diff --git a/.editorconfig b/.editorconfig new file mode 100644 index 0000000..9d08a1a --- /dev/null +++ b/.editorconfig @@ -0,0 +1,9 @@ +root = true + +[*] +charset = utf-8 +indent_style = space +indent_size = 2 +end_of_line = lf +insert_final_newline = true +trim_trailing_whitespace = true diff --git a/.eslintignore b/.eslintignore new file mode 100644 index 0000000..8fb6dcf --- /dev/null +++ b/.eslintignore @@ -0,0 +1,9 @@ +/dist +/src-bex/www +/src-capacitor +/src-cordova +/.quasar +/node_modules +.eslintrc.js +babel.config.js +*.vue diff --git a/.eslintrc.js b/.eslintrc.js new file mode 100644 index 0000000..d456744 --- /dev/null +++ b/.eslintrc.js @@ -0,0 +1,65 @@ +module.exports = { + // https://eslint.org/docs/user-guide/configuring#configuration-cascading-and-hierarchy + // This option interrupts the configuration hierarchy at this file + // Remove this if you have an higher level ESLint config file (it usually happens into a monorepos) + root: true, + + parserOptions: { + parser: "@babel/eslint-parser", + ecmaVersion: 2018, // Allows for the parsing of modern ECMAScript features + sourceType: "module", // Allows for the use of imports + }, + + env: { + browser: true, + "vue/setup-compiler-macros": true, + }, + + // Rules order is important, please avoid shuffling them + extends: [ + // Base ESLint recommended rules + // 'eslint:recommended', + + // Uncomment any of the lines below to choose desired strictness, + // but leave only one uncommented! + // See https://eslint.vuejs.org/rules/#available-rules + "plugin:vue/vue3-essential", // Priority A: Essential (Error Prevention) + // 'plugin:vue/vue3-strongly-recommended', // Priority B: Strongly Recommended (Improving Readability) + // 'plugin:vue/vue3-recommended', // Priority C: Recommended (Minimizing Arbitrary Choices and Cognitive Overhead) + + // https://github.com/prettier/eslint-config-prettier#installation + // usage with Prettier, provided by 'eslint-config-prettier'. + "prettier", + ], + + plugins: [ + // https://eslint.vuejs.org/user-guide/#why-doesn-t-it-work-on-vue-files + // required to lint *.vue files + "vue", + + // https://github.com/typescript-eslint/typescript-eslint/issues/389#issuecomment-509292674 + // Prettier has not been included as plugin to avoid performance impact + // add it as an extension for your IDE + ], + + globals: { + ga: "readonly", // Google Analytics + cordova: "readonly", + __statics: "readonly", + __QUASAR_SSR__: "readonly", + __QUASAR_SSR_SERVER__: "readonly", + __QUASAR_SSR_CLIENT__: "readonly", + __QUASAR_SSR_PWA__: "readonly", + process: "readonly", + Capacitor: "readonly", + chrome: "readonly", + }, + + // add your custom rules here + rules: { + "prefer-promise-reject-errors": "off", + + // allow debugger during development only + "no-debugger": process.env.NODE_ENV === "production" ? "error" : "off", + }, +}; diff --git a/.github/workflows/main.yml b/.github/workflows/deploy.yml similarity index 89% rename from .github/workflows/main.yml rename to .github/workflows/deploy.yml index 3284e57..4e713c0 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/deploy.yml @@ -1,4 +1,4 @@ -name: Build and Deploy +name: Build site on: push: branches: @@ -12,6 +12,7 @@ jobs: - name: Install and Build run: | + sudo apt-get update sudo apt install nodejs npm npm install npx quasar build @@ -22,3 +23,4 @@ jobs: with: personal_token: ${{ secrets.ACCESSTOKEN }} publish_dir: ./dist/spa + diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..553e134 --- /dev/null +++ b/.gitignore @@ -0,0 +1,33 @@ +.DS_Store +.thumbs.db +node_modules + +# Quasar core related directories +.quasar +/dist + +# Cordova related directories and files +/src-cordova/node_modules +/src-cordova/platforms +/src-cordova/plugins +/src-cordova/www + +# Capacitor related directories and files +/src-capacitor/www +/src-capacitor/node_modules + +# BEX related directories and files +/src-bex/www +/src-bex/js/core + +# Log files +npm-debug.log* +yarn-debug.log* +yarn-error.log* + +# Editor directories and files +.idea +*.suo +*.ntvs* +*.njsproj +*.sln diff --git a/.npmrc b/.npmrc new file mode 100644 index 0000000..32bd84d --- /dev/null +++ b/.npmrc @@ -0,0 +1,3 @@ +# pnpm-related options +shamefully-hoist=true +strict-peer-dependencies=false diff --git a/.postcssrc.js b/.postcssrc.js new file mode 100644 index 0000000..02401da --- /dev/null +++ b/.postcssrc.js @@ -0,0 +1,10 @@ +/* eslint-disable */ +// https://github.com/michael-ciniawsky/postcss-load-config + +module.exports = { + plugins: [ + // to edit target browsers: use "browserslist" field in package.json + require("autoprefixer"), + require("tailwindcss"), + ], +}; diff --git a/.vscode/extensions.json b/.vscode/extensions.json new file mode 100644 index 0000000..b6a2eb9 --- /dev/null +++ b/.vscode/extensions.json @@ -0,0 +1,15 @@ +{ + "recommendations": [ + "dbaeumer.vscode-eslint", + "esbenp.prettier-vscode", + "editorconfig.editorconfig", + "vue.volar", + "wayou.vscode-todo-highlight" + ], + "unwantedRecommendations": [ + "octref.vetur", + "hookyqr.beautify", + "dbaeumer.jshint", + "ms-vscode.vscode-typescript-tslint-plugin" + ] +} diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..2200e1c --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,14 @@ +{ + "editor.bracketPairColorization.enabled": true, + "editor.guides.bracketPairs": true, + "editor.formatOnSave": true, + "editor.defaultFormatter": "esbenp.prettier-vscode", + "editor.codeActionsOnSave": ["source.fixAll.eslint"], + "eslint.validate": ["javascript", "javascriptreact", "typescript", "vue"], + "[vue]": { + "editor.defaultFormatter": "Vue.volar" + }, + "[html]": { + "editor.defaultFormatter": "vscode.html-language-features" + } +} diff --git a/404.html b/404.html deleted file mode 100644 index e1d6bf0..0000000 --- a/404.html +++ /dev/null @@ -1 +0,0 @@ -华东师范大学决策智能实验室
\ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..e69de29 diff --git a/babel.config.js b/babel.config.js new file mode 100644 index 0000000..ab0385b --- /dev/null +++ b/babel.config.js @@ -0,0 +1,14 @@ +/* eslint-disable */ + +module.exports = (api) => { + return { + presets: [ + [ + "@quasar/babel-preset-app", + api.caller((caller) => caller && caller.target === "node") + ? { targets: { node: "current" } } + : {}, + ], + ], + }; +}; diff --git a/css/11.79e31209.css b/css/11.79e31209.css deleted file mode 100644 index 3ba77ec..0000000 --- a/css/11.79e31209.css +++ /dev/null @@ -1 +0,0 @@ -ul[data-v-13dcd157]{list-style-type:dot}.my-element[data-v-13dcd157]{animation-duration:1.5s} \ No newline at end of file diff --git a/css/224.a72cf5dc.css b/css/224.a72cf5dc.css deleted file mode 100644 index a735238..0000000 --- a/css/224.a72cf5dc.css +++ /dev/null @@ -1 +0,0 @@ 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norm2 atomic_define atomic_ref execute_command_line leadz trailz storage_size merge_bits bge bgt ble blt dshiftl dshiftr findloc iall iany iparity image_index lcobound ucobound maskl maskr num_images parity popcnt poppar shifta shiftl shiftr this_image IRP_ALIGN irp_here"};return{name:"IRPF90",case_insensitive:!0,keywords:o,illegal:/\/\*/,contains:[e.inherit(e.APOS_STRING_MODE,{className:"string",relevance:0}),e.inherit(e.QUOTE_STRING_MODE,{className:"string",relevance:0}),{className:"function",beginKeywords:"subroutine function program",illegal:"[${=\\n]",contains:[e.UNDERSCORE_TITLE_MODE,n]},e.COMMENT("!","$",{relevance:0}),e.COMMENT("begin_doc","end_doc",{relevance:10}),a]}}e.exports=t},8832:e=>{function t(e){const t="[A-Za-zА-Яа-яёЁ_!][A-Za-zА-Яа-яёЁ_0-9]*",n="[A-Za-zА-Яа-яёЁ_][A-Za-zА-Яа-яёЁ_0-9]*",r="and и else иначе endexcept endfinally endforeach конецвсе endif конецесли endwhile конецпока except exitfor finally foreach все if если in в not не or или try while пока 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SYSRES_CONST_ACCESS_RIGHTS_VIEW_REQUISITE_YES_CODE SYSRES_CONST_ACCESS_TYPE_CHANGE SYSRES_CONST_ACCESS_TYPE_CHANGE_CODE SYSRES_CONST_ACCESS_TYPE_EXISTS SYSRES_CONST_ACCESS_TYPE_EXISTS_CODE SYSRES_CONST_ACCESS_TYPE_FULL SYSRES_CONST_ACCESS_TYPE_FULL_CODE SYSRES_CONST_ACCESS_TYPE_VIEW SYSRES_CONST_ACCESS_TYPE_VIEW_CODE SYSRES_CONST_ACTION_TYPE_ABORT SYSRES_CONST_ACTION_TYPE_ACCEPT SYSRES_CONST_ACTION_TYPE_ACCESS_RIGHTS SYSRES_CONST_ACTION_TYPE_ADD_ATTACHMENT SYSRES_CONST_ACTION_TYPE_CHANGE_CARD SYSRES_CONST_ACTION_TYPE_CHANGE_KIND SYSRES_CONST_ACTION_TYPE_CHANGE_STORAGE SYSRES_CONST_ACTION_TYPE_CONTINUE SYSRES_CONST_ACTION_TYPE_COPY SYSRES_CONST_ACTION_TYPE_CREATE SYSRES_CONST_ACTION_TYPE_CREATE_VERSION SYSRES_CONST_ACTION_TYPE_DELETE SYSRES_CONST_ACTION_TYPE_DELETE_ATTACHMENT SYSRES_CONST_ACTION_TYPE_DELETE_VERSION SYSRES_CONST_ACTION_TYPE_DISABLE_DELEGATE_ACCESS_RIGHTS SYSRES_CONST_ACTION_TYPE_ENABLE_DELEGATE_ACCESS_RIGHTS SYSRES_CONST_ACTION_TYPE_ENCRYPTION_BY_CERTIFICATE SYSRES_CONST_ACTION_TYPE_ENCRYPTION_BY_CERTIFICATE_AND_PASSWORD SYSRES_CONST_ACTION_TYPE_ENCRYPTION_BY_PASSWORD SYSRES_CONST_ACTION_TYPE_EXPORT_WITH_LOCK SYSRES_CONST_ACTION_TYPE_EXPORT_WITHOUT_LOCK SYSRES_CONST_ACTION_TYPE_IMPORT_WITH_UNLOCK SYSRES_CONST_ACTION_TYPE_IMPORT_WITHOUT_UNLOCK SYSRES_CONST_ACTION_TYPE_LIFE_CYCLE_STAGE SYSRES_CONST_ACTION_TYPE_LOCK SYSRES_CONST_ACTION_TYPE_LOCK_FOR_SERVER SYSRES_CONST_ACTION_TYPE_LOCK_MODIFY SYSRES_CONST_ACTION_TYPE_MARK_AS_READED SYSRES_CONST_ACTION_TYPE_MARK_AS_UNREADED SYSRES_CONST_ACTION_TYPE_MODIFY SYSRES_CONST_ACTION_TYPE_MODIFY_CARD SYSRES_CONST_ACTION_TYPE_MOVE_TO_ARCHIVE SYSRES_CONST_ACTION_TYPE_OFF_ENCRYPTION SYSRES_CONST_ACTION_TYPE_PASSWORD_CHANGE SYSRES_CONST_ACTION_TYPE_PERFORM SYSRES_CONST_ACTION_TYPE_RECOVER_FROM_LOCAL_COPY SYSRES_CONST_ACTION_TYPE_RESTART SYSRES_CONST_ACTION_TYPE_RESTORE_FROM_ARCHIVE SYSRES_CONST_ACTION_TYPE_REVISION SYSRES_CONST_ACTION_TYPE_SEND_BY_MAIL SYSRES_CONST_ACTION_TYPE_SIGN SYSRES_CONST_ACTION_TYPE_START SYSRES_CONST_ACTION_TYPE_UNLOCK SYSRES_CONST_ACTION_TYPE_UNLOCK_FROM_SERVER SYSRES_CONST_ACTION_TYPE_VERSION_STATE SYSRES_CONST_ACTION_TYPE_VERSION_VISIBILITY SYSRES_CONST_ACTION_TYPE_VIEW SYSRES_CONST_ACTION_TYPE_VIEW_SHADOW_COPY SYSRES_CONST_ACTION_TYPE_WORKFLOW_DESCRIPTION_MODIFY SYSRES_CONST_ACTION_TYPE_WRITE_HISTORY SYSRES_CONST_ACTIVE_VERSION_STATE_PICK_VALUE SYSRES_CONST_ADD_REFERENCE_MODE_NAME SYSRES_CONST_ADDITION_REQUISITE_CODE SYSRES_CONST_ADDITIONAL_PARAMS_REQUISITE_CODE SYSRES_CONST_ADITIONAL_JOB_END_DATE_REQUISITE_NAME SYSRES_CONST_ADITIONAL_JOB_READ_REQUISITE_NAME SYSRES_CONST_ADITIONAL_JOB_START_DATE_REQUISITE_NAME SYSRES_CONST_ADITIONAL_JOB_STATE_REQUISITE_NAME SYSRES_CONST_ADMINISTRATION_HISTORY_ADDING_USER_TO_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_ADDING_USER_TO_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_COMP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_COMP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_USER_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_USER_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_DATABASE_USER_CREATION SYSRES_CONST_ADMINISTRATION_HISTORY_DATABASE_USER_CREATION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DATABASE_USER_DELETION SYSRES_CONST_ADMINISTRATION_HISTORY_DATABASE_USER_DELETION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_COMP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_COMP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_USER_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_USER_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_USER_FROM_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_USER_FROM_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_FILTERER_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_FILTERER_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_FILTERER_RESTRICTION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_FILTERER_RESTRICTION_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_PRIVILEGE_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_PRIVILEGE_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_RIGHTS_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_RIGHTS_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_IS_MAIN_SERVER_CHANGED_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_IS_MAIN_SERVER_CHANGED_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_IS_PUBLIC_CHANGED_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_IS_PUBLIC_CHANGED_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_FILTERER_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_FILTERER_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_FILTERER_RESTRICTION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_FILTERER_RESTRICTION_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_PRIVILEGE_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_PRIVILEGE_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_RIGHTS_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_RIGHTS_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_SERVER_LOGIN_CREATION SYSRES_CONST_ADMINISTRATION_HISTORY_SERVER_LOGIN_CREATION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_SERVER_LOGIN_DELETION SYSRES_CONST_ADMINISTRATION_HISTORY_SERVER_LOGIN_DELETION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_CATEGORY_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_CATEGORY_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_COMP_TITLE_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_COMP_TITLE_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_FULL_NAME_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_FULL_NAME_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_PARENT_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_PARENT_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_AUTH_TYPE_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_AUTH_TYPE_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_LOGIN_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_LOGIN_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_STATUS_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_STATUS_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_USER_PASSWORD_CHANGE SYSRES_CONST_ADMINISTRATION_HISTORY_USER_PASSWORD_CHANGE_ACTION SYSRES_CONST_ALL_ACCEPT_CONDITION_RUS SYSRES_CONST_ALL_USERS_GROUP SYSRES_CONST_ALL_USERS_GROUP_NAME SYSRES_CONST_ALL_USERS_SERVER_GROUP_NAME SYSRES_CONST_ALLOWED_ACCESS_TYPE_CODE SYSRES_CONST_ALLOWED_ACCESS_TYPE_NAME SYSRES_CONST_APP_VIEWER_TYPE_REQUISITE_CODE SYSRES_CONST_APPROVING_SIGNATURE_NAME SYSRES_CONST_APPROVING_SIGNATURE_REQUISITE_CODE SYSRES_CONST_ASSISTANT_SUBSTITUE_TYPE SYSRES_CONST_ASSISTANT_SUBSTITUE_TYPE_CODE SYSRES_CONST_ATTACH_TYPE_COMPONENT_TOKEN SYSRES_CONST_ATTACH_TYPE_DOC SYSRES_CONST_ATTACH_TYPE_EDOC SYSRES_CONST_ATTACH_TYPE_FOLDER SYSRES_CONST_ATTACH_TYPE_JOB SYSRES_CONST_ATTACH_TYPE_REFERENCE SYSRES_CONST_ATTACH_TYPE_TASK SYSRES_CONST_AUTH_ENCODED_PASSWORD SYSRES_CONST_AUTH_ENCODED_PASSWORD_CODE SYSRES_CONST_AUTH_NOVELL SYSRES_CONST_AUTH_PASSWORD SYSRES_CONST_AUTH_PASSWORD_CODE SYSRES_CONST_AUTH_WINDOWS SYSRES_CONST_AUTHENTICATING_SIGNATURE_NAME SYSRES_CONST_AUTHENTICATING_SIGNATURE_REQUISITE_CODE SYSRES_CONST_AUTO_ENUM_METHOD_FLAG SYSRES_CONST_AUTO_NUMERATION_CODE SYSRES_CONST_AUTO_STRONG_ENUM_METHOD_FLAG SYSRES_CONST_AUTOTEXT_NAME_REQUISITE_CODE SYSRES_CONST_AUTOTEXT_TEXT_REQUISITE_CODE SYSRES_CONST_AUTOTEXT_USAGE_ALL SYSRES_CONST_AUTOTEXT_USAGE_ALL_CODE SYSRES_CONST_AUTOTEXT_USAGE_SIGN SYSRES_CONST_AUTOTEXT_USAGE_SIGN_CODE SYSRES_CONST_AUTOTEXT_USAGE_WORK SYSRES_CONST_AUTOTEXT_USAGE_WORK_CODE SYSRES_CONST_AUTOTEXT_USE_ANYWHERE_CODE SYSRES_CONST_AUTOTEXT_USE_ON_SIGNING_CODE SYSRES_CONST_AUTOTEXT_USE_ON_WORK_CODE SYSRES_CONST_BEGIN_DATE_REQUISITE_CODE SYSRES_CONST_BLACK_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_BLUE_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_BTN_PART SYSRES_CONST_CALCULATED_ROLE_TYPE_CODE SYSRES_CONST_CALL_TYPE_VARIABLE_BUTTON_VALUE SYSRES_CONST_CALL_TYPE_VARIABLE_PROGRAM_VALUE SYSRES_CONST_CANCEL_MESSAGE_FUNCTION_RESULT SYSRES_CONST_CARD_PART SYSRES_CONST_CARD_REFERENCE_MODE_NAME SYSRES_CONST_CERTIFICATE_TYPE_REQUISITE_ENCRYPT_VALUE SYSRES_CONST_CERTIFICATE_TYPE_REQUISITE_SIGN_AND_ENCRYPT_VALUE SYSRES_CONST_CERTIFICATE_TYPE_REQUISITE_SIGN_VALUE SYSRES_CONST_CHECK_PARAM_VALUE_DATE_PARAM_TYPE SYSRES_CONST_CHECK_PARAM_VALUE_FLOAT_PARAM_TYPE SYSRES_CONST_CHECK_PARAM_VALUE_INTEGER_PARAM_TYPE SYSRES_CONST_CHECK_PARAM_VALUE_PICK_PARAM_TYPE SYSRES_CONST_CHECK_PARAM_VALUE_REEFRENCE_PARAM_TYPE SYSRES_CONST_CLOSED_RECORD_FLAG_VALUE_FEMININE SYSRES_CONST_CLOSED_RECORD_FLAG_VALUE_MASCULINE SYSRES_CONST_CODE_COMPONENT_TYPE_ADMIN SYSRES_CONST_CODE_COMPONENT_TYPE_DEVELOPER SYSRES_CONST_CODE_COMPONENT_TYPE_DOCS SYSRES_CONST_CODE_COMPONENT_TYPE_EDOC_CARDS SYSRES_CONST_CODE_COMPONENT_TYPE_EXTERNAL_EXECUTABLE SYSRES_CONST_CODE_COMPONENT_TYPE_OTHER SYSRES_CONST_CODE_COMPONENT_TYPE_REFERENCE SYSRES_CONST_CODE_COMPONENT_TYPE_REPORT SYSRES_CONST_CODE_COMPONENT_TYPE_SCRIPT SYSRES_CONST_CODE_COMPONENT_TYPE_URL SYSRES_CONST_CODE_REQUISITE_ACCESS SYSRES_CONST_CODE_REQUISITE_CODE SYSRES_CONST_CODE_REQUISITE_COMPONENT SYSRES_CONST_CODE_REQUISITE_DESCRIPTION SYSRES_CONST_CODE_REQUISITE_EXCLUDE_COMPONENT SYSRES_CONST_CODE_REQUISITE_RECORD SYSRES_CONST_COMMENT_REQ_CODE SYSRES_CONST_COMMON_SETTINGS_REQUISITE_CODE SYSRES_CONST_COMP_CODE_GRD SYSRES_CONST_COMPONENT_GROUP_TYPE_REQUISITE_CODE SYSRES_CONST_COMPONENT_TYPE_ADMIN_COMPONENTS SYSRES_CONST_COMPONENT_TYPE_DEVELOPER_COMPONENTS SYSRES_CONST_COMPONENT_TYPE_DOCS SYSRES_CONST_COMPONENT_TYPE_EDOC_CARDS SYSRES_CONST_COMPONENT_TYPE_EDOCS SYSRES_CONST_COMPONENT_TYPE_EXTERNAL_EXECUTABLE SYSRES_CONST_COMPONENT_TYPE_OTHER SYSRES_CONST_COMPONENT_TYPE_REFERENCE_TYPES SYSRES_CONST_COMPONENT_TYPE_REFERENCES SYSRES_CONST_COMPONENT_TYPE_REPORTS SYSRES_CONST_COMPONENT_TYPE_SCRIPTS SYSRES_CONST_COMPONENT_TYPE_URL SYSRES_CONST_COMPONENTS_REMOTE_SERVERS_VIEW_CODE SYSRES_CONST_CONDITION_BLOCK_DESCRIPTION SYSRES_CONST_CONST_FIRM_STATUS_COMMON