17th week of 2022 patent applcation highlights part 48 |
Patent application number | Title | Published |
20220129667 | Human Gesture Recognition for Autonomous Aircraft Operation - A method, apparatus, system, and computer program product for training a gesture recognition machine learning model system. Temporal images for a set of gestures used for ground operations for an aircraft are identified by a computer system. Pixel variation data identifying movement on a per image basis from the temporal images is generated by the computer system. The temporal images and the pixel variation data form training data. A set of feature machine learning models is trained by the computer system to recognize features using the training data. | 2022-04-28 |
20220129668 | APPLAUSE GESTURE DETECTION FOR VIDEO CONFERENCES - In various embodiments, a device of a video conferencing system obtains a stream of video data depicting a participant of a video conference. The device analyzes the stream of video data to detect motion of the participant. The device identifies, by analyzing the motion of the participant using a machine learning model, the motion of the participant as clapping by the participant. The device provides the indication that the participant is clapping to one or more user interfaces of the video conferencing system. | 2022-04-28 |
20220129669 | System and Method for Providing Multi-Camera 3D Body Part Labeling and Performance Metrics - A system and method for providing multi-camera 3D body part labeling and performance metrics includes receiving 2D image data and 3D depth data from a plurality image capture units (ICUs) each indicative of a scene viewed by the ICUs, the scene having at least one person, each ICU viewing the person from a different viewing position, determining 3D location data and visibility confidence level for the body parts from each ICU, using the 2D image data and the 3D depth data from each ICU, transforming the 3D location data for the body parts from each ICU to a common reference frame for body parts having at least a predetermined visibility confidence level, averaging the transformed, visible 3D body part locations from each ICU, and determining a performance metric of at least one of the body parts using the averaged 3D body part locations. The person may be a player in a sports scene. | 2022-04-28 |
20220129670 | DISTRACTOR CLASSIFIER - A distractor detector includes a heatmap network and a distractor classifier. The heatmap network operates on an input image to generate a heatmap for a main subject, a heatmap for a distractor, and optionally a heatmap for the background. Each object is cropped within the input image to generate a corresponding cropped image. Regions within the heatmaps that correspond to the objects are identified, and each of the regions is cropped within each of the heatmaps to generate cropped heatmaps. The distractor classifier then operates on the cropped images and the cropped heatmaps to classify each of the objects as being either a main subject or a distractor. | 2022-04-28 |
20220129671 | Document Information Extraction Without Additional Annotations - Disclosed herein are system, method, and computer program product embodiments for document information extraction without additional annotations. An embodiment operates by receiving an input representing a document and a key. The embodiment processes the input using a convolutional neural network to obtain a feature map. The embodiment combines the feature map with positional information to obtain a spatial-aware feature map. The embodiment then repeatedly performs the following decoding process: generate attention weights, generate a context vector based on the spatial-aware feature map and the generated attention weights using an attention layer, process the context vector, the key, and an input vector using a recurrent neural network (RNN) to obtain a RNN state, and generate an output vector based on the RNN state and the context vector using a projection layer. The embodiment then extracts a field based on the result of the decoding process. | 2022-04-28 |
20220129672 | IDENTIFICATION METHOD, IDENTIFICATION SYSTEM, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM STORING A PROGRAM - There is provided an identification method acquiring a first image, a pixel value of each of pixels of which represents a distance from a first position to an imaging target object including an identification target object, acquiring a second image captured from the first position or a second position different the first position, a pixel value of each of pixels of the second image representing at least luminance of reflected light from the imaging target object, identifying a type of the identification target object based on the second image, and calculating, based on the first image, an indicator value indicating a reliability degree of an identification result of the type of the identification target object based on the second image. | 2022-04-28 |
20220129673 | EDGE-BASED PROCESSING OF AGRICULTURAL DATA - Implementations are disclosed for selectively operating edge-based sensors and/or computational resources under circumstances dictated by observation of targeted plant trait(s) to generate targeted agricultural inferences. In various implementations, triage data may be acquired at a first level of detail from a sensor of an edge computing node carried through an agricultural field. The triage data may be locally processed at the edge using machine learning model(s) to detect targeted plant trait(s) exhibited by plant(s) in the field. Based on the detected plant trait(s), a region of interest (ROI) may be established in the field. Targeted inference data may be acquired at a second, greater level of detail from the sensor while the sensor is carried through the ROI. The targeted inference data may be locally processed at the edge using one or more of the machine learning models to make a targeted inference about plants within the ROI. | 2022-04-28 |
20220129674 | METHOD AND DEVICE FOR DETERMINING EXTRACTION MODEL OF GREEN TIDE COVERAGE RATIO BASED ON MIXED PIXELS - A method and a device for determining an extraction model of a green tide coverage ratio based on mixed pixels. The method includes acquiring sample truth values respectively corresponding to a plurality of target regions water body and green tide; acquiring a plurality of first remote sensing data of a first satellite sensor, the plurality of remote sensing data are in one-to-one correspondence with the plurality of target regions; determining reflection index sets respectively corresponding to the plurality of target regions according to the plurality of remote sensing data; and determining the extraction model of the green tide coverage ratio corresponding to the first satellite sensor according to the sample truth value corresponding to each of the plurality of target regions and the reflection index set corresponding to each of the plurality of target regions. | 2022-04-28 |
20220129675 | INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM - An information processing apparatus comprises a first selection unit configured to select, as at least one candidate learning model, at least one learning model from a plurality of learning models learned under learning environments different from each other based on information concerning image capturing of an object, a second selection unit configured to select at least one candidate learning model from the at least one candidate learning model based on a result of object detection processing by the at least one candidate learning model selected by the first selection unit, and a detection unit configured to perform the object detection processing for a captured image of the object using at least one candidate learning model of the at least one candidate learning model selected by the second selection unit. | 2022-04-28 |
20220129676 | INFORMATION PROVIDING METHOD, NON-TRANSITORY COMPUTER READABLE STORAGE MEDIUM STORING PROGRAM, AND INFORMATION PROVIDING APPARATUS - Provided is an information providing method in which a terminal apparatus including an imaging unit and a display unit displays, on the display unit, an image of a vehicle interior captured by the imaging unit. A plurality of devices are installed in the vehicle interior and classified into a plurality of types. The information providing method includes an image acquisition step of acquiring the image of the vehicle interior captured by the imaging unit, and a display step of displaying the image on the display unit, and in a case where at least one of the plurality of devices is included in the image, displaying a graphic corresponding to the device in a superimposed manner on the device and also displaying information indicating a type to which the device belongs. | 2022-04-28 |
20220129677 | Accelerated Video Processing for Feature Recognition via an Artificial Neural Network Configured in a Data Storage Device - Systems, devices, and methods related to video analysis using an Artificial Neural Network (ANN)) are described. For example, a data storage device can be configured to perform the computation of an ANN to recognize or classify features captured in the video images. The recognition or classification results of a prior video frame can be used to accelerate the analysis of the next video frame. The ANN can be organized in layers, where the intermediate result of a current layer can be further analyzed by a next layer for improved accuracy and confidence level. Before or while processing using the next layer, the intermediate result can be compared to the results obtained for the prior frame. If, in view of the results of the prior frame, the confidence level of the intermediate result is boosted to above a threshold, the subsequent layer(s) can be skipped or terminated early. | 2022-04-28 |
20220129678 | PREDICTIVE TARGET TRACKING USING TRAFFICABILITY MAPS - A method includes receiving a query from a user requesting a prediction on future location of a target at a time t>0, receiving a trafficability map wherein the target is located in the trafficability map at an initial time t=0, receiving information about the target including uncertainty on speed of the target, modeling motion of the target using information from the trafficability map including terrain and target mobility on different terrain types to locations on the trafficability map, and answering the query from the user based on output from modeling motion of the target. | 2022-04-28 |
