公开(公告)号：US20220318626A1

公开(公告)日：2022-10-06

申请号：US17711408

申请日：2022-04-01

Applicant: NEC Laboratories America, Inc. ,  NEC Corporation

Inventor： Jingchao Ni ,  Wei Cheng ,  Haifeng Chen ,  Takayoshi Asakura

IPC: G06N3/08

Abstract: A method for performing dialysis event prediction by employing a meta-training strategy for model personalization includes, in a meta-training stage, generating segments from temporal records of patient dialysis data, generating, from the segments, a support set and a query set for each patient of a plurality of patients, formulating tasks for each patient in a pre-training set defined as a meta-training framework (M-DCCN), where each task includes the support set and the query set, and sending the tasks to a two-level meta-training algorithm supported training coordinator. The method further includes, in a finetuning stage, sending the M-DCCN to local machines where a finetuning dataset is collected for new patients, the finetuning dataset including a limited amount of data pertaining the new patients, fine-tuning the M-DCCN for personalization, and using the fine-tuned M-DCCN for future predictive dialysis analysis of future new patients by generating prognostic predictive scores.

公开(公告)号：US20200151563A1

公开(公告)日：2020-05-14

申请号：US16675596

申请日：2019-11-06

Applicant: NEC Laboratories America, Inc.

Inventor： Bo Zong ,  Jingchao Ni ,  Haifeng Chen ,  Cheng Zheng

IPC: G06N3/08 ,  G06N3/04

Abstract: A method for employing a supervised graph sparsification (SGS) network to use feedback from subsequent graph learning tasks to guide graph sparsification is presented. The method includes, in a training phase, generating sparsified subgraphs by edge sampling from input training graphs following a learned distribution, feeding the sparsified subgraphs to a prediction/classification component, collecting a predication/classification error, and updating parameters of the learned distribution based on a gradient derived from the predication/classification error. The method further includes, in a testing phase, generating sparsified subgraphs by edge sampling from input testing graphs following the learned distribution, feeding the sparsified subgraphs to the prediction/classification component, and outputting prediction/classification results to a visualization device.

公开(公告)号：US20200090025A1

公开(公告)日：2020-03-19

申请号：US16553465

申请日：2019-08-28

Applicant: NEC Laboratories America, Inc.

Inventor： Wei Cheng ,  LuAn Tang ,  Dongjin Song ,  Bo Zong ,  Haifeng Chen ,  Jingchao Ni ,  Wenchao Yu

IPC: G06N3/04 ,  G06N3/08

Abstract: Systems and methods for predicting system device failure are provided. The method includes representing device failure related data associated with the devices from a predetermined domain by temporal graphs for each of the devices. The method also includes extracting vector representations based on temporal graph features from the temporal graphs that capture both temporal and structural correlation in the device failure related data. The method further includes predicting, based on the vector representations and device failure related metrics in the predetermined domain, one or more of the devices that is expected to fail within a predetermined time.

公开(公告)号：US20240028898A1

公开(公告)日：2024-01-25

申请号：US18479372

申请日：2023-10-02

Applicant: NEC Laboratories America, Inc.

Inventor： Jingchao Ni ,  Zhengzhang Chen ,  Wei Cheng ,  Bo Zong ,  Haifeng Chen

IPC: G06N3/08 ,  G06N3/04

CPC classification number: G06N3/08 ,  G06N3/04

Abstract: A method interprets a convolutional sequence model. The method converts an input data sequence having input segments into output features. The method clusters the input segments into clusters using respective resolution-controllable class prototypes allocated to each of classes. Each respective class prototype includes a respective output feature subset characterizing a respective associated class. The method calculates, using the clusters, similarity scores that indicate a similarity of an output feature to a respective class prototypes responsive to distances between the output feature and the respective class prototypes. The method concatenates the similarity scores to obtain a similarity vector. The method performs a prediction and prediction support operation that provides a value of prediction and an interpretation for the value responsive to the input segments and similarity vector. The interpretation for the value of prediction is provided using only non-negative weights and lacking a weight bias in the fully connected layer.

公开(公告)号：US20230394309A1

公开(公告)日：2023-12-07

申请号：US18451880

申请日：2023-08-18

Applicant: NEC Laboratories America, Inc.

Inventor： Wei Cheng ,  Haifeng Chen ,  Jingchao Ni ,  Dongkuan Xu ,  Wenchao Yu

IPC: G06N3/08 ,  G06F17/18 ,  G06N5/04 ,  G06F18/214 ,  G06N3/044 ,  G06N3/045

CPC classification number: G06N3/08 ,  G06F17/18 ,  G06N5/04 ,  G06F18/214 ,  G06N3/044 ,  G06N3/045

Abstract: A method for executing a multi-task deep learning model for learning trends in multivariate time series is presented. The method includes collecting multi-variate time series data from a plurality of sensors, jointly learning both local and global contextual features for predicting a trend of the multivariate time series by employing a tensorized long short-term memory (LSTM) with adaptive shared memory (TLASM) to learn historical dependency of historical trends, and employing a multi-task one-dimensional convolutional neural network (1dCNN) to extract salient features from local raw time series data to model a short-term dependency between local time series data and subsequent trends.