SYSRES_CONST_CONST_FIRM_STATUS_INDIVIDUAL SYSRES_CONST_CONST_NEGATIVE_VALUE SYSRES_CONST_CONST_POSITIVE_VALUE SYSRES_CONST_CONST_SERVER_STATUS_DONT_REPLICATE SYSRES_CONST_CONST_SERVER_STATUS_REPLICATE SYSRES_CONST_CONTENTS_REQUISITE_CODE SYSRES_CONST_DATA_TYPE_BOOLEAN SYSRES_CONST_DATA_TYPE_DATE SYSRES_CONST_DATA_TYPE_FLOAT SYSRES_CONST_DATA_TYPE_INTEGER SYSRES_CONST_DATA_TYPE_PICK SYSRES_CONST_DATA_TYPE_REFERENCE SYSRES_CONST_DATA_TYPE_STRING SYSRES_CONST_DATA_TYPE_TEXT SYSRES_CONST_DATA_TYPE_VARIANT SYSRES_CONST_DATE_CLOSE_REQ_CODE SYSRES_CONST_DATE_FORMAT_DATE_ONLY_CHAR SYSRES_CONST_DATE_OPEN_REQ_CODE SYSRES_CONST_DATE_REQUISITE SYSRES_CONST_DATE_REQUISITE_CODE SYSRES_CONST_DATE_REQUISITE_NAME SYSRES_CONST_DATE_REQUISITE_TYPE SYSRES_CONST_DATE_TYPE_CHAR SYSRES_CONST_DATETIME_FORMAT_VALUE SYSRES_CONST_DEA_ACCESS_RIGHTS_ACTION_CODE SYSRES_CONST_DESCRIPTION_LOCALIZE_ID_REQUISITE_CODE SYSRES_CONST_DESCRIPTION_REQUISITE_CODE SYSRES_CONST_DET1_PART SYSRES_CONST_DET2_PART SYSRES_CONST_DET3_PART SYSRES_CONST_DET4_PART SYSRES_CONST_DET5_PART SYSRES_CONST_DET6_PART SYSRES_CONST_DETAIL_DATASET_KEY_REQUISITE_CODE SYSRES_CONST_DETAIL_PICK_REQUISITE_CODE SYSRES_CONST_DETAIL_REQ_CODE SYSRES_CONST_DO_NOT_USE_ACCESS_TYPE_CODE SYSRES_CONST_DO_NOT_USE_ACCESS_TYPE_NAME SYSRES_CONST_DO_NOT_USE_ON_VIEW_ACCESS_TYPE_CODE SYSRES_CONST_DO_NOT_USE_ON_VIEW_ACCESS_TYPE_NAME SYSRES_CONST_DOCUMENT_STORAGES_CODE SYSRES_CONST_DOCUMENT_TEMPLATES_TYPE_NAME SYSRES_CONST_DOUBLE_REQUISITE_CODE SYSRES_CONST_EDITOR_CLOSE_FILE_OBSERV_TYPE_CODE SYSRES_CONST_EDITOR_CLOSE_PROCESS_OBSERV_TYPE_CODE SYSRES_CONST_EDITOR_TYPE_REQUISITE_CODE SYSRES_CONST_EDITORS_APPLICATION_NAME_REQUISITE_CODE SYSRES_CONST_EDITORS_CREATE_SEVERAL_PROCESSES_REQUISITE_CODE SYSRES_CONST_EDITORS_EXTENSION_REQUISITE_CODE SYSRES_CONST_EDITORS_OBSERVER_BY_PROCESS_TYPE SYSRES_CONST_EDITORS_REFERENCE_CODE SYSRES_CONST_EDITORS_REPLACE_SPEC_CHARS_REQUISITE_CODE SYSRES_CONST_EDITORS_USE_PLUGINS_REQUISITE_CODE SYSRES_CONST_EDITORS_VIEW_DOCUMENT_OPENED_TO_EDIT_CODE SYSRES_CONST_EDOC_CARD_TYPE_REQUISITE_CODE SYSRES_CONST_EDOC_CARD_TYPES_LINK_REQUISITE_CODE SYSRES_CONST_EDOC_CERTIFICATE_AND_PASSWORD_ENCODE_CODE SYSRES_CONST_EDOC_CERTIFICATE_ENCODE_CODE SYSRES_CONST_EDOC_DATE_REQUISITE_CODE SYSRES_CONST_EDOC_KIND_REFERENCE_CODE SYSRES_CONST_EDOC_KINDS_BY_TEMPLATE_ACTION_CODE SYSRES_CONST_EDOC_MANAGE_ACCESS_CODE SYSRES_CONST_EDOC_NONE_ENCODE_CODE SYSRES_CONST_EDOC_NUMBER_REQUISITE_CODE SYSRES_CONST_EDOC_PASSWORD_ENCODE_CODE SYSRES_CONST_EDOC_READONLY_ACCESS_CODE SYSRES_CONST_EDOC_SHELL_LIFE_TYPE_VIEW_VALUE SYSRES_CONST_EDOC_SIZE_RESTRICTION_PRIORITY_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_CHECK_ACCESS_RIGHTS_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_COMPUTER_NAME_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_DATABASE_NAME_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_EDIT_IN_STORAGE_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_LOCAL_PATH_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_SHARED_SOURCE_NAME_REQUISITE_CODE SYSRES_CONST_EDOC_TEMPLATE_REQUISITE_CODE SYSRES_CONST_EDOC_TYPES_REFERENCE_CODE SYSRES_CONST_EDOC_VERSION_ACTIVE_STAGE_CODE SYSRES_CONST_EDOC_VERSION_DESIGN_STAGE_CODE SYSRES_CONST_EDOC_VERSION_OBSOLETE_STAGE_CODE SYSRES_CONST_EDOC_WRITE_ACCES_CODE SYSRES_CONST_EDOCUMENT_CARD_REQUISITES_REFERENCE_CODE_SELECTED_REQUISITE SYSRES_CONST_ENCODE_CERTIFICATE_TYPE_CODE SYSRES_CONST_END_DATE_REQUISITE_CODE SYSRES_CONST_ENUMERATION_TYPE_REQUISITE_CODE SYSRES_CONST_EXECUTE_ACCESS_RIGHTS_TYPE_CODE SYSRES_CONST_EXECUTIVE_FILE_STORAGE_TYPE SYSRES_CONST_EXIST_CONST SYSRES_CONST_EXIST_VALUE SYSRES_CONST_EXPORT_LOCK_TYPE_ASK SYSRES_CONST_EXPORT_LOCK_TYPE_WITH_LOCK SYSRES_CONST_EXPORT_LOCK_TYPE_WITHOUT_LOCK SYSRES_CONST_EXPORT_VERSION_TYPE_ASK SYSRES_CONST_EXPORT_VERSION_TYPE_LAST SYSRES_CONST_EXPORT_VERSION_TYPE_LAST_ACTIVE SYSRES_CONST_EXTENSION_REQUISITE_CODE SYSRES_CONST_FILTER_NAME_REQUISITE_CODE SYSRES_CONST_FILTER_REQUISITE_CODE SYSRES_CONST_FILTER_TYPE_COMMON_CODE SYSRES_CONST_FILTER_TYPE_COMMON_NAME SYSRES_CONST_FILTER_TYPE_USER_CODE SYSRES_CONST_FILTER_TYPE_USER_NAME SYSRES_CONST_FILTER_VALUE_REQUISITE_NAME SYSRES_CONST_FLOAT_NUMBER_FORMAT_CHAR SYSRES_CONST_FLOAT_REQUISITE_TYPE SYSRES_CONST_FOLDER_AUTHOR_VALUE SYSRES_CONST_FOLDER_KIND_ANY_OBJECTS SYSRES_CONST_FOLDER_KIND_COMPONENTS SYSRES_CONST_FOLDER_KIND_EDOCS SYSRES_CONST_FOLDER_KIND_JOBS SYSRES_CONST_FOLDER_KIND_TASKS SYSRES_CONST_FOLDER_TYPE_COMMON SYSRES_CONST_FOLDER_TYPE_COMPONENT SYSRES_CONST_FOLDER_TYPE_FAVORITES SYSRES_CONST_FOLDER_TYPE_INBOX SYSRES_CONST_FOLDER_TYPE_OUTBOX SYSRES_CONST_FOLDER_TYPE_QUICK_LAUNCH SYSRES_CONST_FOLDER_TYPE_SEARCH SYSRES_CONST_FOLDER_TYPE_SHORTCUTS SYSRES_CONST_FOLDER_TYPE_USER SYSRES_CONST_FROM_DICTIONARY_ENUM_METHOD_FLAG SYSRES_CONST_FULL_SUBSTITUTE_TYPE SYSRES_CONST_FULL_SUBSTITUTE_TYPE_CODE SYSRES_CONST_FUNCTION_CANCEL_RESULT SYSRES_CONST_FUNCTION_CATEGORY_SYSTEM SYSRES_CONST_FUNCTION_CATEGORY_USER SYSRES_CONST_FUNCTION_FAILURE_RESULT SYSRES_CONST_FUNCTION_SAVE_RESULT SYSRES_CONST_GENERATED_REQUISITE SYSRES_CONST_GREEN_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_GROUP_ACCOUNT_TYPE_VALUE_CODE SYSRES_CONST_GROUP_CATEGORY_NORMAL_CODE SYSRES_CONST_GROUP_CATEGORY_NORMAL_NAME SYSRES_CONST_GROUP_CATEGORY_SERVICE_CODE SYSRES_CONST_GROUP_CATEGORY_SERVICE_NAME SYSRES_CONST_GROUP_COMMON_CATEGORY_FIELD_VALUE SYSRES_CONST_GROUP_FULL_NAME_REQUISITE_CODE SYSRES_CONST_GROUP_NAME_REQUISITE_CODE SYSRES_CONST_GROUP_RIGHTS_T_REQUISITE_CODE SYSRES_CONST_GROUP_SERVER_CODES_REQUISITE_CODE SYSRES_CONST_GROUP_SERVER_NAME_REQUISITE_CODE SYSRES_CONST_GROUP_SERVICE_CATEGORY_FIELD_VALUE SYSRES_CONST_GROUP_USER_REQUISITE_CODE SYSRES_CONST_GROUPS_REFERENCE_CODE SYSRES_CONST_GROUPS_REQUISITE_CODE SYSRES_CONST_HIDDEN_MODE_NAME SYSRES_CONST_HIGH_LVL_REQUISITE_CODE SYSRES_CONST_HISTORY_ACTION_CREATE_CODE SYSRES_CONST_HISTORY_ACTION_DELETE_CODE SYSRES_CONST_HISTORY_ACTION_EDIT_CODE SYSRES_CONST_HOUR_CHAR SYSRES_CONST_ID_REQUISITE_CODE SYSRES_CONST_IDSPS_REQUISITE_CODE SYSRES_CONST_IMAGE_MODE_COLOR SYSRES_CONST_IMAGE_MODE_GREYSCALE SYSRES_CONST_IMAGE_MODE_MONOCHROME SYSRES_CONST_IMPORTANCE_HIGH SYSRES_CONST_IMPORTANCE_LOW SYSRES_CONST_IMPORTANCE_NORMAL SYSRES_CONST_IN_DESIGN_VERSION_STATE_PICK_VALUE SYSRES_CONST_INCOMING_WORK_RULE_TYPE_CODE SYSRES_CONST_INT_REQUISITE SYSRES_CONST_INT_REQUISITE_TYPE SYSRES_CONST_INTEGER_NUMBER_FORMAT_CHAR SYSRES_CONST_INTEGER_TYPE_CHAR SYSRES_CONST_IS_GENERATED_REQUISITE_NEGATIVE_VALUE SYSRES_CONST_IS_PUBLIC_ROLE_REQUISITE_CODE SYSRES_CONST_IS_REMOTE_USER_NEGATIVE_VALUE SYSRES_CONST_IS_REMOTE_USER_POSITIVE_VALUE SYSRES_CONST_IS_STORED_REQUISITE_NEGATIVE_VALUE SYSRES_CONST_IS_STORED_REQUISITE_STORED_VALUE SYSRES_CONST_ITALIC_LIFE_CYCLE_STAGE_DRAW_STYLE SYSRES_CONST_JOB_BLOCK_DESCRIPTION SYSRES_CONST_JOB_KIND_CONTROL_JOB SYSRES_CONST_JOB_KIND_JOB SYSRES_CONST_JOB_KIND_NOTICE SYSRES_CONST_JOB_STATE_ABORTED SYSRES_CONST_JOB_STATE_COMPLETE SYSRES_CONST_JOB_STATE_WORKING SYSRES_CONST_KIND_REQUISITE_CODE SYSRES_CONST_KIND_REQUISITE_NAME SYSRES_CONST_KINDS_CREATE_SHADOW_COPIES_REQUISITE_CODE SYSRES_CONST_KINDS_DEFAULT_EDOC_LIFE_STAGE_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_ALL_TEPLATES_ALLOWED_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_ALLOW_LIFE_CYCLE_STAGE_CHANGING_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_ALLOW_MULTIPLE_ACTIVE_VERSIONS_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_SHARE_ACCES_RIGHTS_BY_DEFAULT_CODE SYSRES_CONST_KINDS_EDOC_TEMPLATE_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_TYPE_REQUISITE_CODE SYSRES_CONST_KINDS_SIGNERS_REQUISITES_CODE SYSRES_CONST_KOD_INPUT_TYPE SYSRES_CONST_LAST_UPDATE_DATE_REQUISITE_CODE SYSRES_CONST_LIFE_CYCLE_START_STAGE_REQUISITE_CODE SYSRES_CONST_LILAC_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_LINK_OBJECT_KIND_COMPONENT SYSRES_CONST_LINK_OBJECT_KIND_DOCUMENT SYSRES_CONST_LINK_OBJECT_KIND_EDOC SYSRES_CONST_LINK_OBJECT_KIND_FOLDER SYSRES_CONST_LINK_OBJECT_KIND_JOB SYSRES_CONST_LINK_OBJECT_KIND_REFERENCE SYSRES_CONST_LINK_OBJECT_KIND_TASK SYSRES_CONST_LINK_REF_TYPE_REQUISITE_CODE SYSRES_CONST_LIST_REFERENCE_MODE_NAME SYSRES_CONST_LOCALIZATION_DICTIONARY_MAIN_VIEW_CODE SYSRES_CONST_MAIN_VIEW_CODE SYSRES_CONST_MANUAL_ENUM_METHOD_FLAG SYSRES_CONST_MASTER_COMP_TYPE_REQUISITE_CODE SYSRES_CONST_MASTER_TABLE_REC_ID_REQUISITE_CODE SYSRES_CONST_MAXIMIZED_MODE_NAME SYSRES_CONST_ME_VALUE SYSRES_CONST_MESSAGE_ATTENTION_CAPTION SYSRES_CONST_MESSAGE_CONFIRMATION_CAPTION SYSRES_CONST_MESSAGE_ERROR_CAPTION SYSRES_CONST_MESSAGE_INFORMATION_CAPTION SYSRES_CONST_MINIMIZED_MODE_NAME SYSRES_CONST_MINUTE_CHAR SYSRES_CONST_MODULE_REQUISITE_CODE SYSRES_CONST_MONITORING_BLOCK_DESCRIPTION SYSRES_CONST_MONTH_FORMAT_VALUE SYSRES_CONST_NAME_LOCALIZE_ID_REQUISITE_CODE SYSRES_CONST_NAME_REQUISITE_CODE SYSRES_CONST_NAME_SINGULAR_REQUISITE_CODE SYSRES_CONST_NAMEAN_INPUT_TYPE SYSRES_CONST_NEGATIVE_PICK_VALUE SYSRES_CONST_NEGATIVE_VALUE SYSRES_CONST_NO SYSRES_CONST_NO_PICK_VALUE SYSRES_CONST_NO_SIGNATURE_REQUISITE_CODE SYSRES_CONST_NO_VALUE SYSRES_CONST_NONE_ACCESS_RIGHTS_TYPE_CODE SYSRES_CONST_NONOPERATING_RECORD_FLAG_VALUE SYSRES_CONST_NONOPERATING_RECORD_FLAG_VALUE_MASCULINE SYSRES_CONST_NORMAL_ACCESS_RIGHTS_TYPE_CODE SYSRES_CONST_NORMAL_LIFE_CYCLE_STAGE_DRAW_STYLE SYSRES_CONST_NORMAL_MODE_NAME SYSRES_CONST_NOT_ALLOWED_ACCESS_TYPE_CODE SYSRES_CONST_NOT_ALLOWED_ACCESS_TYPE_NAME SYSRES_CONST_NOTE_REQUISITE_CODE SYSRES_CONST_NOTICE_BLOCK_DESCRIPTION SYSRES_CONST_NUM_REQUISITE SYSRES_CONST_NUM_STR_REQUISITE_CODE SYSRES_CONST_NUMERATION_AUTO_NOT_STRONG SYSRES_CONST_NUMERATION_AUTO_STRONG SYSRES_CONST_NUMERATION_FROM_DICTONARY SYSRES_CONST_NUMERATION_MANUAL SYSRES_CONST_NUMERIC_TYPE_CHAR SYSRES_CONST_NUMREQ_REQUISITE_CODE SYSRES_CONST_OBSOLETE_VERSION_STATE_PICK_VALUE SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE_CODE SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE_FEMININE SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE_MASCULINE SYSRES_CONST_OPTIONAL_FORM_COMP_REQCODE_PREFIX SYSRES_CONST_ORANGE_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_ORIGINALREF_REQUISITE_CODE SYSRES_CONST_OURFIRM_REF_CODE SYSRES_CONST_OURFIRM_REQUISITE_CODE SYSRES_CONST_OURFIRM_VAR SYSRES_CONST_OUTGOING_WORK_RULE_TYPE_CODE SYSRES_CONST_PICK_NEGATIVE_RESULT SYSRES_CONST_PICK_POSITIVE_RESULT SYSRES_CONST_PICK_REQUISITE SYSRES_CONST_PICK_REQUISITE_TYPE SYSRES_CONST_PICK_TYPE_CHAR SYSRES_CONST_PLAN_STATUS_REQUISITE_CODE SYSRES_CONST_PLATFORM_VERSION_COMMENT SYSRES_CONST_PLUGINS_SETTINGS_DESCRIPTION_REQUISITE_CODE SYSRES_CONST_POSITIVE_PICK_VALUE SYSRES_CONST_POWER_TO_CREATE_ACTION_CODE SYSRES_CONST_POWER_TO_SIGN_ACTION_CODE SYSRES_CONST_PRIORITY_REQUISITE_CODE SYSRES_CONST_QUALIFIED_TASK_TYPE SYSRES_CONST_QUALIFIED_TASK_TYPE_CODE SYSRES_CONST_RECSTAT_REQUISITE_CODE SYSRES_CONST_RED_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_REF_ID_T_REF_TYPE_REQUISITE_CODE SYSRES_CONST_REF_REQUISITE SYSRES_CONST_REF_REQUISITE_TYPE SYSRES_CONST_REF_REQUISITES_REFERENCE_CODE_SELECTED_REQUISITE SYSRES_CONST_REFERENCE_RECORD_HISTORY_CREATE_ACTION_CODE SYSRES_CONST_REFERENCE_RECORD_HISTORY_DELETE_ACTION_CODE SYSRES_CONST_REFERENCE_RECORD_HISTORY_MODIFY_ACTION_CODE SYSRES_CONST_REFERENCE_TYPE_CHAR SYSRES_CONST_REFERENCE_TYPE_REQUISITE_NAME SYSRES_CONST_REFERENCES_ADD_PARAMS_REQUISITE_CODE SYSRES_CONST_REFERENCES_DISPLAY_REQUISITE_REQUISITE_CODE SYSRES_CONST_REMOTE_SERVER_STATUS_WORKING SYSRES_CONST_REMOTE_SERVER_TYPE_MAIN SYSRES_CONST_REMOTE_SERVER_TYPE_SECONDARY SYSRES_CONST_REMOTE_USER_FLAG_VALUE_CODE SYSRES_CONST_REPORT_APP_EDITOR_INTERNAL SYSRES_CONST_REPORT_BASE_REPORT_ID_REQUISITE_CODE SYSRES_CONST_REPORT_BASE_REPORT_REQUISITE_CODE SYSRES_CONST_REPORT_SCRIPT_REQUISITE_CODE SYSRES_CONST_REPORT_TEMPLATE_REQUISITE_CODE SYSRES_CONST_REPORT_VIEWER_CODE_REQUISITE_CODE SYSRES_CONST_REQ_ALLOW_COMPONENT_DEFAULT_VALUE SYSRES_CONST_REQ_ALLOW_RECORD_DEFAULT_VALUE SYSRES_CONST_REQ_ALLOW_SERVER_COMPONENT_DEFAULT_VALUE SYSRES_CONST_REQ_MODE_AVAILABLE_CODE SYSRES_CONST_REQ_MODE_EDIT_CODE SYSRES_CONST_REQ_MODE_HIDDEN_CODE SYSRES_CONST_REQ_MODE_NOT_AVAILABLE_CODE SYSRES_CONST_REQ_MODE_VIEW_CODE SYSRES_CONST_REQ_NUMBER_REQUISITE_CODE SYSRES_CONST_REQ_SECTION_VALUE SYSRES_CONST_REQ_TYPE_VALUE SYSRES_CONST_REQUISITE_FORMAT_BY_UNIT SYSRES_CONST_REQUISITE_FORMAT_DATE_FULL SYSRES_CONST_REQUISITE_FORMAT_DATE_TIME SYSRES_CONST_REQUISITE_FORMAT_LEFT SYSRES_CONST_REQUISITE_FORMAT_RIGHT SYSRES_CONST_REQUISITE_FORMAT_WITHOUT_UNIT SYSRES_CONST_REQUISITE_NUMBER_REQUISITE_CODE SYSRES_CONST_REQUISITE_SECTION_ACTIONS SYSRES_CONST_REQUISITE_SECTION_BUTTON SYSRES_CONST_REQUISITE_SECTION_BUTTONS SYSRES_CONST_REQUISITE_SECTION_CARD SYSRES_CONST_REQUISITE_SECTION_TABLE SYSRES_CONST_REQUISITE_SECTION_TABLE10 SYSRES_CONST_REQUISITE_SECTION_TABLE11 SYSRES_CONST_REQUISITE_SECTION_TABLE12 SYSRES_CONST_REQUISITE_SECTION_TABLE13 SYSRES_CONST_REQUISITE_SECTION_TABLE14 SYSRES_CONST_REQUISITE_SECTION_TABLE15 SYSRES_CONST_REQUISITE_SECTION_TABLE16 SYSRES_CONST_REQUISITE_SECTION_TABLE17 SYSRES_CONST_REQUISITE_SECTION_TABLE18 SYSRES_CONST_REQUISITE_SECTION_TABLE19 SYSRES_CONST_REQUISITE_SECTION_TABLE2 SYSRES_CONST_REQUISITE_SECTION_TABLE20 SYSRES_CONST_REQUISITE_SECTION_TABLE21 SYSRES_CONST_REQUISITE_SECTION_TABLE22 SYSRES_CONST_REQUISITE_SECTION_TABLE23 SYSRES_CONST_REQUISITE_SECTION_TABLE24 SYSRES_CONST_REQUISITE_SECTION_TABLE3 SYSRES_CONST_REQUISITE_SECTION_TABLE4 SYSRES_CONST_REQUISITE_SECTION_TABLE5 SYSRES_CONST_REQUISITE_SECTION_TABLE6 SYSRES_CONST_REQUISITE_SECTION_TABLE7 SYSRES_CONST_REQUISITE_SECTION_TABLE8 SYSRES_CONST_REQUISITE_SECTION_TABLE9 SYSRES_CONST_REQUISITES_PSEUDOREFERENCE_REQUISITE_NUMBER_REQUISITE_CODE SYSRES_CONST_RIGHT_ALIGNMENT_CODE SYSRES_CONST_ROLES_REFERENCE_CODE SYSRES_CONST_ROUTE_STEP_AFTER_RUS SYSRES_CONST_ROUTE_STEP_AND_CONDITION_RUS SYSRES_CONST_ROUTE_STEP_OR_CONDITION_RUS SYSRES_CONST_ROUTE_TYPE_COMPLEX SYSRES_CONST_ROUTE_TYPE_PARALLEL SYSRES_CONST_ROUTE_TYPE_SERIAL SYSRES_CONST_SBDATASETDESC_NEGATIVE_VALUE SYSRES_CONST_SBDATASETDESC_POSITIVE_VALUE SYSRES_CONST_SBVIEWSDESC_POSITIVE_VALUE SYSRES_CONST_SCRIPT_BLOCK_DESCRIPTION SYSRES_CONST_SEARCH_BY_TEXT_REQUISITE_CODE SYSRES_CONST_SEARCHES_COMPONENT_CONTENT SYSRES_CONST_SEARCHES_CRITERIA_ACTION_NAME SYSRES_CONST_SEARCHES_EDOC_CONTENT SYSRES_CONST_SEARCHES_FOLDER_CONTENT SYSRES_CONST_SEARCHES_JOB_CONTENT SYSRES_CONST_SEARCHES_REFERENCE_CODE SYSRES_CONST_SEARCHES_TASK_CONTENT SYSRES_CONST_SECOND_CHAR SYSRES_CONST_SECTION_REQUISITE_ACTIONS_VALUE SYSRES_CONST_SECTION_REQUISITE_CARD_VALUE SYSRES_CONST_SECTION_REQUISITE_CODE SYSRES_CONST_SECTION_REQUISITE_DETAIL_1_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_2_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_3_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_4_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_5_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_6_VALUE SYSRES_CONST_SELECT_REFERENCE_MODE_NAME SYSRES_CONST_SELECT_TYPE_SELECTABLE SYSRES_CONST_SELECT_TYPE_SELECTABLE_ONLY_CHILD SYSRES_CONST_SELECT_TYPE_SELECTABLE_WITH_CHILD SYSRES_CONST_SELECT_TYPE_UNSLECTABLE SYSRES_CONST_SERVER_TYPE_MAIN SYSRES_CONST_SERVICE_USER_CATEGORY_FIELD_VALUE SYSRES_CONST_SETTINGS_USER_REQUISITE_CODE SYSRES_CONST_SIGNATURE_AND_ENCODE_CERTIFICATE_TYPE_CODE SYSRES_CONST_SIGNATURE_CERTIFICATE_TYPE_CODE SYSRES_CONST_SINGULAR_TITLE_REQUISITE_CODE SYSRES_CONST_SQL_SERVER_AUTHENTIFICATION_FLAG_VALUE_CODE SYSRES_CONST_SQL_SERVER_ENCODE_AUTHENTIFICATION_FLAG_VALUE_CODE SYSRES_CONST_STANDART_ROUTE_REFERENCE_CODE SYSRES_CONST_STANDART_ROUTE_REFERENCE_COMMENT_REQUISITE_CODE SYSRES_CONST_STANDART_ROUTES_GROUPS_REFERENCE_CODE SYSRES_CONST_STATE_REQ_NAME SYSRES_CONST_STATE_REQUISITE_ACTIVE_VALUE SYSRES_CONST_STATE_REQUISITE_CLOSED_VALUE SYSRES_CONST_STATE_REQUISITE_CODE SYSRES_CONST_STATIC_ROLE_TYPE_CODE SYSRES_CONST_STATUS_PLAN_DEFAULT_VALUE SYSRES_CONST_STATUS_VALUE_AUTOCLEANING SYSRES_CONST_STATUS_VALUE_BLUE_SQUARE SYSRES_CONST_STATUS_VALUE_COMPLETE SYSRES_CONST_STATUS_VALUE_GREEN_SQUARE SYSRES_CONST_STATUS_VALUE_ORANGE_SQUARE SYSRES_CONST_STATUS_VALUE_PURPLE_SQUARE SYSRES_CONST_STATUS_VALUE_RED_SQUARE SYSRES_CONST_STATUS_VALUE_SUSPEND SYSRES_CONST_STATUS_VALUE_YELLOW_SQUARE SYSRES_CONST_STDROUTE_SHOW_TO_USERS_REQUISITE_CODE SYSRES_CONST_STORAGE_TYPE_FILE SYSRES_CONST_STORAGE_TYPE_SQL_SERVER SYSRES_CONST_STR_REQUISITE SYSRES_CONST_STRIKEOUT_LIFE_CYCLE_STAGE_DRAW_STYLE SYSRES_CONST_STRING_FORMAT_LEFT_ALIGN_CHAR SYSRES_CONST_STRING_FORMAT_RIGHT_ALIGN_CHAR SYSRES_CONST_STRING_REQUISITE_CODE SYSRES_CONST_STRING_REQUISITE_TYPE SYSRES_CONST_STRING_TYPE_CHAR SYSRES_CONST_SUBSTITUTES_PSEUDOREFERENCE_CODE SYSRES_CONST_SUBTASK_BLOCK_DESCRIPTION SYSRES_CONST_SYSTEM_SETTING_CURRENT_USER_PARAM_VALUE SYSRES_CONST_SYSTEM_SETTING_EMPTY_VALUE_PARAM_VALUE SYSRES_CONST_SYSTEM_VERSION_COMMENT SYSRES_CONST_TASK_ACCESS_TYPE_ALL SYSRES_CONST_TASK_ACCESS_TYPE_ALL_MEMBERS SYSRES_CONST_TASK_ACCESS_TYPE_MANUAL SYSRES_CONST_TASK_ENCODE_TYPE_CERTIFICATION SYSRES_CONST_TASK_ENCODE_TYPE_CERTIFICATION_AND_PASSWORD SYSRES_CONST_TASK_ENCODE_TYPE_NONE SYSRES_CONST_TASK_ENCODE_TYPE_PASSWORD SYSRES_CONST_TASK_ROUTE_ALL_CONDITION SYSRES_CONST_TASK_ROUTE_AND_CONDITION SYSRES_CONST_TASK_ROUTE_OR_CONDITION SYSRES_CONST_TASK_STATE_ABORTED SYSRES_CONST_TASK_STATE_COMPLETE SYSRES_CONST_TASK_STATE_CONTINUED SYSRES_CONST_TASK_STATE_CONTROL SYSRES_CONST_TASK_STATE_INIT SYSRES_CONST_TASK_STATE_WORKING SYSRES_CONST_TASK_TITLE SYSRES_CONST_TASK_TYPES_GROUPS_REFERENCE_CODE SYSRES_CONST_TASK_TYPES_REFERENCE_CODE SYSRES_CONST_TEMPLATES_REFERENCE_CODE SYSRES_CONST_TEST_DATE_REQUISITE_NAME SYSRES_CONST_TEST_DEV_DATABASE_NAME SYSRES_CONST_TEST_DEV_SYSTEM_CODE SYSRES_CONST_TEST_EDMS_DATABASE_NAME SYSRES_CONST_TEST_EDMS_MAIN_CODE SYSRES_CONST_TEST_EDMS_MAIN_DB_NAME SYSRES_CONST_TEST_EDMS_SECOND_CODE SYSRES_CONST_TEST_EDMS_SECOND_DB_NAME SYSRES_CONST_TEST_EDMS_SYSTEM_CODE