20220129679 | Summarizing Videos Via Side Information - Machine learning-based techniques for summarizing collections of data such as image and video data leveraging side information obtained from related (e.g., video) data are provided. In one aspect, a method for video summarization includes: obtaining related videos having content related to a target video; and creating a summary of the target video using information provided by the target video and side information provided by the related videos to select portions of the target video to include in the summary. The side information can include video data, still image data, text, comments, natural language descriptions, and combinations thereof. | 2022-04-28 |
20220129680 | ALERT GENERATION BASED ON EVENT DETECTION IN A VIDEO FEED - Methods, systems and computer program products, for processing a stream of image frames captured by a camera system. A hardcoded alert image frame is generated in response to detecting an event. The hardcoded alert image frame includes motion deltas and/or color changes with respect to an event image frame. A stream of encoded image frames is generated, in which stream the hardcoded alert image frame is inserted in display order after the encoded event image frame. | 2022-04-28 |
20220129681 | SYSTEM AND METHOD FOR EARLY DETECTION AND MANAGEMENT OF WILDFIRES - A system and method are disclosed that allow for early detection and management of wildfires. According to various aspects and embodiments of the invention the system and method rapidly and economically detect and help manage and contain, control, suppress the progress of wildfires. | 2022-04-28 |
20220129682 | MACHINE-LEARNING MODEL, METHODS AND SYSTEMS FOR REMOVAL OF UNWANTED PEOPLE FROM PHOTOGRAPHS - Methods and systems for fully-automatic image processing to detect and remove unwanted people from a digital image of a photograph. The system includes the following modules: 1) Deep neural network (DNN)-based module for object segmentation and head pose estimation; 2) classification (or grouping) of wanted versus unwanted people based on information collected in the first module; 3) image inpainting of the unwanted people in the digital image. The classification module can be rules-based in an example. In an example, the DNN-based module generates, from the digital image: 1. A list of object category labels, 2. A list of object scores, 3. A list of binary masks, 4. A list of object bounding boxes, 5. A list of crowd instances, 6. A list of human head bounding boxes, and 7. A list of head poses (e.g., yaws, pitches, and rolls). | 2022-04-28 |
20220129683 | SELECTING DATA FOR DEEP LEARNING - Systems and methods analyze a data set including a plurality of images. In one implementation, at least one processor receives a plurality of images acquired by one or more cameras associated with at least one vehicle; and analyzes the plurality of images using an active learning system configured to determine a relative priority ranking among the plurality of images. The relative priority ranking indicates an ordered sequence for the plurality of images, and is determined based on at least one indicator, determined for each of the plurality of images, of a complexity level and a diversity level associated with representations of one or more objects represented in the plurality of images. The at least one processor then outputs information indicating the relative priority ranking among the plurality of images. | 2022-04-28 |
20220129684 | SYSTEMS AND METHODS FOR CAMERA-LIDAR FUSED OBJECT DETECTION WITH SEGMENT FILTERING - Systems and methods for object detection. Object detection may be used to control autonomous vehicle(s). For example, the methods comprise: obtaining, by a computing device, a LiDAR dataset generated by a LiDAR system of autonomous vehicle; and using, by the computing device, the LiDAR dataset and image(s) to detect an object that is in proximity to the autonomous vehicle. The object is detected by performing the following operations: computing a distribution of object detections that each point of the LiDAR dataset is likely to be in; creating a plurality of segments of LiDAR data points using the distribution of object detections; merging the plurality of segments of LiDAR data points to generate merged segments; and detecting the object in a point cloud defined by the LiDAR dataset based on the merged segments. The object detection may be used by the computing device to facilitate at least one autonomous driving operation. | 2022-04-28 |
20220129685 | System and Method for Determining Object Characteristics in Real-time - System and method for object detection. Images from cameras are provided to an inference engine to detect objects in real time, providing the images to an inference engine to detect the non-background and background pixels of the objects in the images, determining the position and size of the objects in the images based on contemporaneously gathered LiDAR data and the relationship of non-background to background pixels. | 2022-04-28 |
20220129686 | VEHICLE OCCUPANT GAZE DETECTION - A computer includes a processor and a memory, the memory storing instructions executable by the processor to determine respective probabilities of a direction of a gaze of a vehicle occupant toward each of a plurality of points in an image, determine a gaze distance from a center of the image based on the probabilities, and, upon determining that the gaze distance exceeds a threshold, suppress manual control of at least one vehicle component. | 2022-04-28 |
20220129687 | SYSTEMS AND METHODS FOR DETECTING SYMPTOMS OF OCCUPANT ILLNESS - Systems and methods for detecting symptoms of occupant illness is disclosed herein. In embodiments, a storage is configured to maintain a visualization application and data from one or more sources, such as an audio source, an image source, and/or a radar source. A processor is in communication with the storage and a user interface. The processor is programmed to receive data from the one or more sources, execute human-detection models based on the received data, execute activity-recognition models to recognize symptoms of illness based on the data from the one or more sources, determine a location of the recognized symptoms, and execute a visualization application to display information in the user interface. The visualization application can show a background image with an overlaid image that includes an indicator for each location of recognized symptom of illness. Additionally, data from the audio source, image source, and/or radar source can be fused. | 2022-04-28 |
20220129688 | CONTENT EXTRACTION BASED ON GRAPH MODELING - Methods and systems are presented for extracting categorizable information from an image using a graph that models data within the image. Upon receiving an image, a data extraction system identifies characters in the image. The data extraction system then generates bounding boxes that enclose adjacent characters that are related to each other in the image. The data extraction system also creates connections between the bounding boxes based on locations of the bounding boxes. A graph is generated based on the bounding boxes and the connections such that the graph can accurately represent the data in the image. The graph is provided to a graph neural network that is configured to analyze the graph and produce an output. The data extraction system may categorize the data in the image based on the output. | 2022-04-28 |
20220129689 | METHOD AND APPARATUS FOR FACE RECOGNITION ROBUST TO ALIGNMENT STATUS OF THE FACE - A method and apparatus for face recognition robust to an alignment of the face comprising: estimating prior information of a facial shape from an input image cropped from an image including a face using the first deep neural network (DNN); extracting feature information of facial appearance from the input image by using a second DNN; training, by using a face image decoder based on the prior information and the feature information, the face recognition apparatus; and extracting, from a test image, facial shape-aware features in the inference step by using the trained second DNN. | 2022-04-28 |
20220129690 | IDENTIFICATION METHOD, IDENTIFICATION SYSTEM, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM STORING A PROGRAM - There is provided an identification method including acquiring a first image, a pixel value of each of pixels of which represents a distance from a first position to a first imaging target object including a background object and an identification target object, acquiring a second image captured from the first position or a second position different the first position, a pixel value of each of pixels of the second image representing at least luminance of reflected light from the first imaging target object, specifying, based on the first image, a first region occupied by the identification target object in the first image, and identifying a type of the identification target object based on an image of a second region corresponding to the first region in the second image. | 2022-04-28 |
20220129691 | SYSTEM AND METHOD FOR IDENTIFYING NON-STANDARD USER INTERFACE OBJECT - A non-standard user interface object identification system includes an object candidate extractior that extracts one or more objects from an image, a first similarity analyzer that determines object type candidates of the one or more objects in accordance with similarities between the one or more objects and a standard user interface (UI) element, a second similarity analyzer that selects object type-specific weight values in accordance with layout characteristics of the one or more objects and determines object types of the one or more objects using the object type candidates and the object type-specific weight values, and an object identifier that receives type and characteristic information of a search target object and identifies the search target object in accordance with characteristic information and the object types of the one or more objects. | 2022-04-28 |
