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公开(公告)号：US10853937B2
公开(公告)日：2020-12-01
申请号：US16248955
申请日：2019-01-16
Applicant: NEC Laboratories America, Inc.
Inventor： Alexandru Niculescu-Mizil , Renqiang Min , Eric Cosatto , Farley Lai , Hans Peter Graf , Xavier Fontaine
Abstract: A false alarm reduction system is provided that includes a processor cropping each input image at randomly chosen positions to form cropped images of a same size at different scales in different contexts. The system further includes a CONDA-GMM, having a first and a second conditional deep autoencoder for respectively (i) taking each cropped image without a respective center block as input for measuring a discrepancy between a reconstructed and a target center block, and (ii) taking an entirety of cropped images with the target center block. The CONDA-GMM constructs density estimates based on reconstruction error features and low-dimensional embedding representations derived from image encodings. The processor determines an anomaly existence based on a prediction of a likelihood of the anomaly existing in a framework of a CGMM, given the context being a representation of the cropped image with the center block removed and having a discrepancy above a threshold.
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公开(公告)号：US10740676B2
公开(公告)日：2020-08-11
申请号：US15595049
申请日：2017-05-15
Applicant: NEC Laboratories America, Inc.
Inventor： Igor Durdanovic , Hans Peter Graf
Abstract: Methods and systems of training a neural network includes training a neural network based on training data. Weights of a layer of the neural network are multiplied by an attrition factor. A block of weights is pruned from the layer if the block of weights in the layer has a contribution to an output of the layer that is below a threshold.
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公开(公告)号：US10296796B2
公开(公告)日：2019-05-21
申请号：US15478886
申请日：2017-04-04
Applicant: NEC Laboratories America, Inc.
Inventor： Eric Cosatto , Iain Melvin , Hans Peter Graf
Abstract: A video device for predicting driving situations while a person drives a car is presented. The video device includes multi-modal sensors and knowledge data for extracting feature maps, a deep neural network trained with training data to recognize real-time traffic scenes (TSs) from a viewpoint of the car, and a user interface (UI) for displaying the real-time TSs. The real-time TSs are compared to predetermined TSs to predict the driving situations. The video device can be a video camera. The video camera can be mounted to a windshield of the car. Alternatively, the video camera can be incorporated into the dashboard or console area of the car. The video camera can calculate speed, velocity, type, and/or position information related to other cars within the real-time TS. The video camera can also include warning indicators, such as light emitting diodes (LEDs) that emit different colors for the different driving situations.
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24.发明申请公开(公告)号：US20180336431A1
公开(公告)日：2018-11-22
申请号：US15979505
申请日：2018-05-15
Applicant: NEC Laboratories America, Inc.
Inventor： Asim Kadav , Igor Durdanovic , Hans Peter Graf
CPC classification number: G06K9/4628 , G06K9/00624 , G06K9/0063 , G06K9/00771 , G06K9/00805 , G06K9/6217 , G06K9/627 , G06K9/6288 , G06K9/66 , G06N3/04 , G06N3/0445 , G06N3/0454 , G06N3/082 , G06N5/046
Abstract: Systems and methods for predicting changes to an environment, including a plurality of remote sensors, each remote sensor being configured to capture images of an environment. A processing device is included on each remote sensor, the processing device configured to recognize and predict a change to the environment using a pruned convolutional neural network (CNN) stored on the processing device, the pruned CNN being trained to recognize features in the environment by training a CNN with a dataset and removing filters from layers of the CNN that are below a significance threshold for image recognition to produce the pruned CNN. A transmitter is configured to transmit the recognized and predicted change to a notification device such that an operator is alerted to the change.
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公开(公告)号：US20240087672A1
公开(公告)日：2024-03-14
申请号：US18471591
申请日：2023-09-21
Applicant: NEC Laboratories America, Inc.
Inventor： Renqiang Min , Hans Peter Graf , Ziqi Chen
Abstract: A method for generating binding peptides presented by any given Major Histocompatibility Complex (MHC) protein is presented. The method includes, given a peptide and an MHC protein pair, enabling a Reinforcement Learning (RL) agent to interact with and exploit a peptide mutation environment by repeatedly mutating the peptide and observing an observation score of the peptide, learning to form a mutation policy, via a mutation policy network, to iteratively mutate amino acids of the peptide to obtain desired presentation scores, and generating, based on the desired presentation scores, qualified peptides and binding motifs of MHC Class I proteins.