SYSRES_CONST_TEST_NUMERIC_REQUISITE_NAME SYSRES_CONST_TEXT_REQUISITE SYSRES_CONST_TEXT_REQUISITE_CODE SYSRES_CONST_TEXT_REQUISITE_TYPE SYSRES_CONST_TEXT_TYPE_CHAR SYSRES_CONST_TYPE_CODE_REQUISITE_CODE SYSRES_CONST_TYPE_REQUISITE_CODE SYSRES_CONST_UNDEFINED_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_UNITS_SECTION_ID_REQUISITE_CODE SYSRES_CONST_UNITS_SECTION_REQUISITE_CODE SYSRES_CONST_UNOPERATING_RECORD_FLAG_VALUE_CODE SYSRES_CONST_UNSTORED_DATA_REQUISITE_CODE SYSRES_CONST_UNSTORED_DATA_REQUISITE_NAME SYSRES_CONST_USE_ACCESS_TYPE_CODE SYSRES_CONST_USE_ACCESS_TYPE_NAME SYSRES_CONST_USER_ACCOUNT_TYPE_VALUE_CODE SYSRES_CONST_USER_ADDITIONAL_INFORMATION_REQUISITE_CODE SYSRES_CONST_USER_AND_GROUP_ID_FROM_PSEUDOREFERENCE_REQUISITE_CODE SYSRES_CONST_USER_CATEGORY_NORMAL SYSRES_CONST_USER_CERTIFICATE_REQUISITE_CODE SYSRES_CONST_USER_CERTIFICATE_STATE_REQUISITE_CODE SYSRES_CONST_USER_CERTIFICATE_SUBJECT_NAME_REQUISITE_CODE SYSRES_CONST_USER_CERTIFICATE_THUMBPRINT_REQUISITE_CODE SYSRES_CONST_USER_COMMON_CATEGORY SYSRES_CONST_USER_COMMON_CATEGORY_CODE SYSRES_CONST_USER_FULL_NAME_REQUISITE_CODE SYSRES_CONST_USER_GROUP_TYPE_REQUISITE_CODE SYSRES_CONST_USER_LOGIN_REQUISITE_CODE SYSRES_CONST_USER_REMOTE_CONTROLLER_REQUISITE_CODE SYSRES_CONST_USER_REMOTE_SYSTEM_REQUISITE_CODE SYSRES_CONST_USER_RIGHTS_T_REQUISITE_CODE SYSRES_CONST_USER_SERVER_NAME_REQUISITE_CODE SYSRES_CONST_USER_SERVICE_CATEGORY SYSRES_CONST_USER_SERVICE_CATEGORY_CODE SYSRES_CONST_USER_STATUS_ADMINISTRATOR_CODE SYSRES_CONST_USER_STATUS_ADMINISTRATOR_NAME SYSRES_CONST_USER_STATUS_DEVELOPER_CODE SYSRES_CONST_USER_STATUS_DEVELOPER_NAME SYSRES_CONST_USER_STATUS_DISABLED_CODE SYSRES_CONST_USER_STATUS_DISABLED_NAME SYSRES_CONST_USER_STATUS_SYSTEM_DEVELOPER_CODE SYSRES_CONST_USER_STATUS_USER_CODE SYSRES_CONST_USER_STATUS_USER_NAME SYSRES_CONST_USER_STATUS_USER_NAME_DEPRECATED SYSRES_CONST_USER_TYPE_FIELD_VALUE_USER SYSRES_CONST_USER_TYPE_REQUISITE_CODE SYSRES_CONST_USERS_CONTROLLER_REQUISITE_CODE SYSRES_CONST_USERS_IS_MAIN_SERVER_REQUISITE_CODE SYSRES_CONST_USERS_REFERENCE_CODE SYSRES_CONST_USERS_REGISTRATION_CERTIFICATES_ACTION_NAME SYSRES_CONST_USERS_REQUISITE_CODE SYSRES_CONST_USERS_SYSTEM_REQUISITE_CODE SYSRES_CONST_USERS_USER_ACCESS_RIGHTS_TYPR_REQUISITE_CODE SYSRES_CONST_USERS_USER_AUTHENTICATION_REQUISITE_CODE SYSRES_CONST_USERS_USER_COMPONENT_REQUISITE_CODE SYSRES_CONST_USERS_USER_GROUP_REQUISITE_CODE SYSRES_CONST_USERS_VIEW_CERTIFICATES_ACTION_NAME SYSRES_CONST_VIEW_DEFAULT_CODE SYSRES_CONST_VIEW_DEFAULT_NAME SYSRES_CONST_VIEWER_REQUISITE_CODE SYSRES_CONST_WAITING_BLOCK_DESCRIPTION SYSRES_CONST_WIZARD_FORM_LABEL_TEST_STRING SYSRES_CONST_WIZARD_QUERY_PARAM_HEIGHT_ETALON_STRING SYSRES_CONST_WIZARD_REFERENCE_COMMENT_REQUISITE_CODE SYSRES_CONST_WORK_RULES_DESCRIPTION_REQUISITE_CODE SYSRES_CONST_WORK_TIME_CALENDAR_REFERENCE_CODE SYSRES_CONST_WORK_WORKFLOW_HARD_ROUTE_TYPE_VALUE SYSRES_CONST_WORK_WORKFLOW_HARD_ROUTE_TYPE_VALUE_CODE SYSRES_CONST_WORK_WORKFLOW_HARD_ROUTE_TYPE_VALUE_CODE_RUS SYSRES_CONST_WORK_WORKFLOW_SOFT_ROUTE_TYPE_VALUE_CODE_RUS SYSRES_CONST_WORKFLOW_ROUTE_TYPR_HARD SYSRES_CONST_WORKFLOW_ROUTE_TYPR_SOFT SYSRES_CONST_XML_ENCODING SYSRES_CONST_XREC_STAT_REQUISITE_CODE SYSRES_CONST_XRECID_FIELD_NAME SYSRES_CONST_YES SYSRES_CONST_YES_NO_2_REQUISITE_CODE SYSRES_CONST_YES_NO_REQUISITE_CODE SYSRES_CONST_YES_NO_T_REF_TYPE_REQUISITE_CODE SYSRES_CONST_YES_PICK_VALUE SYSRES_CONST_YES_VALUE ",a="CR FALSE nil NO_VALUE NULL TAB TRUE YES_VALUE ",o="ADMINISTRATORS_GROUP_NAME CUSTOMIZERS_GROUP_NAME DEVELOPERS_GROUP_NAME SERVICE_USERS_GROUP_NAME ",s="DECISION_BLOCK_FIRST_OPERAND_PROPERTY DECISION_BLOCK_NAME_PROPERTY DECISION_BLOCK_OPERATION_PROPERTY DECISION_BLOCK_RESULT_TYPE_PROPERTY DECISION_BLOCK_SECOND_OPERAND_PROPERTY ",l="ANY_FILE_EXTENTION COMPRESSED_DOCUMENT_EXTENSION EXTENDED_DOCUMENT_EXTENSION SHORT_COMPRESSED_DOCUMENT_EXTENSION SHORT_EXTENDED_DOCUMENT_EXTENSION ",c="JOB_BLOCK_ABORT_DEADLINE_PROPERTY JOB_BLOCK_AFTER_FINISH_EVENT JOB_BLOCK_AFTER_QUERY_PARAMETERS_EVENT JOB_BLOCK_ATTACHMENT_PROPERTY JOB_BLOCK_ATTACHMENTS_RIGHTS_GROUP_PROPERTY JOB_BLOCK_ATTACHMENTS_RIGHTS_TYPE_PROPERTY JOB_BLOCK_BEFORE_QUERY_PARAMETERS_EVENT JOB_BLOCK_BEFORE_START_EVENT JOB_BLOCK_CREATED_JOBS_PROPERTY JOB_BLOCK_DEADLINE_PROPERTY JOB_BLOCK_EXECUTION_RESULTS_PROPERTY JOB_BLOCK_IS_PARALLEL_PROPERTY JOB_BLOCK_IS_RELATIVE_ABORT_DEADLINE_PROPERTY JOB_BLOCK_IS_RELATIVE_DEADLINE_PROPERTY JOB_BLOCK_JOB_TEXT_PROPERTY JOB_BLOCK_NAME_PROPERTY JOB_BLOCK_NEED_SIGN_ON_PERFORM_PROPERTY JOB_BLOCK_PERFORMER_PROPERTY JOB_BLOCK_RELATIVE_ABORT_DEADLINE_TYPE_PROPERTY JOB_BLOCK_RELATIVE_DEADLINE_TYPE_PROPERTY JOB_BLOCK_SUBJECT_PROPERTY ",u="ENGLISH_LANGUAGE_CODE RUSSIAN_LANGUAGE_CODE ",d="smHidden smMaximized smMinimized smNormal wmNo wmYes ",_="COMPONENT_TOKEN_LINK_KIND DOCUMENT_LINK_KIND EDOCUMENT_LINK_KIND FOLDER_LINK_KIND JOB_LINK_KIND REFERENCE_LINK_KIND TASK_LINK_KIND ",p="COMPONENT_TOKEN_LOCK_TYPE EDOCUMENT_VERSION_LOCK_TYPE ",m="MONITOR_BLOCK_AFTER_FINISH_EVENT MONITOR_BLOCK_BEFORE_START_EVENT MONITOR_BLOCK_DEADLINE_PROPERTY MONITOR_BLOCK_INTERVAL_PROPERTY MONITOR_BLOCK_INTERVAL_TYPE_PROPERTY MONITOR_BLOCK_IS_RELATIVE_DEADLINE_PROPERTY MONITOR_BLOCK_NAME_PROPERTY MONITOR_BLOCK_RELATIVE_DEADLINE_TYPE_PROPERTY MONITOR_BLOCK_SEARCH_SCRIPT_PROPERTY ",g="NOTICE_BLOCK_AFTER_FINISH_EVENT NOTICE_BLOCK_ATTACHMENT_PROPERTY 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edesSlantEqual","PrecedesTilde","Precision","PrecisionGoal","PreDecrement","Predict","PredictionRoot","PredictorFunction","PredictorInformation","PredictorMeasurements","PredictorMeasurementsObject","PreemptProtect","PreferencesPath","Prefix","PreIncrement","Prepend","PrependLayer","PrependTo","PreprocessingRules","PreserveColor","PreserveImageOptions","Previous","PreviousCell","PreviousDate","PriceGraphDistribution","PrimaryPlaceholder","Prime","PrimeNu","PrimeOmega","PrimePi","PrimePowerQ","PrimeQ","Primes","PrimeZetaP","PrimitivePolynomialQ","PrimitiveRoot","PrimitiveRootList","PrincipalComponents","PrincipalValue","Print","PrintableASCIIQ","PrintAction","PrintForm","PrintingCopies","PrintingOptions","PrintingPageRange","PrintingStartingPageNumber","PrintingStyleEnvironment","Printout3D","Printout3DPreviewer","PrintPrecision","PrintTemporary","Prism","PrismBox","PrismBoxOptions","PrivateCellOptions","PrivateEvaluationOptions","PrivateFontOptions","PrivateFrontEndOptions","PrivateKey","PrivateNotebookOptions","PrivatePaths","Probability","ProbabilityDistribution","ProbabilityPlot","ProbabilityPr","ProbabilityScalePlot","ProbitModelFit","ProcessConnection","ProcessDirectory","ProcessEnvironment","Processes","ProcessEstimator","ProcessInformation","ProcessObject","ProcessParameterAssumptions","ProcessParameterQ","ProcessStateDomain","ProcessStatus","ProcessTimeDomain","Product","ProductDistribution","ProductLog","ProgressIndicator","ProgressIndicatorBox","ProgressIndicatorBoxOptions","Projection","Prolog","PromptForm","ProofObject","Properties","Property","PropertyList","PropertyValue","Proportion","Proportional","Protect","Protected","ProteinData","Pruning","PseudoInverse","PsychrometricPropertyData","PublicKey","PublisherID","PulsarData","PunctuationCharacter","Purple","Put","PutAppend","Pyramid","PyramidBox","PyramidBoxOptions","QBinomial","QFactorial","QGamma","QHypergeometricPFQ","QnDispersion","QPochhammer","QPolyGamma","QRDecomposition","QuadraticIrrationalQ","QuadraticOptimization","Quantile","QuantilePlot","Quantity","QuantityArray","QuantityDistribution","QuantityForm","QuantityMagnitude","QuantityQ","QuantityUnit","QuantityVariable","QuantityVariableCanonicalUnit","QuantityVariableDimensions","QuantityVariableIdentifier","QuantityVariablePhysicalQuantity","Quartics","QuartileDeviation","Quartiles","QuartileSkewness","Query","QueueingNetworkProcess","QueueingProcess","QueueProperties","Quiet","Quit","Quotient","QuotientRemainder","RadialGradientImage","RadialityCentrality","RadicalBox","RadicalBoxOptions","RadioButton","RadioButtonBar","RadioButtonBox","RadioButtonBoxOptions","Radon","RadonTransform","RamanujanTau","RamanujanTauL","RamanujanTauTheta","RamanujanTauZ","Ramp","Random","RandomChoice","RandomColor","RandomComplex","RandomEntity","RandomFunction","RandomGeoPosition","RandomGraph","RandomImage","RandomInstance","RandomInteger","RandomPermutation","RandomPoint","RandomPolygon","RandomPolyhedron","RandomPrime","RandomReal","RandomSample","RandomSeed","RandomSeeding","RandomVariate","RandomWalkProcess","RandomWord","Range","RangeFilter","RangeSpecification","RankedMax","RankedMin","RarerProbability","Raster","Raster3D","Raster3DBox","Raster3DBoxOptions","RasterArray","RasterBox","RasterBoxOptions","Rasterize","RasterSize","Rational","RationalFunctions","Rationalize","Rationals","Ratios","RawArray","RawBoxes","RawData","RawMedium","RayleighDistribution","Re","Read","ReadByteArray","ReadLine","ReadList","ReadProtected","ReadString","Real","RealAbs","RealBlockDiagonalForm","RealDigits","RealExponent","Reals","RealSign","Reap","RebuildPacletData","RecognitionPrior","RecognitionThreshold","Record","RecordLists","RecordSeparators","Rectangle","RectangleBox","RectangleBoxOptions","RectangleChart","RectangleChart3D","RectangularRepeatingElement","RecurrenceFilter","RecurrenceTable","RecurringDigitsForm","Red","Reduce","RefBox","ReferenceLineStyle","ReferenceMarkers","ReferenceMarkerStyle","Refine","ReflectionMatrix","ReflectionTransform","Refresh","RefreshRate","Region","RegionBinarize","RegionBoundary","RegionBoundaryStyle","RegionBounds","RegionCentroid","RegionDifference","RegionDimension","RegionDisjoint","RegionDistance","RegionDistanceFunction","RegionEmbeddingDimension","RegionEqual","RegionFillingStyle","RegionFunction","RegionImage","RegionIntersection","RegionMeasure","RegionMember","RegionMemberFunction","RegionMoment","RegionNearest","RegionNearestFunction","RegionPlot","RegionPlot3D","RegionProduct","RegionQ","RegionResize","RegionSize","RegionSymmetricDifference","RegionUnion","RegionWithin","RegisterExternalEvaluator","RegularExpression","Regularization","RegularlySampledQ","RegularPolygon","ReIm","ReImLabels","ReImPlot","ReImStyle","Reinstall","RelationalDatabase","RelationGraph","Release","ReleaseHold","ReliabilityDistribution","ReliefImage","ReliefPlot","RemoteAuthorizationCaching","RemoteConnect","RemoteConnectionObject","RemoteFile","RemoteRun","RemoteRunProcess","Remove","Remov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ize","Standardized","StandardOceanData","StandbyDistribution","Star","StarClusterData","StarData","StarGraph","StartAsynchronousTask","StartExternalSession","StartingStepSize","StartOfLine","StartOfString","StartProcess","StartScheduledTask","StartupSound","StartWebSession","StateDimensions","StateFeedbackGains","StateOutputEstimator","StateResponse","StateSpaceModel","StateSpaceRealization","StateSpaceTransform","StateTransformationLinearize","StationaryDistribution","StationaryWaveletPacketTransform","StationaryWaveletTransform","StatusArea","StatusCentrality","StepMonitor","StereochemistryElements","StieltjesGamma","StippleShading","StirlingS1","StirlingS2","StopAsynchronousTask","StoppingPowerData","StopScheduledTask","StrataVariables","StratonovichProcess","StreamColorFunction","StreamColorFunctionScaling","StreamDensityPlot","StreamMarkers","StreamPlot","StreamPoints","StreamPosition","Streams","StreamScale","StreamStyle","String","StringBreak","StringByteCount","StringCases","StringContainsQ","StringCount","StringDelete","StringDrop","StringEndsQ","StringExpression","StringExtract","StringForm","StringFormat","StringFreeQ","StringInsert","StringJoin","StringLength","StringMatchQ","StringPadLeft","StringPadRight","StringPart","StringPartition","StringPosition","StringQ","StringRepeat","StringReplace","StringReplaceList","StringReplacePart","StringReverse","StringRiffle","StringRotateLeft","StringRotateRight","StringSkeleton","StringSplit","StringStartsQ","StringTake","StringTemplate","StringToByteArray","StringToStream","StringTrim","StripBoxes","StripOnInput","StripWrapperBoxes","StrokeForm","StructuralImportance","StructuredArray","StructuredArrayHeadQ","StructuredSelection","StruveH","StruveL","Stub","StudentTDistribution","Style","StyleBox","StyleBoxAutoDelete","StyleData","StyleDefinitions","StyleForm","StyleHints","StyleKeyMapping","StyleMenuListing","StyleNameDialogSettings","StyleNames","StylePrint","StyleSheetPath","Subdivide","Subfactorial","Subgraph","SubMinus","SubPlus","SubresultantPolynomialRemainders","SubresultantPolynomials","Subresultants","Subscript","SubscriptBox","SubscriptBoxOptions","Subscripted","Subsequences","Subset","SubsetCases","SubsetCount","SubsetEqual","SubsetMap","SubsetPosition","SubsetQ","SubsetReplace","Subsets","SubStar","SubstitutionSystem","Subsuperscript","SubsuperscriptBox","SubsuperscriptBoxOptions","SubtitleEncoding","SubtitleTracks","Subtract","SubtractFrom","SubtractSides","SubValues","Succeeds","SucceedsEqual","SucceedsSlantEqual","SucceedsTilde","Success","SuchThat","Sum","SumConvergence","SummationLayer","Sunday","SunPosition","Sunrise","Sunset","SuperDagger","SuperMinus","SupernovaData","SuperPlus","Superscript","SuperscriptBox","SuperscriptBoxOptions","Superset","SupersetEqual","SuperStar","Surd","SurdForm","SurfaceAppearance","SurfaceArea","SurfaceColor","SurfaceData","SurfaceGraphics","SurvivalDistribution","SurvivalFunction","SurvivalModel","SurvivalModelFit","SuspendPacket","SuzukiDistribution","SuzukiGroupSuz","SwatchLegend","Switch","Symbol","SymbolName","SymletWavelet","Symmetric","SymmetricGroup","SymmetricKey","SymmetricMatrixQ","SymmetricPolynomial","SymmetricReduction","Symmetrize","SymmetrizedArray","SymmetrizedArrayRules","SymmetrizedDependentComponents","SymmetrizedIndependentComponents","SymmetrizedReplacePart","SynchronousInitialization","SynchronousUpdating","Synonyms","Syntax","SyntaxForm","SyntaxInformation","SyntaxLength","SyntaxPacket","SyntaxQ","SynthesizeMissingValues","SystemCredential","SystemCredentialData","SystemCredentialKey","SystemCredentialKeys","SystemCredentialStoreObject","SystemDialogInput","SystemException","SystemGet","SystemHelpPath","SystemInformation","SystemInformationData","SystemInstall","SystemModel","SystemModeler","SystemModelExamples","SystemModelLinearize","SystemModelParametricSimulate","SystemModelPlot","SystemModelProgressReporting","SystemModelReliability","SystemModels","SystemModelSimulate","SystemModelSimulateSensitivity","SystemModelSimulationData","SystemOpen","SystemOptions","SystemProcessData","SystemProcesses","SystemsConnectionsModel","SystemsModelDelay","SystemsModelDelayApproximate","SystemsModelDelete","SystemsModelDimensions","SystemsModelExtract","SystemsModelFeedbackConnect","SystemsModelLabels","SystemsModelLinearity","SystemsModelMerge","SystemsModelOrder","SystemsModelParallelConnect","SystemsModelSeriesConnect","SystemsModelStateFeedbackConnect","SystemsModelVectorRelativeOrders","SystemStub","SystemTest","Tab","TabFilling","Table","TableAlignments","TableDepth","TableDirections","TableForm","TableHeadings","TableSpacing","TableView","TableViewBox","TableViewBoxBackground","TableViewBoxItemSize","TableViewBoxOptions","TabSpacings","TabView","TabViewBox","TabViewBoxOptions","TagBox","TagBoxNote","TagBoxOptions","TaggingRules","TagSet","TagSetDelayed","TagStyle","TagUnset","Take","TakeDrop","TakeLargest","TakeLargestBy","TakeList","TakeSmallest","TakeSmallestBy","TakeWhile","Tally","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b/quasar.config.js @@ -0,0 +1,233 @@ +/* eslint-env node */ + +/* + * This file runs in a Node context (it's NOT transpiled by Babel), so use only + * the ES6 features that are supported by your Node version. https://node.green/ + */ + +// Configuration for your app +// https://v2.quasar.dev/quasar-cli-webpack/quasar-config-js + +const ESLintPlugin = require("eslint-webpack-plugin"); + +const { configure } = require("quasar/wrappers"); + +module.exports = configure(function (ctx) { + return { + // https://v2.quasar.dev/quasar-cli-webpack/supporting-ts + supportTS: false, + + // https://v2.quasar.dev/quasar-cli-webpack/prefetch-feature + // preFetch: true, + + // app boot file (/src/boot) + // --> boot files are part of "main.js" + // https://v2.quasar.dev/quasar-cli-webpack/boot-files + boot: [ + 'i18n' + ], + boot: ["i18n"], + + // https://v2.quasar.dev/quasar-cli-webpack/quasar-config-js#Property%3A-css + css: ["app.scss"], + + // https://github.com/quasarframework/quasar/tree/dev/extras + extras: [ + // 'ionicons-v4', + // 'mdi-v5', + // 'fontawesome-v6', + // 'eva-icons', + // 'themify', + // 'line-awesome', + // 'roboto-font-latin-ext', // this or either 'roboto-font', NEVER both! + + "roboto-font", // optional, you are not bound to it + "material-icons", // optional, you are not bound to it + ], + + // Full list of options: https://v2.quasar.dev/quasar-cli-webpack/quasar-config-js#Property%3A-build + build: { + vueRouterMode: "history", // available values: 'hash', 'history' + + // transpile: false, + // publicPath: '/', + + // Add dependencies for transpiling with Babel (Array of string/regex) + // (from node_modules, which are by default not transpiled). + // Applies only if "transpile" is set to true. + // transpileDependencies: [], + + // rtl: true, // https://quasar.dev/options/rtl-support + // preloadChunks: true, + // showProgress: false, + // gzip: true, + // analyze: true, + + // Options below are automatically set depending on the env, set them if you want to override + // extractCSS: false, + + // https://v2.quasar.dev/quasar-cli-webpack/handling-webpack + // "chain" is a webpack-chain object https://github.com/neutrinojs/webpack-chain + + chainWebpack(chain) { + chain + .plugin("eslint-webpack-plugin") + .use(ESLintPlugin, [{ extensions: ["js", "vue"] }]); + }, + }, + + // Full list of options: https://v2.quasar.dev/quasar-cli-webpack/quasar-config-js#Property%3A-devServer + devServer: { + server: { + type: "http", + }, + port: 8080, + open: true, // opens browser window automatically + }, + + // https://v2.quasar.dev/quasar-cli-webpack/quasar-config-js#Property%3A-framework + framework: { + config: {}, + + // iconSet: 'material-icons', // Quasar icon set + // lang: 'en-US', // Quasar language pack + + // For special cases outside of where the auto-import strategy can have an impact + // (like functional components as one of the examples), + // you can manually specify Quasar components/directives to be available everywhere: + // + // components: [], + // directives: [], + + // Quasar plugins + plugins: ["Notify"], + }, + + animations: "all", // --- includes all animations + // https://quasar.dev/options/animations + // animations: [], + + // https://v2.quasar.dev/quasar-cli-webpack/developing-ssr/configuring-ssr + ssr: { + pwa: false, + + // manualStoreHydration: true, + // manualPostHydrationTrigger: true, + + prodPort: 3000, // The default port that the production server should use + // (gets superseded if process.env.PORT is specified at runtime) + + maxAge: 1000 * 60 * 60 * 24 * 30, + // Tell browser when a file from the server should expire from cache (in ms) + + chainWebpackWebserver(chain) { + chain + .plugin("eslint-webpack-plugin") + .use(ESLintPlugin, [{ extensions: ["js"] }]); + }, + + middlewares: [ + ctx.prod ? "compression" : "", + "render", // keep this as last one + ], + }, + + // https://v2.quasar.dev/quasar-cli-webpack/developing-pwa/configuring-pwa + pwa: { + workboxPluginMode: "GenerateSW", // 'GenerateSW' or 'InjectManifest' + workboxOptions: {}, // only for GenerateSW + + // for the custom service worker ONLY (/src-pwa/custom-service-worker.