20220129692 | COMPUTER READABLE RECORDING MEDIUM WHICH CAN PERFORM IMAGE PATTERN DETERMINING METHOD - A computer readable recording medium storing at least one program, wherein an image pattern determining method for determining a stripe image can be performed when the program is executed. The image pattern determining method comprises: (a) classifying a single target image to a plurality of image blocks, wherein each of the image blocks comprises at least one pixel; (b) calculating pixel differences between pixel value sums of at least one of the image blocks and neighboring image blocks of the image block; (c) calculating image variation levels of each of the image blocks in a plurality of directions according to the pixel differences; and (d) determining whether the single target image comprises the strip image or not according to the image variation levels. An image pattern determining method for determining a check board image is also disclosed. | 2022-04-28 |
20220129693 | STATE DETERMINATION APPARATUS AND IMAGE ANALYSIS APPARATUS - According to one embodiment, a state determination apparatus includes a processor. The processor acquires a targeted image. The processor acquires a question concerning the targeted image and an expected answer to the question. The processor generates an estimated answer estimated with respect to the question concerning the targeted image using a trained model trained to estimate an answer based on a question concerning an image. The processor determines a state of a target for determination in accordance with a similarity between the expected answer and the estimated answer. | 2022-04-28 |
20220129694 | ELECTRONIC DEVICE AND METHOD FOR SCREENING SAMPLE - An electronic device and a method for screening a sample are provided. The method includes the following steps. N samples corresponding to a first object are received, in which the N samples include a first sample. N similarity vectors respectively corresponding to the N samples are calculated, in which the N similarity vectors include a first similarity vector corresponding to the first sample. The first similarity vector includes multiple first similarities between the first sample and each of the N samples except the first sample. The first sample is determined to be a representative sample of the first object in response to an average value of the first similarities of the first similarity vector being the maximum value among average values of N similarities respectively corresponding to the N similarity vectors. | 2022-04-28 |
20220129695 | BILEVEL METHOD AND SYSTEM FOR DESIGNING MULTI-AGENT SYSTEMS AND SIMULATORS - A computer-implemented system and method learn an optimized interacting set of operational policies for implementation by multiple agents, where each agent is capable of learning an operational policy of the interacting set of operational policies. The system includes a first framework sub-system and a second framework sub-system. The first framework sub-system is configured modify one or both of reward functions and transition functions of a stochastic game undertaken by a plurality of agents in a simulated environment of the second framework sub-system; and update the reward and/or the transition functions based on feedback from the second framework sub-system. The system may generate policies that are capable of coping with deviations in the domains in which they are deployed and may perform alterations to the environment so as to induce optimal system outcomes. | 2022-04-28 |
20220129696 | USING TEMPORAL FILTERS FOR AUTOMATED REAL-TIME CLASSIFICATION - In various examples, the present disclosure relates to using temporal filters for automated real-time classification. The technology described herein improves the performance of a multiclass classifier that may be used to classify a temporal sequence of input signals—such as input signals representative of video frames. A performance improvement may be achieved, at least in part, by applying a temporal filter to an output of the multiclass classifier. For example, the temporal filter may leverage classifications associated with preceding input signals to improve the final classification given to a subsequent signal. In some embodiments, the temporal filter may also use data from a confusion matrix to correct for the probable occurrence of certain types of classification errors. The temporal filter may be a linear filter, a nonlinear filter, an adaptive filter, and/or a statistical filter. | 2022-04-28 |
20220129697 | TRAINING ROBUST MACHINE LEARNING MODELS - Training a robust machine learning model by mapping an input data set to a first feature space, applying a transformation to the first feature space, yielding a second feature space, and training a dense model using the first feature space, and the second feature space. | 2022-04-28 |
20220129698 | METHOD AND SYSTEM FOR FORELINE DEPOSITION DIAGNOSTICS AND CONTROL - Systems, apparatus, and methods are disclosed for foreline diagnostics and control. A foreline coupled to a chamber exhaust is instrumented with one or more sensors, in some embodiments placed between the chamber exhaust and an abatement system. The one or more sensors are positioned to measure pressure in the foreline as an indicator of conductance. The sensors are coupled to a trained machine learning model configured to provide a signal when the foreline needs a cleaning cycle or when preventive maintenance should be performed. In some embodiments, the trained machine learning predicts when cleaning or preventive maintenance will be needed. | 2022-04-28 |
20220129699 | UNSUPERVISED TRAINING OF A VIDEO FEATURE EXTRACTOR - A computer-implemented unsupervised learning method of training a video feature extractor. The video feature extractor is configured to extract a feature representation from a video sequence. The method uses training data representing multiple training video sequences. From a training video sequence of the multiple training video sequences, a current subsequence; a preceding subsequence preceding the current subsequence; and a succeeding subsequence succeeding the current subsequence are selected. The video feature extractor is applied to the current subsequence to extract a current feature representation of the current subsequence. A training signal is derived from a joint predictability of the preceding and succeeding subsequences given the current feature representation. The parameters of the video feature extractor are updated based on the training signal. | 2022-04-28 |
20220129700 | METHODS, APPARATUSES, AND SYSTEMS FOR UPDATING SERVICE MODEL BASED ON PRIVACY PROTECTION - A computer-implemented method, medium, and system are disclosed. One example method includes determining multiple model bases by multiple service parties. A respective local service model is constructed by each service party. Respective local training samples are processed by each service party using the respective local service model to determine respective gradient data corresponding to each model basis. The respective gradient data is sent to a server. In response to determining that the first model basis satisfies a gradient update condition, corresponding gradient data of the first model basis received from each service party are combined to obtain global gradient data corresponding to the first model basis. The global gradient data is sent to each service party. Reference parameters in local model basis corresponding to the first model basis are updated by each service party using the global gradient data to train the respective local service model. | 2022-04-28 |
20220129701 | SYSTEM AND METHOD FOR DETECTING OBJECTS IN A DIGITAL IMAGE, AND SYSTEM AND METHOD FOR RESCORING OBJECT DETECTIONS - The invention relates to a system for detecting objects in a digital image. The system comprises a neural network which is configured to generate candidate windows indicating object locations, and to generate for each candidate window a score representing the confidence of detection. Generating the scores comprises:
| 2022-04-28 |
20220129702 | IMAGE SEARCHING APPARATUS, CLASSIFIER TRAINING METHOD, AND RECORDING MEDIUM - An image searching apparatus includes: a processor; and a memory, wherein the processor is configured to attach, to an image with a first correct label attached thereto, a second correct label, the first correct label being a correct label attached to each image included in an image dataset for training for use in supervised training, the second correct label being a correct label based on a degree of similarity from a predetermined standpoint; execute main training processing to train a classifier by using the images and one of the first correct label and the second correct label, fine-tune a training state of the classifier; trained by the main training processing, by using the images and the other one of the first correct label and the second correct label; and search, by using the classifier that is fine-tuned, for images similar to a query image. | 2022-04-28 |
20220129703 | ARTIFICIAL INTELLIGENCE APPARATUS FOR GENERATING TRAINING DATA FOR ARTIFICIAL INTELLIGENCE MODEL AND METHOD THEREOF - An embodiment of the present disclosure provides an artificial intelligence apparatus for generating training data including a memory configured to store an artificial intelligence model, an input interface including a microphone or a camera, and a processor configured to receive, via the input interface, input data, generate an inference result corresponding to the input data by using the artificial intelligence model, receive feedback corresponding to the inference result, determine suitability of the input data and the feedback for updating the artificial intelligence model, and generate training data based on the input data and the feedback if the input data and the feedback are determined as data suitable for updating of the artificial intelligence model. | 2022-04-28 |
20220129704 | COMPUTING DEVICE - A computing device includes: an inference circuit that calculates a recognition result of a recognition target and reliability of the recognition result using sensor data from a sensor group that detects the recognition target and a first classifier that classifies the recognition target; and a classification circuit that classifies the sensor data into either an associated target with which the recognition result is associated or a non-associated target with which the recognition result is not associated, based on the reliability of the recognition result calculated by the inference circuit. | 2022-04-28 |
