Disclosed here are various techniques for improving the testing and training of datasets comprising sequences of skeletal representations performing various actions. The dataset can be denoised by applying various techniques to determine noisy frames within each sequence and eliminating the sequences from the dataset when the number of noisy frames in the sequence is too large. In addition, the dataset may be augmented by various data augmentation techniques to manipulate the skeletal representations, after denoising.
Provided is a method for locating an epilepsy seizure onset zone and prediction of seizure outcome including receiving interictal electroencephalographs from two or more points in a patient's cerebral cortex. The interictal electroencephalographs are used to determine directional information flow values which indicate dominant information flow from a non-seizure zone to a seizure onset zone. The directional informational flow values may be input into a classification model trained to predict whether the two or more points in the patient's cerebral cortex are a seizure onset zone and/or classify the patient's predicted post-treatment seizure outcome after epilepsy treatment based on the directional information flow values. An output from the classification model may indicate a location of seizure onset zone in the patient's cerebral cortex and/or the patient's predicted post-treatment seizure outcome after epilepsy treatment. Systems and computer program products are also provided.
A61B 5/374 - Détection de la répartition de fréquence dans les signaux, p.ex. détection des ondes delta, thêta, alpha, bêta ou gamma
A61B 5/00 - Mesure servant à établir un diagnostic ; Identification des individus
A61B 5/291 - Détection, mesure ou enregistrement de signaux bioélectriques ou biomagnétiques du corps ou de parties de celui-ci Électrodes bioélectriques à cet effet spécialement adaptées à des utilisations particulières pour l’électroencéphalographie [EEG]
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p.ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p.ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
A bipedal walking robot uses a quasi-passive control scheme and a simplified mechanical design. The walking robot has a pair of legs connected to a body through a passive hip joint, which is offset from a center of gravity of the walking robot. A nonconcentric, curved foot is attached at to each leg by a prismatic joint. Extension and retraction of the prismatic joint initiates the walking sequence of the robot, with each foot retracted during the swing phase and extended during the stance phase. Directional changes are controlled by changing a phase offset in the actuation of each foot.
B62D 57/032 - Véhicules caractérisés par des moyens de propulsion ou de prise avec le sol autres que les roues ou les chenilles, seuls ou en complément aux roues ou aux chenilles avec moyens de propulsion en prise avec le sol, p.ex. par jambes mécaniques avec des pieds ou des patins soulevés alternativement ou dans un ordre déterminé
This document describes a process of producing gel microparticles, which are consistent in size and morphology. Through the process of coacervation, large volumes of gel microparticle slurry can be produced by scaling up reactor vessel size. Particles can be repeatedly dehydrated and rehydrated in accordance to their environment, allowing for the storage of particles in a non-solvent such as ethanol. Gel slurries exhibit a Bingham plastic behavior in which the slurry behaves as a solid at shear stresses that are below a critical value. Upon reaching the critical shear stress, the slurry undergoes a rapid decrease in viscosity and behaves as a liquid. The rheological behavior of these slurries can be adjusted by changing the compaction processes such as centrifugation force to alter the yield-stress. The narrower distribution and reduced size of these particles allows for an increase in FRESH printing fidelity.
Disclosed herein are devices comprising stretchable 3D circuits and methods for fabricating the circuits. The fabrication process includes providing in the elastomeric polymer as a substrate and providing conductive interconnects within the substrate encased in an insulating polymer, such as polyimide, to provide a stiffness gradient between the conductive interconnects and the flexible elastomeric substrate. The circuit may be fabricated as a multilayer construction using three-dimensional pillars as vias and as external interconnects to the circuit.
Systems and methods for generating new images for training a machine-learning model are disclosed. Image data is produced regarding an image captured by an image sensor. The image data is altered such that the style of the image (e.g., color, shading, orientation, etc.) is altered. The altered image data is encoded into a first latent space. An image from a database is selected based on its similarity to the altered image and a decoding of the first latent space. Style encodings of the first latent space are extracted to classify a style of the altered image data in a second latent space. New images are then generated utilizing a reconstructor model that combines the two latent spaces. These new images can be used to train an image-recognition model.
G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
G06F 16/532 - Formulation de requêtes, p.ex. de requêtes graphiques
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 20/58 - Reconnaissance d’objets en mouvement ou d’obstacles, p.ex. véhicules ou piétons; Reconnaissance des objets de la circulation, p.ex. signalisation routière, feux de signalisation ou routes
In one aspect, a method includes providing support material within which the structure is fabricated, depositing, into the support material, structure material to form the fabricated structure, and removing the support material to release the fabricated structure from the support material. The provided support material is stationary at an applied stress level below a threshold stress level and flows at an applied stress level at or above the threshold stress level during fabrication of the structure. The provided support material is configured to mechanically support at least a portion of the structure and to prevent deformation of the structure during the fabrication of the structure. The deposited structure material is suspended in the support material at a location where the structure material is deposited. The structure material comprises a fluid that transitions to a solid or semi-solid state after deposition of the structure material.
A61L 27/18 - Matériaux macromoléculaires obtenus par des réactions autres que celles faisant intervenir uniquement des liaisons non saturées carbone-carbone
B29C 64/112 - Procédés de fabrication additive n’utilisant que des matériaux liquides ou visqueux, p.ex. dépôt d’un cordon continu de matériau visqueux utilisant des gouttelettes individuelles, p.ex. de buses de jet
B29C 64/118 - Procédés de fabrication additive n’utilisant que des matériaux liquides ou visqueux, p.ex. dépôt d’un cordon continu de matériau visqueux utilisant un matériau filamentaire mis en fusion, p.ex. modélisation par dépôt de fil en fusion [FDM]
B29C 64/40 - Structures de support des objets en 3D pendant la fabrication, lesdites structures devant être sacrifiées après réalisation de la fabrication
A system for classifying structured medical data, with each item of structured medical data, the system comprising a processing module that parses items of structured medical data to retrieve values of respective fields of the one or more items of structured medical data, the one or more retrieved values representing a set of medical attributes; a classification module that selects a classifier based at least one of the attributes in the set and applies the classifier to the set of attributes to classify one or more items of structured medical data into a particular risk profile; a user interface that renders one or more controls for input data that confirms one or more of the risk factors of the risk profile; and a transmitter to transmit to a remote medical device, an alert that specifies confirmation of the one or more of the risk factors.
G16H 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour extraire des données médicales, p.ex. pour analyser les cas antérieurs d’autres patients
A61B 5/00 - Mesure servant à établir un diagnostic ; Identification des individus
G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
G06F 16/901 - Indexation; Structures de données à cet effet; Structures de stockage
G06N 5/02 - Représentation de la connaissance; Représentation symbolique
G16H 10/20 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des essais ou des questionnaires cliniques électroniques
G16H 40/67 - TIC spécialement adaptées à la gestion ou à l’administration de ressources ou d’établissements de santé; TIC spécialement adaptées à la gestion ou au fonctionnement d’équipement ou de dispositifs médicaux pour le fonctionnement d’équipement ou de dispositifs médicaux pour le fonctionnement à distance
G16H 50/30 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour l’évaluation des risques pour la santé d’une personne
9.
Methods and Software for Bundle-Based Content Organization, Manipulation, and/or Task Management
Methods for assisting one or more users in organizing content-items, such as location information (e.g., URLs) for online information resources (e.g., webpages) and clips taken from information resources, accessed via content-access software, such as one or more web browsers, using a content item-bundle primitive that allows users to create, build, manipulate, and/or populate their own content-item bundles according to their information investigation and collection desires/needs. In some embodiments, the methods include automatically bundling content items into suggested content-item bundles based on learned relationships among various content items. In some embodiments, the methods can be implemented to provide bundle-based task managers that allow users to not only organize their content items but also define tasks and/or projects rooted in the content-item-bundle primitive. Further embodiments are disclosed, as is software for executing disclosed methods.
A high-density storage system for goods is described in which totes carrying the goods are storage in a storage structure and stored and retrieved by robotic carriers. The carriers move laterally and/or longitudinally along the exterior of the support structure and retrieve totes from the interior of the structure by manipulating rows of coupled totes. Totes at the ends of rows are quickly removed and stored in another row until the desired tote appears at the end of the row, at which point the carrier proceeds with the tote to the exit point of the storage system. Storing totes is also a quick action by pushing them into any row. As a tote is pushed into the row, it will automatically couple with a tote inside the row that it comes into contact with.
A high-density storage system for goods is described in which totes carrying the goods are stored in a storage structure and stored and retrieved via stationary or mobile conveyors running along opposite ends of each layer of the storage structure. The totes may be moved to or from the conveyors as the rows move at a constant velocity toward or away from the conveyors. Totes at the ends of rows are quickly moved and stored in another row until the desired tote appears at the end of the row, at which point the desired tote is carried to an exit point of the storage structure by one of the conveyors.
Disclosed herein is a system and method for increasing the confidence of a match between the test image and an image stored in a library database. Features are extracted from the test image and compared to features stored in the image database and, if a match is determined, one or more transformations are performed on the test image to generate pose-altered images. Features are then extracted from the pose-altered images and matched with pose-altered images in the database. The scores for the subsequent matchings can be aggregated to determine an overall probability of a match between the test image in an image in the library database.
