International Business Machines Corporation

Boe Technology Group Co., Ltd.

Beijing Boe Optoelectronics Technology Co., Ltd.

Thomson Licensing

Chengdu Boe Optoelectronics Technology Co., Ltd.

Other

Tsinghua University

Beijing Boe Technology Development Co., Ltd.

Interdigital Ce Patent Holdings

Beijing Institute of Technology

Sun Yat-senuniversity

China Agricultural University

Thomson Licensing Dtv

Beijing Boe Optoelectronics

Xiao Ming Ma

Jing James Xu

Si Er Han

Xue Ying Zhang

Hao Wu

Zheng Zhang

Gang Ci

Na An

Zhenhua Luo

Baolei Guo

Xinfeng Gong

Ji Hui Yang

Shaopeng Xu

Qingzhu Guan

Bin Xu

Wei Su

Xuzhong Liu

Zongwei Luo

Jun Wang

Zuoxin Yu

A Peng Zhang

Jing Xu

Guochun Wang

Lei Gao

Jun Xu

Jinzhao Zhou

Wen Pei Yu

Dianzhong Liu

Chang Wang

Jian Chen

Bin Zhang

Sheng Yan Sun

Jun Song Wang

Site Cai

Ning Liao

Chun Lei Xu

Huan Zhang

Ren Lei Chen

Dong Hai Yu

Xu Zhao

Charles Chuanming Wang

Hyungkyu Kim

Sier Han

Jia Meng

Binbin Liu

Libao Cui

Rui Wang

Hui Yin

Shuo Li

Peng Hui Jiang

Xiaobo Wang

Yu Gu

Lin Du

Hui Zhang

Jing Min Xu

Yang Li

Jian Jun Wang

Jun Li

Yi Shan Jiang

Shuang Shuang Li

Xiaoping Zhang

Wei Zhou

Wei Yan

Junbiao Zhang

ShengYan Sun

Liguang Deng

Ping Xiao

Shan Gao

Dong Zhang

Xian Wang

Chao Yuan Huang

Jiaqiang Wang

Zhigang Zhang

Yanjun Liu

Litao Fan

Guo Qiang Li

Xing Wei

Fuzheng Xie

Yangli Zheng

Jingyu Zhang

Xu He

Tao Jia

Zhuo Hua Li

Li Juan Gao

Yao Dong Liu

Luo Zhang

Zhiqiang Fan

Jin Wang

Chong Liu

Zhi Feng Zhao

Jianxin Wang

Bangcan Xue

Ying Lin Xu

Jiang Bo Kang

Zhi Gang Zhang

Wenfeng Jin

Xingpan Sun

Jin Rong Wang

Yanfeng Zhang

Jianguo Zhu

A method, system, and computer program product for incremental machine learning for a parametric machine learning model are disclosed. The method may include processing samples comprising historical samples and new samples with an existing parametric machine learning model to obtain at least one prediction residual of each of the samples, wherein the existing parametric machine learning model was trained based on the historical samples. The method may further include clustering the samples based on the at least one prediction residual of each of the samples and features of each of the samples. The method may further include sampling samples in each cluster to ensure that each cluster includes substantially similar number of sampled samples. The method may further include updating the existing parametric machine learning model to obtain an updated parametric machine learning model based on sampled samples in each cluster.

Incremental machine learning for a parametric machine learning model

A method, a computer system, and a computer program product for receiving a plurality of unreduced training data records (TDRs), using a first autoencoder on the plurality of unreduced TDRs to obtain a first plurality of reduced TDRs, clustering the first plurality of reduced TDRs into a plurality of clusters, wherein each first reduced TDR cluster is assigned a respective cluster label, transferring the respective cluster label from each first reduced TDR cluster to a corresponding unreduced TDR cluster, and performing stratified sampling to form K data blocks of unreduced TDRs from a plurality of unreduced TDR clusters.

Dimension reduction in the context of unsupervised learning

Provided is a method for reducing methane production in animal stomachs by adding melatonin to gastric juice of the animal or feeding the animal with melatonin. In the method of the present disclosure, 10-7 mol/L to 10-3 mol/L of melatonin can reduce the methane production from the in vitro gastric fermentation fluid, and feeding melatonin of 8.0 to 35.0 mg/kg/day can reduce the methane production from the animal respiration from day 7 of feeding melatonin. The method of the present disclosure can reduce greenhouse gas emissions of animals, control environmental pollution and realize low-carbon farming.

Method for reducing methane production in animal stomach

A computer-implemented method for anomaly detection for a time series data is provided. Aspects include receiving a time series data including a plurality of sequential data points, calculating an expected next value for the time series data based on the plurality of sequential data points, and receiving an actual next value corresponding to the time series data. Aspects also include calculating an anomaly strength estimate based on the expected next value and the actual next value, identifying one of a plurality of anomaly detection pipelines based on the anomaly strength estimate and a portrait associated with each of the plurality of anomaly detection pipelines, and obtaining an anomaly prediction by inputting the time series data and the actual next value into the one of the plurality of anomaly detection pipelines.

Anomaly detection for time series data

A method for improving a query performance of a query of a database application using rewrite includes introducing a database query into a database application to obtain a query result, analyzing the database query to identify index columns and a predicate column; identifying associated columns by determining if any associations exist between the index columns and the predicate column, calculating a confidence score value for each of the associated columns responsive to the associated columns and the predicate column, generating a list of associated columns with the confidence score values, generating a rewritten query by rewriting the database query based on the list of associated columns and the confidence score values and validating the rewritten query.