20220129705 | APPARATUS AND METHOD OF IMAGE CLUSTERING - An apparatus includes a modified image generator generating modified images by modifying each unlabeled image, a pre-trainer to generate a feature vector for each modified image by using an artificial neural network-based encoder and train the encoder based on the feature vector for each modified image, a pseudo-label generator to generate a feature vector for each unlabeled training image, cluster the training images based on the feature vector for each training image, and generate a pseudo-label for at least one training image among the training images based on the clustering result, and a further trainer to generate a predicted label by using the trained encoder and a classification model including a classifier to generate a predicted label for an image input to the trained encoder based on a feature vector, and train the classification model based on the pseudo-label and predicted label for the at least one training image. | 2022-04-28 |
20220129706 | Systems and Methods for Heterogeneous Federated Transfer Learning - The technology disclosed relates to a system and method of exporting learned features between federated endpoints whose learning is confined to respective training datasets. The system includes logic to access a first training dataset to train a first federated endpoint and a second training dataset to train a second federated endpoint. The first and second training datasets have first and second sample sets that share one or more shared sample features. The shared sample features are common between the first and second sample sets. The system includes logic to train a first generator on the first federated endpoint. The system includes logic to use the first trained generator for a second inference on a second performance task executed on the second federated endpoint. | 2022-04-28 |
20220129707 | QUANTIFYING MACHINE LEARNING MODEL UNCERTAINTY - Evaluating machine learning model classifications, training a machine learning classification model using a training data set from a first data distribution, determining a classification for test data from a second data distribution using the machine learning classification model, wherein the first data distribution and the second data distribution are disjoint distributions, determining an uncertainty for the classification of the test data according to a difference between the first data distribution and the second data distribution. | 2022-04-28 |
20220129708 | SEGMENTING AN IMAGE USING A NEURAL NETWORK - A method for image segmentation includes receiving, by a processing device, an image. The method further includes applying a machine learning model to the image, wherein the machine-learning model is trained by a training process comprising evaluating training outputs generated during the training process using a loss function. The method further includes obtaining, for each pixel of multiple pixels of the image, an output of the machine learning model within a multi-dimensional domain, wherein the output is obtained by providing the machine-learning model with pixels of different classes of segments of the image that are mapped to spaced apart clusters associated with different axes of the multi-dimensional domain. The method further includes determining, using the machine-learning model and for each pixel of multiple pixels of the image, a class of a segment that comprises the pixel by finding a closest axis to the output. | 2022-04-28 |
20220129709 | Systems and Methods for Preference and Similarity Learning - Systems and methods for preference and similarity learning arc disclosed. The systems and methods improve efficiency for both searching datasets and embedding objects within the datasets. The systems and methods for preference embedding include identifying paired comparisons closest to a user's true preference point. The processes include removing obvious paired comparisons and/or ambiguous paired comparisons from subsequent queries The systems and methods for similarity learning include providing larger rank orderings of tuples to increase the context of the information in a dataset In each embodiment, the systems and methods can embed user responses in a Euclidean space such that distances between objects are indicative of user preference or similarity. | 2022-04-28 |
20220129710 | SYSTEM AND METHOD FOR CLASSIFYING ELEMENTS OF A PRODUCT - Disclosed is a method and system for classifying elements of a product. The method comprises identifying elements of the product. Thereupon, features of the one or more elements are determined, using a feature recognition technique. The features correspond to manufacturing operations required for manufacturing the elements, and include sheet metal operations, turn operations, injection moulding operations, and machining operations. The manufacturing operations are determined in a priority order with the sheet metal operation having a highest priority and the machining operation having a least priority. | 2022-04-28 |
20220129711 | Systems and Methods for Per-Cluster Intensity Correction and Base Calling - The technology disclosed generates variation correction coefficients on a cluster-by-cluster basis to correct inter-cluster intensity profile variation for improved base calling. An amplification coefficient corrects scale variation. Channel-specific offset coefficients correct shift variation along respective intensity channels. The variation correction coefficients for a target cluster are generated based on combining analysis of historic intensity data generated for the target cluster at preceding sequencing cycles of a sequencing run with analysis of current intensity data generated for the target cluster at a current sequencing cycle of the sequencing run. The variation correction coefficients are then used to correct next intensity data generated for the target cluster at a next sequencing cycle of the sequencing run. The corrected next intensity data is then used to base call the target cluster at the next sequencing cycle. | 2022-04-28 |
20220129712 | DEEP NEURAL NETWORK HARDENER - Discussed herein are devices, systems, and methods for classification using a clustering autoencoder. A method can include obtaining content to be classified by the DNN classifier, and operating the DNN classifier to determine a classification of the received content, the DNN classifier including a clustering classification layer that clusters based on a latent feature vector representation of the content, the classification corresponding to one or more clusters that are closest to the latent feature vector providing the classification and a corresponding confidence. | 2022-04-28 |
20220129713 | IMAGE FORMING APPARATUS AND IMAGE PROCESSING APPARATUS - An image forming apparatus includes: an image forming unit configured to form an image on a sheet of perforated paper which has been perforated; an input unit configured to receive first information and second information; and a processor configured to display an alarm display without performing sheet-passing of the sheet of perforated paper. The first information is information concerning the sheet of perforated paper on which an image is to be formed by the image forming unit and wherein the second information is information concerning whether to perform automatic double-sided printing on the sheet of perforated paper or not. The processor is configured to display the alarm display in a case where the automatic double-sided printing is instructed and it is determined that the sheet of perforated paper has a cut-off portion. | 2022-04-28 |
20220129714 | LOT CODE INFORMATION FOR PRINTING DEVICE CALIBRATION - Examples of printing device calibration based on a lot code are described. In an example, a lot code of a print media is determined. Calibration parameters for a printing device are determined based on the lot code. The printing device is calibrated to print on the print media based on the calibration parameters. | 2022-04-28 |
20220129715 | TEMPORAL CORRECTION OF TONE SCALE ERRORS IN A DIGITAL PRINTER - A method for correcting tone-level non-uniformities in a digital printing system includes printing a test target having a set of uniform test patches. The printed test target is automatically analyzed to determine tone-level errors as a function of cross-track position for each of the test patches. A tone-level correction function is determined and represented using a set of one-dimensional feature vectors which specifies tone-level corrections as a function of cross-track position, pixel value and time. Corrected image data is determined by using the tone-level correction function to determine a tone-level correction value for each image pixel responsive to the input pixel value, cross-track position and time. The corrected image data is printed using the digital printing system to provide a printed image with reduced tone-level errors. | 2022-04-28 |
20220129716 | SYSTEM LINKED ITEM - An information product or item, such as a for sale sign, can be configured with contact information and/or elements that link the information product to a networked system and related services (with the product being called a for information product or FIP in the description). These services can make available applications such as phone and web application that can be helpful in providing an additional mechanism/electronic media as it relates to the FIP and/or any FIP related product/object/item. These services may aid someone to whom the FIP belongs in fielding, FIP related, viewer inquires while providing the FIP viewer with more options, upon making requests to said system services. The FIP may make available linked application system services to viewers of the FIP, which may belong to someone who does not know how to link such services in other ways. | 2022-04-28 |
20220129717 | Payment Card with Removable Insert and Identification Elements - Aspects described herein may allow for a payment card assembly including a payment card having a first surface, an opposed second surface, and an aperture extending through the payment card from the first surface to the second surface. An insert may be removably received in the aperture. Each of a plurality of identification elements may be configured to be removably received in the aperture and have an identification characteristic different than an identification characteristic of each of the other identification elements. | 2022-04-28 |