G06V 10/74 - Appariement de motifs d’image ou de vidéo; Mesures de proximité dans les espaces de caractéristiques
G06T 3/60 - Rotation d'une image entière ou d'une partie d'image
G06T 19/20 - Transformation de modèles ou d'images tridimensionnels [3D] pour infographie Édition d'images tridimensionnelles [3D], p.ex. modification de formes ou de couleurs, alignement d'objets ou positionnements de parties
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p.ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersections; Analyse de connectivité, p.ex. de composantes connectées
G06V 10/56 - Extraction de caractéristiques d’images ou de vidéos relative à la couleur
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
Disclosed herein is a system and method for generating quadrilateral bonding boxes which tightly cover the most representative faces of retail products having arbitrary poses. The quadrilateral boxes do not include unnecessary background information or miss parts of the objects, as would the axis-aligned bounding boxes produced by prior art detectors. A simple projection transformation can correct the pose of products for downstream tasks.
G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p.ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersections; Analyse de connectivité, p.ex. de composantes connectées
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
14.
METHOD FOR COMPRESSING AN AI-BASED OBJECT DETECTION MODEL FOR DEPLOYMENT ON RESOURCE-LIMITED DEVICES
Disclosed herein is a method for efficiently reducing the computational footprint of any AI-based object detection model, so as to enable its real-time deployment on computing resource-limited (i.e., low-power, embedded) devices. The disclosed method provides a step-by-step framework using an optimized combination of compression techniques to effectively compress any given AI-based object detection model.
G06V 10/70 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique
G06N 3/082 - Méthodes d'apprentissage modifiant l’architecture, p.ex. par ajout, suppression ou mise sous silence de nœuds ou de connexions
G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
15.
SYSTEM AND METHOD FOR THE DISCOVERING EFFICIENT RANDOM NEURAL NETWORKS
Disclosed herein is a system and method for novel neural architecture search using a random graph network backbone to facilitate the creation of an efficient network structure. The method utilizes reinforcement learning algorithms to build a complex relationship between intra-connections (i.e., links between blocks in a random graph network) and extra-connections (i.e., links among blocks across the random graphs network) for discovering an efficient random neural architecture.
A system comprises a IoT resource and a computing device of a user. The computing device comprises a processor that executes a personal privacy app that receives data about the IoT resource and communicates a preference setting for the user with respect to the IoT device. The preference setting is based on the data received about the IoT resource.
Disclosed herein is a system and method for evolving a deep neural network model by searching for hidden sub-networks within the model. The model is evolved by adding convolutional layers to the model, then pruning the model to remove redundant filters. The model is exposed to training samples of increasing complexity each time the model is evolved, until a desired level of performance is achieved, at which time, the model is exposed to all available training data.
A process for micro-tissue encapsulation of cells includes coating a tissue scaffold stamp with an extracellular matrix compound. The process includes depositing the tissue scaffold stamp onto a thermoresponsive substrate and seeding the tissue scaffold stamp with a cell culture. A cell culture forms a cell patch that is attached to the extracellular matrix compound. A monolayer on the tissue scaffold stamp for which borders of the monolayer maintain expressions for cell-cell junctions, wherein the cell-cell junctions of the monolayer are configured to express tension forces. The process includes removing the thermoresponsive substrate. The process includes folding the micro-tissue structure by suspending the micro-tissue in the solvent. The folded micro-tissue structure is collected from the solvent and administered to an organism.
A61F 9/00 - Procédés ou dispositifs pour le traitement des yeux; Dispositifs pour mettre en place des verres de contact; Dispositifs pour corriger le strabisme; Appareils pour guider les aveugles; Dispositifs protecteurs pour les yeux, portés sur le corps ou dans la main
A61L 27/18 - Matériaux macromoléculaires obtenus par des réactions autres que celles faisant intervenir uniquement des liaisons non saturées carbone-carbone
A61L 27/36 - Matériaux pour prothèses ou pour revêtement de prothèses contenant des constituants de constitution indéterminée ou leurs produits réactionnels
Disclosed herein is a system and method for selecting a battery for a particular application, for example, batteries used in portable electronics, electric vehicles, satellites, etc. The method uses an end-to-end differentiable modeling approach that allows the selection of batteries directly from the parameters of the battery and a specification of the particular application for which the batteries are being selected.
G01R 31/36 - Dispositions pour le test, la mesure ou la surveillance de l’état électrique d’accumulateurs ou de batteries, p.ex. de la capacité ou de l’état de charge
G01R 31/367 - Logiciels à cet effet, p.ex. pour le test des batteries en utilisant une modélisation ou des tables de correspondance
20.
System, Method, and Device for Automated Energy Remediation
Provided is a system, method, and device for automated energy remediation. The system includes at least one processor programmed or configured to: store energy usage data for a plurality of households, store environmental data associated with the plurality of households, the environmental data including outdoor temperature measurements, determine an inflection temperature for each household of the plurality of households based on a nonlinear regression model, determine a gap metric value based on a maximum median inflection temperature and a minimum inflection temperature from the plurality of households, form a plurality of groups based on the plurality of households and household data associated with each household, each group including a subset of households of the plurality of households, determine at least one group of the plurality of groups, and automatically initiate at least one energy protocol for households in the at least one group.
H02J 13/00 - Circuits pour pourvoir à l'indication à distance des conditions d'un réseau, p.ex. un enregistrement instantané des conditions d'ouverture ou de fermeture de chaque sectionneur du réseau; Circuits pour pourvoir à la commande à distance des moyens de commutation dans un réseau de distribution d'énergie, p.ex. mise en ou hors circuit de consommateurs de courant par l'utilisation de signaux d'impulsion codés transmis par le réseau
H02J 3/00 - Circuits pour réseaux principaux ou de distribution, à courant alternatif
21.
SYSTEM AND METHOD FOR DOMAIN-AGNOSTIC BIAS REDUCTION WITH SELECTED SAMPLING FOR FEW-SHOT LEARNING
Disclosed herein is a methodology for refining novel-class features in a few-shot learning scenario by fine-tuning the feature extractor by reducing both class-agnostic biases and class-specific biases. A distribution calibration module is used to reduce the class-agnostic bias by normalizing the overall feature distribution for novel classes and further reshaping the feature manifold for fast adaptation during fine-tuning. Selected sampling is used to reduce class-specific bias by augmenting more data for better estimation.
G06V 10/77 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
G06V 10/771 - Sélection de caractéristiques, p.ex. sélection des caractéristiques représentatives à partir d’un espace multidimensionnel de caractéristiques
22.
System and Method for Tracking an Object Based on Skin Images
University of Pittsburgh - Of the Commonwealth System of Higher Education (USA)
Inventeur(s)
Galeotti, John Michael
Stetten, George Dewitt
Huang, Chun-Yin
Abrégé
Provided is a system, method, and computer program product for tracking an object based on skin images. A method includes capturing, with at least one computing device, a sequence of images with a stationary or movable camera unit arranged in a room, the sequence of images including the subject and an object moving relative to the subject, and determining, with at least one computing device, the pose of the object with respect to the subject in at least one image of the sequence of images based on computing or using a prior surface model of the subject, a surface model of the object, and an optical model of the stationary or movable camera unit.
Disclosed herein is a method of soft anchor-point detection (SAPD), which implements a concise, single-stage anchor-point detector with both faster speed and higher accuracy. Also disclosed is a novel training strategy with two softened optimization techniques: soft-weighted anchor points and soft-selected pyramid levels.
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p.ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersections; Analyse de connectivité, p.ex. de composantes connectées
G06V 10/74 - Appariement de motifs d’image ou de vidéo; Mesures de proximité dans les espaces de caractéristiques
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
24.
SYSTEM AND METHOD FOR SCENE RECTIFICATION VIA HOMOGRAPHY ESTIMATION
Disclosed herein is a system and method for performing pose-correction on images containing objects within a scene, or the entire scene, to compensate for off-centered camera views. The system and method generates a more frontal view of the object or scene by applying planar homography by identifying corner endpoints of the object or the scene and repositioning the corner endpoints to provide a more frontal view. The pose-corrected scene may then be input to an object detector to determine a location of a bounding box of an object-of-interest which would be more accurate than a bounding box from the original off-centered image.
G06V 10/24 - Alignement, centrage, détection de l’orientation ou correction de l’image
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p.ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersections; Analyse de connectivité, p.ex. de composantes connectées
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
Disclosed herein is a system and method for pooling local features for fine-grained image classification. The deep features learned by the deep network are augmented with low level local landmark features by learning a pooling strategy that pools landmark features from earlier layers of the deep network. These low level landmark features are combined with the deep features and sent to the classifier.
G06V 10/80 - Fusion, c. à d. combinaison des données de diverses sources au niveau du capteur, du prétraitement, de l’extraction des caractéristiques ou de la classification
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 10/77 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source
G06V 10/46 - Descripteurs pour la forme, descripteurs liés au contour ou aux points, p.ex. transformation de caractéristiques visuelles invariante à l’échelle [SIFT] ou sacs de mots [BoW]; Caractéristiques régionales saillantes
26.
SYSTEM AND METHOD FOR PHOTOREALISTIC IMAGE SYNTHESIS USING UNSUPERVISED SEMANTIC FEATURE DISENTANGLEMENT
Disclosed herein is a method to disentangle linear-encoded facial semantics from facial images without external supervision. The method uses linear regression and sparse representation learning concepts to make the disentangled latent representations easily interpreted and manipulated. Generated facial images are decomposed into multiple semantic features and latent representations are extracted to capture interpretable facial semantics. The semantic features may be manipulated to synthesize photorealistic facial images by sampling along vectors representing the semantic features, thereby changing the associate semantics.