[js|ts]) + // if using workbox in InjectManifest mode + + chainWebpackCustomSW(chain) { + chain + .plugin("eslint-webpack-plugin") + .use(ESLintPlugin, [{ extensions: ["js"] }]); + }, + + manifest: { + name: `华东师范大学XXX实验室`, + short_name: `华东师范大学XXX实验室`, + description: `华东师范大学XXX实验室`, + display: "standalone", + orientation: "portrait", + background_color: "#ffffff", + theme_color: "#027be3", + icons: [ + { + src: "icons/icon-128x128.png", + sizes: "128x128", + type: "image/png", + }, + { + src: "icons/icon-192x192.png", + sizes: "192x192", + type: "image/png", + }, + { + src: "icons/icon-256x256.png", + sizes: "256x256", + type: "image/png", + }, + { + src: "icons/icon-384x384.png", + sizes: "384x384", + type: "image/png", + }, + { + src: "icons/icon-512x512.png", + sizes: "512x512", + type: "image/png", + }, + ], + }, + }, + + // Full list of options: https://v2.quasar.dev/quasar-cli-webpack/developing-cordova-apps/configuring-cordova + cordova: { + // noIosLegacyBuildFlag: true, // uncomment only if you know what you are doing + }, + + // Full list of options: https://v2.quasar.dev/quasar-cli-webpack/developing-capacitor-apps/configuring-capacitor + capacitor: { + hideSplashscreen: true, + }, + + // Full list of options: https://v2.quasar.dev/quasar-cli-webpack/developing-electron-apps/configuring-electron + electron: { + bundler: "packager", // 'packager' or 'builder' + + packager: { + // https://github.com/electron-userland/electron-packager/blob/master/docs/api.md#options + // OS X / Mac App Store + // appBundleId: '', + // appCategoryType: '', + // osxSign: '', + // protocol: 'myapp://path', + // Windows only + // win32metadata: { ... } + }, + + builder: { + // https://www.electron.build/configuration/configuration + + appId: "ex-site", + }, + + // "chain" is a webpack-chain object https://github.com/neutrinojs/webpack-chain + + chainWebpackMain(chain) { + chain + .plugin("eslint-webpack-plugin") + .use(ESLintPlugin, [{ extensions: ["js"] }]); + }, + + chainWebpackPreload(chain) { + chain + .plugin("eslint-webpack-plugin") + .use(ESLintPlugin, [{ extensions: ["js"] }]); + }, + }, + }; +}); diff --git a/src/App.vue b/src/App.vue new file mode 100644 index 0000000..1cc60b2 --- /dev/null +++ b/src/App.vue @@ -0,0 +1,11 @@ + + + diff --git a/src/assets/img/cp.jpg b/src/assets/img/cp.jpg new file mode 100644 index 0000000..105fd74 Binary files /dev/null and b/src/assets/img/cp.jpg differ diff --git a/src/assets/img/cyx.jpg b/src/assets/img/cyx.jpg new file mode 100644 index 0000000..1a6735b Binary files /dev/null and b/src/assets/img/cyx.jpg differ diff --git a/src/assets/img/fyw.jpg 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Zhang", + "password": "cp12345", + "pic": "https://xwyzsn.site/picbed/张旭.jpg", + "description": "2024级硕士生", + "tag": "Reinforcement learning", + "type": "master2024", + "username": "zqs", + "link": null + }, + { + "id": 56, + "name": "Xingze Zheng", + "password": "cp12345", + "pic": "https://xwyzsn.site/picbed/郑兴泽.jpg", + "description": "2024级硕士生", + "tag": "Time series", + "type": "master2024", + "username": "zqs", + "link": null + }, + { + "id": 58, + "name": "Ruicheng Qian", + "password": "cp12345", + "pic": "https://xwyzsn.site/picbed/钱睿澄.jpg", + "description": "2024级硕士生", + "tag": "LLM-Based Agent", + "type": "master2024", + "username": "zqs", + "link": null + }, + { + "id": 59, + "name": "Hengyan Tu", + "password": "cp12345", + "pic": "https://xwyzsn.site/picbed/屠恒彦.jpeg", + "description": "2025级硕士生", + "tag": "Time series analysis", + "type": "master2025", + "username": "zqs", + "link": null + }, + { + "id": 60, + "name": "Yifan Zheng", + "password": "cp12345", + "pic": 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"https://xwyzsn.site/picbed/周孙睿.jpg", + "description": "2025级硕士生", + "tag": "Agent", + "type": "master2025", + "username": "zqs", + "link": null + }, + { + "id": 65, + "name": "Yuning Lu", + "password": "cp12345", + "pic": "https://xwyzsn.site/picbed/卢雨凝.jpg", + "description": "2025级硕士生", + "tag": "Time Series", + "type": "master2025", + "username": "zqs", + "link": null + }, + { + "id": 66, + "name": "Yiting Hao", + "password": "cp12345", + "pic": "https://xwyzsn.site/picbed/郝以婷.jpg", + "description": "2025级硕士生", + "tag": "Time Series Anomaly Detection", + "type": "master2025", + "username": "zqs", + "link": null + }, + { + "id": 67, + "name": "Yifei Ding", + "password": "cp12345", + "pic": "https://xwyzsn.site/picbed/丁逸飞.jpg", + "description": "2025级硕士生", + "tag": "Spatiotemporal forecasting", + "type": "master2025", + "username": "zqs", + "link": null + }, + { + "id": 68, + "name": "Linfeng Wang", + "password": "cp12345", + "pic": "https://xwyzsn.site/picbed/汪琳丰.jpeg", + 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"description": "2025级硕士生", + "tag": "LLM, Agent", + "type": "master2025", + "username": "zqs", + "link": null + }, + { + "id": 73, + "name": "Yuhan Zhu", + "password": "cp12345", + "pic": "https://xwyzsn.site/picbed/祝予晗.jpg", + "description": "2025级硕士生", + "tag": "time series forecasting", + "type": "master2025", + "username": "zqs", + "link": null + }, + { + "id": 74, + "name": "Dongxu Guo", + "password": "cp12345", + "pic": "https://xwyzsn.site/picbed/郭东旭.jpg", + "description": "2025级硕士生", + "tag": "LLM Agents, Multi-Agent System", + "type": "master2025", + "username": "zqs", + "link": null + } +] diff --git a/src/assets/news.json b/src/assets/news.json new file mode 100644 index 0000000..221463e --- /dev/null +++ b/src/assets/news.json @@ -0,0 +1,226 @@ +[ + { + "new_time": "2022年11月", + "content": "AutoCTS+: Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting被SIGMOD 2023（CCF A类）录用。首次提出了网络架构和超参数联合搜索框架，用于自动构建高精度关联时序预测模型。 " + }, + { + "new_time": "2022年11月", + "content": "Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs被IEEE TKDE（CCF A类）录用。提出一种连续型时空建模架构，用于多维时序预测。" + }, + { + "new_time": "2022年11月", + "content": "Profit Optimization in Spatial Crowdsourcing: Effectiveness and Efficiency 被IEEE TKDE（CCF A类）录用。" + }, + { + "new_time": "2022年9月", + "content": "Interpreting Operation Selection in Differentiable Architecture Search: A Perspective from Influence-Directed Explanations被NeurIPS 2022（CCF A类）录用。提出一种解释可微分架构搜索中操作选择的方法。 " + }, + { + "new_time": "2022年9月", + "content": "Weighted Mutual Learning with Diversity-Driven Model Compression 被NeurIPS 2022（CCF A类）录用。提出一种新的在线蒸馏方法，用于模型压缩。" + }, + { + "new_time": "2022年9月", + "content": "欢迎2022级研究生魏涵玥、宋晏如、欧阳彪、庄志豪、田锦东、陈鹏、王赫、张布昂加入实验室！" + }, + { + "new_time": "2023年2月", + "content": "LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation被SIGMOD 2023（CCF A类）录用。LightTS可以有效地对大型集成学习Ensemble模型进行压缩，并自动配置出满足不同资源限制下的小模型，用于时间序列分类。" + }, + { + "new_time": "2023年2月", + "content": "CGF: A Category Guidance Based PM2.5 Sequence Forecasting Training Framework被IEEE TKDE （CCF A类）录用，提出一种弱监督，自步学习的时序预测框架，用于PM2.5值的预测。" + }, + { + "new_time": "2023年5月", + "content": "LightPath: Lightweight and Scalable Path Representation Learning被SIGKDD 2023（CCF A类）录用。提出一种新颖的轻量化无监督路径表征学习方法。其核心基于稀疏自编码器和知识蒸馏，并采用基于关系推理的自监督方式进行训练。" + }, + { + "new_time": "2023年5月", + "content": "MagicScaler: Uncertainty-aware, Predictive Autoscaling被PVLDB 2023（CCF A类）Industrial Track录用。提出一种不确定性感知的，预测性自动伸缩方法，可用于云计算资源的自动扩缩容。" + }, + { + "new_time": "2023年5月", + "content": "Origin-Destination Travel Time Oracle for Map-based Services被SIGMOD 2024（CCF A类）录用。提出一种新颖的基于Diffusion模型的OD旅行时间预测方法，达到高精度和可解释性。" + }, + { + "new_time": "2023年9月", + "content": "胡吉林教授获评2023年度ACM SIGSPATIAL中国分会新星奖。胡吉林教授结合大数据技术与人工智能方法，深入研究时空数据管理和大数据管理与分析等方向，提出了一系列新理论、新技术和新方法，从而推动数字城市和智慧交通等发展。" + }, + { + "new_time": "2023年9月", + "content": "欢迎2023级研究生陈宇轩、樊亦文、高洪帆、胡诗彦、黄姗姗、李北步、李修文、刘思聪、梅杰源、邱翔飞、邱钰莹、申屠琦超、王海臣、王益杭、魏永富、吴行健、徐榕荟、张滕雪、张馨元、郑朴辰、钟青松加入实验室！" + }, + { + "new_time": "2023年11月", + "content": "Weakly Guided Adaptation for Robust Time Series Forecasting被PVLDB 2024（CCF A类）录用。提出一种基于节假日等易获取的弱监督信号进行训练的对抗学习方法，可以有效提高时序预测的鲁棒性。" + }, + { + "new_time": "2023年11月", + "content": "Multiple Time Series Forecasting with Dynamic Graph Modeling被PVLDB 2024（CCF A类）录用。提出一种能够有效估计多条时间序列的未来关联关系的方法，并结合动态图神经网络进行有效的时序预测。" + }, + { + "new_time": "2024年1月", + "content": "Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting被ICLR 2024 （CORE A*）录用。提出一种基于Pathway的多尺度自适应时序建模方法用于时序预测。" + }, + { + "new_time": "2024年2月", + "content": "实验室承担的“基于负载预测的云计算资源调度优化算法”项目获得阿里巴巴2024优秀学术合作项目奖。" + }, + { + "new_time": "2024年4月", + "content": "TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods被PVLDB 2024 （CCF A类）录用。提出了一套全面的时间序列预测方法的评测基准。" + }, + { + "new_time": "2024年5月", + "content": "A Crystal Knowledge-enhanced Pre-training Framework for Crystal Property Estimation被ECML PKDD 2024录用。提出了用于晶体性质预测的预训练框架，可以有效降低标注数据的需求量，并融入了相关晶体知识。" + }, + { + "new_time": "2024年5月", + "content": "What Can Large Language Models Tell Us about Time Series Analysis被ICML 2024 （CCF A类） Position Paper Track录用。展望了大模型和时间序列分析结合的前景。" + }, + { + "new_time": "2024年6月", + "content": "Efficient Stochastic Routing in Path-Centric Uncertain Road Networks被PVLDB 2024 （CCF A类）录用。基于能对旅行时间的不确定性进行准确建模的路径中心不确定图，提出了高效的随机路由导航算法。" + }, + { + "new_time": "2024年6月", + "content": "QCore: Data-Efficient, On-Device Continual Calibration for Quantized Models被PVLDB 2024 （CCF A类）录用。针对端侧量化模型，提出了数据高效的持续模型校准方法，可以高效支撑边端智能。" + }, + { + "new_time": "2024年7月", + "content": "AutoCTS++: Zero-shot Joint Neural Architecture and Hyperparameter Search forCorrelated Time Series Forecasting被The VLDB Journal （CCF A类）录用。提出了Zero-shot的神经架构和超参数联合搜索框架，用于对不同时间序列数据快速构建预测模型及其合适的超参数。" + }, + { + "new_time": "2024年7月", + "content":"Ocean Significant Wave Height Estimation with Spatio-temporally Aware Large Language Models被CIKM 2024录用。提出了基于时空感知大语言模型的海洋有效波高预测模型Orca。" + }, + { + "new_time":"2024年7月", + "content":"Dependency-aware Differentiable Neural Architecture Search被ECCV 2024录用。提出了一种考虑神经网络架构图中边之间相关性的可微神经架构搜索方法。" + }, + { + "new_time": "2024年8月", + "content": "TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods在VLDB 2024 （CCF A类）大会上获得最佳论文提名。" + }, + { + "new_time":"2024年9月", + "content": "欢迎2024级研究生成涵吟、金建新、雷智、李哲、李正宇、裘王辉、沈王梦、汪思远、徐鼎力、严思雨、杨柳、张旭、郑兴泽、朱林威加入实验室！" + }, + { + "new_time":"2024年9月", + "content": "Less is More: Efficient Time Series Dataset Condensation via Two-fold Modal Matching被PVLDB 2025（CCF A类）录用。提出了时序数据压缩方法，可以大幅提高需要重复训练场景的效率，比如流式分析、模型选择和NAS等。" + }, + { + "new_time":"2024年9月", + "content": "TEAM: Topological Evolution-aware Framework for Traffic Forecasting被PVLDB 2025（CCF A类）录用。提出了在交通道路网路发生动态演变情况下的高效时空交通流预测方法。" + }, + { + "new_time":"2024年9月", + "content": "A Memory Guided Transformer for Time Series Forecasting被PVLDB 2025（CCF A类）录用。提出了有效建模长时间序列的动态关联性和突变关联性，有效提高长期预测精度。" + }, + { + "new_time":"2024年10月", + "content":"Fully Automated Correlated Time Series Forecasting in Minutes被PVLDB 2025（CCF A类）录用。提出了高效的自动化时序预测框架，可以在分钟级别构建针对特定时序的预测模型。" + }, + { + "new_time":"2024年11月", + "content":"DUET: Dual Clustering Enhanced Multivariate Time Series Forecasting被KDD2025（CCF A类）录用。提出一种时间和通道双向聚类架构，可以有效应对时序数据分布漂移并动态建模多变量时间序列中的变量相关性。" + }, + { + "new_time":"2024年11月", + "content":"MM-Path: Multi-modal, Multi-granularity Path Representation Learning被KDD 2025（CCF A类）录用。提出了多模态、多尺度路径表征学习框架，进而融合道路拓扑结合和卫星图像进行通用路径表征学习。" + }, + { + "new_time":"2024年12月", + "content":"Assessing Pre-trained Models for Transfer Learning through Distribution of Spectral Components被AAAI 2025（CCF A类）录用。提出了从模型库中选择出最适合下游任务的预训练模型的方法，可以灵活用于分类和回归任务。" + }, + { + "new_time":"2025年1月", + "content":"Air-DualODE: Air Quality Prediction with Physics-guided Dual Neural ODEs in Open Systems被ICLR 2025（CORE A*）录用。提出了一种双分支动态系统建模的知识-数据双驱动算法，在开放系统的空气污染物预测任务上提升了可解释性和预测准确性。" + }, + { + "new_time":"2025年1月", + "content":"Learning Generalizable Skills from Offline Multi-Task Data for Multi-Agent Cooperation被ICLR 2025（CORE A*）录用。提出了一种基于分层策略与多视角技能学习的离线多智能体强化学习算法，实现了多任务场景中的策略迁移。" + }, + { + "new_time":"2025年1月", + "content":"CATCH: Channel-Aware Multivariate Time Series Anomaly Detection via Frequency Patching被ICLR 2025（CORE A*）录用。提出了一种基于通道感知的多变量异常检测算法，通过划分不同的频段，并在每个频段中自适应地发现通道关系，增强了模型对于多变量时间序列的重构能力，显著提升了异常检测的能力。" + }, + { + "new_time":"2025年1月", + "content":"Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoders被ICLR 2025（CORE A*）录用。提出首个通用时序异常检测模型，在多个数据集上zero-shot性能达到SOTA。" + }, + { + "new_time":"2025年1月", + "content":"Path-LLM: A Multi-Modal Path Representation Learning by Aligning and Fusing with Large Language Models被The Web Conference 2025（CCF A类）录用。提出了一种基于大语言模型的多模态（路段名称和路段标号）路径表征方法，进一步提升路径表征在下游任务上的泛化能力。" + }, + { + "new_time":"2025年1月", + "content":"RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems被VLDB 2025（CCF A类）录用。提出了一种估计和排序根因影响程度的多模态慢查询根因诊断算法，可以优先处理慢查询的重要根因。" + }, + { + "new_time":"2025年1月", + "content":"Noise Matters: Cross Contrastive Learning for Flink Anomaly Detection被VLDB 2025（CCF A类）录用。提出了一种能够异常感知的、基于跨窗口对比学习的多元时序无监督异常检测算法。" + }, + { + "new_time":"2025年2月", + "content":"Enhancing Diversity for Data-free Quantization 被CVPR 2025（CCF A类）录用。从样本数据多样性的角度提出了一种基于生成器架构的无数据模型量化方式，提升了量化模型的精度。" + }, + { + "new_time":"2025年3月", + "content":"AimTS: Augmented Series and Image Contrastive Learning for Time Series Classification 被ICDE 2025（CCF A类）录用。提出了一种基于Prototype和时序-图像对比学习的预训练框架，在不同的下游时序分类数据实现了良好的分类效果。" + }, + { + "new_time":"2025年3月", + "content":"AID-SQL: Adaptive In-Context Learning of Text-to-SQL with Difficulty-Aware Instruction and Retrieval-Augmented Generation 被ICDE 2025（CCF A类）录用。从动态自适应上下文学习的角度提出了一种基于检索增强生成的大语言模型Text-to-SQL任务解决方案，增强了生成SQL的精度。" + }, + { + "new_time":"2025年4月", + "content":"ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting 被IJCAI 2025（CCF A类）录用。该方法将差分注意力机制引入到时空领域，抑制了无关的空间关联，使得模型关注到关键空间信息。模型充分考虑多层级的时空关联，提升了城市需求预测精度。" + }, + { + "new_time":"2025年5月", + "content":"决策智能实验室硕士研究生邱翔飞获第五届CCF优秀大学生学术秀硕士组一等奖" + }, + { + "new_time":"2025年5月", + "content":"TAB: Unified Benchmarking of Time Series Anomaly Detection Methods 被PVLDB 2025（CCF A类）录用。提出了时间序列异常检测方法的统一测评基准." + }, + { + "new_time":"2025年5月", + "content":"Unsupervised Time Series Anomaly Prediction with Importance-based Generative Contrastive Learning 被KDD 2025（CCF A类）录用。结合Maximum Mean Discrepancy理论，首次解决无监督时间序列异常预测问题，并提出了一种基于控制扩散过程的生成式对比学习，显著提升了精度和效率。" + }, + { + "new_time":"2025年5月", + "content":"TSFM-Bench: Comprehensive and Unified Benchmarking of Foundation Models for Time Series Forecasting 被KDD 2025（CCF A类）录用。提出了一套全面统一的时间序列预测基础模型的测评基准。" + }, + { + "new_time":"2025年5月", + "content":"SSD-TS: Exploring the potential of linear state space models for diffusion models in time series imputation被KDD 2025（CCF A类）录用。该研究基于状态空间模型提出了适用于扩散模型的有效骨干网络，显著提升了时间序列插补任务的精度。" + }, + { + "new_time":"2025年5月", + "content":"K²VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting 被ICML 2025（CCF A类）录用。结合库普曼理论与卡尔曼滤波，提出了一种高效的变分自编码器，显著提升了长程时间序列概率预测的精度和效率。" + }, + { + "new_time":"2025年5月", + "content":"LightGTS: A Lightweight General Time Series Forecasting Model 被ICML 2025（CCF A类）录用。该研究提出了一种创新的时序基础模型，通过周期tokenization和并行解码机制，有效融合时序数据的固有归纳偏置。在保持轻量化参数量级的前提下，模型在零样本预测任务中取得了当前最先进的性能表现。" + }, + { + "new_time":"2025年5月", + "content":"Towards a General Time Series Forecasting Model with Unified Representation and Adaptive Transfer 被ICML 2025（CCF A类）录用。结合分解频率掩码的预训练任务和时序寄存器，提出了一个有效的时序基础模型框架，显著提升了时序基础模型少样本和零样本的预测能力。" + }, + { + "new_time":"2025年6月", + "content":"欢送决策智能实验室首届（2025届）毕业生！祝贺实验室首届毕业生王赫、宋晏如、魏涵玥、庄志豪、张布昂顺利毕业，取得硕士学位！其中庄志豪同学获评上海市优秀毕业生！" + }, + { + "new_time":"2025年9月", + "content":"欢迎2025级研究生屠恒彦、郑一钒、鲍啸天、陈亦楠、张睿桐、周孙睿、卢雨凝、郝以婷、丁逸飞、汪琳丰、唐益、晏康佳、陆骏凯、祝予晗、郭东旭、杨锦涛加入实验室！" + }, + { + "new_time":"2026年6月", + "content":"欢送决策智能实验室2026届毕业生！祝贺欧阳彪顺利毕业，取得博士学位！祝贺陈宇轩、樊亦文、黄姗姗、李北步、李修文、梅杰源、邱钰莹、申屠琦超、王海臣、王益杭、魏永富、张馨元顺利毕业，取得硕士学位！祝贺王益杭获得上海市优秀毕业生！