20220129718 | CANNABIS FACILITY OPERATION METHOD - The invention comprises a method and/or an apparatus for operating a Cannabis facility generating a tetrahydrocannabinol (THC) containing product, comprising the step of labeling the THC containing product with a matrix bar code/quick response (QR) code, the cannabis containing product comprising at least two milligrams of a tetrahydrocannabinol substance, the QR code linking to product information associated with the THC containing product, the product information comprising at least one of a location, date of harvest, strain, and/or type of a plant containing a tetrahydrocannabinol substance in the THC containing product; an extraction method; a date and/or a location of manufacture of the THC product; a related laboratory report; and/or a link to a related web page. | 2022-04-28 |
20220129719 | LIGHT EMITTING DEVICE FOR OPTICALLY REPRODUCING A CODED INFORMATION AND METHOD FOR OPERATING THE LIGHT EMITTING DEVICE - A light emitting device for optically reproducing a coded information includes a plurality of optical components. Each of the components is configured to emit light. The combination of the light emitted from the optical components provides coded information. | 2022-04-28 |
20220129720 | COMBINED EVENT DRIVER AND FINANCIAL CARD - Combined use transaction cards are disclosed including preferably the functionality of being usable for conducting a financial transaction along with the additional functionality for activation or access to at least one other event. Many types of non-financial events are contemplated such as for access or entry, employee need, student need, and the like. Such cards preferably utilize electronically readable means as may be provided to such cards, such as including magnetic stripes, RFID transponders, OCR text, and one dimensional or two dimensional bar codes. | 2022-04-28 |
20220129721 | RFID TAG MODULE HAVING TEMPERATURE AND HUMIDITY LOGGING FUNCTION - This application relates to a radio frequency identification (RFID) tag module. In one aspect, the RFID tag module may have a temperature/humidity logging function that can log temperature and humidity inside a box, based on an RFID tag, to stably manage the quality of a product contained in the box. The RFID tag module may be installed in the box in which the product is packed. The RFID tag module may include a temperature/humidity sensor detecting a temperature/humidity inside the box in which the product is packed. The RFID tag module may also include an RFID tag logging the temperature/humidity detected by the temperature/humidity sensor. | 2022-04-28 |
20220129722 | RF TAG AND RF TAG-EQUIPPED CONDUCTOR - An RF tag includes an RF tag antenna and an IC chip. The RF tag antenna is provided with: an insulation base material having a first main surface, a second main surface, and a first lateral surface; a first waveguide element provided on the first main surface; a second waveguide element provided to extend from the second main surface to the first lateral surface and the first main surface; and a power supply part and a short circuiting part that are provided on the first main surface. A planar inverted-F antenna is formed from the insulation base material, the first waveguide element, the second waveguide element, the power supply part, and the short circuiting part. The lengths of the power supply part and the short circuiting part are set such that the resonant frequency of an LC resonant circuit coincides with the reception frequency of radio waves. | 2022-04-28 |
20220129723 | Dual Passive Technology RFID Temperature Activated Media - RFID devices are provided for use in combination with a food item or other temperature-sensitive item. The RFID devices include an RFID chip and an antenna electrically coupled to the RFID chip, along with a temperature-sensitive member. The temperature-sensitive member is configured to be in a first condition below a selected temperature and a second condition above the selected temperature to signify that the RFID device and associated food item or other temperature-sensitive item have been exposed to a temperature above the selected temperature. The temperature-sensitive member may be incorporated into the antenna to render the antenna at least partially inoperative above the selected temperature. The temperature-sensitive member may instead be configured to exhibit different colors below and above the selected temperature, or a single RFID device may include both types of temperature-sensitive members. Such RFID devices may also incorporate tamper-resistant features and/or accommodate human- and/or machine-readable printed symbols. | 2022-04-28 |
20220129724 | SYSTEM AND METHOD FOR FACILITATING AFFECTIVE-STATE-BASED ARTIFICIAL INTELLIGENCE - In some embodiments, affective-state-based artificial intelligence may be facilitated. One or more growth or decay factors for a set of affective attributes of an artificial intelligence entity may be determined, and a set of affective values, which are associated with the set of affective attributes, may be continuously updated based on the growth or decay factors. An input may be obtained, and a response related to the input may be generated based on the continuously-updated set of affective values of the artificial intelligence entity. In some embodiments, the growth or decay factors may be updated based on the input and subsequent to the updating of the decay factors, the affective values may be updated based on the updated growth or decay factors. | 2022-04-28 |
20220129725 | METHOD AND SYSTEM FOR CONVOLUTION MODEL HARDWARE ACCELERATOR - A method and system for a convolution model hardware accelerator. The method comprises receiving a stream of an input feature map into the one or more processors utilizing a convolution model that includes a plurality of convolution layers, for a given convolution layer within the plurality of convolution layers, reconfiguring a computational order for a plurality of hardware accelerator sub-blocks by re-shuffling a plurality of output filters among the plurality of sub-blocks, and in accordance with the reconfigured computational order, generating output features that are interpretive of the input feature map. | 2022-04-28 |
20220129726 | DETERMINATION OF THE DRIVING CONTEXT OF A VEHICLE - To determine a driving context of a vehicle, in a first step, sensor data of one or more sensors of the vehicle are received. Then an occupancy grid is determined based on the sensor data. Finally, the occupancy grid is parsed with a convolutional neural network for determining the driving context. | 2022-04-28 |
20220129727 | Multi-Phase Training Techniques for Machine Learning Models Using Weighted Training Data - Techniques are disclosed relating to multi-phase training of machine learning models using weighted training data. In some embodiments, a computer system may train a machine learning classification model in at least two phases. During an initial training phase, the computer system may train an initial version of the classification model based on a training dataset, applying equal weight to the training samples in the training dataset. The computer system may then generate model scores for the training samples using the initial version of the classification model. Based on these model scores, the computer system may generate, for the training samples, corresponding weighting values. The computer system may then perform a subsequent training phase to generate an updated version of the classification model, where, during this subsequent training phase, at least some of the training samples are weighted using their respective weighting values. | 2022-04-28 |
20220129728 | REINFORCEMENT LEARNING-BASED RECLOSER CONTROL FOR DISTRIBUTION CABLES WITH DEGRADED INSULATION LEVEL - Reinforcement learning (RL)-based recloser control for distribution cables with degraded insulation level is provided. Utilities continuously observe cable failures on aged cables that have an unknown degraded basic insulation level (BIL). One of the root causes is the transient overvoltage (TOV) associated with circuit breaker reclosing. Since it is hard to model TOV due to its complexity, embodiments described herein provide a model-free stochastic control method for reclosers under the existence of uncertainty and noise. Concretely, to capture high-dimensional dynamics patterns, the recloser control problem is formulated herein by incorporating the temporal sequence reward mechanism into a deep Q-network (DQN). Meanwhile, physical understanding of the problem is embedded into the action probability allocation to develop an infeasible-action-space-elimination algorithm. The learning efficiency is proved to be outstanding due to the proposed time sequence reward mechanism and infeasible action elimination method. | 2022-04-28 |
20220129729 | PROCESSOR FOR NEURAL NETWORK, PROCESSING METHOD FOR NEURAL NETWORK, AND NON-TRANSITORY COMPUTER READABLE STORAGE MEDIUM - Provided is a processor for a neural network whose high-performance compact model can be incorporated into low-spec devices such as embedded devices or mobile devices without requiring re-training. The processor for a neural network, which uses a multi-valued basis matrix, widens the range of integer values that can be taken by each element of the multi-valued basis matrix; thus, the number of dimensions (the number of elements) of a scaling coefficient vector is reduced accordingly. The elements of the scaling coefficient vector are real numbers, and thus reducing the amount of processing of real number calculation processing allows for reducing the number of dimensions (the number of elements) of the scaling coefficient vector. As a result, this neural network processor significantly reduces the amount of calculation processing while ensuring the calculation accuracy when performing matrix calculation processing using the binary basis matrix. | 2022-04-28 |
