Abstract: System and method for automatic remediation for a distributed file system uses a file system (FS) remediation module running in a cluster management server and FS remediation agents running in a cluster of host computers. The FS remediation module monitors the cluster of host computers for related events. When a first file system service (FSS)-impacting event is detected, a cluster-level remediation action is executed at the cluster management server by the FS remediation module in response to the detected first FSS-impacting event. When a second FSS-impacting event is detected, a host-level remediation action is executed at one or more of the host computers in the cluster by the FS remediation agents in response to the detected second FSS-impacting event.

Abstract: An action recognition system is provided that includes a device configured to capture a video sequence formed from a set of unlabeled testing video frames. The system further includes a processor configured to pre-train a recognition engine formed from a reference set of CNNs on a still image domain that includes labeled training still image frames. The processor adapts the recognition engine to a video domain to form an adapted engine, by applying non-reference CNNs to domains that include the still image and video domains and a degraded image domain that includes labeled synthetically degraded versions of the frames in the still image domain. The video domain includes random unlabeled training video frames. The processor recognizes, using the adapted engine, an action performed by at least one object in the sequence, and controls a device to perform a response action in response to an action type of the action.

Abstract: An adaptive filter system and a method for controlling the adaptive filter system are described herein. The system can includes one or more filters to attenuate incoming light. The one or more filters can be moved by one or more actuators. The method can capture image data from an imaging device through the one or more filters. Information can be determined from the captured image data. The one or more filters can be moved to a position for capturing image data based on the information.

Abstract: Embodiments of the present disclosure relate to attribute-based selectable item recommendation. A computer-implemented method is disclosed. A computer generates respective attribute profiles of selectable items based on one or more attribute elements included in the selectable items. The profiles include at least one attribute elements respectively correspond to the selectable items. The computer generates an attribute requirement description of an item requesting agent based on a historical search dataset of the item requesting agent. The attribute requirement description includes at least part of the one or more attribute elements. The computer determines recommended selectable items included in the selectable items for the item requesting agent based on the attribute profiles of the selectable items and the attribute requirement description of the item requesting agent.

Abstract: A sensor enclosure comprising a domed cover and a base. The base can be encased by the domed cover. The base comprises an inner frame, an outer frame, one or more wipers, and a powertrain. The inner frame can provide surfaces for one or more sensors. The outer frame, disposed underneath the inner frame, the outer frame includes a slewing ring. The slewing ring comprises an inner ring to which the domed cover is attached and an outer ring attached to the outer frame. The one or more wipers extends vertically from the outer frame, each wiper having a first end attached to the outer frame and a second end attached to a support ring, and each wiper making a contact with the dome cover. The powertrain, disposed within the outer frame, configured to rotate the ring and the dome cover attached to the inner ring.

Abstract: A method for multi-task learning via gradient split for rich human analysis is presented. The method includes extracting images from training data having a plurality of datasets, each dataset associated with one task, feeding the training data into a neural network model including a feature extractor and task-specific heads, wherein the feature extractor has a feature extractor shared component and a feature extractor task-specific component, dividing filters of deeper layers of convolutional layers of the feature extractor into N groups, N being a number of tasks, assigning one task to each group of the N groups, and manipulating gradients so that each task loss updates only one subset of filters.

Abstract: A wearable medical device comprising an annular housing configured to attach to a wrist of a patient. The wearable medical device having a first receptacle attached to the annular housing for receiving a first portion of a tube of an intravenous delivery system. The wearable medical device comprising a flow regulator attached to the annular housing and in contact with the first portion of the tube, where the flow regulator is configured to modify a geometric characteristic of the first portion of the tube. The wearable medical device further comprising a wireless transmitter for communicating with a user console.

Abstract: A method for employing a general label space voting-based differentially private federated learning (DPFL) framework is presented. The method includes labeling a first subset of unlabeled data from a first global server, to generate first pseudo-labeled data, by employing a first voting-based DPFL computation where each agent trains a local agent model by using private local data associated with the agent, labeling a second subset of unlabeled data from a second global server, to generate second pseudo-labeled data, by employing a second voting-based DPFL computation where each agent maintains a data-independent feature extractor, and training a global model by using the first and second pseudo-labeled data to provide provable differential privacy (DP) guarantees for both instance-level and agent-level privacy regimes.

Abstract: Embodiments of this application relate to the field of communications technologies, and disclose a flow control method and apparatus, to resolve a prior-art problem such as packet loss, packet accumulation, or network congestion that occurs after a packet is switched between priority queues. A specific solution is as follows: A first device receives a first packet sent by a second device, where the first packet carries a first field and a second field, the first field carries a first priority, and the second field carries a second priority; the first device performs flow control based on the first priority in the first packet; and the first device performs queue scheduling on the first packet based on the second priority in the first packet.

Abstract: Disclosed herein are battery separators that include a microporous membrane and a coating. The coating may comprise, consist, or consist essentially of polymeric components, inorganic components, or combinations thereof. The battery separators described herein are, among other things, thinner, stronger, and more wettable with electrolyte than some prior battery separators. The battery separators may be used in secondary or rechargeable batteries, including lithium ion batteries. The batteries may be used in vehicles or devices such as cell phones, tablets, laptops, and e-vehicles.