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p.ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersections; Analyse de connectivité, p.ex. de composantes connectées
G06V 40/16 - Visages humains, p.ex. parties du visage, croquis ou expressions
A MEMS/NEMS actuator based on a phase change material is described in which the volumetric change observed when the phase change material changes from a crystalline phase to an amorphous phase is used to effectuate motion in the device. The phase change material may be changed from crystalline phase to amorphous phase by heating with a heater or by passing current directly through the phase change material, and thereafter quenched quickly by dissipating heat into a substrate. The phase change material may be changed from the amorphous phase to a crystalline phase by heating at a lower temperature. An application of the actuator is described to fabricate a phase change nano relay in which the volumetric expansion of the actuator is used to push a contact across an airgap to bring it into contact with a source/drain.
H01H 37/36 - Interrupteurs actionnés thermiquement - Détails Éléments thermosensibles actionnés par l'expansion ou la contraction d'un fluide avec ou sans vaporisation
Provided is a method for classification of diseases including receiving image data associated with an image at a first resolution. The image may be processed, for example by removing a background from the image, deconstructing the image into separate layers, and segmenting the image to define a plurality of single-cell images. A single-cell image may be processed, for example, by applying a filter to the single-cell image to decrease a resolution of the single-cell image as compared to the first resolution, to a second resolution. A label may be assigned to the single-cell image. A machine learning model is trained to predict a classification of the single-cell image based on inputting a plurality of single-cell images into the model. The trained machine learning model may be used to predict the outcome of a treatment. Systems and computer program products are also provided.
G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
G06V 20/70 - RECONNAISSANCE OU COMPRÉHENSION D’IMAGES OU DE VIDÉOS Éléments spécifiques à la scène Étiquetage du contenu de scène, p.ex. en tirant des représentations syntaxiques ou sémantiques
G06V 10/776 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source Évaluation des performances
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06T 3/40 - Changement d'échelle d'une image entière ou d'une partie d'image
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p.ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p.ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
29.
System and Method for Deep Learning for Tracking Cortical Spreading Depression Using EEG
Disclosed herein is a system and method implementing an automated, generalizable model for tracking cortical spreading depressions using EEG. The model comprises convolutional neural networks and graph neural networks to leverage both the spatial and the temporal properties of CSDs in the detection. The trained model is generalizable to different head models such that it can be applied to new patients without re-training. Further, the model is scalable to different densities of EEG electrodes, even when trained on a specific electride density.
Disclosed herein an effective detach strategy which suppresses the flow of gradients from context sub-networks through the detection backbone path to obtain a more discriminative context by forcing the representation of context sub-network to be dissimilar from the detection network. A sub-network is defined to generate the context information from early layers of the detection backbone. Because instance and context focus on perceptually different parts of an image, the representations from either of them should also be discrepant. In addition, a stacked complementary loss is generated to and backpropagated to the detection network.
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 10/77 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source
31.
SYSTEM AND METHOD FOR UNSUPERVISED OBJECT DEFORMATION USING FEATURE MAP-LEVEL DATA AUGMENTATION
Disclosed herein is a methodology implementing feature map-level data augmentation in a feature map. Two or more units in the feature map are selected and the values of locations in the two or more units are swapped among the two or more units. Value perturbations applied around local units in the feature map implicitly lead to an unused data augmentation at the image level.
G06V 10/771 - Sélection de caractéristiques, p.ex. sélection des caractéristiques représentatives à partir d’un espace multidimensionnel de caractéristiques
32.
System and Method for Detecting, Reading and Matching in a Retail Scene
Disclosed herein are designs for two baselines to detect products in a retail setting. A novel detector, referred to herein as RetailDet, detects quadrilateral products. To match products using visual texts on 2D space, text features are encoded with spatial positional encoding and the Hungarian Algorithm that calculates optimal assignment plans between varying text sequences is used.
G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
G06V 10/77 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 10/766 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la régression, p.ex. en projetant les caractéristiques sur des hyperplans
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
G06V 10/776 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source Évaluation des performances
G06V 30/18 - Extraction d’éléments ou de caractéristiques de l’image
G06V 30/19 - Reconnaissance utilisant des moyens électroniques
University of Pittsburgh - Of the Commonwealth System of Higher Education (USA)
Inventeur(s)
Li, Lu
Schwerin, Michael B.
Choset, Howie
Cook, Keith E.
Rose, Jason
Abrégé
Provided is a system for operating a ventilator. The system includes a motorized proportional valve actuator including a stepper motor and an actuator. The actuator is connected to the stepper motor and configured to output pressurized air by controlling a pressure on a valve diaphragm. A conduit provides for fluid communication of the pressurized air to a breathing apparatus. A sensor arrangement is in fluid communication with the conduit between the at least one motorized proportional valve actuator and the breathing apparatus. The sensor arrangement includes: (i) an intake manifold configured to output a restricted flow of air from the pressurized air transported in the conduit, and (ii) a sensor device in fluid communication with an outlet of the intake manifold, the sensor device configured to measure an air pressure of the conduit based on the restricted flow of air.
Disclosed herein is an improved method for identifying images containing objects-of-interest from a large set of images. The method comprises mixing two or more of the images to create a grouped image and exposing the grouped image to an object detector trained on grouped images to make an initial determination that the grouped image was formed from at least one image containing an object-of-interest. The images which formed the grouped image are then exposed to regular object detectors to determine a classification of the object-of-interest.
G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
35.
SYSTEM AND METHOD FOR IMPROVED FEW-SHOT OBJECT DETECTION USING A DYNAMIC SEMANTIC NETWORK
Disclosed herein is an improved few-shot detector which utilizes a dynamic semantic network which takes as input a language feature and generates trainable parameters for a visual network. The visual network takes a visual feature as input and generates a classification and localization of an object.
G06F 18/2136 - Extraction de caractéristiques, p.ex. en transformant l'espace des caractéristiques; Synthétisations; Mappages, p.ex. procédés de sous-espace basée sur des critères de parcimonie, p.ex. avec une base trop complète
A computer-implemented system and method relate to test-time adaptation of a machine learning system from a source domain to a target domain. Sensor data is obtained from a target domain. The machine learning system generates prediction data based on the sensor data. Pseudo-reference data is generated based on a gradient of a predetermined function evaluated with the prediction data. Loss data is generated based on the pseudo-reference data and the prediction data. One or more parameters of the machine learning system is updated based on the loss data. The machine learning system is configured to perform a task in the target domain after the one or more parameters has been updated.
The Trustees of the University of Pennsylvania (USA)
Carnegie Mellon University (USA)
Inventeur(s)
Hsu, David Hwei-Yu
Tambe, Prasanna
Lee, Dokyun
Abrégé
Methods, systems, and computer readable media for using machine learning models to determine predicted values of patent documents. In some examples, a method includes training, by at least one processor, a machine learning model to predict patent value based on unstructured text from training patents and, for each training patent, a measure of patent value. The method includes supplying, by the at least one processor, unstructured text from a patent document to the machine learning model. The method includes outputting, by the at least one processor, a predicted measure of value of the patent document.
G06N 3/0442 - Réseaux récurrents, p.ex. réseaux de Hopfield caractérisés par la présence de mémoire ou de portes, p.ex. mémoire longue à court terme [LSTM] ou unités récurrentes à porte [GRU]
38.
METHODS AND SYSTEMS FOR GEO-REFERENCING MAPPING SYSTEMS
A method includes receiving a trajectory dataset including a plurality of geospatial points forming a point cloud and acquired along a trajectory wherein for each of the plurality of geospatial points there is a defined an x-coordinate, a y-coordinate and a z-coordinate and at least one mapping device orientation attribute, segmenting the trajectory dataset into a plurality of segments, determining at least one relative constraint for each of the plurality of segments and utilizing, for each of the plurality of segments, at least one of the determined relative constraints to determine a relative position of at least two of the plurality of segments.
A method of identifying an attack comprising receiving an input of one or more images, wherein the one or more images includes a patch size and size, divide the image into a first sub-image and a second sub-image, classify the first sub-image and the second sub-image, wherein classifying is accomplished via introducing a variable in a pixel location associated with the first and second sub-image, and in response to classifying the first and second sub-image and identifying an adversarial patch, output a notification indicating that the input is not certified.
G06F 21/64 - Protection de l’intégrité des données, p.ex. par sommes de contrôle, certificats ou signatures
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p.ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersections; Analyse de connectivité, p.ex. de composantes connectées
40.
LIQUID METAL CIRCUITS AND METHODS OF MAKING THE SAME
Manufacturing technology to fabricate liquid metal-based soft and flexible electronics (sensors, antennas, etc.) in a high-throughput fashion, with fabrication rates that may approach that of the traditional integrated circuit components and circuits, are described. The technique allows creation of liquid-metal-only circuits, as well as seamless integration of solid IC chips into the circuits, in which liquid metal traces are used as flexible interconnects and/or as other circuit elements. The process may be applied at the wafer scale and may be integrated into the traditional microelectronics fabrication processes. Many sensors, antennas, and other circuit elements may be directly created using liquid metal, and when combined with the IC chips, a broad range of electronic functionality may be provided in a flexible, soft circuit that can be conformable, wearable.
H01L 23/498 - Connexions électriques sur des substrats isolants
H01Q 1/36 - Forme structurale pour éléments rayonnants, p.ex. cône, spirale, parapluie
H05K 3/38 - Amélioration de l'adhérence entre le substrat isolant et le métal
H05K 3/12 - Appareils ou procédés pour la fabrication de circuits imprimés dans lesquels le matériau conducteur est appliqué au support isolant de manière à former le parcours conducteur recherché utilisant la technique de l'impression pour appliquer le matériau conducteur
H01L 21/48 - Fabrication ou traitement de parties, p.ex. de conteneurs, avant l'assemblage des dispositifs, en utilisant des procédés non couverts par l'un uniquement des groupes
41.