20220129730 | PRIORITY DETERMINATION DEVICE, PRIORITY DETERMINATION METHOD, AND PRIORITY DETERMINATION SYSTEM - Aspects relate to determining a priority based on impact on the surrounding environment according to the type of the event, thereby preventing the occurrence of secondary events resulting from the event and improving the efficiency and safety of event countermeasures in smart cities. Provided is a priority determination device including a sensor group for acquiring sensor information, an analysis unit for detecting, by analyzing the sensor information, occurrence of an event and determining an event feature related to the detected event, a surrounding impact determination unit for determining, for an impact region that has a possibility of being impacted by the event, a surrounding impact parameter that indicates an impact of the event on the impact region based on the event feature related to the event, and a priority calculation unit for determining a priority of the event based on at least the surrounding impact parameter. | 2022-04-28 |
20220129731 | METHOD AND APPARATUS FOR TRAINING IMAGE RECOGNITION MODEL, AND METHOD AND APPARATUS FOR RECOGNIZING IMAGE - The present disclosure provides a method and apparatus for training an image recognition model, and a method and apparatus for recognizing an image, and relates to the field of artificial intelligence, and particularly to the fields of deep learning and computer vision. A specific implementation comprises: acquiring a tagged sample set, an untagged sample set and a knowledge distillation network; and performing following training steps: selecting an input sample from the tagged sample set and the untagged sample set, and accumulating a number of iterations; inputting respectively the input sample into a student network and a teacher network of the knowledge distillation network to train the student network and the teacher network; and selecting an image recognition model from the student network and the teacher network, if a training completion condition is satisfied. | 2022-04-28 |
20220129732 | EVALUATING FUNCTIONAL FAULT CRITICALITY OF STRUCTURAL FAULTS FOR CIRCUIT TESTING - A system for evaluating fault criticality using machine learning includes a first machine learning module that is trained on a subset of a circuit and used for evaluating whether a node in a netlist of the entire circuit is a critical node, and a second machine learning module specialized to minimize classification errors in nodes predicted as benign. A generative adversarial network can be used to generate synthetic test escape data to supplement data used to train the second machine learning module. | 2022-04-28 |
20220129733 | DETERMINING AT LEAST ONE NODE - A method including providing a graph database representation, wherein the graph database representation represents a plurality of nodes in a graph which are interconnected by respective edges, wherein each node of the plurality of the nodes represents a data sample and is assigned to at least one node feature, wherein each edge of the plurality of the edges represents a relationship between the data samples, transforming the graph database representation into a data matrix using a first machine learning algorithm suitable for graph data and an architecture as first layers of a joint machine learning architecture, determining at least one node of the plurality of nodes based on the transformed data matrix using a second machine learning algorithm and a second architecture as second layers of a joint machine learning architecture, and providing the at least one determined node. Further, a computing unit and a computer program product is provided. | 2022-04-28 |
20220129734 | FORECASTING DEVICE, METHOD, AND PROGRAM - A monetary risk prediction apparatus according to an embodiment includes: a predictive model recording unit that records a predictive model to predict time-series data showing a future asset amount of a user; and prediction means for receiving evaluation data that is time-series data including an asset amount and a numeric value showing a health condition of the user, inputting the evaluation data to the predictive model recorded on the predictive model recording unit, and outputting the time-series data showing the future asset amount of the user predicted by the predictive model according to the input. | 2022-04-28 |
20220129735 | Semi-supervised Hyperspectral Data Quantitative Analysis Method Based on Generative Adversarial Network - Disclosed is a hyperspectral data analysis method based on a semi-supervised learning strategy, which includes: hyperspectral sample data is acquired; a sample training set and a prediction set are constructed, herein an unlabeled prediction set sample is used; a regression network based on a generative adversarial network is constructed, including a generator network that generates a sample, and a discriminator/regressor network that has functions of judging the authenticity of the sample and outputting a quantitative analysis value at the same time; a loss function of the generative adversarial network is constructed, including a loss function of the discriminator, a loss function of the regressor, and a loss function of the generator with a sample distribution matching function. The generative adversarial network is used to generate a sample. A sample distribution matching strategy is used to supplement an existing unlabeled sample set. So the accuracy of hyperspectral quantitative analysis is improved. | 2022-04-28 |
20220129736 | MIXED-PRECISION QUANTIZATION METHOD FOR NEURAL NETWORK - A mixed-precision quantization method for a neural network is provided. The neural network has a first precision and includes several layers and an original final output. For a particular layer, quantization of second precision on the particular layer and an input is performed. An output of the particular layer is obtained according to the particular layer of second precision and the input. De-quantization on the output of the particular layer is performed, and the de-quantized output is inputted to a next layer to obtain a final output. A value of an objective function is obtained according to the final output and the original final output. Above steps are repeated until the value of the objective function of each layer is obtained. A precision of quantization for each layer is decided according to the value of the objective function. The precision of quantization is one of first to fourth precision. | 2022-04-28 |
20220129737 | SYSTEMS AND METHODS FOR IMPROVING COMPUTER OPERATION WITH FASTER NEURAL NETWORKS - System and methods are provided that can address a slowdown during neural network execution. The machine learning system precomputes values at the input layer that are not going to change for subsequent inferences. Using the precomputed values during execution reduces the computation costs for determining inferences in neural networks. Some of the improved neural networks are configured to maximize performance of an agent. Further, some of the improved neural networks are configured to process multiple agents where the input layer is configured to receive agent feature vectors in the input layer. | 2022-04-28 |
20220129738 | Deep Learning Accelerator in Memory Utilized in Factory Safety Monitoring Systems - Systems, devices, and methods related to safety monitoring in a factory using an artificial neural network are described. For example, the system can use a plurality of sensors installed at different locations of a factory to generate a plurality of streams of sensor data. At least one memory device can be configured in the system to perform matrix computations of the artificial neural network according to the plurality of streams of sensor data written into at least one memory device. Based on an output of the artificial neural network responsive to the plurality of streams of sensor data, the system generates an event identification representative of a hazard or anomaly in the factory and activates safety control or notification responsive to the event identification. | 2022-04-28 |
20220129739 | METHOD AND SYSTEM FOR CONVOLUTION MODEL MULTI-MODE HARDWARE ACCELERATOR - A method and system for a convolution model multi-mode hardware accelerator. The method comprises receiving a stream of an input feature map into the one or more processors utilizing a convolution model that includes a plurality of convolution layers, estimating a sparsity characteristic of a data portion that encompasses at least one of the plurality of convolution layers, the data portion comprising at least one of weights and input data, processing, in accordance with the sparsity characteristic, the data portion of the convolution model using a first and a second hardware accelerator modes, and in accordance with the processing, generating a plurality of output features that are interpretive of the input feature map. | 2022-04-28 |
20220129740 | CONVOLUTIONAL NEURAL NETWORKS WITH SOFT KERNEL SELECTION - Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing inputs using neural networks that include one or more conditional convolutional layers. A conditional convolutional layer has a plurality of kernels and determines a respective input-dependent weight for each of the plurality of kernels and generates an input-dependent kernel by computing a weighted sum of the plurality of kernels in accordance with the respective input-dependent weights. | 2022-04-28 |
20220129741 | IMPLEMENTATION OF A NEURAL NETWORK IN MULTICORE HARDWARE - A multicore hardware implementation of a deep neural network includes a plurality of layers arranged in plurality of layer groups. The input data to the network comprises a multidimensional tensor including one or more traversed dimensions that are traversed by strides in at least one layer of a first layer group, and one or more non-traversed dimensions. The hardware implementation splits the evaluation of the first layer group into a first pass and a second pass, along one of the traversed dimensions or one of the non-traversed dimensions. A first core evaluates the first layer group for the first pass, to generate a first portion of output data. A second core evaluates the first layer group for the second pass, to generate a second portion of output data. The hardware implementation combines the first portion of output data and the second portion of output data to produce the output data of the first layer group. | 2022-04-28 |