祝贺欧阳彪、王海臣、李北步、申屠琦超、魏永富获得华东师范大学优秀毕业生！" + } +] diff --git a/src/assets/news_en.json b/src/assets/news_en.json new file mode 100644 index 0000000..bfb7600 --- /dev/null +++ b/src/assets/news_en.json @@ -0,0 +1,226 @@ +[ + { + "new_time": "November 2022", + "content": "[SIGMOD 2023] AutoCTS+: Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting." + }, + { + "new_time": "November 2022", + "content": "[IEEE TKDE] Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs." + }, + { + "new_time": "November 2022", + "content": "[IEEE TKDE] Profit Optimization in Spatial Crowdsourcing: Effectiveness and Efficiency." + }, + { + "new_time": "September 2022", + "content": "[NeurIPS 2022] Interpreting Operation Selection in Differentiable Architecture Search: A Perspective from Influence-Directed Explanations." + }, + { + "new_time": "September 2022", + "content": "[NeurIPS 2022] Weighted Mutual Learning with Diversity-Driven Model Compression." + }, + { + "new_time": "September 2022", + "content": "Welcome to the lab, 2022 graduate students 魏涵玥、宋晏如、欧阳彪、庄志豪、田锦东、陈鹏、王赫、张布昂！" + }, + { + "new_time": "February 2023", + "content": "[SIGMOD 2023] LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation." + }, + { + "new_time": "February 2023", + "content": "[IEEE TKDE] CGF: A Category Guidance Based PM2.5 Sequence Forecasting Training Framework." + }, + { + "new_time": "May 2023", + "content": "[SIGKDD 2023] LightPath: Lightweight and Scalable Path Representation Learning." + }, + { + "new_time": "May 2023", + "content": "[PVLDB 2023] MagicScaler: Uncertainty-aware, Predictive Autoscaling." + }, + { + "new_time": "May 2023", + "content": "[SIGMOD 2024] Origin-Destination Travel Time Oracle for Map-based Services." + }, + { + "new_time": "September 2023", + "content": "Professor Hu Jilin has been awarded the 2023 ACM SIGSPATIAL China Rising Star Award. Combining big data technology with artificial intelligence methods, Professor Hu Jilin conducts in-depth research in spatiotemporal data management, big data management, and analysis. He has proposed a series of new theories, technologies, and methods, contributing to the advancement of digital cities and intelligent transportation." + }, + { + "new_time": "September 2023", + "content": "Welcome to the lab, 2023 graduate students 陈宇轩、樊亦文、高洪帆、胡诗彦、黄姗姗、李北步、李修文、刘思聪、梅杰源、邱翔飞、邱钰莹、申屠琦超、王海臣、王益杭、魏永富、吴行健、徐榕荟、张滕雪、张馨元、郑朴辰、钟青松！" + }, + { + "new_time": "November 2023", + "content": "[PVLDB 2024] Weakly Guided Adaptation for Robust Time Series Forecasting." + }, + { + "new_time": "November 2023", + "content": "[PVLDB 2024] Multiple Time Series Forecasting with Dynamic Graph Modeling." + }, + { + "new_time": "January 2024", + "content": "[ICLR 2024] Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting." + }, + { + "new_time": "February 2024", + "content": "The lab's project, “Cloud Computing Resource Scheduling Optimization Algorithm Based on Workload Prediction,“ has been awarded the 2024 Alibaba Outstanding Academic Collaboration Project Award." + }, + { + "new_time": "April 2024", + "content": "[PVLDB 2024] TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods." + }, + { + "new_time": "May 2024", + "content": "[ECML PKDD] A Crystal Knowledge-enhanced Pre-training Framework for Crystal Property Estimation." + }, + { + "new_time": "May 2024", + "content": "[ICML 2024] What Can Large Language Models Tell Us about Time Series Analysis." + }, + { + "new_time": "June 2024", + "content": "[PVLDB 2024] Efficient Stochastic Routing in Path-Centric Uncertain Road Networks." + }, + { + "new_time": "June 2024", + "content": "[PVLDB 2024] QCore: Data-Efficient, On-Device Continual Calibration for Quantized Models." + }, + { + "new_time": "July 2024", + "content": "[VLDBJ 2024] AutoCTS++: Zero-shot Joint Neural Architecture and Hyperparameter Search forCorrelated Time Series Forecasting." + }, + { + "new_time": "July 2024", + "content":"[CIKM 2024] Ocean Significant Wave Height Estimation with Spatio-temporally Aware Large Language Models." + }, + { + "new_time":"July 2024", + "content":"[ECCV 2024] Dependency-aware Differentiable Neural Architecture Search." + }, + { + "new_time": "August 2024", + "content": "[PVLDB 2024] TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods. Best paper nomination." + }, + { + "new_time":"September 2024", + "content": "Welcome to the lab, 2024 graduate students 成涵吟、金建新、雷智、李哲、李正宇、裘王辉、沈王梦、汪思远、徐鼎力、严思雨、杨柳、张旭、郑兴泽、朱林威！" + }, + { + "new_time":"September 2024", + "content": "[PVLDB 2025] Less is More: Efficient Time Series Dataset Condensation via Two-fold Modal Matching." + }, + { + "new_time":"September 2024", + "content": "[PVLDB 2025] TEAM: Topological Evolution-aware Framework for Traffic Forecasting." + }, + { + "new_time":"September 2024", + "content": "[PVLDB 2025] A Memory Guided Transformer for Time Series Forecasting." + }, + { + "new_time":"September 2024", + "content":"[PVLDB 2025] Fully Automated Correlated Time Series Forecasting in Minutes." + }, + { + "new_time":"November 2024", + "content":"[KDD 2025] DUET: Dual Clustering Enhanced Multivariate Time Series Forecasting." + }, + { + "new_time":"November 2024", + "content":"[KDD 2025] MM-Path: Multi-modal, Multi-granularity Path Representation Learning." + }, + { + "new_time":"December 2024", + "content":"[AAAI 2025] Assessing Pre-trained Models for Transfer Learning through Distribution of Spectral Components." + }, + { + "new_time":"January 2025", + "content":"[ICLR 2025] Air-DualODE: Air Quality Prediction with Physics-guided Dual Neural ODEs in Open Systems." + }, + { + "new_time":"January 2025", + "content":"[ICLR 2025] Learning Generalizable Skills from Offline Multi-Task Data for Multi-Agent Cooperation." + }, + { + "new_time":"January 2025", + "content":"[ICLR 2025] CATCH: Channel-Aware Multivariate Time Series Anomaly Detection via Frequency Patching." + }, + { + "new_time":"January 2025", + "content":"[ICLR 2025] Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoders." + }, + { + "new_time":"January 2025", + "content":"[WWW 2025] Path-LLM: A Multi-Modal Path Representation Learning by Aligning and Fusing with Large Language Models." + }, + { + "new_time":"January 2025", + "content":"[PVLDB 2025] RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems." + }, + { + "new_time":"January 2025", + "content":"[PVLDB 2025] Noise Matters: Cross Contrastive Learning for Flink Anomaly Detection." + }, + { + "new_time":"February 2025", + "content":"[CVPR 2025] Enhancing Diversity for Data-free Quantization." + }, + { + "new_time":"March 2025", + "content":"[ICDE 2025] AimTS: Augmented Series and Image Contrastive Learning for Time Series Classification." + }, + { + "new_time":"March 2025", + "content":"[ICDE 2025] AID-SQL: Adaptive In-Context Learning of Text-to-SQL with Difficulty-Aware Instruction and Retrieval-Augmented Generation." + }, + { + "new_time":"April 2025", + "content":"[IJCAI 2025] ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting." + }, + { + "new_time":"May 2025", + "content":"Master's student Qiu Xiangfei won the First Prize in the Master's Group at the 5th CCF Outstanding Undergraduate Academic Showcase." + }, + { + "new_time":"May 2025", + "content":"[PVLDB 2025] TAB: Unified Benchmarking of Time Series Anomaly Detection Methods." + }, + { + "new_time":"May 2025", + "content":"[KDD 2025] Unsupervised Time Series Anomaly Prediction with Importance-based Generative Contrastive Learning." + }, + { + "new_time":"May 2025", + "content":"[KDD 2025] TSFM-Bench: Comprehensive and Unified Benchmarking of Foundation Models for Time Series Forecasting." + }, + { + "new_time":"May 2025", + "content":"[KDD 2025] SSD-TS: Exploring the potential of linear state space models for diffusion models in time series imputation." + }, + { + "new_time":"May 2025", + "content":"[ICML 2025] K²VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting." + }, + { + "new_time":"May 2025", + "content":"[ICML 2025] LightGTS: A Lightweight General Time Series Forecasting Model." + }, + { + "new_time":"May 2025", + "content":"[ICML 2025] Towards a General Time Series Forecasting Model with Unified Representation and Adaptive Transfer." + }, + { + "new_time":"June 2025", + "content":"Farewell to the first graduating class (Class of 2025) of the Decision Intelligence Lab! Congratulations to our inaugural graduates — 王赫, 宋晏如, 魏涵玥, 庄志豪, 张布昂 — on successfully completing their studies and receiving their Master's degrees! A special congratulations to 庄志豪 for being recognized as an Outstanding Graduate of Shanghai!" + }, + { + "new_time":"September 2025", + "content":"Welcome to the lab, 2025 graduate students 屠恒彦、郑一钒、鲍啸天、陈亦楠、张睿桐、周孙睿、卢雨凝、郝以婷、丁逸飞、汪琳丰、唐益、晏康佳、陆骏凯、祝予晗、郭东旭、杨锦涛！" + }, + { + "new_time":"June 2026", + "content":"Farewell to the 2026 graduates of the Decision Intelligence Lab! Congratulations to 欧阳彪 on successfully graduating with a PhD degree! Congratulations to 陈宇轩、樊亦文、黄姗姗、李北步、李修文、梅杰源、邱钰莹、申屠琦超、王海臣、王益杭、魏永富、张馨元 on successfully graduating with master’s degrees! Congratulations to 王益杭 on being named a Shanghai Outstanding Graduate! Congratulations to 欧阳彪、王海臣、李北步、申屠琦超、魏永富 on being named Outstanding Graduates of East China Normal University!" + } +] diff --git a/src/assets/paper_table.json b/src/assets/paper_table.json new file mode 100644 index 0000000..091b298 --- /dev/null +++ b/src/assets/paper_table.json @@ -0,0 +1,636 @@ +[ + { + "id": 1, + "data": { + "data": [ + { + "tag": "l1_tag", + "desc": "l1_desc", + "content": [ + { + "content": [ + { + "title": "lst1_1_1", + "papers": [ + "Xiangfei Qiu, Xingjian Wu, Yan Lin, Chenjuan Guo, Jilin Hu, Bin Yang: DUET: Dual Clustering Enhanced Multivariate Time Series Forecasting. KDD 2025." + ] + }, + { + "title": "lst1_1_2", + "papers": [ + "Razvan-Gabriel Cirstea, Bin Yang, Chenjuan Guo, Tung Kieu, Shirui Pan: Towards Spatio-Temporal Aware Traffic Time Series Forecasting. ICDE 2022: 2900-2913", + "Razvan-Gabriel Cirstea, Tung Kieu, Chenjuan Guo, Bin Yang, Sinno Jialin Pan: EnhanceNet: Plugin Neural Networks for Enhancing Correlated Time Series Forecasting. ICDE 2021: 1739-1750", + "Haomin Yu, Jilin Hu, Xinyuan Zhou, Chenjuan Guo, Bin Yang, Qingyong Li: CGF: A Category Guidance Based PM2.5 Sequence Forecasting Training Framework. IEEE TKDE, 2023. " + ] + }, + { + "title": "lst1_1_3", + "papers": [ + "Xinle Wu, Dalin Zhang, Chenjuan Guo, Chaoyang He, Bin Yang, Christian S. Jensen: AutoCTS:Automated Correlated Time Series Forecasting. Proc. VLDB Endow. 15(4): 971-983 (2021)", + "AutoCTS+: Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting. SIGMOD 2023.", + "Xinle Wu, Xingjian Wu, Bin Yang, Lekui Zhou, Chenjuan Guo, Xiangfei Qiu, Jilin Hu, Zhenli Sheng, Christian S. Jensen: AutoCTS++: zero-shot joint neural architecture and hyperparameter search for correlated time series forecasting. VLDB J. 33(5): 1743-1770 (2024)", + "Xinle Wu, Xingjian Wu, Bin Yang, Dalin Zhang, Miao Zhang, Chenjuan Guo, Christian S. Jensen: Fully Automated Correlated Time Series Forecasting in Minutes. PVLDB, 2025." + ] + }, + { + "title": "lst1_1_4", + "papers": [ + "Razvan-Gabriel Cirstea, Chenjuan Guo, Bin Yang, Tung Kieu, Xuanyi Dong, and Shirui Pan. Triformer: Triangular, Variable-Specific Attention for Long Sequence Multivariate Time Series Forecasting. IJCAI 2022.", + "Peng Chen, Yingying Zhang, Yunyao Cheng, Yang Shu, Yihang Wang, Qingsong Wen, Bin Yang, Chenjuan Guo: Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting. ICLR 2024.", + "A Memory Guided Transformer for Time Series Forecasting. PVLDB 2025." + ] + }, + { + "title": "lst1_1_5", + "papers":[ + "Yunyao Cheng, Peng Chen, Chenjuan Guo, Kai Zhao, Qingsong Wen, Bin Yang, Christian S. Jensen: Weakly Guided Adaptation for Robust Time Series Forecasting. PVLDB 2023." + ] + }, + { + "title": "lst1_1_6", + "papers": [ + "Kai Zhao, Chenjuan Guo, Yunyao Cheng, Peng Han, Miao Zhang, Bin Yang: Multiple Time Series Forecasting with Dynamic Graph Modeling. PVLDB 2023." + ] + }, + { + "title": "lst1_1_7", + "papers": [ + "Xiangfei Qiu, Jilin Hu, Lekui Zhou, Xingjian Wu, Junyang Du, Buang Zhang, Chenjuan Guo, Aoying Zhou, Christian S. Jensen, Zhenli Sheng, Bin Yang: TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods. PVLDB 2024. " + ] + }, + { + "title": "lst1_1_8", + "papers" :[ + "Hao Miao, Yan Zhao, Chenjuan Guo, Bin Yang, Kai Zheng, Feiteng Huang, Jiandong Xie, Christian S. Jensen: A Unified Replay-based Continuous Learning Framework for Spatio-Temporal Prediction on Streaming Data. ICDE 2024.", + "David Campos, Bin Yang, Tung Kieu, Miao Zhang, Chenjuan Guo, Christian S. Jensen: QCore: Data-Efficient, On-Device Continual Calibration for Quantized Models. PVLDB 2024. " + ] + }, + { + "title": "lst1_1_9", + "papers": [ + "Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs. Ming Jin, Yu Zheng Yuan-Fang Li, Siheng Chen, Bin Yang, Shirui Pan. TKDE, 2022.", + "Jindong Tian, Yuxuan Liang, Ronghui Xu, Peng Chen, Chenjuan Guo, Aoying Zhou, Lujia Pan, Zhongwen Rao, Bin Yang: Air Quality Prediction with Physics-Informed Dual Neural ODEs in Open Systems. ICLR 2025" + ] + }, + { + "title": "lst1_1_10", + "papers": [ + "Bin Yang, Chenjuan Guo, Christian S. Jensen: Travel Cost Inference from Sparse, SpatioTemporally Correlated Time Series Using Markov Models. Proc. VLDB Endow. 6(9): 769-780 (2013)" + ] + }, + { + "title": "lst1_1_11", + "papers": [ + "Razvan-Gabriel Cirstea, Darius-Valer Micu, Gabriel-Marcel Muresan, Chenjuan Guo, Bin Yang: Correlated Time Series Forecasting using Multi-Task Deep Neural Networks. CIKM 2018: 1527 1530", + "Nicolaj Casanova Abildgaard, Casper Weiss Bang, Jonas Hansen, Tobias Lambek Jacobsen,Thomas Højriis Knudsen, Nichlas Ørts Lisby, Chenjuan Guo, Bin Yang: A Correlated Time Series Forecast System. MDM 2020: 242-24" + ] + } + ], + "subsection": "ls1_1" + }, + { + "content": [ + { + "title": "lst1_2_4", + "papers": [ + "Qichao Shentu, Beibu Li, Kai Zhao, Yang Shu, Zhongwen Rao, Lujia Pan, Bin Yang, Chenjuan Guo: Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoders. ICLR 2025" + ] + }, + { + "title": "lst1_2_1", + "papers": [ + "David Campos, Tung Kieu, Chenjuan Guo, Feiteng Huang, Kai Zheng, Bin Yang, Christian S. Jensen: Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles. Proc. VLDB Endow. 15(3): 611-623 (2021)", + "Tung Kieu, Bin Yang, Chenjuan Guo, Christian S. Jensen: Outlier Detection for Time Series with Recurrent Autoencoder Ensembles. IJCAI 2019: 2725-2732" + ] + }, + { + "title": "lst1_2_2", + "papers": [ + "Tung Kieu, Bin Yang, Chenjuan Guo, Christian S. Jensen, Yan Zhao, Feiteng Huang, Kai Zheng: Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection. ICDE 2022: 3038-3050 ", + "Tung Kieu, Bin Yang, Chenjuan Guo, Razvan-Gabriel Cirstea, Yan Zhao, Yale Song, Christian S. Jensen: Anomaly Detection in Time Series with Robust Variational Quasi-Recurrent Autoencoders. ICDE 2022: 1342-1354" + ] + }, + { + "title": "lst1_2_3", + "papers": [ + "Xingjian Wu, Xiangfei Qiu, Zhengyu Li, Yihang Wang, Jilin Hu, Chenjuan Guo, Hui Xiong, Bin Yang: CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patching. ICLR 2025" + ] + }, + { + "title": "lst1_2_5", + "papers": [ + "Tung Kieu, Bin Yang, Chenjuan Guo, Christian S. Jensen, Yan Zhao, Feiteng Huang, Kai Zheng: Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection. ICDE 2022: 3038-3050" + ] + } + ], + "subsection": "ls1_2" + }, + { + "content": [ + { + "title": "", + "papers": [ + "David Campos, Miao Zhang, Bin Yang, Tung Kieu, Chenjuan Guo, Christian S. Jensen: LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation. SIGMOD 2023", + "David Campos, Bin Yang, Tung Kieu, Miao Zhang, Chenjuan Guo, Christian S. Jensen: QCore: Data-Efficient, On-Device Continual Calibration for Quantized Models. PVLDB 2024. " + ] + } + ], + "subsection": "ls1_3" + } + ], + "section": "l1", + "id":"1" + }, + { + "tag": "l4_tag", + "desc": "l4_desc", + "content": [ + { + "content": [ + { + "title": "lst4_1_1", + "papers": [ + "Ronghui Xu, Hanyin Cheng, Chenjuan Guo, Hongfan Gao, Jilin Hu, Sean Bin Yang, Bin Yang: MM-Path: Multi-modal, Multi-granularity Path Representation Learning. KDD 2025. ", + "Sean Bin Yang, Jilin Hu, Chenjuan Guo, Bin Yang, Christian S. Jensen: LightPath: Lightweight and Scalable Path Representation Learning. KDD 2023. ", + "Sean Bin Yang, Chenjuan Guo, Jilin Hu, Bin Yang, Jian Tang, Christian S. Jensen: Weaklysupervised Temporal Path Representation Learning with Contrastive Curriculum Learning. ICDE 2022: 2873-2885", + "Sean Bin Yang, Chenjuan Guo, Bin Yang: Context-Aware Path Ranking in Road Networks. IEEE Trans. Knowl. Data Eng. 34(7): 3153-3168 (2022)", + "Sean Bin Yang, Chenjuan Guo, Jilin Hu, Jian Tang, Bin Yang: Unsupervised Path Representation Learning with Curriculum Negative Sampling. IJCAI 2021: 3286-3292 ", + "Sean Bin Yang, Bin Yang: Learning to Rank Paths in Spatial Networks. ICDE 2020: 2006- 2009 ", + "Jilin Hu, Bin Yang, Chenjuan Guo, Christian S. Jensen: Risk-aware path selection with time-varying, uncertain travel costs: a time series approach. VLDB J. 27(2): 179-200 (2018)", + "Jian Dai, Bin Yang, Chenjuan Guo, Christian S. Jensen, Jilin Hu: Path Cost Distribution Estimation Using Trajectory Data. Proc. VLDB Endow. 10(3): 85-96 (2016)" + ] + }, + { + "title": "lst4_1_2", + "papers": [ + "Jilin Hu, Bin Yang, Chenjuan Guo, Christian S. 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Jensen: Learning to Route with Sparse Trajectory Sets. ICDE 2018: 1073-1084 ", + "Bin Yang, Chenjuan Guo, Yu Ma, Christian S. Jensen: Toward personalized, context-aware routing. VLDB J. 24(2): 297-318 (2015)", + "Jian Dai, Bin Yang, Chenjuan Guo, Zhiming Ding: Personalized route recommendation using big trajectory data. ICDE 2015: 543-554" + ] + } + ], + "subsection": "ls4_2" + }, + { + "content": [ + { + "title": "lst4_3_1", + "papers": [ + "Chenjuan Guo, Ronghui Xu, Bin Yang, Ye Yuan, Tung Kieu, Yan Zhao, Christian S. Jensen. Efficient Stochastic Routing in Path-Centric Uncertain Road Networks. PVLDB 2024. ", + "Simon Aagaard Pedersen, Bin Yang, Christian S. Jensen, Jesper Møller: Stochastic Routing with Arrival Windows. ACM TSAS, 2023. ", + "Simon Aagaard Pedersen, Bin Yang, Christian S. Jensen: A Hybrid Learning Approach to Stochastic Routing. ICDE 2020: 1910-1913", + "Simon Aagaard Pedersen, Bin Yang, Christian S. Jensen: Anytime Stochastic Routing with Hybrid Learning. Proc. VLDB Endow. 