System and Method for Domain Generalization Across Variations in Medical Images
Provided is a method of training a machine-learning-based artificial intelligence (AI) model to handle diverse types of motions occurring during image acquisition, including capturing image data including motion between an imaging device and tissue, modifying the captured image data, resulting in modified image data, by at least one of: altering an amount of time between any two frames; removing a subsequence of frames from the captured image data; and adding a subsequence of one or more new frames to the captured image data, and training a machine-learning-based AI model based on the modified image data. Other systems and methods are also described.
G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
42.
BINARY NEURAL NETWORKS WITH GENERALIZED ACTIVATION FUNCTIONS
Disclosed herein is a design for a 1-bit CNN that closes the performance gap between binary neural networks and real-valued networks on challenging large-scale datasets. The design starts with a high-performance baseline network. Blocks with identity shortcuts which bypass 1-bit generic convolutions are adopted to replace the convolutions in the baseline network. Reshaping and shifting of activation functions is introduced. Finally, a distributional loss to further is adopted enforce the binary network to learn similar output distributions as those of a real-valued network.
Disclosed herein is a system and method for augmenting data by generating a plurality of pose-altered images of an item from one or more 2D images of the item and using the augmented data to train a train a feature extractor. In other aspects of the invention, the trained feature extractor is used to enroll features extracted from images of new products in a library database of known products or to identify images of unknown products by matching features of an image of the unknown product with features stored in the library database.
G06V 10/56 - Extraction de caractéristiques d’images ou de vidéos relative à la couleur
G06V 10/74 - Appariement de motifs d’image ou de vidéo; Mesures de proximité dans les espaces de caractéristiques
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p.ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersections; Analyse de connectivité, p.ex. de composantes connectées
44.
ELECTRODE SURFACE ENGINEERING IN LITHIUM ION BATTERIES
A method to form a coated cathode material may generally include forming, via chemical vapor deposition, an interfacial layer coating on an exterior surface of a cathode active material, wherein the interfacial layer comprises an organic polymer; and wherein the interfacial layer is substantially uniform on and conformal to the exterior surface of the cathode active material. The polymer may include poly(3,4-ethylenedioxythiophene) (PEDOT). Methods of making and using the same are also described.
H01M 4/525 - Emploi de substances spécifiées comme matériaux actifs, masses actives, liquides actifs d'oxydes ou d'hydroxydes inorganiques de nickel, de cobalt ou de fer d'oxydes ou d'hydroxydes mixtes contenant du fer, du cobalt ou du nickel pour insérer ou intercaler des métaux légers, p.ex. LiNiO2, LiCoO2 ou LiCoOxFy
H01M 10/0525 - Batteries du type "rocking chair" ou "fauteuil à bascule", p.ex. batteries à insertion ou intercalation de lithium dans les deux électrodes; Batteries à l'ion lithium
H01M 4/505 - Emploi de substances spécifiées comme matériaux actifs, masses actives, liquides actifs d'oxydes ou d'hydroxydes inorganiques de manganèse d'oxydes ou d'hydroxydes mixtes contenant du manganèse pour insérer ou intercaler des métaux légers, p.ex. LiMn2O4 ou LiMn2OxFy
C23C 16/44 - Revêtement chimique par décomposition de composés gazeux, ne laissant pas de produits de réaction du matériau de la surface dans le revêtement, c. à d. procédés de dépôt chimique en phase vapeur (CVD) caractérisé par le procédé de revêtement
C23C 16/00 - Revêtement chimique par décomposition de composés gazeux, ne laissant pas de produits de réaction du matériau de la surface dans le revêtement, c. à d. procédés de dépôt chimique en phase vapeur (CVD)
H01M 4/36 - Emploi de substances spécifiées comme matériaux actifs, masses actives, liquides actifs
H01M 4/62 - Emploi de substances spécifiées inactives comme ingrédients pour les masses actives, p.ex. liants, charges
H01M 4/1391 - Procédés de fabrication d'électrodes à base d'oxydes ou d'hydroxydes mixtes, ou de mélanges d'oxydes ou d'hydroxydes, p.ex. LiCoOx
45.
USER-SPACE EMULATION FRAMEWORK FOR HETEROGENEOUS SOC DESIGN
Arizona Board of Regents on Behalf of Arizona State University (USA)
Arizona Board of Regents on Behalf of the University of Arizona (USA)
Board of Regents, The University of Texas System (USA)
Carnegie Mellon University (USA)
Inventeur(s)
Ogras, Umit
Marculescu, Radu
Akoglu, Ali
Chakrabarti, Chaitali
Bliss, Daniel
Arda, Samet Egemen
Sartor, Anderson
Kumbhare, Nirmal
Krishnakumar, Anish
Mack, Joshua
Goksoy, Ahmet
Mandal, Sumit
Abrégé
A user-space emulation framework for heterogeneous system-on-chip (SoC) design is provided. Embodiments described herein propose a portable, Linux-based emulation framework to provide an ecosystem for hardware-software co-design of heterogenous SoCs (e.g., domain-specific SoCs (DSSoCs)) and enable their rapid evaluation during the pre-silicon design phase. This framework holistically targets three key challenges of heterogeneous SoC design: accelerator integration, resource management, and application development. These challenges are addressed via a flexible and lightweight user-space runtime environment that enables easy integration of new accelerators, scheduling heuristics, and user applications, and the utility of each is illustrated through various case studies. A prototype compilation toolchain is introduced that enables automatic mapping of unlabeled C code to heterogeneous SoC platforms. Taken together, this environment offers a unique ecosystem to rapidly perform functional verification and obtain performance and utilization estimates that help accelerate convergence towards a final heterogeneous SoC design.
A therapeutic delivery system uses an engineered extracellular vesicle-albumin hybrid carrier for curcumin, which is embedded in dissolvable microneedle arrays. The co-encapsulation of curcumin with albumin in extracellular vesicles extends curcumin's stability. The incorporation of therapeutic loaded carrier into microneedle arrays does not alter its cell uptake properties or bioactivity. Moreover, the bioactivity of therapeutic loaded carrier can be preserved for at least one year when encapsulated in microneedle arrays and stored under room temperature storage conditions. The microneedle arrays of the delivery system are fabricated using molding and casting processes. The extracellular vesicle carrier can be loaded using sonication.
A61K 47/26 - Hydrates de carbone, p.ex. polyols ou sucres alcoolisés, sucres aminés, acides nucléiques, mono-, di- ou oligosaccharides; Leurs dérivés, p.ex. polysorbates, esters d’acide gras de sorbitan ou glycyrrhizine
A61M 37/00 - Autres appareils pour introduire des agents dans le corps; Percutanisation, c. à d. introduction de médicaments dans le corps par diffusion à travers la peau
A method for a passive single-viewpoint 3D imaging system comprises capturing an image from a camera having one or more phase masks. The method further includes using a reconstruction algorithm, for estimation of a 3D or depth image.
G06T 7/55 - Récupération de la profondeur ou de la forme à partir de plusieurs images
H04N 13/122 - Raffinement de la perception 3D des images stéréoscopiques par modification du contenu des signaux d’images, p.ex. par filtrage ou par ajout d’indices monoscopiques de profondeur
H04N 13/111 - Transformation de signaux d’images correspondant à des points de vue virtuels, p.ex. interpolation spatiale de l’image
H04N 13/128 - Ajustement de la profondeur ou de la disparité
H04N 13/229 - Générateurs de signaux d’images utilisant des caméras à images stéréoscopiques utilisant un seul capteur d’images 2D utilisant des lentilles lenticulaires, p.ex. dispositions de lentilles cylindriques
48.
PERFORMANCE OF NEURAL NETWORKS UNDER DISTRIBUTION SHIFT
Methods and systems of estimating an accuracy of a neural network on out-of-distribution data. In-distribution accuracies of a plurality of machine learning models trained with in-distribution data are determined. The plurality of machine learning models includes a first model, and a remainder of models. In-distribution agreement is determined between (i) an output of the first machine learning model executed with an in-distribution dataset and (ii) outputs of a remainder of the plurality of machine learning models executed with the in-distribution dataset. The machine learning models are also executed with an unlabeled out-of-distribution dataset, and an out-of-distribution agreement is determined. The in-distribution agreement is compared with the out-of-distribution agreement. Based on a result of the comparison being within a threshold, an accuracy of the first machine learning model on the unlabeled out-of-distribution dataset is estimated based on (i) the in-distribution accuracies, (ii) the in-distribution agreement, and (iii) the out-of-distribution agreement.
B60W 60/00 - Systèmes d’aide à la conduite spécialement adaptés aux véhicules routiers autonomes
G06V 20/56 - Contexte ou environnement de l’image à l’extérieur d’un véhicule à partir de capteurs embarqués
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
Provided is a sensing device including an elastomer, a magnetic device positioned within the elastomer and associated with a magnetic field, and a magnetometer configured to sense a change in the magnetic field of the magnetic device. A method and computer program product are also provided.