20220129742 | HORIZONTAL AND VERTICAL ASSERTIONS FOR VALIDATION OF NEUROMORPHIC HARDWARE - Simulation and validation of neural network systems is provided. In various embodiments, a description of an artificial neural network is read. A directed graph is constructed comprising a plurality of edges and a plurality of nodes, each of the plurality of edges corresponding to a queue and each of the plurality of nodes corresponding to a computing function of the neural network system. A graph state is updated over a plurality of time steps according to the description of the neural network, the graph state being defined by the contents of each of the plurality of queues. Each of a plurality of assertions is tested at each of the plurality of time steps, each of the plurality of assertions being a function of a subset of the graph state. Invalidity of the neural network system is indicated for each violation of one of the plurality of assertions. | 2022-04-28 |
20220129743 | NEURAL NETWORK ACCELERATOR OUTPUT RANKING - Neural network accelerator output ranking is provided. In various embodiments, a system comprises a data memory; a memory controller configured to access the data memory; a plurality of comparators configured in a tree; a register; and a two-way comparator. The memory controller is configured to provide a first plurality of values from the data memory to the comparator tree. The comparator tree is configured to perform a plurality of concurrent pairwise comparisons of the first plurality of values to arrive at a first greatest value of the first plurality of values. The two-way comparator is configured to output the greater of the greatest value from the comparator tree and a stored value from the register. The register is configured to store the output of the two-way comparator. | 2022-04-28 |
20220129744 | METHOD FOR PERMUTING DIMENSIONS OF A MULTI-DIMENSIONAL TENSOR - A method performed by a processor for permuting dimensions of a multi-dimensional tensor is described. The multi-dimensional tensor contains an array of tensor values in three or more dimensions that are stored in a first storage unit. The array of tensor values is transferred from the first storage unit to a second storage unit by reading tensor values from the first storage that are arrayed along a first dimension of the multi-dimensional tensor and writing the corresponding tensor values to the second storage in locations corresponding to a second dimension of the multi-dimensional tensor. The dimensions of the multi-dimensional tensor may be further permuted by a programmable engine within the processor. | 2022-04-28 |
20220129745 | Prediction and Management of System Loading - Supervised learning creates and trains a model to predict resource consumption by a remote system. Historical time-series data (e.g., monitor logs of CPU consumption, memory consumption) are collected from systems called upon to perform a task. This raw data is transformed into a labeled data set ready for supervised learning. Using the labeled data set, a model is constructed to correlate the input data with a resulting load. The constructed model may be a Sequence to Sequence (Seq2Seq) model based upon Gated Recurrent Units of a Recurrent Neural Network. After training, the model is saved for re-use to predict future load based upon an existing input. For example, the existing input may be data from a most recent 24 hour period (hour0-hour23), and the output of the model may be the load predicted for the next 24 hour period (hour24-hour47). This prediction promotes efficient reservation remote server resources. | 2022-04-28 |
20220129746 | DECENTRALIZED PARALLEL MIN/MAX OPTIMIZATION - Techniques are provided for decentralized parallel min/max optimizations. In one embodiment, the techniques involve generating gradients based on a first set of weights associated with a first node of a neural network, exchanging the first set of weights with a second set of weights associated with a second node, generating an average weight based on the first set of weights and the second set of weights, and updating the first set of weights and the second set of weights via a decentralized parallel optimistic stochastic gradient (DPOSG) algorithm based on the gradients and the average weight. | 2022-04-28 |
20220129747 | SYSTEM AND METHOD FOR DEEP CUSTOMIZED NEURAL NETWORKS FOR TIME SERIES FORECASTING - The present teaching relates to method, system, medium, and implementations for machine learning for time series via hierarchical learning. First, global model parameters of a base model are learned via deep learning for forecasting time series measurements of a plurality of time series. Based on the learned base model, target model parameters of a target model are obtained by customizing the base model, wherein the target model corresponds to a specific target time series from the plurality of time series for forecasting time series measurements of the specific target time series. | 2022-04-28 |
20220129748 | SYSTEM AND METHOD FOR MONITORING A MACHINE - A system for monitoring a machine includes a transducer mounted to the machine, and a processing unit coupled to the transducer. The transducer converts a sound produced by the machine during operation into a to-be-tested dataset. The processing unit receives the to-be-tested dataset from the transducer, performs time-frequency analysis on the to-be-tested dataset to generate a to-be-tested spectrogram based on the to-be-tested dataset, inputs the to-be-tested spectrogram to an analysis model of a deep neural network to obtain an analysis result, determines whether the machine is abnormal based on the analysis result, and outputs an abnormal signal when it is determined that the machine is abnormal. | 2022-04-28 |
20220129749 | Training a Neural Network using Graph-Based Temporal Classification - A method for training a neural network with a graph-based temporal classification (GTC) objective function, using a directed graph of nodes connected by edges representing labels and transitions among the labels, is provided. The directed graph specifies one or a combination of non-monotonic alignment between a sequence of labels and a sequence of probability distributions and constraints on the label repetitions. The method comprises executing a neural network to transform a sequence of observations into the sequence of probability distributions, and updating parameters of the neural network based on the GTC objective function configured to maximize a sum of conditional probabilities of all possible sequences of labels that are generated by unfolding the directed graph to the length of the sequence of observations and mapping each unfolded sequence of nodes and edges to a possible sequence of labels. | 2022-04-28 |
20220129750 | METHOD FOR THE AUTOMATIC IDENTIFICATION AND QUANTIFICATION OF RADIOISOTOPES IN GAMMA SPECTRA - A method for identifying and quantifying radioisotopes in a gamma spectrum. and an algorithm based on convolutional neural networks (CNN) with a direct acyclic graph (DAG) structure are provided. The capacity to capture relevant attributes of CNNs combined with the possibility of carrying out several tasks of a DAG simultaneously allows performing precise, automatic identification and quantification in a single process. After appropriate training of the network, the only input needed is the raw spectrum measured by the device, without intervention of human operators and intermediate measurement processings. | 2022-04-28 |
20220129751 | SCALABLE AND DISTRIBUTED MACHINE LEARNING FRAMEWORK WITH UNIFIED ENCODER (SULU) - A computer implemented system for interpreting data using machine learning, including one or more processors; one or more memories; and one or more computer executable instructions embedded on the one or more memories, wherein the computer executable instructions are configured to execute a unified encoder comprising a neural network encoding data into one or more feature vectors, wherein the encoder is trained using machine learning to generate the one or more feature vectors useful for performing a plurality of different tasks each comprising different interpretations of the data. A plurality of decoders are connected to the unified encoder, each of the decoders comprising a neural network interpreting the one or more feature vectors so as to decode one or more of the feature vectors to output one of the interpretations. | 2022-04-28 |
20220129752 | MEMORY BANDWIDTH REDUCTION TECHNIQUES FOR LOW POWER CONVOLUTIONAL NEURAL NETWORK INFERENCE APPLICATIONS - Systems, apparatuses, and methods for implementing memory bandwidth reduction techniques for low power convolutional neural network inference applications are disclosed. A system includes at least a processing unit and an external memory coupled to the processing unit. The system detects a request to perform a convolution operation on input data from a plurality of channels. Responsive to detecting the request, the system partitions the input data from the plurality of channels into 3D blocks so as to minimize the external memory bandwidth utilization for the convolution operation being performed. Next, the system loads a selected 3D block from external memory into internal memory and then generates convolution output data for the selected 3D block for one or more features. Then, for each feature, the system adds convolution output data together across channels prior to writing the convolution output data to the external memory. | 2022-04-28 |
20220129753 | PRE-TRAINING METHOD OF NEURAL NETWORK MODEL, ELECTRONIC DEVICE AND MEDIUM - A pre-training method of a neural network model, an electronic device, and a medium. The pre-training data is inputted to the initial neural network model, and the initial neural network model is pre-trained in the first training mode, in the first training mode, the plurality of hidden layers share one hidden layer parameter, and the loss value of the initial neural network model is obtained, if the loss value of the initial neural network model is less than a preset threshold, the initial neural network model continues to be pre-trained in the second training mode, in the second training mode, each of the plurality of hidden layers has its own hidden layer parameter. | 2022-04-28 |
20220129754 | UTILIZING MACHINE LEARNING TO PERFORM A MERGER AND OPTIMIZATION OPERATION - A device may comprise a memory and a processor coupled to the memory. The processor may receive transaction information and entity information for a plurality of entities and may generate a first model based on the transaction information, the entity information, and information identifying an event, a theme, or a transaction parameter. The processor may process, using the first model, the transaction information and the entity information to identify a set of related entities and a type of relationship associated with the set of related entities. The processor may determine, using a second model, one or more modifications to a first set of accounts and a second set of accounts associated with the first and second entities based on the type of relationship and may perform one or more actions based on the one or more modifications. | 2022-04-28 |
