公开(公告)号：US20240078430A1

公开(公告)日：2024-03-07

申请号：US18449748

申请日：2023-08-15

Applicant: NEC Laboratories America, Inc.

Inventor： Renqiang Min ,  Hans Peter Graf ,  Tianxiao Li

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: A computer-implemented method for learning disentangled representations for T-cell receptors to improve immunotherapy is provided. The method includes optionally introducing a minimal number of mutations to a T-cell receptor (TCR) sequence to enable the TCR sequence to bind to a peptide, using a disentangled Wasserstein autoencoder to separate an embedding space of the TCR sequence into functional embeddings and structural embeddings, feeding the functional embeddings and the structural embeddings to a long short-term memory (LSTM) or transformer decoder, using an auxiliary classifier to predict a probability of a positive binding label from the functional embeddings and the peptide, and generating new TCR sequences with enhanced binding affinity for immunotherapy to target a particular virus or tumor.

公开(公告)号：US20240071571A1

公开(公告)日：2024-02-29

申请号：US18471641

申请日：2023-09-21

Applicant: NEC Laboratories America, Inc.

Inventor： Renqiang Min ,  Hans Peter Graf ,  Ziqi Chen

IPC: G16B40/00 ,  G06N3/08 ,  G16B15/30

CPC classification number: G16B40/00 ,  G06N3/08 ,  G16B15/30

Abstract: A system for binding peptide search for immunotherapy is presented. The system includes employing a deep neural network to predict a peptide presentation given Major Histocompatibility Complex allele sequences and peptide sequences, training a Variational Autoencoder (VAE) to reconstruct peptides by converting the peptide sequences into continuous embedding vectors, running a Monte Carlo Tree Search to generate a first set of positive peptide vaccine candidates, running a Bayesian Optimization search with the trained VAE and a Backpropagation search with the trained VAE to generate a second set of positive peptide vaccine candidates, using a sampling from a Position Weight Matrix (sPWM) to generate a third set of positive peptide vaccine candidates, screening and merging the first, second, and third sets of positive peptide vaccine candidates, and outputting qualified peptides for immunotherapy from the screened and merged sets of positive peptide vaccine candidates.

公开(公告)号：US20240070232A1

公开(公告)日：2024-02-29

申请号：US18452664

申请日：2023-08-21

Applicant: NEC Laboratories America, Inc.

Inventor： Wei Cheng ,  Jingchao Ni ,  Liang Tong ,  Haifeng Chen ,  Yizhou Zhang

IPC: G06F18/2413 ,  G06F18/2415 ,  H04B10/69

CPC classification number: G06F18/24133 ,  G06F18/2415 ,  H04B10/697

Abstract: Methods and systems for training a model include determining class prototypes of time series samples from a training dataset. A task corresponding to the time series samples is encoded using the class prototypes and a task-level configuration. A likelihood value is determined based on outputs of a time series density model, a task-class distance from a task embedding model, and a task density model. Parameters of the time series density model, the task embedding model, and the task density model are adjusted responsive to the likelihood value.

公开(公告)号：US20240064161A1

公开(公告)日：2024-02-22

申请号：US18359179

申请日：2023-07-26

Applicant: NEC Laboratories America, Inc.

Inventor： Yanchi Liu ,  Haifeng Chen ,  Yufei Li

IPC: H04L9/40 ,  H04L41/16

CPC classification number: H04L63/1425 ,  H04L41/16

Abstract: A computer-implemented method for employing a graph-based log anomaly detection framework to detect relational anomalies in system logs is provided. The method includes collecting log events from systems or applications or sensors or instruments, constructing dynamic graphs to describe relationships among the log events and log fields by using a sliding window with a fixed time interval to snapshot a batch of the log events, capturing sequential patterns by employing temporal-attentive transformers to learn temporal dependencies within the sequential patterns, and detecting anomalous patterns in the log events based on relationships between the log events and temporal context determined from the temporal-attentive transformers.

公开(公告)号：US20240062043A1

公开(公告)日：2024-02-22

申请号：US18364746

申请日：2023-08-03

Applicant: NEC Laboratories America, Inc.