G01B 7/24 - Dispositions pour la mesure caractérisées par l'utilisation de techniques électriques ou magnétiques pour mesurer les déformations dans un solide, p.ex. au moyen d'une jauge de contrainte à résistance en utilisant la variation des propriétés magnétiques
Arizona Board of Regents on Behalf of Arizona State University (USA)
Arizona Board of Regents on Behalf of the University of Arizona (USA)
Board of Regents, The University of Texas System (USA)
Carnegie Mellon University (USA)
Inventeur(s)
Ogras, Umit
Marculescu, Radu
Akoglu, Ali
Chakrabarti, Chaitali
Bliss, Daniel
Arda, Samet Egemen
Sartor, Anderson
Kumbhare, Nirmal
Krishnakumar, Anish
Mack, Joshua
Goksoy, Ahmet
Mandal, Sumit
Abrégé
Runtime task scheduling using imitation learning (IL) for heterogenous many-core systems is provided. Domain-specific systems-on-chip (DSSoCs) are recognized as a key approach to narrow down the performance and energy-efficiency gap between custom hardware accelerators and programmable processors. Reaching the full potential of these architectures depends critically on optimally scheduling the applications to available resources at runtime. Existing optimization-based techniques cannot achieve this objective at runtime due to the combinatorial nature of the task scheduling problem. In an exemplary aspect described herein, scheduling is posed as a classification problem, and embodiments propose a hierarchical IL-based scheduler that learns from an Oracle to maximize the performance of multiple domain-specific applications. Extensive evaluations show that the proposed IL-based scheduler approximates an offline Oracle policy with more than 99% accuracy for performance- and energy-based optimization objectives. Furthermore, it achieves almost identical performance to the Oracle with a low runtime overhead and high adaptivity.
G06F 9/48 - Lancement de programmes; Commutation de programmes, p.ex. par interruption
G06F 15/80 - Architectures de calculateurs universels à programmes enregistrés comprenant un ensemble d'unités de traitement à commande commune, p.ex. plusieurs processeurs de données à instruction unique
51.
METHOD AND APPARATUS WITH IMAGE QUALITY IMPROVEMENT
Samsung Electronics Co., Ltd. (République de Corée)
CARNEGIE MELLON UNIVERSITY (USA)
Inventeur(s)
Kang, Eunhee
Yang, Anqi
Sankaranarayanan, Aswin
Lee, Hyong Euk
Abrégé
A device with image acquisition includes: a first phase mask disposed at a front end of a display layer and configured to modulate external light; the display layer comprising pixel areas between hole areas through which the modulated light that has passed through the first phase mask passes; a second phase mask disposed at a rear end of the display layer and configured to modulate the modulated light that has passed through the first phase mask; an image sensor disposed at a rear end of the second phase mask and configured to generate a raw image by sensing the modulated light that has passed through the second phase mask; and a processor configured to perform image processing on the raw image, based on blur information corresponding to the raw image.
H04N 23/55 - Pièces optiques spécialement adaptées aux capteurs d'images électroniques; Leur montage
G02B 26/06 - Dispositifs ou dispositions optiques pour la commande de la lumière utilisant des éléments optiques mobiles ou déformables pour commander la phase de la lumière
52.
HILITE: HIERARCHICAL AND LIGHTWEIGHT IMITATION LEARNING FOR POWER MANAGEMENT OF EMBEDDED SOCS
Arizona Board of Regents on Behalf of Arizona State University (USA)
Arizona Board of Regents on Behalf of the University of Arizona (USA)
Board of Regents, The University of Texas System (USA)
Carnegie Mellon University (USA)
Inventeur(s)
Ogras, Umit
Marculescu, Radu
Akoglu, Ali
Chakrabarti, Chaitali
Bliss, Daniel
Arda, Samet Egemen
Sartor, Anderson
Kumbhare, Nirmal
Krishnakumar, Anish
Mack, Joshua
Goksoy, Ahmet
Mandal, Sumit
Abrégé
Hierarchical and lightweight imitation learning (IL) for power management of embedded systems-on-chip (SoCs), also referred to herein as HiLITE, is provided. Modern SoCs use dynamic power management (DPM) techniques to improve energy efficiency. However, existing techniques are unable to efficiently adapt the mntime decisions considering multiple objectives (e.g., energy and real-time requirements) simultaneously on heterogeneous platforms. To address this need, embodiments described herein propose HiLITE, a hierarchical IL framework that maximizes energy efficiency while satisfying soft real-time constraints on embedded SoCs. This approach first trains DPM policies using IL; then, it applies a regression policy at runtime to minimize deadline misses. HiLITE improves the energy-delay product by 40% on average, and reduces deadline misses by up to 76%, compared to state-of-the-art approaches. In addition, the trained policies not only achieve high accuracy, but also have negligible prediction time overhead and small memory footprint.
Disclosed herein is a method providing a flexible way to transfer knowledge from base to novel classes in a few shot learning scenario. The invention introduces a partial transfer paradigm for the few-shot classification task in which a model is first trained on the base classes. Then, instead of transferring the learned representation by freezing the whole backbone network, an efficient evolutionary search method is used to automatically determine which layer or layers need to be frozen and which will be fine-tuned on the support set of the novel class.
G06V 10/776 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source Évaluation des performances
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
54.
SYSTEM AND METHOD FOR DETECTING AND CLASSIFYING ABNORMAL CELLS
Disclosed herein is a method for training a network to detect and classify abnormal pathologies in images if cells. Specifically, the network uses a deep framework optimally trained to detect and classify abnormal cervical cells in pap smear images.
G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
55.
ARTIFICIAL VALVED CONDUITS FOR CARDIAC RECONSTRUCTIVE PROCEDURES AND METHODS FOR THEIR PRODUCTION
UNIVERSITY OF PITTSBURGH - OF THE COMMONWEALTH SYSTEM OF HIGHER EDUCATION (USA)
Inventeur(s)
Yoshida, Masahiro
Bernstein, C. Douglas
Dur, Onur
Pekkan, Kerem
Abrégé
Artificial heart valve structures and methods of their fabrication are disclosed. The heart valve structures may be fabricated from a biocompatible polymer and include one or more heart valve leaflet structures incorporated within a conduit. The valve structures may incorporate one or more conduit sinuses, as well as a gap between the lower margin of the valve leaflets and the interior of the conduit. In addition, the valve structures may include one or more valve sinuses created in a space between the valve leaflets and the conduit inner surface. Computational fluid dynamics and mechanical modeling may be used to design the valve leaflets with optimal characteristics. A heart valve structure may also incorporate a biodegradable component to which cells may adhere. The incorporated cells may arise from patient cells migrating to the biodegradable component, or the component may be pre-seeded with cells prior to implantation in a patient.
A61L 27/16 - Matériaux macromoléculaires obtenus par des réactions faisant intervenir uniquement des liaisons non saturées carbone-carbone
A61L 33/00 - Traitement antithrombogénique d'articles chirurgicaux, p.ex. de sutures, cathéters, prothèses ou d'articles pour la manipulation ou le conditionnement du sang; Matériaux pour un tel traitement
A61L 27/36 - Matériaux pour prothèses ou pour revêtement de prothèses contenant des constituants de constitution indéterminée ou leurs produits réactionnels
A61L 27/58 - Matériaux au moins partiellement résorbables par le corps
A system and method utilize capacitance sensor data to identify cell events with single-cell resolution. The method identifies patterns in the sensor data related to events such as mitosis, migration-in to the sensor field, and migration-out. The system may include a processor co-located with the sensor to perform the pattern recognition. Further, microfluidic channels can be provided to direct cells to the sensors.
G01N 33/487 - Analyse physique de matériau biologique de matériau biologique liquide
G01N 33/50 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique
57.
Method, System, and Computer Program Product for Estimating Intracranial Pressure Using Near-Infrared Spectroscopy
The disclosed method includes generating first waveform data using near-infrared spectroscopy (NIRS) to measure at least one light-based signal in a plurality of patients, wherein each waveform of the plurality of waveforms of the first waveform data is associated with at least one blood attribute. The method also includes training a machine learning model based on the first waveform data to produce a trained machine learning model. The method further includes generating second waveform data using NIRS to measure at least one light-based signal in a patient. The method further includes determining an estimated ICP in the patient based on the trained machine learning model. Determining the estimated ICP includes inputting the second waveform data to the trained machine learning model and generating an output from the trained machine learning model including the estimated ICP based on a shape feature of a waveform of the second waveform data.
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p.ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p.ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
G06N 20/20 - Techniques d’ensemble en apprentissage automatique
58.
TRANSPARENT SUPPORT BATH FOR EMBEDDED 3D PRINTING AND SYSTEM FOR IN PROCESS MONITORING
An additive manufacturing method, an additive manufacturing system (1200), a support material for additive manufacturing, an assembly of the support material and a structure material, and a product thereof are provided. The method comprises depositing, by a nozzle (1210a), a structure material into a support material based on a computer model of an object, thereby forming a portion of the object. Image data of at least the portion of the object can be obtained in-process by a detector (1240). The image data is compared to the computer model. Based on the comparison, the method can comprise modifying the computer model, modifying a print parameter, modifying machine path instructions for an additive manufacturing machine that comprises the nozzle, aborting the additive formation, indicating a discrepancy, indicating validation of the shape, or a combination thereof. The depositing of the structure material is repeated by the nozzle (1210a) as necessary to additively form the object.
B29C 64/393 - Acquisition ou traitement de données pour la fabrication additive pour la commande ou la régulation de procédés de fabrication additive
B29C 64/40 - Structures de support des objets en 3D pendant la fabrication, lesdites structures devant être sacrifiées après réalisation de la fabrication
Disclosed herein are systems and methods for processing ash. For example, in certain embodiments, the method comprises dissolving at least a portion of ash in acid. In some embodiments, the acid is produced in a reactor. In some embodiments, dissolving at least a portion of ash in acid produces refined silica (SiO2) (e.g., amorphous silica, substantially pure silica, and/or a substantial amount of silica). According to certain embodiments, the ash can be further processed (e.g., using electro winning, pH-based precipitation, and/or electrorefining) to obtain other components instead of or in addition to refined silica.
