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公开(公告)号：US20240089592A1
公开(公告)日：2024-03-14
申请号：US18466296
申请日：2023-09-13
Applicant: NEC Laboratories America, Inc.
Inventor： Kunal Rao , Sibendu Paul , Giuseppe Coviello , Murugan Sankaradas , Oliver Po , Srimat Chakradhar
Abstract: Systems and methods are provided for dynamically tuning camera parameters in a video analytics system to optimize analytics accuracy. A camera captures a current scene, and optimal camera parameter settings are learned and identified for the current scene using a Reinforcement Learning (RL) engine. The learning includes defining a state within the RL engine as a tuple of two vectors: a first representing current camera parameter values and a second representing measured values of frames of the current scene. Quality of frames is estimated using a quality estimator, and camera parameters are adjusted based on the quality estimator and the RL engine for optimization. Effectiveness of tuning is determined using perceptual Image Quality Assessment (IQA) to quantify a quality measure. Camera parameters are adaptively tuned in real-time based on learned optimal camera parameter settings, state, quality measure, and set of actions, to optimize the analytics accuracy for video analytics tasks.
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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.
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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
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.
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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.
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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
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.
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Patent Agency Ranking
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.
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477.发明公开公开(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
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.
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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
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).
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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
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.
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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
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.
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