13(9): 1555-1567 (2020)", + "Simon Aagaard Pedersen, Bin Yang, Christian S. Jensen: Fast stochastic routing under time-varying uncertainty. VLDB J. 29(4): 819-839 (2020)", + "Jilin Hu, Bin Yang, Chenjuan Guo, Christian S. Jensen: Risk-aware path selection with time-varying, uncertain travel costs: a time series approach. VLDB J. 27(2): 179-200 (2018)", + "Bin Yang, Jian Dai, Chenjuan Guo, Christian S. Jensen, Jilin Hu: PACE: a PAth-CEntric paradigm for stochastic path finding. VLDB J. 27(2): 153-178 (2018)", + "Georgi Andonov, Bin Yang: Stochastic Shortest Path Finding in Path-Centric Uncertain Road Networks. MDM 2018: 40-45" + ] + }, + { + "title": "lst4_3_2", + "papers": [ + "Simon Aagaard Pedersen, Bin Yang, Christian S. Jensen: A Hybrid Learning Approach to Stochastic Routing. ICDE 2020: 1910-1913", + "Simon Aagaard Pedersen, Bin Yang, Christian S. Jensen: Anytime Stochastic Routing with Hybrid Learning. Proc. VLDB Endow. 13(9): 1555-1567 (2020)", + "Chenjuan Guo, Bin Yang, Jilin Hu, Christian S. Jensen, Lu Chen: Context-aware, preference-based vehicle routing. VLDB J. 29(5): 1149-1170 (2020)", + "Chenjuan Guo, Bin Yang, Jilin Hu, Christian S. Jensen: Learning to Route with Sparse Trajectory Sets. ICDE 2018: 1073-1084" + ] + }, + { + "title": "lst4_3_3", + "papers": [ + "Chenjuan Guo, Bin Yang, Jilin Hu, Christian S. Jensen, Lu Chen: Context-aware, preference-based vehicle routing. VLDB J. 29(5): 1149-1170 (2020)", + "Bin Yang, Chenjuan Guo, Yu Ma, Christian S. Jensen: Toward personalized, context-aware routing. VLDB J. 24(2): 297-318 (2015) ", + "Bin Yang, Chenjuan Guo, Christian S. Jensen, Manohar Kaul, Shuo Shang: Stochastic skyline route planning under time-varying uncertainty. ICDE 2014: 136-147" + ] + }, + { + "title": "lst4_3_4", + "papers": [ + "Huiping Liu, Cheqing Jin, Bin Yang, Aoying Zhou: Finding Top-k Shortest Paths with Diversity. IEEE Trans. Knowl. Data Eng. 30(3): 488-502 (2018)", + "Huiping Liu, Cheqing Jin, Bin Yang, Aoying Zhou: Finding Top-k Optimal Sequenced Routes. ICDE 2018: 569-580", + "Huiping Liu, Cheqing Jin, Bin Yang, Aoying Zhou: Finding Top-k Shortest Paths with Diversity. ICDE 2018: 1761-1762" + ] + }, + { + "title": "lst4_3_5", + "papers": [ + "Jilin Hu, Bin Yang, Christian S. Jensen, Yu Ma: Enabling time-dependent uncertain eco-weights for road networks. GeoInformatica 21(1): 57-88 (2017)", + "Chenjuan Guo, Bin Yang, Ove Andersen, Christian S. Jensen, Kristian Torp: EcoMark 2.0: empowering eco-routing with vehicular environmental models and actual vehicle fuel consumption data. GeoInformatica 19(3): 567-599 (2015)", + "Chenjuan Guo, Bin Yang, Ove Andersen, Christian S. Jensen, Kristian Torp: EcoSky: Reducing vehicular environmental impact through eco-routing. ICDE 2015: 1412-1415 ", + "Ove Andersen, Christian S. Jensen, Kristian Torp, Bin Yang: EcoTour: Reducing the Environmental Footprint of Vehicles Using Eco-routes. MDM (1) 2013: 338-3", + "Chenjuan Guo, Yu Ma, Bin Yang, Christian S. Jensen, Manohar Kaul: EcoMark: evaluating models of vehicular environmental impact. SIGSPATIAL/GIS 2012: 269-278" + ] + } + ], + "subsection": "ls4_3" + }, + { + "content": [ + { + "title": "", + "papers": [ + "Tung Kieu, Bin Yang, Chenjuan Guo, Christian S. Jensen: Distinguishing Trajectories from Different Drivers using Incompletely Labeled Trajectories. CIKM 2018: 863-872", + "Nicklas K. Breum, Martin N. Joergensen, Christian A. Knudsen, Laerke B. Kristensen, Bin Yang: A Charging Scheduling System for Electric Vehicles using Vehicle-to-Grid. MDM 2019: 351-352 ", + "Marc Beuchert, Steffen Hald Jensen, Omar Ali Sheikh-Omar, Mathias Bach Svendsen, Bin Yang: aSTEP: Aau's Spatio-TEmporal Data Analytics Platform. MDM 2018: 278-279", + "Asger Gitz-Johansen, Mikkel Elkjaer Holm, Laurids Vinther Kirkeby, Dan Kristiansen, Alexander Stoica Ostenfeld, Morten Konggaard Schou, Bin Yang: A Practical Delivery Route Planning System. MDM 2019: 349-350 " + ] + } + ], + "subsection": "ls4_4" + } + ], + "section": "l4", + "id":"2" + }, + { + "tag": "l7_tag", + "desc": "l7_desc", + "content": [ + { + "content": [ + { + "title": "", + "papers": [ + "Sean Bin Yang, Chenjuan Guo, Jilin Hu, Bin Yang, Jian Tang, Christian S. Jensen: Weaklysupervised Temporal Path Representation Learning with Contrastive Curriculum Learning. ICDE 2022: 2873-2885 ", + "Sean Bin Yang, Chenjuan Guo, Bin Yang: Context-Aware Path Ranking in Road Networks. IEEE Trans. Knowl. Data Eng. 34(7): 3153-3168 (2022)", + "Sean Bin Yang, Chenjuan Guo, Jilin Hu, Jian Tang, Bin Yang: Unsupervised Path Representation Learning with Curriculum Negative Sampling. IJCAI 2021: 3286-3292 （Robustness）", + "Sean Bin Yang, Bin Yang: Learning to Rank Paths in Spatial Networks. ICDE 2020: 2006- 2009 ", + "Sean Bin Yang, Jilin Hu, Chenjuan Guo, Bin Yang, Christian S. Jensen: LightPath: Lightweight and Scalable Path Representation Learning. KDD 2023: 2999-3010" + ] + } + ], + "subsection": "ls7_1" + }, + { + "content": [ + { + "title": "", + "papers": [ + "Jilin Hu, Bin Yang, Chenjuan Guo, Christian S. Jensen, Hui Xiong: Stochastic OriginDestination Matrix Forecasting Using Dual-Stage Graph Convolutional, Recurrent Neural Networks. ICDE 2020: 1417-1428", + "Razvan-Gabriel Cirstea, Hilmar Gústafsson, Rasmus Riis Grønbæk Pedersen, Rolf Hakon Verder Sehested, Tamas Imre Winkler, Bin Yang: A Road Segment Attribute Completion System. MDM 2020: 236-237", + "Jilin Hu, Chenjuan Guo, Bin Yang, Christian S. Jensen: Stochastic Weight Completion for Road Networks Using Graph Convolutional Networks. ICDE 2019: 1274-12", + "Bin Yang, Manohar Kaul, Christian S. Jensen: Using Incomplete Information for Complete Weight Annotation of Road Networks. IEEE Trans. Knowl. Data Eng. 26(5): 1267-1279 (2014) " + ] + } + ], + "subsection": "ls7_2" + }, + { + "content": [ + { + "title": "", + "papers": [ + "Razvan-Gabriel Cirstea, Bin Yang, Chenjuan Guo, Tung Kieu, Shirui Pan: Towards Spatio-Temporal Aware Traffic Time Series Forecasting. ICDE 2022: 2900-2913 ", + "Razvan-Gabriel Cirstea, Tung Kieu, Chenjuan Guo, Bin Yang, Sinno Jialin Pan: EnhanceNet: Plugin Neural Networks for Enhancing Correlated Time Series Forecasting. ICDE 2021: 1739-1750", + "Xinle Wu, Dalin Zhang, Chenjuan Guo, Chaoyang He, Bin Yang, Christian S. Jensen: AutoCTS: Automated Correlated Time Series Forecasting. Proc. VLDB Endow. 15(4): 971-983 (2021)" + ] + } + ], + "subsection": "ls7_3" + }, + { + "content": [ + { + "title": "", + "papers": [ + "Simon Aagaard Pedersen, Bin Yang, Christian S. Jensen: A Hybrid Learning Approach to Stochastic Routing. ICDE 2020: 1910-1913 ", + "Simon Aagaard Pedersen, Bin Yang, Christian S. Jensen: Anytime Stochastic Routing with Hybrid Learning. Proc. VLDB Endow. 13(9): 1555-1567 (2020)", + "Chenjuan Guo, Bin Yang, Jilin Hu, Christian S. Jensen, Lu Chen: Context-aware, preference-based vehicle routing. VLDB J. 29(5): 1149-1170 (2020) ", + "Simon Aagaard Pedersen, Bin Yang, Christian S. Jensen: Fast stochastic routing under time-varying uncertainty. VLDB J. 29(4): 819-839 (2020) ", + "Bin Yang, Jian Dai, Chenjuan Guo, Christian S. Jensen, Jilin Hu: PACE: a PAth-CEntric paradigm for stochastic path finding. VLDB J. 27(2): 153-178 (2018) ", + "Georgi Andonov, Bin Yang: Stochastic Shortest Path Finding in Path-Centric Uncertain Road Networks. MDM 2018: 40-45 ", + "Bin Yang, Chenjuan Guo, Yu Ma, Christian S. Jensen: Toward personalized, context-aware routing. VLDB J. 24(2): 297-318 (2015)", + "Jian Dai, Bin Yang, Chenjuan Guo, Zhiming Ding: Personalized route recommendation using big trajectory data. ICDE 2015: 543-554", + "Bin Yang, Chenjuan Guo, Christian S. Jensen, Manohar Kaul, Shuo Shang: Stochastic skyline route planning under time-varying uncertainty. ICDE 2014: 136-147" + ] + } + ], + "subsection": "ls7_4" + } + ], + "section": "l7", + "id":"3" + }, + { + "tag": "l2_tag", + "desc": "l2_desc", + "content": [ + { + "content": [ + { + "title": "", + "papers": [ + "Miao Zhang, Shirui Pan, Xiaojun Chang, Steven Su, Jilin Hu, Gholamreza Haffari, Bin Yang: BaLeNAS: Differentiable Architecture Search via the Bayesian Learning Rule. CVPR 2022: 11861- 11870", + "Miao Zhang, Wei Huang, and Bin Yang. Interpreting Operation Selection in Differentiable Architecture Search: A Perspective from Influence-Directed Explanations. NeurIPS 2022. ", + "Xinle Wu, Dalin Zhang, Chenjuan Guo, Chaoyang He, Bin Yang, Christian S. Jensen: AutoCTS: Automated Correlated Time Series Forecasting. Proc. VLDB Endow. 15(4): 971-983 (2021). ", + "Xinle Wu, Dalin Zhang, Miao Zhang, Chenjuan Guo, Bin Yang, Christian S. Jensen. AutoCTS+: Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting. SIGMOD 2023. ", + "Xinle Wu, Dalin Zhang, Miao Zhang, Chenjuan Guo, Shuai Zhao, Huai Wang, and Bin Yang. AutoPINN: When AutoML Meets Physics-Informed Neural Networks. CoRR. " + ] + } + ], + "subsection": "" + } + ], + "section": "l2", + "id":"4" + }, + { + "tag": "l5_tag", + "desc": "l5_desc", + "content": [ + { + "content": [ + { + "title": "", + "papers": [ + "Frederik Baymler Mathiesen, Bin Yang, Jilin Hu: Hyperverlet: A Symplectic Hypersolver for Hamiltonian Systems. AAAI 2022: 4575-4582 ", + "Shufang Xie, Rui Yan, Peng Han, Yingce Xia, Lijun Wu, Chenjuan Guo, Bin Yang, Tao Qin: RetroGraph: Retrosynthetic Planning with Graph Search. KDD 2022: 2120-2129", + "Xinle Wu, Dalin Zhang, Miao Zhang, Chenjuan Guo, Shuai Zhao, Huai Wang, and Bin Yang. AutoPINN: When AutoML Meets Physics-Informed Neural Networks. CoRR. " + ] + } + ], + "subsection": "" + } + ], + "section": "l5", + "id":"5" + }, + { + "tag": "l8_tag", + "desc": "l8_desc", + "content": [ + { + "content": [ + { + "title": "", + "papers": [ + "David Campos, Bin Yang, Tung Kieu, Miao Zhang, Chenjuan Guo, Christian S. Jensen: QCore: Data-Efficient, On-Device Continual Calibration for Quantized Models. PVLDB 2024. ", + "Miao Zhang, Li Wang, David Campos, Wei Huang, Chenjuan Guo, and Bin Yang. Weighted Mutual Learning with Diversity-Driven Model Compression. NeurIPS 2022.", + "David Campos, Miao Zhang, Bin Yang, Tung Kieu, Chenjuan Guo, Christian S. Jensen: LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation. SIGMOD 2023. ", + "Sean Bin Yang, Jilin Hu, Chenjuan Guo, Bin Yang, Christian S. Jensen: LightPath: Lightweight and Scalable Path Representation Learning. KDD 2023: 2999-3010" + ] + } + ], + "subsection": "" + } + ], + "section": "l8", + "id":"6" + }, + { + "tag": "l3_tag", + "desc": "l3_desc", + "content": [ + { + "content": [ + { + "title": "Ensemble learning based outlier detection：", + "papers": [ + "David Campos, Tung Kieu, Chenjuan Guo, Feiteng Huang, Kai Zheng, Bin Yang, Christian S. Jensen: Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles. Proc. VLDB Endow. 15(3): 611-623 (2021)", + "Tung Kieu, Bin Yang, Chenjuan Guo, Christian S. Jensen: Outlier Detection for Time Series with Recurrent Autoencoder Ensembles. IJCAI 2019: 2725-2732 " + ] + }, + { + "title": "Robust outlier detection：", + "papers": [ + "Tung Kieu, Bin Yang, Chenjuan Guo, Christian S. Jensen, Yan Zhao, Feiteng Huang, Kai Zheng: Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection. ICDE 2022: 3038-3050", + "Tung Kieu, Bin Yang, Chenjuan Guo, Razvan-Gabriel Cirstea, Yan Zhao, Yale Song, Christian S. Jensen: Anomaly Detection in Time Series with Robust Variational Quasi-Recurrent Autoencoders. ICDE 2022: 1342-1354" + ] + }, + { + "title": "Explainable outlier detection: ", + "papers": [ + "Tung Kieu, Bin Yang, Chenjuan Guo, Christian S. Jensen, Yan Zhao, Feiteng Huang, Kai Zheng: Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection. ICDE 2022: 3038-3050" + ] + }, + { + "title": "Autoencoder based outlier detection: ", + "papers": [ + "Tung Kieu, Bin Yang, Christian S. Jensen: Outlier Detection for Multidimensional Time Series Using Deep Neural Networks. MDM 2018: 125-134" + ] + } + ], + "subsection": "" + } + ], + "section": "l3", + "id":"7" + }, + { + "tag": "l6_tag", + "desc": "l6_desc", + "content": [ + { + "content": [ + { + "title": "lst6_4_1", + "papers": [ + "Razvan-Gabriel Cirstea, Bin Yang, Chenjuan Guo, Tung Kieu, Shirui Pan: Towards SpatioTemporal Aware Traffic Time Series Forecasting. ICDE 2022: 2900-2913", + "Razvan-Gabriel Cirstea, Tung Kieu, Chenjuan Guo, Bin Yang, Sinno Jialin Pan: EnhanceNet: Plugin Neural Networks for Enhancing Correlated Time Series Forecasting. ICDE 2021: 1739-1750", + "Xinle Wu, Dalin Zhang, Chenjuan Guo, Chaoyang He, Bin Yang, Christian S. Jensen: AutoCTS: Automated Correlated Time Series Forecasting. Proc. VLDB Endow. 15(4): 971-983 (2021)", + "Xinle Wu, Dalin Zhang, Miao Zhang, Chenjuan Guo, Bin Yang, Christian S. Jensen. AutoCTS+: Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting. SIGMOD 2023.", + "Razvan-Gabriel Cirstea, Tung Kieu, Chenjuan Guo, Bin Yang, Sinno Jialin Pan: EnhanceNet: Plugin Neural Networks for Enhancing Correlated Time Series Forecasting. ICDE 2021: 1739-1750", + "Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs. Ming Jin, Yu Zheng, Yuan-Fang Li, Siheng Chen, Bin Yang, Shirui Pan. TKDE, 2022.", + "Bin Yang, Chenjuan Guo, Christian S. Jensen: Travel Cost Inference from Sparse, SpatioTemporally Correlated Time Series Using Markov Models. Proc. VLDB Endow. 6(9): 769-780 (2013)" + ] + }, + { + "title": "lst6_4_2", + "papers": [ + "Sean Bin Yang, Chenjuan Guo, Jilin Hu, Bin Yang, Jian Tang, Christian S. Jensen: Weaklysupervised Temporal Path Representation Learning with Contrastive Curriculum Learning. ICDE 2022: 2873-2885", + "Sean Bin Yang, Chenjuan Guo, Bin Yang: Context-Aware Path Ranking in Road Networks. IEEE Trans. Knowl. Data Eng. 34(7): 3153-3168 (2022)", + "Sean Bin Yang, Chenjuan Guo, Jilin Hu, Jian Tang, Bin Yang: Unsupervised Path Representation Learning with Curriculum Negative Sampling. IJCAI 2021: 3286-3292", + "Sean Bin Yang, Bin Yang: Learning to Rank Paths in Spatial Networks. ICDE 2020: 2006- 2009", + "Jian Dai, Bin Yang, Chenjuan Guo, Christian S. Jensen, Jilin Hu: Path Cost Distribution Estimation Using Trajectory Data. Proc. VLDB Endow. 10(3): 85-96 (2016)", + "Jilin Hu, Bin Yang, Chenjuan Guo, Christian S. Jensen, Hui Xiong: Stochastic OriginDestination Matrix Forecasting Using Dual-Stage Graph Convolutional, Recurrent Neural Networks. ICDE 2020: 1417-1428", + "Razvan-Gabriel Cirstea, Hilmar Gústafsson, Rasmus Riis Grønbæk Pedersen, Rolf Hakon Verder Sehested, Tamas Imre Winkler, Bin Yang: A Road Segment Attribute Completion System. MDM 2020: 236-237", + "Jilin Hu, Chenjuan Guo, Bin Yang, Christian S. Jensen: Stochastic Weight Completion for Road Networks Using Graph Convolutional Networks. ICDE 2019: 1274-12", + "Bin Yang, Manohar Kaul, Christian S. Jensen: Using Incomplete Information for Complete Weight Annotation of Road Networks. IEEE Trans. Knowl. Data Eng. 26(5): 1267-1279 (2014)" + ] + } + ], + "subsection": "" + } + ], + "section": "l6", + "id":"8" + }, + { + "tag": "l9_tag", + "desc": "l9_desc", + "content": [ + { + "subsection": "", + "content": [ + { + "title": "lst9_1_1", + "papers": [ + "Simon Aagaard Pedersen, Bin Yang, Christian S. Jensen: A Hybrid Learning Approach to Stochastic Routing. ICDE 2020: 1910-1913", + "Simon Aagaard Pedersen, Bin Yang, Christian S. Jensen: Anytime Stochastic Routing with Hybrid Learning. Proc. VLDB Endow. 13(9): 1555-1567 (2020)", + "Simon Aagaard Pedersen, Bin Yang, Christian S. Jensen: Fast stochastic routing under time-varying uncertainty. VLDB J. 29(4): 819-839 (2020)", + "Jilin Hu, Bin Yang, Chenjuan Guo, Christian S. Jensen: Risk-aware path selection with time-varying, uncertain travel costs: a time series approach. VLDB J. 27(2): 179-200 (2018)", + "Bin Yang, Jian Dai, Chenjuan Guo, Christian S. Jensen, Jilin Hu: PACE: a PAth-CEntric paradigm for stochastic path finding. VLDB J. 27(2): 153-178 (2018) ", + "Georgi Andonov, Bin Yang: Stochastic Shortest Path Finding in Path-Centric Uncertain Road Networks. MDM 2018: 40-45 " + ] + }, + { + "title": "lst9_1_2", + "papers": [ + "Simon Aagaard Pedersen, Bin Yang, Christian S. Jensen: A Hybrid Learning Approach to Stochastic Routing. ICDE 2020: 1910-1913 ", + "Simon Aagaard Pedersen, Bin Yang, Christian S. Jensen: Anytime Stochastic Routing with Hybrid Learning. Proc. VLDB Endow. 13(9): 1555-1567 (2020)", + "Chenjuan Guo, Bin Yang, Jilin Hu, Christian S. Jensen, Lu Chen: Context-aware, preference-based vehicle routing. VLDB J. 29(5): 1149-1170 (2020)", + "Chenjuan Guo, Bin Yang, Jilin Hu, Christian S. Jensen: Learning to Route with Sparse Trajectory Sets. ICDE 2018: 1073-1084" + ] + }, + { + "title": "lst9_1_3", + "papers": [ + "Chenjuan Guo, Bin Yang, Jilin Hu, Christian S. Jensen, Lu Chen: Context-aware, preference-based vehicle routing. VLDB J. 29(5): 1149-1170 (2020) ", + "Bin Yang, Chenjuan Guo, Yu Ma, Christian S. Jensen: Toward personalized, context-aware routing. VLDB J. 24(2): 297-318 (2015)", + "Jian Dai, Bin Yang, Chenjuan Guo, Zhiming Ding: Personalized route recommendation using big trajectory data. ICDE 2015: 543-554", + "Bin Yang, Chenjuan Guo, Christian S. Jensen, Manohar Kaul, Shuo Shang: Stochastic skyline route planning under time-varying uncertainty. ICDE 2014: 136-147" + ] + }, + { + "title": "lst9_1_4", + "papers": [ + "Huiping Liu, Cheqing Jin, Bin Yang, Aoying Zhou: Finding Top-k Shortest Paths with Diversity. IEEE Trans. Knowl. Data Eng. 30(3): 488-502 (2018)", + "Huiping Liu, Cheqing Jin, Bin Yang, Aoying Zhou: Finding Top-k Optimal Sequenced Routes. ICDE 2018: 569-580", + "Huiping Liu, Cheqing Jin, Bin Yang, Aoying Zhou: Finding Top-k Shortest Paths with Diversity. ICDE 2018: 1761-1762" + ] + }, + { + "title": "lst9_1_5", + "papers": [ + "Jilin Hu, Bin Yang, Christian S. Jensen, Yu Ma: Enabling time-dependent uncertain eco-weights for road networks. GeoInformatica 21(1): 57-88 (2017) ", + "Chenjuan Guo, Bin Yang, Ove Andersen, Christian S. Jensen, Kristian Torp: EcoMark 2.0: empowering eco-routing with vehicular environmental models and actual vehicle fuel consumption data. GeoInformatica 19(3): 567-599 (2015) ", + "Chenjuan Guo, Bin Yang, Ove Andersen, Christian S. Jensen, Kristian Torp: EcoSky: Reducing vehicular environmental impact through eco-routing. ICDE 2015: 1412-1415 ", + "Ove Andersen, Christian S. Jensen, Kristian Torp, Bin Yang: EcoTour: Reducing the Environmental Footprint of Vehicles Using Eco-routes. MDM (1) 2013: 338-3 ", + "Chenjuan Guo, Yu Ma, Bin Yang, Christian S. Jensen, Manohar Kaul: EcoMark: evaluating models of vehicular environmental impact. SIGSPATIAL/GIS 2012: 269-278 " + ] + }, + { + "title": "lst9_1_6", + "papers":[ + "Zhicheng Pan, Yihang Wang, Yingying Zhang, Sean Bin Yang, Yunyao Cheng, Peng Chen, Chenjuan Guo, Qingsong Wen, Xiduo Tian, Yunliang Dou, Zhiqiang Zhou, Chengcheng Yang, Aoying Zhou, Bin Yang:MagicScaler: Uncertainty-aware, Predictive Autoscaling. Proc. VLDB Endow. 