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Patent Agency Ranking
公开(公告)号：US20240087179A1
公开(公告)日：2024-03-14
申请号：US18462703
申请日：2023-09-07
Applicant: NEC Laboratories America, Inc.
Inventor： Renqiang Min , Kai Li , Hans Peter Graf , Haomiao Ni
CPC classification number: G06T11/00 , G06T3/0093 , G06V20/46
Abstract: Methods and systems for training a model include training an encoder in an unsupervised fashion based on a backward latent flow between a reference frame and a driving frame taken from a same video. A diffusion model is trained that generates a video sequence responsive to an input image and a text condition, using the trained encoder to determine a latent flow sequence and occlusion map sequence of a labeled training video.
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公开(公告)号：US20240071563A1
公开(公告)日：2024-02-29
申请号：US18471597
申请日：2023-09-21
Applicant: NEC Laboratories America, Inc.
Inventor： Renqiang Min , Hans Peter Graf , Ziqi Chen
Abstract: A method for generating binding peptides presented by any given Major Histocompatibility Complex (MHC) protein is presented. The method includes, given a peptide and an MHC protein pair, enabling a Reinforcement Learning (RL) agent to interact with and exploit a peptide mutation environment by repeatedly mutating the peptide and observing an observation score of the peptide, learning to form a mutation policy, via a mutation policy network, to iteratively mutate amino acids of the peptide to obtain desired presentation scores, and generating, based on the desired presentation scores, qualified peptides and binding motifs of MHC Class I proteins.
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公开(公告)号：US20230377682A1
公开(公告)日：2023-11-23
申请号：US18319803
申请日：2023-05-18
Applicant: NEC Laboratories America, Inc.
Inventor： Renqiang Min , Hans Peter Graf
Abstract: Methods and systems for peptide generation include training a peptide mutation policy neural network using reinforcement learning that includes a peptide presentation score as a reward. New peptides are generated using the peptide mutation policy. A binding motif of a major histocompatibility complex is calculated using the new peptides. Library peptides are screened in accordance with the binding motif.
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29.发明申请公开(公告)号：US20230129568A1
公开(公告)日：2023-04-27
申请号：US17969883
申请日：2022-10-20
Applicant: NEC Laboratories America, Inc.
Inventor： Renqiang Min , Hans Peter Graf , Erik Kruus , Yiren Jian
Abstract: Systems and methods for predicting T-Cell receptor (TCR)-peptide interaction, including training a deep learning model for the prediction of TCR-peptide interaction by determining a multiple sequence alignment (MSA) for TCR-peptide pair sequences from a dataset of TCR-peptide pair sequences using a sequence analyzer, building TCR structures and peptide structures using the MSA and corresponding structures from a Protein Data Bank (PDB) using a MODELLER, and generating an extended TCR-peptide training dataset based on docking energy scores determined by docking peptides to TCRs using physical modeling based on the TCR structures and peptide structures built using the MODELLER. TCR-peptide pairs are classified and labeled as positive or negative pairs using pseudo-labels based on the docking energy scores, and the deep learning model is iteratively retrained based on the extended TCR-peptide training dataset and the pseudo-labels until convergence.
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公开(公告)号：US20230085160A1
公开(公告)日：2023-03-16
申请号：US17899004
申请日：2022-08-30
Applicant: NEC Laboratories America, Inc.
Inventor： Renqiang Min , Hans Peter Graf , Ziqi Chen
Abstract: A method for generating binding peptides presented by any given Major Histocompatibility Complex (MHC) protein is presented. The method includes, given a peptide and an MHC protein pair, enabling a Reinforcement Learning (RL) agent to interact with and exploit a peptide mutation environment by repeatedly mutating the peptide and observing an observation score of the peptide, learning to form a mutation policy, via a mutation policy network, to iteratively mutate amino acids of the peptide to obtain desired presentation scores, and generating, based on the desired presentation scores, qualified peptides and binding motifs of MHC Class I proteins.
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