20220129755 | INCORPORATING A TERNARY MATRIX INTO A NEURAL NETWORK - Artificial neural networks (ANNs) are computing systems inspired by the human brain by learning to perform tasks by considering examples. These ANNs are typically created by connecting several layers of artificial neurons using connections, where each artificial neuron is connected to every other artificial neuron either directly or indirectly to create fully connected layers within the ANN. By substituting ternary matrices for one or more fully connected layers within the ANN, a complexity and resource usage of the ANN may be reduced, while improving the performance of the ANN. | 2022-04-28 |
20220129756 | SELF-PRUNING NEURAL NETWORKS FOR WEIGHT PARAMETER REDUCTION - A technique to prune weights of a neural network using an analytic threshold function h(w) provides a neural network having weights that have been optimally pruned. The neural network includes a plurality of layers in which each layer includes a set of weights w associated with the layer that enhance a speed performance of the neural network, an accuracy of the neural network, or a combination thereof. Each set of weights is based on a cost function C that has been minimized by back-propagating an output of the neural network in response to input training data. The cost function C is also minimized based on a derivative of the cost function C with respect to a first parameter of the analytic threshold function h(w) and on a derivative of the cost function C with respect to a second parameter of the analytic threshold function h(w). | 2022-04-28 |
20220129757 | PREDICTION METHOD, DEVICE AND SYSTEM FOR ROCK MASS INSTABILITY STAGES - Embodiments of the present application provide a prediction method, device and system for rock mass instability stages, and belong to the technical field of rock mass instability prediction. The method includes the steps: acquiring acoustic emission signals of rock mass; extracting feature parameters from the acquired acoustic emission signals; and predicting instability stages of the rock mass in accordance with the feature parameters and a preset back propagation (BP) neural network model, wherein the preset BP neural network model is obtained by training a BP neural network and a genetic algorithm by virtue of the feature parameters of the acoustic emission signals at different rock mass instability stages. According to the technical solution in the present application, the problem in the training process of the BP neural network model that model parameter optimization may be easily trapped in a locally optimal solution is effectively solved. | 2022-04-28 |
20220129758 | CLUSTERING AUTOENCODER - Discussed herein are devices, systems, and methods for classification using a clustering autoencoder. A method can include receiving, by an encoder of an autoencoder, content, the autoencoder trained using other content and corresponding labels, providing, by the encoder, a latent feature representation of the content to a decoder of the autoencoder, providing, by a clustering layer situated between the encoder and the decoder, a probability that the content belongs to a class of classes represented by respective clusters in a latent feature representation space based on a distance between the feature representation and the cluster, and providing, by the decoder, reconstructed content that is a construction of the content based on the latent feature representation. | 2022-04-28 |
20220129759 | Universal Loss-Error-Aware Quantization for Deep Neural Networks with Flexible Ultra-Low-Bit Weights and Activations - Apparatuses, methods, and GPUs are disclosed for universal loss-error-aware quantization (ULQ) of a neural network (NN). In one example, an apparatus includes data storage to store data including activation sets and weight sets, and a network processor coupled to the data storage. The network processor is configured to implement the ULQ by constraining a low-precision NN model based on a full-precision NN model, to perform a loss-error-aware activation quantization to quantize activation sets into ultra-low-bit versions with given bit-width values, to optimize the NN with respect to a loss function that is based on the full-precision NN model, and to perform a loss-error-aware weight quantization to quantize weight sets into ultra-low-bit versions. | 2022-04-28 |
20220129760 | TRAINING NEURAL NETWORKS WITH LABEL DIFFERENTIAL PRIVACY - Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training neural networks with label differential privacy. One of the methods includes, for each training example: processing the network input in the training example using the neural network in accordance with the values of the network parameters as of the beginning of the training iteration to generate a network output, generating a private network output for the training example from the target output in the training example and the network output for the training example, and generating a modified training example that includes the network input in the training example and the private network output for the training example; and training the neural network on at least the modified training examples to update the values of the network parameters. | 2022-04-28 |
20220129761 | TUNING A NEURAL NETWORK LEARNT BY UNSUPERVISED LEARNING - There may be provided a neural network (NN) and a learning process. The learning process may include (a) feeding media units to the NN, (b) generating signatures by the NN—till obtaining many (for example, at least 1,000,000 signatures), and (c) performing an optimization (or a sub-optimal process) of distances between signatures—and assign weights that will lead to the optimal or sub-optimal distances. | 2022-04-28 |
20220129762 | Removing Bias from Artificial Intelligence Models - Data is received characterizing a population and a target trait characteristic for selecting candidates from the population. The population is segmented into at least a first subpopulation and a second subpopulation. A first number of candidates is selected from the first subpopulation and using a first model. The first number of candidates is selected according to the target trait characteristic. The first model having been trained using a first training population in which all members of the first training population are part of the first class of the two or more classes. A second number of candidates is selected from the second subpopulation and using a second model. The second model having been trained using a second training population in which all members of the second training population are part of the second class of the two or more classes. Related apparatus, systems, techniques and articles are also described. | 2022-04-28 |
20220129763 | HIGH CONFIDENCE MULTIPLE BRANCH OFFSET PREDICTOR - An embodiment of an integrated circuit may comprise a front end unit, and circuitry coupled to the front end unit, the circuitry to provide a high confidence, multiple branch offset predictor. For example, the circuitry may be configured to identify an entry in a multiple-taken-branch prediction table that corresponds to a conditional branch instruction, determine if a confidence level of the entry exceeds a threshold confidence level, and, if so determined, provide multiple taken branch predictions that stem from the conditional branch instruction from the entry in the multiple-taken-branch prediction table. Other embodiments are disclosed and claimed. | 2022-04-28 |
20220129764 | ANOMALY DETECTION APPARATUS, ANOMALY DETECTION METHOD, AND COMPUTER-READABLE MEDIUM - An anomaly detection apparatus according to the present disclosure includes a binary tree structure creation unit, a score calculation unit, and a learning unit. The binary tree structure creation unit creates a binary tree structure using a plurality of data pieces. The score calculation unit calculates a score using a node evaluation value for a node feature vector, the node feature vector being a feature of each node passing from a root node to a leaf node of the binary tree structure. The learning unit learns a node evaluation model for calculating the node evaluation value for the node feature vector of the each node of the binary tree structure. | 2022-04-28 |
20220129765 | A/B TESTING USING SEQUENTIAL HYPOTHESIS - A method of executing an A/B test includes, during execution of the A/B test, determining, by a processing device using a sequential frequentist test, that a sample ratio mismatch has occurred, wherein the sample ratio mismatch is determined before the A/B test ends the execution. The method further includes, in response to the determining, ending the execution of the A/B test before a previously scheduled end of the A/B test. | 2022-04-28 |
20220129766 | DATA STORAGE AND RETRIEVAL SYSTEM INCLUDING A KNOWLEDGE GRAPH EMPLOYING MULTIPLE SUBGRAPHS AND A LINKING LAYER INCLUDING MULTIPLE LINKING NODES, AND METHODS, APPARATUS AND SYSTEMS FOR CONSTRUCTING AND USING SAME - A graph-based data storage and retrieval system in which multiple subgraphs representing respective datasets in different namespaces are interconnected via a linking or “canonical” layer. Datasets represented by subgraphs in different namespaces may pertain to a particular information domain (e.g., the health care domain), and may include heterogeneous datasets. The canonical layer provides for a substantial reduction of graph complexity required to interconnect corresponding nodes in different subgraphs, which in turn offers advantages as the number of subgraphs (and the number of corresponding nodes in different subgraphs) increases for the particular domain(s) of interest. Examples of such advantages include reductions in data storage and retrieval times, and enhanced query/search efficacy, discovery of relationships in different parts of the system, ability to infer relationships in different parts of the system, and ability to train data models for natural language processing (NLP) and other purposes based on information extracted from the system. | 2022-04-28 |