16(12): 3808-3821 (2023)" + ] + } + ] + } + ], + "section": "l9", + "id":"9" + }, + { + "tag": "l10_tag", + "desc": "l10_desc", + "content": [ + { + "subsection": "", + "content": [ + { + "title": "lst10_1_1", + "papers": [ + "Yang Shu, Zhangjie Cao, Jinghan Gao, Jianmin Wang, Philip S. Yu, Mingsheng Long: Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot Learning. TPAMI 2023", + "Yang Shu, Zhangjie Cao, Ziyang Zhang, Jianmin Wang, Mingsheng Long: Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models. NeurIPS 2022: 32913-32927", + "Yang Shu, Zhi Kou, Zhangjie Cao, Jianmin Wang, Mingsheng Long: Zoo-Tuning: Adaptive Transfer from A Zoo of Models. ICML 2021: 9626-9637 " + ] + }, + { + "title": "lst10_1_2", + "papers": [ + "Yang Shu, Xingzhuo Guo, Jialong Wu, Ximei Wang, Jianmin Wang, Mingsheng Long: CLIPood: Generalizing CLIP to Out-of-Distributions. ICML 2023", + "Yang Shu, Zhangjie Cao, Chenyu Wang, Jianmin Wang, Mingsheng Long: Open Domain Generalization with Domain-Augmented Meta-Learning. CVPR 2021: 9624-9633" + ] + }, + { + "title": "lst10_1_3", + "papers": [ + "Yang Shu, Zhangjie Cao, Mingsheng Long, Jianmin Wang: Transferable Curriculum for Weakly-Supervised Domain Adaptation. AAAI 2019: 4951-4958" + ] + } + ] + } + ], + "section": "l10", + "id":"10" + }, + { + "tag": "l11_tag", + "desc": "l11_desc", + "content": [ + { + "content": [ + { + "title": "", + "papers": [ + "Yihang Wang, Yuying Qiu, Peng Chen, Kai Zhao, Yang Shu, Zhongwen Rao, Lujia Pan, Bin Yang, Chenjuan Guo. Towards a General Time Series Forecasting Model with Unified Representation and Adaptive Transfer. ICML 2025.", + "Yihang Wang, Yuying Qiu, Peng Chen, Yang Shu, Zhongwen Rao, Lujia Pan, Bin Yang, Chenjuan Guo. LightGTS: A Lightweight General Time Series Forecasting Model. ICML 2025.", + "Qichao Shentu, Beibu Li, Kai Zhao, Yang Shu, Zhongwen Rao, Lujia Pan, Bin Yang, Chenjuan Guo. Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoders. ICLR 2025.", + "Yuxuan Chen, Shanshan Huang, Yunyao Cheng, Peng Chen, Zhongwen Rao, Yang Shu, Bin Yang, Lujia Pan, Chenjuan Guo. AimTS: Augmented Series and Image Contrastive Learning for Time Series Classification. ICDE 2025." + ] + } + ], + "subsection": "" + } + ], + "section": "l11", + "id":"11" + }, + { + "tag": "l12_tag", + "desc": "l12_desc", + "content": [ + { + "content": [ + { + "title": "", + "papers": [ + "Zhe Li, Xiangfei Qiu, Peng Chen, Yihang Wang, Hanyin Cheng, Yang Shu, Jilin Hu, Chenjuan Guo, Aoying Zhou, Christian S. Jensen, Bin Yang. FM4TS-Bench: A Comprehensive and Unified Benchmark of Foundation Models for Time Series Forecasting. KDD 2025." + ] + } + ], + "subsection": "" + } + ], + "section": "l12", + "id":"12" + }, + { + "tag": "l13_tag", + "desc": "l13_desc", + "content": [ + + ], + "section": "l13", + "id":"13" + }, + { + "tag": "l14_tag", + "desc": "l14_desc", + "content": [ + + ], + "section": "l14", + "id":"14" + } + ] + }, + "modify_date": "2022-11-12" + } +] diff --git a/src/assets/quasar-logo-vertical.svg b/src/assets/quasar-logo-vertical.svg new file mode 100644 index 0000000..8210831 --- /dev/null +++ b/src/assets/quasar-logo-vertical.svg @@ -0,0 +1,15 @@ + + + + + + + + + \ No newline at end of file diff --git a/src/boot/.gitkeep b/src/boot/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/src/boot/axios.js b/src/boot/axios.js new file mode 100644 index 0000000..26aea25 --- /dev/null +++ b/src/boot/axios.js @@ -0,0 +1,26 @@ +import { boot } from "quasar/wrappers"; +import axios from "axios"; + +const api = axios.create({ baseURL: "https://xwyzsn.site" }); +api.interceptors.request.use((config) => { + let token = localStorage.getItem("access_token"); + if (localStorage.getItem("access_token") !== null) { + config.headers.Authorization = + "Bearer " + localStorage.getItem("access_token"); + } + return config; +}); +export default boot(({ app }) => { + // for use inside Vue files (Options API) through this.$axios and this.$api + + app.config.globalProperties.$axios = axios; + // ^ ^ ^ this will allow you to use this.$axios (for Vue Options API form) + // so you won't necessarily have to import axios in each vue file + + app.config.globalProperties.$api = api; + + // ^ ^ ^ this will allow you to use this.$api (for Vue Options API form) + // so you can easily perform requests against your app's API +}); + +export { axios, api }; diff --git a/src/boot/i18n.js b/src/boot/i18n.js new file mode 100644 index 0000000..fe9bab3 --- /dev/null +++ b/src/boot/i18n.js @@ -0,0 +1,14 @@ +import { createI18n } from "vue-i18n"; +import messages from "src/i18n"; + +export default ({ app }) => { + // Create I18n instance + const i18n = createI18n({ + locale: "en-US", + legacy: false, // comment this out if not using Composition API + messages, + }); + + // Tell app to use the I18n instance + app.use(i18n); +}; diff --git a/src/components/EssentialLink.vue b/src/components/EssentialLink.vue new file mode 100644 index 0000000..9ffa860 --- /dev/null +++ b/src/components/EssentialLink.vue @@ -0,0 +1,41 @@ + + + diff --git a/src/components/MyCard.vue b/src/components/MyCard.vue new file mode 100644 index 0000000..0331d11 --- /dev/null +++ b/src/components/MyCard.vue @@ -0,0 +1,97 @@ + + + diff --git a/src/components/blogCard.vue b/src/components/blogCard.vue new file mode 100644 index 0000000..8981e81 --- /dev/null +++ b/src/components/blogCard.vue @@ -0,0 +1,73 @@ + + + diff --git a/src/css/app.scss b/src/css/app.scss new file mode 100644 index 0000000..ecac98f --- /dev/null +++ b/src/css/app.scss @@ -0,0 +1 @@ +// app global css in SCSS form diff --git a/src/css/main.css b/src/css/main.css new file mode 100644 index 0000000..b5c61c9 --- /dev/null +++ b/src/css/main.css @@ -0,0 +1,3 @@ +@tailwind base; +@tailwind components; +@tailwind utilities; diff --git a/src/css/quasar.variables.scss b/src/css/quasar.variables.scss new file mode 100644 index 0000000..2605a0d --- /dev/null +++ b/src/css/quasar.variables.scss @@ -0,0 +1,25 @@ +// Quasar SCSS (& Sass) Variables +// -------------------------------------------------- +// To customize the look and feel of this app, you can override +// the Sass/SCSS variables found in Quasar's source Sass/SCSS files. + +// Check documentation for full list of Quasar variables + +// Your own variables (that are declared here) and Quasar's own +// ones will be available out of the box in your .vue/.scss/.sass files + +// It's highly recommended to change the default colors +// to match your app's branding. +// Tip: Use the "Theme Builder" on Quasar's documentation website. + +$primary: #1976d2; +$secondary: #26a69a; +$accent: #9c27b0; + +$dark: #1d1d1d; +$dark-page: #121212; + +$positive: #21ba45; +$negative: #c10015; +$info: #31ccec; +$warning: #f2c037; diff --git a/src/i18n/en-US/index.js b/src/i18n/en-US/index.js new file mode 100644 index 0000000..7b1e454 --- /dev/null +++ b/src/i18n/en-US/index.js @@ -0,0 +1,171 @@ +export default { + IndexPage: "Home", + publicationPage: "Research", + MemberPage: "Members", + ProjectPage: "Projects", + JoinPage: "Join us", + LabName: "Decision Intelligence Lab", + desc_p1: "The Decision Intelligence Lab, part of the School of Data Science and Engineering at East China Normal University, conducts cutting-edge research on artificial intelligence and data analytics, with a fouse on enabling data-driven decision making. ", + desc_b1: "Innovating AI to impact business and society, and making AI accessible to all.", + desc_p2: "Our research is guided by the AGREE principles—Automation, Generalization, Robustness, Explainability, and Efficiency, which we apply to fundamental tasks including, e.g., forecasting, anomaly detection, classification, ranking, searching, and decision making. These innovations have been successfully deployed across multiple domains, including Intelligent transportation, Digital energy, AI4DB, AIOps, Predictive maintainence, AI for Science, Smart ocean, Autoscaling, and Intelligent emergency response. ", + l1: "Time Series Analytics", + l2: "AutoML", + l3: "Anomaly Detection", + l4: "Spatial-Temporal Analytics", + l5: "AI for Science", + l6: "Forecasting", + l7: "Graph Analytics", + l8: "Model Compression", + l9: "Intelligent Decision Making", + l10: "Transfer Learning and Model Generalization", + l11: "Time Series Large Models", + l12: "Large Model Benchmark", + l13: "Multi-Agent", + l14: "Embodied Intelligence", + + ro: "Research Outcomes", + roc: "Our research is applied in a variety of application domains, such as Intelligent Transportation, Digital Energy, AIOps, Smart Ocean, AI4DB, Predictive Maintenance, Intelligent emergency response, materials design, and drug discovery.", + + l1_tag: + "Intelligent Operations & Maintenance, AIOps, Digital Twins, Digital Energy", + l1_desc: + "Enabling digital transformation, empowering intelligent operations, smart transportation, smart cities, AIOps, digital energy, smart water resource management, etc. Time series is a common data type brought by digital transformation. Time series data contains dynamic information that changes over time. Effective analysis of time series data can provide effective decision support for different application scenarios. For example, traffic condition prediction can help drivers divert in advance, load forecasting can help suppliers prepare for adjustments in advance, and machine condition monitoring can detect potential failures early and carry out predictive maintenance. Our research mainly focuses on two types of analysis: forecasting and anomaly detection. While pursuing high accuracy, we also pay attention to efficiency, robustness, interpretability, and automation.", + l4_tag: "Sustainable Transportation, Smart City, Smart Logistics", + l4_desc: + "Effective analysis of spatiotemporal trajectory data, empowering sustainable transportation, smart cities, and autonomous driving. Spatiotemporal data includes spatial information that changes over time, such as trajectory data. Our research mainly focuses on how to effectively utilize large-scale spatiotemporal trajectory data to empower smart cities, sustainable transportation, and autonomous driving. Specifically, spatiotemporal data analysis includes traffic state estimation, preference recognition, and navigation decision-making. It involves representation learning, graph learning, multi-task learning, and graph algorithms. The research results have been applied to intelligent transportation systems of several partner units in Denmark, Germany, the Netherlands, Greece, and Cyprus. For example, the proposed Eco-routing algorithm can effectively reduce CO2 emissions in the transportation sector and has been applied in several services of FlexDanmark, the largest transportation scheduling company in North Jutland, Denmark.", + l7_tag: "Smart Transportation, Social Network, Molecular Design", + l7_desc: + "Efficient graph learning and graph algorithms, supporting sustainable transportation, smart cities, and molecular design. Graphs consist of nodes and edges, which can effectively model complex relationships between multiple entities. Nodes are often used to represent entities, and edges connecting two nodes represent a certain relationship between the corresponding entities. For example, a city's transportation network can be represented as a graph, where nodes represent intersections and edges represent roads. Our graph analysis work mainly includes: graph representation learning (based on contrastive learning, weakly supervised learning, curriculum learning), graph attribute prediction, temporal correlation learning, and shortest path algorithms for uncertain graphs.", + l2_tag: + "Reducing the Threshold and Costs of Using Machine Learning across Industries", + l2_desc: + "Automatically building high-precision model architectures and automatically selecting hyperparameters, reducing the threshold and costs of using machine learning across different industries. Currently, efficient machine learning models are usually manually designed by machine learning experts, and this manual design process is lengthy and laborious. AutoML attempts to automate this design process with algorithms, automatically constructing efficient machine learning models. This will greatly reduce the threshold for using machine learning and the human, material, and time costs of using machine learning across industries. Our research focuses on differentiable neural architecture search algorithms and their interpretability, joint search of neural architectures and hyperparameters, and the application of these methods in automatically building time series analysis models and physics-informed neural networks.", + l5_tag: "Empowering Scientific Discoveries and Industrial Applications", + l5_desc: + "Artificial intelligence is empowering scientists to make scientific discoveries and is expected to become a new paradigm for scientific discovery. Our research mainly focuses on neural network-based solvers and drug discovery.", + l8_tag: + "Edge Device Intelligence, Industrial Intelligence, Digital Twins, Digital Power", + l8_desc: + "Many decisions need to be made on edge devices, which usually have limited computing and storage resources, requiring lightweight decision models. Our research mainly focuses on model compression.", + l3_tag: "Intelligent Operations & Maintenance, AIOps", + l3_desc: + "Empowering intelligent operations, AIOps, and fraud detection. Anomaly detection is used to discover states that differ from normal states. Since abnormal states are rare and can vary widely, traditional supervised anomaly detection algorithms often struggle to work effectively. Our research focuses on unsupervised anomaly detection, which can accurately detect anomalies without requiring abnormal labels. We have proposed anomaly detection algorithms based on autoencoders (AutoEncoder) and ensemble learning, focusing on their robustness and interpretability.", + l6_tag: "Time Series Forecasting, Graph Forecasting", + l6_desc: + "Many decision-making scenarios require forecasting unknown states, such as predicting future states and estimating missing parts of data. Our work mainly focuses on time series forecasting and graph data forecasting.", + l9_tag: + "", + l9_desc: + "We make many decisions every day, with a typical example being route choice during travel. Decision-making can be complex as it often involves considering many factors, not just one (e.g., travel time and carbon emissions), and these factors are often uncertain. We have conducted in-depth research on these issues, using route navigation decision-making as an example. The research results have been applied to intelligent transportation systems of several partner units in Denmark, Germany, the Netherlands, Greece, and Cyprus. For example, the proposed Eco-routing algorithm can effectively reduce CO2 emissions in the transportation sector and has been applied in several services of FlexDanmark, the largest transportation scheduling company in North Jutland, Denmark.", + l10_tag: + "", + l10_desc: "Transfer Learning and Model Generalization", + l11_tag: "", + l11_desc: "The research on foundation models for time series aims to build time series analysis models that possess generality across different tasks and transferability across various domains by training on large-scale time series data. This approach reduces the cost of data collection and model training for specific downstream tasks, enhances the usability of time series analysis technologies, and empowers intelligent analysis, decision-making, and digital transformation in areas such as intelligent operations and maintenance, smart cities, and digital energy. The research covers pretraining techniques for time series foundation models, model architecture design, large-scale time series datasets, and applications of foundation models in typical analysis tasks such as forecasting, anomaly detection, and classification.", + l12_tag: "", + l12_desc: "The research on foundation model evaluation aims to systematically assess and compare various large models—such as large language models and time series foundation models—from the perspectives of accuracy, efficiency, and generalization. It involves both qualitative and quantitative analyses of the strengths and limitations of different models, providing guidance for model optimization and serving as a reference for model selection. The research includes the construction of evaluation datasets, the design of evaluation frameworks, and the development of evaluation systems for large models.", + l13_tag: "", + l13_desc: + "Multi-agent systems, by simulating complex social interactions and autonomous decision-making, can effectively analyze collective behavior and system dynamics. Our research focuses on leveraging multi-agent simulation technologies to empower areas such as education policy evaluation, traffic scheduling, and resource allocation. Specifically, the research encompasses the construction of social simulators (e.g., forecasting the