Disclosed herein are devices and method for realizing field-free deterministic switching of a perpendicularly polarized magnet using SOTs in a quantum material with low-symmetry crystal structure. In preferred embodiments, SOT devices are fabricated using a perpendicularly polarized van der Waals (vdW) based layered quantum material platform and thin films of WTe2 are used as a spin-source material for generating the SOTs for magnetic memory and spin logic devices.
H01F 10/32 - Multicouches couplées par échange de spin, p.ex. superréseaux à structure nanométrique
G11C 11/16 - Mémoires numériques caractérisées par l'utilisation d'éléments d'emmagasinage électriques ou magnétiques particuliers; Eléments d'emmagasinage correspondants utilisant des éléments magnétiques utilisant des éléments dans lesquels l'effet d'emmagasinage est basé sur l'effet de spin
H10N 52/80 - Dispositifs à effet Hall - Détails de structure
A multiblock, cationic-functionalized norbornene copolymer is formed by a process including performing a vinyl addition polymerization in the presence of a metal catalyst of a first norbornene monomer substituted with a first alkyl group and a second norbornene monomer substituted with a second alkyl group by adding a predetermined amount of the first norbornene monomer and a predetermined amount of the second norbornene monomer sequentially to the reaction to form blocks of an intermediate norbornene multiblock copolymer. The second alkyl group includes a substituent which undergoes a reaction with a precursor for a cationic group having a volume less than 0.25 cm3/mol. The process further includes reacting the precursor for the cationic group with the intermediate norbornene multiblock copolymer to form the multiblock, cationic-functionalized norborene copolymer.
C08F 299/00 - Composés macromoléculaires obtenus par des interréactions de polymères impliquant uniquement des réactions entre des liaisons non saturées carbone-carbone, en l'absence de monomères non macromoléculaires
C08F 232/08 - Copolymères de composés cycliques ne contenant pas de radicaux aliphatiques non saturés dans une chaîne latérale et contenant une ou plusieurs liaisons doubles carbone-carbone dans un système carbocyclique contenant des cycles condensés
C08F 287/00 - Composés macromoléculaires obtenus par polymérisation de monomères sur des polymères séquencés
62.
TIP-LOADED MICRONEEDLE ARRAYS FOR TRANSDERMAL INSERTION
University of Pittsburgh - Of the Commonwealth System of Higher Education (USA)
Carnegie Mellon University (USA)
Inventeur(s)
Falo, Jr., Louis D.
Erdos, Geza
Ozdoganlar, O. Burak
Abrégé
A method of forming a microneedle array can include forming a microneedle array that has one or more bioactive component. The microneedle array can include a base portion and plurality of microneedles extending from the base portion, and the one or more bioactive components are present in a higher concentration in the plurality of microneedles than in the base portion.
B29C 67/00 - Techniques de façonnage non couvertes par les groupes , ou
A61K 9/00 - Préparations médicinales caractérisées par un aspect particulier
A61M 37/00 - Autres appareils pour introduire des agents dans le corps; Percutanisation, c. à d. introduction de médicaments dans le corps par diffusion à travers la peau
B23C 3/20 - Usinage de surfaces à double courbure pour le façonnage de matrices
Polymeric nanoparticles are provided for use in delivery of cargoes to plants. A method of delivering cargoes to plants, and to particular plant parts is provided. A method of treating heat stress in a plant also is provided.
C08L 53/00 - Compositions contenant des copolymères séquencés possédant au moins une séquence d'un polymère obtenu par des réactions ne faisant intervenir que des liaisons non saturées carbone-carbone; Compositions contenant des dérivés de tels polymères
64.
FLUORINATED ELECTRODES AND BATTERIES CONTAINING THE SAME
In some aspects, the present disclosure is directed to fluorinated electrodes that comprises layers of AFx, where A is a single-element material selected from B, Al, Si, and P or a multi-element material comprising two different elements selected from B, C, N, Al, Si, and P, where F is fluorine, where x is the degree to which A is fluorinated on an atom basis, and where x is between 0.5 to 20. In other aspects, the present disclosure is directed to batteries that contain such fluorinated electrodes and to methods of making such fluorinated electrodes.
University of Pittsburgh - Of the Commonwealth System of Higher Education (USA)
Inventeur(s)
Panat, Rahul
Gao, Shou-Jiang
Ali, Azahar
Hu, Chunshan
Yuan, Bin
Saleh, Mohammad Sadeq
Abrégé
A method of preparing a functionalized electrode array is provided. The method includes depositing a conductive material onto the surface of a substrate by droplet-based printing of particles comprising an electrically-conductive material. The surface of the conductive material is functionalized with a binding reagent that binds to an analyte. A three-dimensional electrode array and microfluidic test device are also provided.
H01B 5/14 - Conducteurs ou corps conducteurs non isolés caractérisés par la forme comprenant des couches ou pellicules conductrices sur supports isolants
G01N 33/543 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet avec un support insoluble pour l'immobilisation de composés immunochimiques
G01N 33/569 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour micro-organismes, p.ex. protozoaires, bactéries, virus
A method includes retrieving a map of a 3D geometry of an environment the map including a plurality of non-spatial attribute values each corresponding to one of a plurality of non-spatial attributes and indicative of a plurality of non-spatial sensor readings acquired throughout the environment, receiving a plurality of sensor readings from a device within the environment wherein each of the sensor readings corresponds to at least one of the non-spatial attributes and matching the plurality of received sensor readings to at least one location in the map to produce a determined sensor location.
G01C 21/20 - Instruments pour effectuer des calculs de navigation
G01S 5/02 - Localisation par coordination de plusieurs déterminations de direction ou de ligne de position; Localisation par coordination de plusieurs déterminations de distance utilisant les ondes radioélectriques
H04W 4/38 - Services spécialement adaptés à des environnements, à des situations ou à des fins spécifiques pour la collecte d’informations de capteurs
H04W 4/029 - Services de gestion ou de suivi basés sur la localisation
G01C 21/16 - Navigation; Instruments de navigation non prévus dans les groupes en utilisant des mesures de la vitesse ou de l'accélération exécutées à bord de l'objet navigant; Navigation à l'estime en intégrant l'accélération ou la vitesse, c. à d. navigation par inertie
67.
Discriminative Cosine Embedding in Machine Learning
During training of deep neural networks, a Copernican loss (LC) is designed to augment a primary loss function, for example, a standard Softmax loss, to explicitly minimize intra-class variation and simultaneously maximize inter-class variation. Copernican loss operates using the cosine distance and thereby affects angles leading to a cosine embedding, which removes the disconnect between training and testing.
A method and system is for receiving data representing gene clusters, the gene clusters including one or more genes configured to encode one or more polypeptides or other small molecules; accessing a machine learning model, the machine learning model being trained with a training dataset that associates the gene clusters to structures of one or more small molecules represented in the data; applying the machine learning model to the data representing the gene clusters; identifying, based on applying the machine learning model, one or more monomers associated with at least one gene cluster represented in the data; and determining a structure for a natural product including the one or more monomers.
A micro-electromechanical system (MEMS) device includes a silicon substrate; and a Tantalum (Ta) layer comprising a first portion and a second portion, a first portion being suspended over the silicon substrate and configured to move relative to the silicon substrate, and the second portion of the structure being coupled to the silicon substrate and fixed in place relative to the silicon substrate. MEMS devices including accelerometers, gyroscopes, microphones, etc. can be fabricated in which Ta forms the structure material of the MEMS components on a chip. The Ta and integrated circuit (IC) can be fabricated together in a single package in which the MEMS structure is able to use the full area above the IC in the package.
THE BOARD OF TRUSTEES OF THE UNIVERSITY OF ILLINOIS (USA)
CARNEGIE MELLON UNIVERSITY (USA)
Inventeur(s)
Bao, Zhipeng
Tokmakov, Pavel
Gaidon, Adrien David
Jabri, Allan
Wang, Yuxiong
Hebert, Martial
Abrégé
A method of compositional feature representation learning for video understanding is described. The method includes individually processing a sequence of video frames received as an input of a feature map network to generate a plurality of feature maps. The method also includes binding the plurality of feature maps to a fixed set of slot variables using an attention model according to a motion segmentation signal. The method further includes combining slot states corresponding to the fixed set of slot variables into a combined feature map. The method also includes decoding the combined feature map to form a reconstructed sequence of video frames, in which objects discovered in the reconstructed sequence of video frames are identified.
G06V 20/58 - Reconnaissance d’objets en mouvement ou d’obstacles, p.ex. véhicules ou piétons; Reconnaissance des objets de la circulation, p.ex. signalisation routière, feux de signalisation ou routes
G06T 7/246 - Analyse du mouvement utilisant des procédés basés sur les caractéristiques, p.ex. le suivi des coins ou des segments
G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
G06V 10/77 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source
A method for the efficient management of a fleet of electric vehicles in a target area couples vehicle dynamics and battery dynamics modeling with environmental factors to accurately incorporate the impact that the environment has on the range of the battery into the placement of the chargers by simulating trips of fleets of electric vehicles. The vehicles can be of various types, for example, motorcycles, cars, trucks or aircraft, and will each have their battery state of charge monitored as they traverse a simulated trip through the target area.