Inventor： Liang Tong ,  Zhengzhang Chen ,  Wei Cheng ,  Haifeng Chen ,  Zhuohang Li

IPC: G06N3/0455 ,  G06N3/08

CPC classification number: G06N3/0455 ,  G06N3/08

Abstract: A computer-implemented method for employing a graph-based adaptive domain generation framework is provided. The method includes, in a training phase, performing domain prototypical network training on source domains, constructing an autoencoding domain relation graph by applying a graph autoencoder to produce domain node embeddings, and performing, via a domain-adaptive classifier, domain-adaptive classifier training to make an informed decision. The method further includes, in a testing phase, given testing samples from a new source domain, computing a prototype by using a pretrained domain prototypical network, inferring node embedding, and making a prediction by the domain-adaptive classifier based on the domain node embeddings.

公开(公告)号：US20240061740A1

公开(公告)日：2024-02-22

申请号：US18359350

申请日：2023-07-26

Applicant: NEC Laboratories America, Inc.

Inventor： Zhengzhang Chen ,  Haifeng Chen ,  Liang Tong ,  Dongjie Wang

IPC: G06F11/07 ,  G06F11/34

CPC classification number: G06F11/079 ,  G06F11/3447

Abstract: A computer-implemented method for locating root causes is provided. The method includes detecting a trigger point from entity metrics data and key performance indicator (KPI) data, generating a learned causal graph by fusing a state-invariant causal graph with a state-dependent causal graph, and locating the root causes by employing a random walk-based technique to estimate a probability score for each of the entity metrics data by starting from a KPI node.

公开(公告)号：US20240055842A1

公开(公告)日：2024-02-15

申请号：US18492902

申请日：2023-10-24

Applicant: NEC Laboratories America, Inc.

Inventor： Yangmin Ding ,  Yuanda Xu ,  Sarper Ozharar ,  Yue Tian ,  Ting Wang

IPC: H02G1/02 ,  G01S19/01 ,  G01D5/353

CPC classification number: H02G1/02 ,  G01S19/01 ,  G01D5/35354

Abstract: Systems and methods for performing the dynamic anomaly localization of utility pole aerial/suspended/supported wires/cables by distributed fiber optic sensing. In sharp contrast to the prior art, our inventive systems and methods according to aspects of the present disclosure advantageously identify a “location region” on a utility pole supporting an affected wire/cable, thereby permitting the identification and reporting of service personnel that are uniquely responsible for responding to such anomalous condition(s).

公开(公告)号：US20240054373A1

公开(公告)日：2024-02-15

申请号：US18471570

申请日：2023-09-21

Applicant: NEC Laboratories America, Inc.

Inventor： Wenchao Yu ,  Wei Cheng ,  Haifeng Chen ,  Yuncong Chen ,  Xuchao Zhang ,  Tianxiang Zhao

IPC: G06N7/01 ,  G06N20/00

CPC classification number: G06N7/01 ,  G06N20/00

Abstract: A method for learning a self-explainable imitator by discovering causal relationships between states and actions is presented. The method includes obtaining, via an acquisition component, demonstrations of a target task from experts for training a model to generate a learned policy, training the model, via a learning component, the learning component computing actions to be taken with respect to states, generating, via a dynamic causal discovery component, dynamic causal graphs for each environment state, encoding, via a causal encoding component, discovered causal relationships by updating state variable embeddings, and outputting, via an output component, the learned policy including trajectories similar to the demonstrations from the experts.

公开(公告)号：US20240046091A1

公开(公告)日：2024-02-08

申请号：US18484793

申请日：2023-10-11

Applicant: NEC Laboratories America, Inc.

Inventor： Wenchao Yu ,  Wei Cheng ,  Haifeng Chen ,  Yiwei Sun

IPC: G06N3/08 ,  G06N20/00

CPC classification number: G06N3/08 ,  G06N20/00

Abstract: A method for acquiring skills through imitation learning by employing a meta imitation learning framework with structured skill discovery (MILD) is presented. The method includes learning behaviors or tasks, by an agent, from demonstrations: by learning to decompose the demonstrations into segments, via a segmentation component, the segments corresponding to skills that are transferrable across different tasks, learning relationships between the skills that are transferrable across the different tasks, employing, via a graph generator, a graph neural network for learning implicit structures of the skills from the demonstrations to define structured skills, and generating policies from the structured skills to allow the agent to acquire the structured skills for application to one or more target tasks.

公开(公告)号：US20240037397A1

公开(公告)日：2024-02-01

申请号：US18479385

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.