Query performance improvement of a query for a database application using rewrite

A computer-implemented method for treating post-modeling data includes computing, sequentially for each category of a feature, a category importance (CI) value. The CI value is based on a model accuracy change when records of a category being examined are reassigned to a remaining set of categories of the feature according to a cumulative distribution of records among the remaining set of categories of the feature, wherein the remaining set of categories include all categories of the feature, except for the category being examined. A post-modeling category is performed to merge of each category having the CI value less than a CI value threshold.

Key category identification and visualization

An approach to time-series data point anomaly detection may be presented. Data point anomalies in time-series data can cause a cascade of incorrect predictions in a time-series data prediction model. Presented herein may be an approach to decompose a time-series training data set into elementary components, such as seasonal, trend and residual. The approach may determine one or more confidence intervals for elementary components of data points including level shift, variance, and outlier. From these confidence intervals, new data points can be analyzed and identified as anomaly data points. The approach may also prevent anomaly data points from being incorporated into a time series data prediction model, reducing prediction error in the prediction model.

Optimization of time-series anomaly detection

In an approach for post-modeling data visualization and analysis, a processor presents a first visualization of a training dataset in a first plot. Responsive to receiving a selection of a data group of the training dataset to analyze, a processor identifies three or fewer key model features of the data group of the training dataset. A processor ascertains a representative record of each key model feature of the three or fewer key model features using a Local Interpretable Model-Agnostic Explanation technique. A processor presents a second visualization of the three or fewer key model features and the representative record of each key model feature in a second plot.

Visualize data and significant records based on relationship with the model

Embodiments analyze a query pattern of an incoming query on a database, perform a semantic analysis of the query pattern of the incoming query, generate a re-write query that has an improved query performance in comparison to a query performance of the incoming query based on the analyzed query pattern and the semantic analysis; build a query model using machine learning based on at least one of the query pattern and the semantic analysis; and apply the re-write query by performing the re-write query on the database to provide the improved query performance.

Query performance discovery and improvement

A method, computer system, and a computer program product are provided for post-modeling feature evaluation. In one embodiment, at least at least one post model visual output and associated data is obtained that at least includes an individual conditional expectation (ICE) plot and a partial dependence (PDP) plot. Using the associated data and the plots, a Feature Importance (PI) plot is provided. A plurality of features is then determined for each PI, PDP and ICE plots to calculate at least one Interesting Value for each plot. An overall score is also calculated for each plurality of features based on the associated Interesting Values for each PDP, ICE and PI plots. At least one top feature is selected based on said scores. A final plot is then generated at least reflecting the top feature. The final plot combines the PI, PDP and ICE plots together.

Visual representation using post modeling feature evaluation

Disclosed are a computer-implemented method, a system and a computer program product for model exploration. Model feature importance of each model of a plurality of models can be obtained, the plurality of models can be grouped into a plurality of model clusters based on the model feature importance of each model, and the model feature importance can be presented by box-plot or confidence interval.

Feature importance based model optimization

A method for forecasting server demand includes collecting a historical number of scoring requests from a network using a serverless architecture. A scoring request capacity per server is determined using the historical number of scoring requests. A prediction model predicts a first future value of scoring requests for a first future time span. A current number of servers in a pool of servers handling the scoring requests. Using the prediction model, a determination of whether the current number of servers is capable of handling the first future value of scoring requests for the first future time span is made. Upon determining that the current number of servers is incapable of handling the first future value of scoring requests, one or more additional servers are warmed up. The warmed-up additional servers are added to the pool of servers prior to an arrival of the first future time span.

Efficient serverless method and system of serving artificial intelligence models

The present disclosure relates to privacy protection in a search process. According to a method, a target emotion vector is extracted from a search interaction, the target emotion vector representing emotional information in the search interaction. Respective emotion distances between the target emotion vector and respective emotion vectors associated with a plurality of text clusters are determined. The plurality of text clusters is clustered from a dictionary of text elements. A first number of text clusters are selected from the plurality of text clusters based on the determined respective emotion distances. The first number of text clusters have emotion distances larger than at least one unselected text cluster among the plurality of text clusters. A plurality of confused search interactions are constructed for the search interaction based on the first number of text clusters, and the plurality of confused search interactions are performed.

Privacy protection in a search process

A computer-implemented method, system and computer program product for improving performance of a distributed database. A query is received to store version data in the distributed database. Upon receiving the query to store the version data, the version data is stored in a row of a data page of a main table of a heap organized table/index organized table of the distributed database, where the row of the data page of the main table of the heap organized table/index organized table of the distributed database contains a pointer pointing to a later/previous version of the version data if the later/previous version of the version data is stored in the data page thereby chaining version data bi-directionally.

Chaining version data bi-directionally in data page to avoid additional version data accesses

The present application discloses an electronic device including a housing, support plates and a display panel. The housing is recessed inward to form one or more clearances penetrating the housing along a first direction. A number of the support plates is at least two, and one clearance is correspondingly arranged below adjoining parts of two adjacent support plates. The support plates are configured to support the display panel. The display panel is switchable between a folded position and an unfolded position. When the display panel is in the unfolded position, first surfaces of the support plates that abut against the display panel are flush. When the display panel is in the folded position, any two oppositely folded and adjacent support plates are separated by a part of the display panel that covers the any two oppositely folded and adjacent support plates when the display panel is in the unfolded position, and a part of the any two oppositely folded and adjacent support plates and a part of the display panel that covers the part of the any two oppositely folded and adjacent support plates when the display panel is in the unfolded position enter the clearance.

Electronic devices

Growing community of inventors

Xi'an, China

Xiao Ming Ma