B60L 53/62 - Surveillance et commande des stations de charge en réponse à des paramètres de charge, p.ex. courant, tension ou charge électrique
B60L 53/66 - Transfert de données entre les stations de charge et le véhicule
G01C 21/34 - Recherche d'itinéraire; Guidage en matière d'itinéraire
B60L 58/13 - Maintien de l’état de charge [SoC] à l'intérieur d'une plage déterminée
B60L 53/30 - PROPULSION DES VÉHICULES À TRACTION ÉLECTRIQUE; FOURNITURE DE L'ÉNERGIE ÉLECTRIQUE À L'ÉQUIPEMENT AUXILIAIRE DES VÉHICULES À TRACTION ÉLECTRIQUE; SYSTÈMES DE FREINS ÉLECTRODYNAMIQUES POUR VÉHICULES, EN GÉNÉRAL; SUSPENSION OU LÉVITATION MAGNÉTIQUES POUR VÉHICULES; CONTRÔLE DES PARAMÈTRES DE FONCTIONNEMENT DES VÉHICULES À TRACTION ÉLECTRIQUE; DISPOSITIFS ÉLECTRIQUES DE SÉCURITÉ POUR VÉHICULES À TRACTION ÉLECTRIQUE Échange d'éléments d’emmagasinage d'énergie dans les véhicules électriques - Détails de construction des stations de charge
Described herein is a functional graphene composition comprising a graphene scaffold and one or more metal chelating functional groups covalently bonded to the graphene scaffold and a porous substrate that includes the functional graphene composition. Also provided is a method of removing dissolved metals from an aqueous liquid, such as, acid mine drainage.
B01J 20/22 - Compositions absorbantes ou adsorbantes solides ou compositions facilitant la filtration; Absorbants ou adsorbants pour la chromatographie; Procédés pour leur préparation, régénération ou réactivation contenant une substance organique
C02F 1/28 - Traitement de l'eau, des eaux résiduaires ou des eaux d'égout par absorption ou adsorption
C02F 1/68 - Traitement de l'eau, des eaux résiduaires ou des eaux d'égout par addition de substances spécifiées, pour améliorer l'eau potable, p.ex. par addition d'oligo-éléments
B01J 20/20 - Compositions absorbantes ou adsorbantes solides ou compositions facilitant la filtration; Absorbants ou adsorbants pour la chromatographie; Procédés pour leur préparation, régénération ou réactivation contenant une substance inorganique contenant du carbone obtenu par des procédés de carbonisation
B01J 20/28 - Compositions absorbantes ou adsorbantes solides ou compositions facilitant la filtration; Absorbants ou adsorbants pour la chromatographie; Procédés pour leur préparation, régénération ou réactivation caractérisées par leur forme ou leurs propriétés physiques
A novel class of imaging systems that combines diffractive optics with 1D line sensing is disclosed. When light passes through a diffraction grating or prism, it disperses as a function of wavelength. This property is exploited to recover 2D and 3D positions from line images. A detailed image formation model and a learning-based algorithm for 2D position estimation are disclosed. The disclosure includes several extensions of the imaging system to improve the accuracy of the 2D position estimates and to expand the effective field-of-view. The invention is useful for fast passive imaging of sparse light sources, such as streetlamps, headlights at night and LED-based motion capture, and structured light 3D scanning with line illumination and line sensing.
A computing system for generating image data representing a speaker's face includes a detection device configured to route data representing a voice signal to one or more processors and a data processing device comprising the one or more processors configured to generate a representation of a speaker that generated the voice signal in response to receiving the voice signal. The data processing device executes a voice embedding function to generate a feature vector from the voice signal representing one or more signal features of the voice signal, maps a signal feature of the feature vector to a visual feature of the speaker by a modality transfer function specifying a relationship between the visual feature of the speaker and the signal feature of the feature vector; and generates a visual representation of at least a portion of the speaker based on the mapping, the visual representation comprising the visual feature.
A micro-electromechanical system (MEMS) device includes a silicon substrate; and a Tantalum (Ta) layer comprising a first portion and a second portion, a first portion being suspended over the silicon substrate and configured to move relative to the silicon substrate, and the second portion of the structure being coupled to the silicon substrate and fixed in place relative to the silicon substrate.
Provided is a vision sensing device including a housing, a camera, a laser pattern generator, an inertial measurement unit, and at least one processor configured to project a laser pattern within the field of view of the camera, capture inertial data from the inertial measurement unit as a user moves the housing, capture visual data from the field of view with the camera as the user moves the housing, capture depth data with the laser pattern generator as the user moves the housing, and generate an RGB-D point cloud based on the visual data, the inertial data, and the depth data.
G01C 11/02 - Dispositions de prises de vues spécialement adaptées pour la photogrammétrie ou les levers photographiques, p.ex. pour commander le recouvrement des photos
G01C 11/28 - Adaptation particulière pour enregistrer les données relatives au point de la photo, p.ex. pour les profils
H04N 25/589 - Commande de la gamme dynamique impliquant plusieurs expositions acquises de manière séquentielle, p. ex. en utilisant la combinaison de champs d'image pairs et impairs avec des temps d'intégration différents, p. ex. des expositions courtes et longues
Processes for producing a metal superhydride include obtaining a metal or metal alloy electrode comprising one or more metal atoms, obtaining an electrolyte comprising hydrogen atoms, the electrolyte configured to kinetically suppress a hydrogen evolution reaction in the metal electrode, disposing the metal electrode in the electrolyte, applying pressure to the metal electrode and the electrolyte while the metal electrode is disposed in the electrolyte, and forming, based on applying the pressure, a metal superhydride comprising a plurality of hydrogen atoms of the electrolyte being bonded to each of the one or more metal atoms of the metal electrode. Generally, the metal superhydride is stable at a pressure less than 100 gigapascal (GPa).
Tire sensing systems operable to determine one or more physical characteristics of a tire include millimeter wave transmitting and receiving devices. A processor is communicatively coupled with a memory that includes instructions to transmit and receive a millimeter wave toward and from the tire. Memory also includes instructions to image first and second radial extents of the tire based on the received millimeter wave as well as instructions to determine a dimensional difference between the first and second radial extents of the tire. Vehicles including such tire sensing systems as well as non-transitory machine-readable storage mediums and methods are also included.
The present disclosure is directed to a battery that comprise at least one electrochemical cell that comprises a cathode, an anode or an anode current collector and an electrolyte disposed between the cathode and the anode or the current collector, wherein (a) the anode comprises an isotopically enriched metal; (b) the cathode comprises isotopically enriched metal ions; (c) the electrolyte comprises an isotopically enriched metal salt; (d) a combination of (a) and (b); (e) a combination of (a) and (c); (f) a combination of (b) and (c); or (g) a combination of (a), (b) and (c).
H01M 4/134 - PROCÉDÉS OU MOYENS POUR LA CONVERSION DIRECTE DE L'ÉNERGIE CHIMIQUE EN ÉNERGIE ÉLECTRIQUE, p.ex. BATTERIES Électrodes Électrodes composées d'un ou comprenant un matériau actif Électrodes pour accumulateurs à électrolyte non aqueux, p.ex. pour accumulateurs au lithium; Leurs procédés de fabrication Électrodes à base de métaux, de Si ou d'alliages
An expandable valved conduit for pediatric right ventricular outflow tract (RVOT) reconstruction is disclosed. The valved conduit may provide long-term patency and resistance to thrombosis and stenosis. The valved conduit may enlarge radially and/or longitudinally to accommodate the growing anatomy of the patient. Further, a method is disclosed for the manufacture of the valved conduit based in part on a plastically deformable biocompatible polymer and a computer-optimized valve design developed for such an expandable valved conduit.
A device to collect a biological sample may generally include an elongated rod having a handle and terminating in a tip; and a core and a plurality of projections extending in a radial direction from a surface of the core. The projections may have curved geometric pattern, such as a sinusoidal pattern, a circular arc pattern, and/or a helical pattern, for example. The device may include a layer of fibers disposed on a surface of the projections by flocking. Method of making and using the device are also described.
A61B 10/02 - Instruments pour prélever des échantillons cellulaires ou pour la biopsie
A61B 10/00 - Autres méthodes ou instruments pour le diagnostic, p.ex. pour le diagnostic de vaccination; Détermination du sexe; Détermination de la période d'ovulation; Instruments pour gratter la gorge
A hybrid microneedle array and a method of fabricating the array is used for delivery of drugs, vaccines, and other therapeutic agents into tissues, including skin, heart, inner ear, and other tissues. The microneedle array can facilitate precise and reproducible intradermal delivery. Each microneedle has a dissolvable tip with a hollow body permitting the delivery of a variety of therapeutic agents into the skin. A fabrication process utilizes a two part mold to separately mold a dissolvable tip and a solid body portion of each microneedle in the array.
A high-density storage system for goods is described in which totes carrying the goods are stored in a storage structure and stored and retrieved via stationary or mobile conveyers running along opposite ends of each layer of the storage structure. The totes may be moved to or from the conveyers as the rows move at a constant velocity toward or away from the conveyers. Totes at the ends of rows are quickly moved and stored in another row until the desired tote appears at the end of the row, at which point the desired tote is carried to an exit point of the storage structure by one of the conveyers.
A system configured to derive a motion estimate for a SLAM device using an IMU forming a part of the SLAM system. The system may be configured to refine the motion estimate via a visual-inertial odometry optimization process to produce a refined estimate and refine the refined estimate via a laser odometry optimization process by minimizing at least one residual squared error between at least one feature in a current scan and at least one previously scanned feature.
G01S 7/48 - DÉTERMINATION DE LA DIRECTION PAR RADIO; RADIO-NAVIGATION; DÉTERMINATION DE LA DISTANCE OU DE LA VITESSE EN UTILISANT DES ONDES RADIO; LOCALISATION OU DÉTECTION DE LA PRÉSENCE EN UTILISANT LA RÉFLEXION OU LA RERADIATION D'ONDES RADIO; DISPOSITIONS ANALOGUES UTILISANT D'AUTRES ONDES - Détails des systèmes correspondant aux groupes , , de systèmes selon le groupe
G01S 17/66 - Systèmes de poursuite utilisant d'autres ondes électromagnétiques que les ondes radio
85.