macro-level impact of education policies), collaborative decision-making algorithms (e.g., ride-hailing supply-demand matching and route optimization), and multi-agent reinforcement learning (e.g., dynamic scheduling of water reservoir usage), drawing on methodologies from game theory, distributed optimization, reinforcement learning, and mechanism design. The research outcomes will be applied in domestic education policy simulation platforms and smart city projects. For example: 1) The developed education policy simulator uses multi-agent modeling to quantify the impact of resource allocation on regional education equity, providing decision support for the National Institute of Education Policy Research; 2) The designed collaborative ride-hailing scheduling algorithm will be implemented in the dispatching system of one of China's top ten ride-hailing platforms; 3) The proposed multi-agent water resource allocation framework will be applied to address saline tide challenges at the Yangtze River water source in Shanghai.", + l14_tag: "", + l14_desc: + "Embodied intelligence research focuses on developing algorithms for embodied intelligent devices such as quadruped robots and drones. By integrating reinforcement learning, motion control, and multi-agent collaboration technologies, the goal is to build efficient and robust ground-air integrated intelligent systems. Key research areas include: dynamic environment perception (e.g., adaptation to complex terrains and real-time obstacle avoidance), autonomous motion control (e.g., high-dynamic gait optimization for quadruped robots and precise hovering for drones), and cross-platform collaboration (e.g., joint search and task allocation between drones and ground robots). Core technologies involve bio-inspired motion modeling, multimodal sensor fusion, distributed reinforcement learning, and real-time path planning.", + + ls1_1: "Time Series Forecasting", + ls1_2: "Time Series Anomaly Detection", + ls1_3: "Time Series Classification", + lst1_1_1: "Time-frequency domain based forecasting:", + lst1_1_2: "Model-agnostic plugin:", + lst1_1_3: "Automated correlated time series forecasting:", + lst1_1_4: "Transformer-based forecasting:", + lst1_1_5: "Robust forecasting: ", + lst1_1_6: "Graph neural network based forecasting:", + lst1_1_7: "Forecasting benchmarking:", + lst1_1_8: "Continual learning for forecasting:", + lst1_1_9: "Neural ODE-based forecasting：", + lst1_1_10: "Markov Model-based forecasting:", + lst1_1_11: "Others:", + lst1_2_1: "Ensemble learning based outlier detection：", + lst1_2_2: "Robust outlier detection：", + lst1_2_3: "Frequency domain based outlier detection：", + lst1_2_4: "Outlier detection foundation model： ", + lst1_2_5: "Explainable outlier detection: ", + lst1_2_6: "Ensemble learning based outlier detection：", + + ls4_1: "Traffic State Estimation", + ls4_2: "Preference Identification", + ls4_3: "Data-Driven Navigation Decision-Making", + ls4_4: "Others", + lst4_1_1: "Route Traffic State:", + lst4_1_2: "Road Network Traffic State:", + lst4_3_1: "Decision-Making Under Uncertainty:", + lst4_3_2: "Learning-Based Decision-Making:", + lst4_3_3: "Multi-Criteria Decision-Making:", + lst4_3_4: "Top-K Decision-Making:", + lst4_3_5: "Eco-routing:", + + ls7_1: "Path Representation Learning", + ls7_2: "Graph Attribute Prediction", + ls7_3: "Spatiotemporal Sequence Correlation Learning", + ls7_4: "Shortest Path Algorithm for Uncertain Graphs", + + lst6_4_1: "Time Series Forecasting：", + lst6_4_2: "Graph-Based Prediction：", + + lst9_1_1: "Decision-Making Under Uncertainty：", + lst9_1_2: "Learning-Based Decision-Making：", + lst9_1_3: "Multi-Criteria Decision-Making：", + lst9_1_4: "Top-K Decision-Making:", + lst9_1_5: "Eco-routing:", + lst9_1_6: "Elastic Scaling, Auto Scaling (AutoScaling):", + + lst10_1_1: "Model Pre-training and Transfer Learning (Pre-training, Fine-tuning, Model Zoo, Few-shot Learning):", + lst10_1_2: "OOD Generalization:", + lst10_1_3: "Domain Adaptation:", + + + + r1: "Machine Learning, Artificial Intelligence, Data Management and Analysis", + r2: "Time Series Analysis, Spatial-Temporal Analysis, Graph Analysis, AI for Science", + r3: "AutoML, Model Interpretability, Robustness, Continuous Learning, Lightweight Models, DB4AI", + r4: "Self-proposed Directions (not limited to the above)", + + r5: "Expectations for Students", + r51: "Self-motivated, with goals for their future", + r52: "Solid programming and mathematics foundation", + r53: "Good English reading and writing skills, capable of presenting in English", + r54: "Diligent, responsible, good at communication, and team-oriented", + + r6: "Training Methods", + r61: "Provide a research environment combining research and application", + r62: "Topic selection balances high-level paper publication and engineering practice", + r63: "Teaching according to individual needs, fully considering students' actual conditions and requirements, achieving win-win development for students and supervisors", + r64: "Focus on internationalization, actively providing various international exchange opportunities", + + r7: "Job Description", + r71: "Independently undertake research projects", + r72: "Assist in guiding graduate students", + + r8: "Job Requirements", + r81: "Have or are about to obtain a PhD in computer science or related information science, or other science and engineering PhD (mainly for AI for Science direction)", + r82: "Published high-level papers as the first author in the past three years (e.g., CCF Class A)", + r83: "Ability to independently complete research projects", + + r9: "Contact Us", + r91: "Yang Bin:", + r92: "Guo Chenjuan:", + r93: "3663 North Zhongshan Road, Putuo District, Shanghai", + + r10: "Benefits", + r101: '
    Benefits
    ', + address: "Address", + email: "Email", + wechat: "Wechat Official Account", + + mp1: "Faculty", + mp2: "Ph.D. students", + mp3: "Master Students", + mp4: "Students admitted in 2023", + mp5: "Students admitted in 2024", + mp6: "Students admitted in 2025", +}; diff --git a/src/i18n/index.js b/src/i18n/index.js new file mode 100644 index 0000000..eb72fc4 --- /dev/null +++ b/src/i18n/index.js @@ -0,0 +1,6 @@ +import enUS from "./en-US"; +import zhCN from "./zh-CN"; +export default { + en: enUS, + "zh-CN": zhCN, +}; diff --git a/src/i18n/zh-CN/index.js b/src/i18n/zh-CN/index.js new file mode 100644 index 0000000..446b1f5 --- /dev/null +++ b/src/i18n/zh-CN/index.js @@ -0,0 +1,177 @@ +export default { + IndexPage: "主页", + publicationPage: "成果", + MemberPage: "成员", + ProjectPage: "项目", + JoinPage: "加入我们", + LabName: "决策智能实验室（Decision Intelligence Lab）", + desc_p1: "决策智能实验室依托华东师范大学，数据科学与工程学院，具有一支国际化，高水平导师团队，一人入选国家级领军人才，两人入选国家级青年人才。主要研究方向涵盖人工智能、机器学习和数据管理。通过对复杂异构数据（例如时间序列、时空数据、图、图像和分子结构等）进行高精度、高效率、自动的、高鲁棒性、可解释的分析和管理，助力不同行业的数字化转型和不同应用领域的决策支持。", + desc_b1: "", + desc_p2: "", + l1: "时间序列分析", + l2: "自动机器学习", + l3: "异常检测", + l4: "时空数据分析", + l5: "AI for Science", + l6: "预测", + l7: "图分析", + l8: "模型轻量化", + l9: "决策智能", + l10: "迁移学习与模型泛化", + l11: "时序大模型", + l12: "大模型评测", + l13: "多智能体", + l14: "具身智能", + + + ro: "研究成果", + roc: "实验室科研成果应用于绿色交通、智能运维、智慧城市、AIOps、数字能源、智能水资源管理、数字孪生、分子设计和药物设计等领域。", + + l1_tag: "智能运维、AIOps、数字孪生、数字能源", + l1_desc: + "助力数字化转型，赋能智能运维、智能交通、智慧城市、AIOps、数字能源、智能水资源管理等。\n时间序列是数字化转型带来的一种常见数据类型。时序数据中蕴含了大量的随时间变化的动态信息，对时序数据的有效分析能够为不同应用场景提供有效的决策支持。比如道路交通状况预测可以帮助司机提早进行分流，负载预测可以帮助适配器提早做好调整的准备，机器运行状况检测可以提早发现可能发生的故障，进而开展预测性维护等。我们的研究主要集中在两大类分析——预测和异常检测。在追求高精度的同时，还关注其时效性，鲁棒性，可解释性，以及自动化。\n", + + l4_tag: "绿色交通、智慧城市、智慧物流 ", + l4_desc: + "有效分析时空轨迹数据，赋能绿色交通、智慧城市和自动驾驶等。 时空数据包含随时间变化的空间信息，比如轨迹数据。我们的研究主要集中在如何有效利用大规模的时空轨迹数据，赋能智慧城市、绿色交通和自动驾驶等。具体来说，时空数据分析包括交通状态预估、偏好识别和导航决策。涉及到了表征学习、图学习、多任务学习和图算法等。研究成果已经应用于丹麦，德国，荷兰，希腊和塞浦路斯的若干合作单位的智能交通系统中。比如，提出的环保导航算法（Eco-routing）可以有效的减少交通领域的二氧化碳排量，已应用于丹麦北日德兰地区最大的交通调度公司FlexDanmark的多项服务中。", + + l7_tag: "智能交通、社交网络、分子设计 ", + l7_desc: + "高效图学习和图算法，支持绿色交通、智慧城市和分子设计。\n\n图由节点和边组成，可以有效的对多个实体之间的复杂关系进行建模。节点常常用于表示实体，连接两个节点的边则用于表示其对应的两个实体之间的某种关系。比如，一个城市的交通网络就可以表示成一个图，这里节点表示路口，边表示道路。我们的图分析工作主要包括：图表征学习（基于对比学习，弱监督学习，课程学习）、图属性预测、时序相关性学习和不确定性图的最短路径算法。 ", + + l2_tag: "降低各行业使用机器学习的门槛和成本", + l2_desc: + "自动构建高精度模型架构并自动选择超参数，降低不同行业使用机器学习门槛和成本。\n\n 当前高效的机器学习模型通常是由机器学习专家手工设计的，而这一人工设计过程冗长费力。自动机器学习则尝试由算法来自动化这一设计过程，自动构建高效的机器学习模型。这将大大降低各行业使用机器学习的门槛和使用机器学习的人力、物力和时间成本。我们的研究集中在可微神经架构搜索算法及其可解释性，神经架构和超参数联合搜索，以及这些方法在自动构建时序分析模型和基于物理信息的神经网络（Physics Informed Neural Network）的应用研究。", + + l5_tag: "助力科学发现和工业应用", + l5_desc: + "人工智能正在助力科学家进行科学发现，有望成为进行科学发现的新范式。我们的研究主要集中在基于神经网络的求解器和药物发现。", + + l8_tag: "边端设备智能化、工业智能、数字孪生、数字能源", + l8_desc: + "很多决策需要在边端设备完成，而边端设备通常只有有限的计算和存储资源，这就要求轻量化的决策模型。我们的研究主要集中在模型压缩。", + + l3_tag: "智能运维、AIOps", + l3_desc: + "助力智能运维，AIOps，欺诈检测。异常检测用于发现和正常状态不同的状态。由于异常状态很少出现，异常的种类也可能多种多样，这就造成传统的有监督异常检测算法很难有效工作。我们的研究集中在无监督异常检测，即在不需要异常标签的情况下，可以准确地检测异常。我们提出了基于自编码器（AutoEncoder）和集成学习（Ensemble Learning）的异常检测算法，并关注其健壮性和可解释性。 ", + + l6_tag: "时序预测、图预测", + l6_desc: + "很多决策场景都需要对未知状态进行预测，比如预测未来将要发生的状态和估计数据缺失部分的状态。我们的工作主要集中在基于时间序列预测和基于图数据的预测。", + + l9_tag: "不确定决策、学习决策、多元决策等", + l9_desc: + "我们每个人每天都在做大量的决策。一个最典型的例子就是出行时的路径选择。决策之所以有时很复杂，是因为其要考虑很多因素而不仅仅是一个因素（比如要考虑旅行时间和碳排放），而这些因素又常常是不确定的。我们针对这些问题，以路径导航决策为例，进行了深入的研究。其研究成果已经应用于丹麦，德国，荷兰，希腊和塞浦路斯的若干合作单位的智能交通系统中。比如，提出的环保导航算法（Eco-routing）可以有效的减少交通领域的二氧化碳排量，已应用于丹麦北日德兰地区最大的交通调度公司FlexDanmark的多项服务中。", + + l10_tag: "", + l10_desc: "迁移学习与模型泛化性", + l11_tag: "", + l11_desc: "时序大模型的研究旨在通过大规模时序数据的训练，构建在不同任务上具有通用性，在不同领域间具有泛化性的时间序列分析模型，从而减小模型在具体下游任务中的数据收集、模型训练成本，提升时间序列分析技术在各领域的易用性，赋能智能运维、智慧城市、数字能源等领域的智能分析决策与数字化转型。研究内容涵盖时序大模型预训练技术、时序大模型架构设计、大规模时序数据集、时序大模型在预测、异常检测、分类等典型分析任务上的应用等方面。", + l12_tag: "", + l12_desc: "大模型评测的研究旨在对大语言模型、时序大模型等各类大模型从精度、效率、通用性等角度进行系统性评测对比，对不同模型的优势与存在的问题进行定性与定量的分析，为大模型的优化提供方向，为大模型的选型提供参考。研究内容涵盖大模型评测集构建、大模型评测框架与系统设计等。", + l13_tag: "", + l13_desc: + "多智能体系统通过模拟复杂社会交互和自主决策，能有效分析群体行为与系统动态。我们的研究聚焦于利用多智能体仿真技术，赋能教育政策评估、交通调度和资源分配等领域。具体而言，研究涵盖社会模拟器构建（如教育政策宏观影响预测）、协同决策算法（如网约车供需匹配与路径优化）以及多智能体强化学习（如水库用水动态调度），涉及博弈论、分布式优化、强化学习与机制设计等方法。研究成果将应用于国内教育政策仿真平台与智慧城市项目，例如：1）开发的教育政策模拟器通过多智能体建模量化了资源分配对区域教育公平的影响，为国家宏观教育政策研究院提供决策支持；2）设计的网约车协同调度算法，并将落地与国内前十的网约车出行平台调度系统中；3）提出的水资源多智能体分配框架将应用于上海市长江水源地咸潮应对中。", + l14_tag: "", + l14_desc: + "具身智能研究致力于四足机器人、无人机等具身智能设备的算法，通过强化学习、运动控制与多智能体协同技术，打造高效、鲁棒的地-空一体化智能系统。研究重点包括：动态环境感知（如复杂地形适应与实时避障）、自主运动控制（如四足机器人的高动态步态优化与无人机的精准悬停）以及跨平台协同（如无人机-机器人联合搜索与任务分配）。关键技术涉及仿生运动建模、多模态传感器融合、分布式强化学习与实时路径规划。", + + + ls1_1: "时间序列预测", + ls1_2: "时序异常检测", + ls1_3: "时序分类", + lst1_1_1: "Time-frequency domain based forecasting:", + lst1_1_2: "Model-agnostic plugin:", + lst1_1_3: "Automated correlated time series forecasting:", + lst1_1_4: "Transformer-based forecasting:", + lst1_1_5: "Robust forecasting: ", + lst1_1_6: "Graph neural network based forecasting:", + lst1_1_7: "Forecasting benchmarking:", + lst1_1_8: "Continual learning for forecasting:", + lst1_1_9: "Neural ODE-based forecasting：", + lst1_1_10: "Markov Model-based forecasting:", + lst1_1_11: "Others:", + lst1_2_1: "Ensemble learning based outlier detection：", + lst1_2_2: "Robust outlier detection：", + lst1_2_3: "Frequency domain based outlier detection：", + lst1_2_4: "Outlier detection foundation model： ", + lst1_2_5: "Explainable outlier detection: ", + lst1_2_6: "Ensemble learning based outlier detection：", + + ls4_1: "交通状态预估", + ls4_2: "偏好识别", + ls4_3: "数据驱动的导航决策", + ls4_4: "其他", + lst4_1_1: "路径交通状态:", + lst4_1_2: "路网交通状态:", + lst4_3_1: "不确定性下的决策:", + lst4_3_2: "基于学习的决策:", + lst4_3_3: "多元决策:", + lst4_3_4: "Top-K 决策:", + lst4_3_5: "Eco-routing:", + + ls7_1: "路径表征学习", + ls7_2: "图属性预测", + ls7_3: "时空序列关联性学习", + ls7_4: "不确定性图的最短路径算法", + + lst6_4_1: "时间序列预测：", + lst6_4_2: "基于图预测：", + + lst9_1_1: "不确定性下的决策：", + lst9_1_2: "基于学习的决策：", + lst9_1_3: "多元决策：", + lst9_1_4: "Top-K 决策:", + lst9_1_5: "Eco-routing:", + lst9_1_6: "弹性伸缩、自动扩缩容（AutoScaling）:", + + lst10_1_1: "模型预训练与迁移学习（Pre-training, Fine-tuning, Model Zoo, Few-shot Learning）:", + lst10_1_2: "分布外泛化（OOD Generalization）:", + lst10_1_3: "领域适应（Domain Adaptation）:", + + + r1: "机器学习，人工智能，数据管理与分析", + r2: "时间序列分析，时空序列分析，图分析，AI for Science", + r3: "自动机器学习，模型可解释性，健壮性，持续学习，轻量化模型，DB4AI", + r4: "自拟方向（不局限于以上）", + + r5: "对学生的期望", + r51: "具有自我驱动能、对自己未来有目标", + r52: "具备扎实的编程和数学基础", + r53: "具备良好的英文阅读和撰写能力，能用英文做口头报告", + r54: "踏实、有责任心、善于沟通、有团队合作精神", + + r6: "培养方法", + r61: "提供科研与应用结合的科研环境", + r62: "课题选择兼顾高水平论文发表与工程实践应用", + r63: "因人施教，充分考虑学生实际情况和需求，实现学生与导师共赢发展", + r64: "注重国际化，积极提供各类国际交流机会", + + r7: "岗位描述", + r71: "独立承担课题研究", + r72: "协助指导研究生", + + r8: "岗位要求", + r81: "具有或即将获得计算机等相关信息科学相关的博士学位，或其他理工科博士学位（主要针对AI for Science方向）", + r82: "近三年以第一作者发表过高水平论文（比如CCF A类）", + r83: "具有独立完成科研课题能力", + + r9: "联系我们", + r91: "杨彬：", + r92: "郭晨娟：", + r93: "上海市普陀区中山北路3663号", + + r10: "待遇", + r101: '
    待遇
    • 薪资待遇按华东师范大学博士后有关规定执行，博士后基础年薪20-40万/年，具体规定参照http://postdoctor.ecnu.edu.cn/34/1e/c14591a275486/page.htm。优先支持申请国家，上海市和学校的各类博士后计划
      • 国家“博新计划” （年薪40万+经费23万）
      • 国家“博士后国际交流计划” （年薪30+万）
      • 上海市“博士后创新创业计划” （年薪30万+经费10万）
      • 学校“卓越博士后计划” (年薪25+万) ，条件优秀者将推荐申请华师大“光华”优秀博士后（年薪40万）;
    • 全职博士后在站期间，学校按照国家规定为其缴纳社会保险和住房公积金。同时，学校为全职博士后提供2万元安家补贴，进站后一次性发放。
      • 享受课题研究补贴
        • 享受华东师范大学相应的工会待遇，住房、户口等其他方面根据华东师范大学相关政策执行。
          ', + + address: "地址", + email: "邮箱", + wechat: "微信公众号", + + mp1: "导师", + mp2: "博士生", + mp3: "硕士生", + mp4: "2023 级", + mp5: "2024 级", + mp6: "2025 级", +}; diff --git a/src/index.template.html b/src/index.template.html new file mode 100644 index 0000000..22c3d15 --- /dev/null +++ b/src/index.template.html @@ -0,0 +1,47 @@ + + + + 华东师范大学决策智能实验室 + + + + + + + + + + + + + + + + + +
          + + diff --git a/src/layouts/MainLayout.vue b/src/layouts/MainLayout.vue new file mode 100644 index 0000000..9abbaee --- /dev/null +++ b/src/layouts/MainLayout.vue @@ -0,0 +1,136 @@ + + + diff --git a/src/pages/ErrorNotFound.vue b/src/pages/ErrorNotFound.vue new file mode 100644 index 0000000..6de077d --- /dev/null +++ b/src/pages/ErrorNotFound.vue @@ -0,0 +1,29 @@ + + + diff --git a/src/pages/IndexPage.vue b/src/pages/IndexPage.vue new file mode 100644 index 0000000..f806098 --- /dev/null +++ b/src/pages/IndexPage.vue @@ -0,0 +1,260 @@ + + + + + diff --git a/src/pages/MemberPage.vue b/src/pages/MemberPage.vue new file mode 100644 index 0000000..9421ebc --- /dev/null +++ b/src/pages/MemberPage.vue @@ -0,0 +1,363 @@ + + + + diff --git a/src/pages/PageDetail.vue b/src/pages/PageDetail.vue new file mode 100644 index 0000000..9c193fb --- /dev/null +++ b/src/pages/PageDetail.vue @@ -0,0 +1,66 @@ + + + diff --git a/src/pages/PaperPage.vue b/src/pages/PaperPage.vue new file mode 100644 index 0000000..0ddf646 --- /dev/null +++ b/src/pages/PaperPage.vue @@ -0,0 +1,103 @@ + + + + + diff --git a/src/pages/project.vue b/src/pages/project.vue new file mode 100644 index 0000000..63f6a70 --- /dev/null +++ b/src/pages/project.vue @@ -0,0 +1,152 @@ + + + + diff --git a/src/pages/recruit.vue b/src/pages/recruit.vue new file mode 100644 index 0000000..b8c5f2b --- /dev/null +++ b/src/pages/recruit.vue @@ -0,0 +1,291 @@ + + + diff --git a/src/router/index.js b/src/router/index.js new file mode 100644 index 0000000..eb5cecd --- /dev/null +++ b/src/router/index.js @@ -0,0 +1,39 @@ +import { route } from "quasar/wrappers"; +import { + createRouter, + createMemoryHistory, + createWebHistory, + createWebHashHistory, +} from "vue-router"; +import routes from "./routes"; +import { api } from "boot/axios"; + +/* + * If not building with SSR mode, you can + * directly export the Router instantiation; + * + * The function below can be async too; either use + * async/await or return a Promise which resolves + * with the Router instance. + */ + +export default route(function (/* { store, ssrContext } */) { + const createHistory = process.env.SERVER + ? createMemoryHistory + : process.env.VUE_ROUTER_MODE === "history" + ? createWebHistory + : createWebHashHistory; + + const Router = createRouter({ + scrollBehavior: () => ({ left: 0, top: 0 }), + routes, + // Leave this as is and make changes in quasar.conf.js instead! + // quasar.conf.js -> build -> vueRouterMode + // quasar.conf.js -> build -> publicPath + history: createHistory( + process.env.MODE === "ssr" ? void 0 : process.env.VUE_ROUTER_BASE + ), + }); + + return Router; +}); diff --git a/src/router/routes.js b/src/router/routes.js new file mode 100644 index 0000000..e6266ce --- /dev/null +++ b/src/router/routes.js @@ -0,0 +1,29 @@ +const routes = [ + { + path: "/", + component: () => import("layouts/MainLayout.vue"), + redirect: "index", + children: [ + { path: "index", component: () => import("pages/IndexPage.vue") }, + { path: "papers", component: () => import("pages/PaperPage.vue") }, + { + path: "member", + component: () => import("pages/MemberPage.vue"), + }, + { path: "recruit", component: () => import("pages/recruit.vue") }, + { path: "project", component: () => import("pages/project.vue") }, + { + path: "/paper/:id", + component: () => import("pages/PageDetail.vue"), + }, + ], + }, + // Always leave this as last one, + // but you can also remove it + { + path: "/:catchAll(.*)*", + component: () => import("pages/ErrorNotFound.vue"), + }, +]; + +export default routes; diff --git a/src/state.js b/src/state.js new file mode 100644 index 0000000..8ba52e2 --- /dev/null +++ b/src/state.js @@ -0,0 +1,20 @@ +import { reactive } from "vue"; +import { api } from "boot/axios"; + +class State { + constructor() { + this.papers = reactive([]); + } + getPaper() { + return fetch( + "https://raw.githubusercontent.com/decisionintelligence/files/main/paper_table.json" + ) + .then((res) => res.json()) + .then((res) => { + return res[0]; + }); + } +} +export default new State(); + +// export function diff --git a/tailwind.config.js b/tailwind.config.js new file mode 100644 index 0000000..098750c --- /dev/null +++ b/tailwind.config.js @@ -0,0 +1,10 @@ +/** @type {import('tailwindcss').Config} */ +module.exports = { + content: ["./src/**/*.html", "./src/**/*.vue", "./src/**/*.jsx"], + theme: { + extend: {}, + }, + plugins: [require("@tailwindcss/typography")], + prefix: "tw-", + important: true, +};