Abstract: This application provides a method and an apparatus for determining a port orientation. The method includes: A first network device receives, through a first port, a protocol data unit PDU sent by a second network device. The first network device determines, based on the PDU, that the first port is an upstream port or a downstream port. In this embodiment of this application, the first network device may determine, based on the PDU sent by the second network device, whether the first port is the upstream port or the downstream port, to provide a basis for subsequently helping an SDN controller reduce load.

Abstract: Example packet control methods and apparatus are described. One example method includes detecting, by a network node, a packet in a packet flow causing a congestion from an upstream node. The network node reduces a scheduling priority of the packet in the packet flow and generates a congestion isolation message, where the congestion isolation message includes description information of the packet flow. The congestion isolation message is sent to the upstream node to instruct the upstream node to reduce the scheduling priority of the packet in the packet flow.

Abstract: A method for acquiring privacy-enhancing encodings in an optical domain before image capture is presented. The method includes feeding a differentiable sensing model with a plurality of images to obtain encoded images, the differentiable sensing model including parameters for sensor optics, integrating the differentiable sensing model into an adversarial learning framework where parameters of attack networks, parameters of utility networks, and the parameters of the sensor optics are concurrently updated, and, once adversarial training is complete, validating efficacy of a learned sensor design by fixing the parameters of the sensor optics and training the attack networks and the utility networks to learn to estimate private and public attributes, respectively, from a set of the encoded images.

Abstract: Systems, methods, and non-transitory computer-readable media are provided for implementing a tracking camera system onboard an autonomous vehicle. Coordinate data of an object can be received. The tracking camera system actuates, based on the coordinate data, to a position such that the object is in view of the tracking camera system. Vehicle operation data of the autonomous vehicle can be received. The position of the tracking camera system can be adjusted, based on the vehicle operation data, such that the object remains in view of the tracking camera system while the autonomous vehicle is in motion. A focus of the tracking camera system can be adjusted to bring the object in focus. The tracking camera system captures image data corresponding to the object.

Abstract: Lithium ion batteries and battery modules are disclosed. The lithium ion battery includes a housing where a positive electrode, a negative electrode, and an electrolyte are provided. Some of the areas of the housing are made from the fusible material. When the temperature in the housing exceeds a set threshold, the area, made from the fusible material, on the housing is communicated, where the electrolyte flows out to separate from the positive electrode and the negative electrode. Timely separation of the electrolyte in the single lithium ion battery is achieved when the temperature thereof rises abnormally, the electrolyte is effectively protected from being ignited or decomposed, the single lithium ion battery is protected from being ignited and exploded, and damages to the rest surrounding single lithium ion batteries caused by a chain reaction brought by the single lithium ion battery of which the temperature rises abnormally are avoided.

Abstract: A computer-implemented method for implementing face spoofing detection using a physical-cue-guided multi-source multi-channel framework includes receiving a set of data including face recognition data, liveness data and material data associated with at least one face image, obtaining a shared feature from the set of data using a backbone neural network structure, performing, based on the shared feature, a pretext task corresponding to face recognition, a first proxy task corresponding to depth estimation, a liveness detection task, and a second proxy task corresponding to material prediction, and aggregating outputs of the pretext task, the first proxy task, the liveness detection task and the second proxy task using an attention mechanism to boost face spoofing detection performance.

Abstract: Systems and methods implemented via a broker in a cloud-based system include steps of, responsive to a user and associated user device executing a client connector being authenticated, receiving a notification from the client connector; determining private applications accessible by the user based on policy, wherein the private applications are located in one of a public cloud, a private cloud, and an enterprise network; and sending a Top-Level Domain+1 (TLD+1) list of the accessible private applications to the user device, wherein the TLD+1 includes a TLD and a domain name.

Abstract: Disclosed herein are embodiments for committing changes of an image of a virtual machine-based container running on a host computer system. A starting image for a container is obtained from a guest starting folder, and while the container runs the starting image changes to the image made by the running container are captured in a guest folder that is union mounted with the guest starting folder. Another union folder, which is the union of a new host folder and the starting folder, contains the changed image which can be used for the container or transferred to a different container.

Abstract: An aspect of the present invention discloses a method for extracting content from a document. The method includes one or more processors identifying a visual anchor corresponding to a text element depicted in a first document utilizing an edge detection analysis. The method further includes determining edge coordinates of the text element depicted in the first document. The method further includes determining text at a leading edge of the text element depicted in the first document and text at a trailing edge of the text element depicted in the first document, based on the determined edge coordinates. The method further includes extracting a complete version of the text element depicted in the first document, from a plain text version of the first document, utilizing the determined text at the leading edge of the text element and the determined text at the trailing edge of the text element.

Abstract: A system included and a computer-implemented method performed in an autonomous-driving vehicle are described. The system performs: detecting one or more movable traffic objects; determining one or more target movable traffic objects from the one or more detected movable traffic objects; determining a type of the one or more target movable traffic objects and an traffic object that has a right of way (ROW) in a situation involving the autonomous-driving vehicle. The system further performs: determining a manner of generating a vehicle behavior notification to the target movable traffic object based on the type of the one or more target movable traffic objects and the ROW; and causing a vehicle behavior notification of the determined manner to be generated to the one or more target movable traffic objects.