SYSTEM AND METHOD FOR TRAINING A NEURAL NETWORK TO PERFORM OBJECT DETECTION USING LIDAR SENSORS AND RADAR SENSORS
A method includes generating a radar-based intensity map and a lidar-based intensity map and performing one or more augmentation routines on the radar-based intensity map and the lidar-based intensity map to generate a radar input and a lidar input. The method includes generating a plurality of teacher-based bounding boxes and a plurality of student-based bounding boxes based on the radar input and the lidar input. The method includes determining a loss value of the plurality of student-based bounding boxes based on the plurality of teacher-based bounding boxes and a plurality of ground truth bounding boxes, updating one or more weights of the student neural network based on the loss value, and updating one or more weights of the teacher neural network based on a moving average associated with the one or more weights of the student neural network.
G01S 7/41 - DÉTERMINATION DE LA DIRECTION PAR RADIO; RADIO-NAVIGATION; DÉTERMINATION DE LA DISTANCE OU DE LA VITESSE EN UTILISANT DES ONDES RADIO; LOCALISATION OU DÉTECTION DE LA PRÉSENCE EN UTILISANT LA RÉFLEXION OU LA RERADIATION D'ONDES RADIO; DISPOSITIONS ANALOGUES UTILISANT D'AUTRES ONDES - Détails des systèmes correspondant aux groupes , , de systèmes selon le groupe utilisant l'analyse du signal d'écho pour la caractérisation de la cible; Signature de cible; Surface équivalente de cible
G01S 13/89 - Radar ou systèmes analogues, spécialement adaptés pour des applications spécifiques pour la cartographie ou la représentation
G01S 17/89 - Systèmes lidar, spécialement adaptés pour des applications spécifiques pour la cartographie ou l'imagerie
G06V 20/58 - Reconnaissance d’objets en mouvement ou d’obstacles, p.ex. véhicules ou piétons; Reconnaissance des objets de la circulation, p.ex. signalisation routière, feux de signalisation ou routes
86.
Interactive System Using Speech Recognition and Digital Media
A system for interactive system using speech recognition and digital media is described. The system uses automated speech recognition and recognizes interactions from users to execute digital media items. The interactions are based on behavior of the user. The user is given a prompt. If the student responds to a prompt correctly, the student is rewarded with an animation. Otherwise the user experience continues without a reward. The system recognizes natural language responses for interactions of the user. The media item is dynamically generated as the user interacts with the digital media item.
A method of generating virtual sensor data of a virtual single-photon avalanche diode (SPAD) lidar sensor includes generating a two-dimensional (2D) lidar array having a plurality of cells. The method further includes interpolating image data from a virtual camera with the 2D lidar array to define auxiliary image data, generating a virtual ambient image based on a red-channel (R-channel) data of the auxiliary image data, identifying a plurality of virtual echoes of the virtual SPAD lidar sensor based on the R-channel data and a defined photon threshold, defining a virtual point cloud indicative of virtual photon measurements of the virtual SPAD lidar sensor based on the plurality of virtual echoes, and outputting data indicative of the virtual ambient image, the virtual photon measurements, the virtual point cloud, or a combination thereof, as the virtual sensor data of the virtual SPAD lidar sensor.
This document describes a data processing system for processing a feature vector that comprises features (one or more) that are indicative of dyslexic behavior that are indicative of dyslexic behavior. The data processing system includes a feature classification engine that generates classification metrics for a feature vector. Machine learning logic is used to determine a classification metric for each feature. Features that have a classification metric below a pre-determined threshold are removed. The data processing system includes a prediction engine that generates a prediction value indicative of a predicted likelihood of dyslexia. The prediction engine assigns, to each remaining feature, based on the classification metric of the respective remaining feature, a prediction weight and determines the prediction value based on the prediction weights.
G06N 5/04 - Modèles d’inférence ou de raisonnement
G06N 20/10 - Apprentissage automatique utilisant des méthodes à noyaux, p.ex. séparateurs à vaste marge [SVM]
G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p.ex. basé sur des systèmes experts médicaux
Methods and systems for inferring data to supplement an input utilizing a neural network, and training such a system, are disclosed. In embodiments, an input is received from a sensor at the neural network. Iterations of approximate probabilities can be determined based on hidden-to-hidden Markov random field (MRF) potentials, observed-to-hidden MRF potentials, and unary MRF potentials. A constant can be identified using a root-finding algorithm. The iterations can continue until convergence. The final iteration of the approximate probability can be used to supplement the input to produce an output.
A convolutional neural network system includes a sensor and a controller, wherein the controller is configured to receive an image from the sensor, divide the image into patches, each patch of size p, extract, via a first convolutional layer, a feature map having a number of channels based on a feature detector of size p, wherein the feature detector has a stride equal to size p, refine the feature map by alternatingly applying depth-wise convolutional layers and point-wise convolutional layers to obtain a refined feature map, wherein the number of channels in the feature map and the size of the feature map remains constant throughout all operations in the refinement; and output the refined feature map.
A computer-program product storing instructions which, when executed by a computer, cause the computer to, for one or more iterations, update parameters associated with a machine-learning network utilizing perturbations for input data, wherein the perturbations are sampled utilizing Markov chain Monte Carlo, identify a loss value associated with each perturbation in each iteration, and evaluate the machine learning network by identifying an average loss value across each iteration and outputting the average loss value.
G06N 7/08 - Agencements informatiques fondés sur des modèles mathématiques spécifiques utilisant des modèles de chaos ou des modèles de systèmes non linéaires
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
Materials and methods for conducting an atom transfer radical polymerization in the presence of oxygen by interlocking enzymatic activities are provided herein.
C08F 4/10 - Composés métalliques autres que les hydrures et autres que les composés organiques de métal; Complexes d'halogénures de bore ou d'halogénures d'aluminium avec des composés organiques contenant de l'oxygène de métaux alcalino-terreux, de zinc, de cadmium, de mercure, de cuivre ou d'argent
C08F 2/16 - Polymérisation en milieu non solvant en milieu aqueux
C08F 4/26 - Composés métalliques autres que les hydrures et autres que les composés organiques de métal; Complexes d'halogénures de bore ou d'halogénures d'aluminium avec des composés organiques contenant de l'oxygène de manganèse, des métaux du groupe du fer ou des métaux du groupe du platine
Provided herein is method of modulating a plurality of neurons in a patient, by stimulating an area of the patient's central nervous system. The stimulation includes alternating first periods when a plurality of pulses of electrical stimulation are delivered and second periods when no pulses of electrical stimulation are delivered. The first periods have a duration of about 100 to about 400 ms and the second periods have a duration of about 500 ms to about 1900 ms. The pulses have a frequency of about 100 Hz to about 250 Hz.
A61N 1/36 - Application de courants électriques par électrodes de contact courants alternatifs ou intermittents pour stimuler, p.ex. stimulateurs cardiaques
A61N 1/05 - Electrodes à implanter ou à introduire dans le corps, p.ex. électrode cardiaque
A fast imaging apparatus and method for high resolution diffuse optical tomography with a line imaging and illumination system is disclosed. The method uses an algorithm comprising a convolution approximation of the forward heterogeneous scattering model that can be inverted to produce deeper than ever before structured beneath the surface. The method can detect reasonably accurate boundaries and relative depth of absorption variations up to a depth of approximately 8 mm below highly scattering medium such as skin.
2 and drying the aqueous solution, the method further includes heating the composition after drying to a temperature sufficiently high to carbonize the nitrogen-containing polymer to form the mesoporous nitrogen-doped carbon.
Provided are systems, methods, and devices for interactive neurological training. The method includes traversing a graph data structure based on a user profile for a user operating a device, the graph data structure including a plurality of nodes, each node of the plurality of nodes associated with at least one of a logical statement and an audible output, presenting at least one audible output to the user based on at least one node of the graph data structure, receiving a gesture input from the user through the device in response to the at least one audible output, determining a next node of the graph data structure from at least two different nodes connected to the at least one node, and presenting at least one further audible output to the user based on the next node of the graph data structure.
G06F 3/0488 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] utilisant des caractéristiques spécifiques fournies par le périphérique d’entrée, p.ex. des fonctions commandées par la rotation d’une souris à deux capteurs, ou par la nature du périphérique d’entrée, p.ex. des gestes en fonction de la pression exer utilisant un écran tactile ou une tablette numérique, p.ex. entrée de commandes par des tracés gestuels
G10L 25/30 - Techniques d'analyses de la parole ou de la voix qui ne se limitent pas à un seul des groupes caractérisées par la technique d’analyse utilisant des réseaux neuronaux
A computer implemented method for controlling a load aggregator for a grid includes receiving a predicted power demand over a horizon of time steps associated with one of at least two buildings, aggregating the predicted power demand at each time step to obtain an aggregate power demand, applying a learnable convolutional filter on the aggregate power demand to obtain a target load, computing a difference between the predicted power demand of the one building with the target load to obtain a power shift associated with the one building over the horizon of time steps, apportioning the power shift according to a learnable weighted vector to obtain an apportioned power shift, optimizing the learnable weighted vector and the learnable convolutional filter via an evolutionary strategy based update to obtain an optimized apportioned power shift, and transmitting the optimized apportioned power shift to a building level controller associated with the one building.