公开(公告)号：US11783181B2

公开(公告)日：2023-10-10

申请号：US16987789

申请日：2020-08-07

Applicant: NEC Laboratories America, Inc.

Inventor： Wei Cheng ,  Haifeng Chen ,  Jingchao Ni ,  Dongkuan Xu ,  Wenchao Yu

IPC: G06N3/08 ,  G06F17/18 ,  G06N5/04 ,  G06F18/214 ,  G06N3/044 ,  G06N3/045

CPC classification number: G06N3/08 ,  G06F17/18 ,  G06F18/214 ,  G06N3/044 ,  G06N3/045 ,  G06N5/04

Abstract: A method for executing a multi-task deep learning model for learning trends in multivariate time series is presented. The method includes collecting multi-variate time series data from a plurality of sensors, jointly learning both local and global contextual features for predicting a trend of the multivariate time series by employing a tensorized long short-term memory (LSTM) with adaptive shared memory (TLASM) to learn historical dependency of historical trends, and employing a multi-task one-dimensional convolutional neural network (1dCNN) to extract salient features from local raw time series data to model a short-term dependency between local time series data and subsequent trends.

公开(公告)号：US20230094623A1

公开(公告)日：2023-03-30

申请号：US17950203

申请日：2022-09-22

Applicant: NEC Laboratories America, Inc.

Inventor： Jingchao Ni ,  Wei Cheng ,  Haifeng Chen

IPC: G06N3/08 ,  G06N7/00

Abstract: A computer-implemented method for model building is provided. The method includes receiving a training set of medical records and model hyperparameters. The method further includes initializing an encoder as a Dual-Channel Combiner Network (DCNN) and initialize distribution related parameters. The method also includes performing, by a hardware processor, a forward computation to (1) the DCNN to obtain the embeddings of the medical records, and (2) the distribution related parameters to obtain class probabilities. The method additionally includes checking by a convergence evaluator if the iterative optimization has converged. The method further includes performing model personalization responsive to model convergence by encoding the support data of a new patient and using the embeddings and event subtype labels to train a personalized classifier.

公开(公告)号：US11606393B2

公开(公告)日：2023-03-14

申请号：US17004547

申请日：2020-08-27

Applicant: NEC Laboratories America, Inc.

Inventor： Jingchao Ni ,  Haifeng Chen ,  Bo Zong ,  LuAn Tang ,  Wei Cheng

IPC: H04L9/40 ,  G06N3/049 ,  H04L41/16 ,  G06N3/08

Abstract: Methods and systems for detecting and responding to anomalous nodes in a network include inferring temporal factors, using a computer-implemented neural network, that represent changes in a network graph across time steps, with a temporal factor for each time step depending on a temporal factor for a previous time step. An invariant factor is inferred that represents information about the network graph that does not change across the time steps. The temporal factors and the invariant factor are combined into a combined temporal-invariant representation. It is determined that an unlabeled node is anomalous, based on the combined temporal-invariant representation. A security action is performed responsive to the determination that unlabeled node is anomalous.

公开(公告)号：US20230076575A1

公开(公告)日：2023-03-09

申请号：US17883729

申请日：2022-08-09

Applicant: NEC Laboratories America, Inc.

Inventor： Jingchao Ni ,  Wei Cheng ,  Haifeng Chen

IPC: G16H50/30 ,  G16H50/70 ,  G16H50/50

Abstract: A method for making prognostic prediction scores during a pre-dialysis period on an incidence of events in future dialysis includes learning a meta-training model that simultaneously classifies dialysis in-distribution events and detects out-of-distribution (OOD) events during model personalization by employing a data preprocessing component to extract different parts of data from historical medical records of patients to generate a meta-training dataset, a meta-training component to analyze the meta-training dataset, the meta-training component including a class pool generator, a task generator, a prototype network, an attention component, and a model training component, the class pool generator splitting training classes into a first class pool and a second class pool for generating a distribution statistics dictionary, a storage component to store the meta-training model for distribution to local machines, and a personalization component including a local data collection component, and a class and OOD detector component.

公开(公告)号：US11323465B2

公开(公告)日：2022-05-03

申请号：US16562805

申请日：2019-09-06

Applicant: NEC Laboratories America, Inc.

Inventor： Wei Cheng ,  LuAn Tang ,  Haifeng Chen ,  Bo Zong ,  Jingchao Ni

IPC: G06N3/08 ,  H04L29/06

Abstract: Systems and methods for implementing sequence data based temporal behavior analysis (SDTBA) to extract features for characterizing temporal behavior of network traffic are provided. The method includes extracting communication and profile data associated with one or more devices to determine sequences of data associated with the devices. The method includes generating temporal features to model anomalous network traffic. The method also includes inputting, into an anomaly detection process for anomalous network traffic, the temporal features and the sequences of data associated with the devices and formulating a list of prediction results of anomalous network traffic associated with the devices.