Abstract: A device is provided, and includes a first bus; a first circuit and a first bus unit circuit, a second circuit and a second bus unit circuit. The first circuit is coupled to the first bus through the first bus unit circuit, and the first bus unit circuit passes a first output signal from the first circuit as a first bus data signal on the first bus. The second circuit is coupled to the first bus through the second bus unit circuit, and the second bus unit circuit performs an OR operation between the first bus data signal and a second bus data signal on the first bus to generate a first input signal to the second circuit.

Abstract: A device is provided, and includes a first bus; a first circuit and a first bus unit circuit, a second circuit and a second bus unit circuit. The first circuit is coupled to the first bus through the first bus unit circuit, and the first bus unit circuit passes a first output signal from the first circuit as a first bus data signal on the first bus. The second circuit is coupled to the first bus through the second bus unit circuit, and the second bus unit circuit performs an OR operation between the first bus data signal and a second bus data signal on the first bus to generate a first input signal to the second circuit.

Abstract: A semiconductor structure includes a substrate, at least one first fin protruded from the substrate, and a 3D capacitor disposed over the substrate. The 3D capacitor includes a doped electrode conformally disposed in the first fin, a metal electrode disposed over the doped electrode, and a dielectric layer disposed between the doped electrode and the metal electrode.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an image embedding model. In one aspect, a method comprises: obtaining training data comprising a plurality of training examples, wherein each training example comprises: an image pair comprising a first image and a second image; and selection data indicating one or more of: (i) a co-click rate of the image pair, and (ii) a similar-image click rate of the image pair; and using the training data to train an image embedding model having a plurality of image embedding model parameters.

Abstract: An edge device extracts a data segment of monitoring data and generates a summary of the data segment. The summary is classified using a generative artificial intelligence (AI) model. The classification is validated based on similarity of the summary to previous summaries that have the same classification as the summary. The summary and the previous summaries are stored as event data in an event database. Event data in the event database are marked for upload to a backend system as feedback data, which is used to update the generative AI model.

Abstract: A semiconductor device includes: first and second input circuits in a central region and correspondingly configured to operate in a first voltage domain; first and second single bit level shifters (SBLSs) correspondingly in first and second regions at first and second sides of the central region relative to a first direction and electrically coupled correspondingly to the first and second input circuits, and correspondingly configured to operate in a second voltage domain; and a control circuit configured to toggle each of the first and second SBLSs between a normal state and a standby state when a control signal is received from the control circuit.

Abstract: A fabric searching system includes: a memory storing a fabric database and a user database corresponding to user information; a processor selecting, in the fabric database, a first fabric data group conformed to a first fabric searching condition, and store a first selected fabric data group in the first fabric data group into the user database according to the user information; and a display displaying the first fabric data group, in which after the first selected fabric data group is stored into the user database, the processor selects, in the fabric database, a second fabric data group conformed to a second fabric searching condition, and arranging the second fabric data group according to the first selected fabric data group, and the display displays the second fabric data group which is arranged according to the first selected fabric data group.

Abstract: A method for forming a semiconductor structure includes following operations. First fins are formed in a first region of a substrate, and second fins are formed in a second region of the substrate. Widths of the first fins are greater than widths of the second fins. An isolation structure is formed over the substrate. A first ion implantation is performed on the first fins. A portion of the isolation structure is removed to expose a portion of each first fin and a portion of each second fin. The widths of the first fins are equal to or less than the widths of the second fins after the removing of the portion of the isolation structure. A 3D capacitor is formed in the first region, and a FinFET device is formed in the second region. The 3D capacitor includes the first fins, and the FinFET device includes the second fins.

Abstract: A case for an electronic device is provided. The case includes an outer surface and an inner surface. The outer surface includes a plurality of first blind holes. The inner surface includes a plurality of second blind holes corresponding to the first blind holes. Each first blind hole and the corresponding second blind hole are three-dimensionally overlapped to form a through region extending through the case.

Abstract: A connected vehicle includes electronic control units (ECUs) that host onboard cybersecurity modules. The cybersecurity modules are part of a cybersecurity framework that protects the connected vehicle at various logical layers using signature-based modules and rule-based modules that are onboard hosted by the ECUs, and signature-based and anomaly-based modules that are offboard hosted by a computer system that is external to the connected vehicle. Attack techniques of a cyberattack are detected and correlated to validate detection of the cyberattack onboard the connected vehicle and offboard by the external computer system.

Abstract: A firewall manager automates traffic route configuration, compliance monitoring, and remediation. An administrator specifies a firewall policy that includes rules and traffic description, and specifies accounts, isolated virtual networks (IVNs) and/or subnets for firewall deployment. For automated traffic route configuration, the manager provisions and configures firewalls for the specified networks. The manager uses discovered network resource of the specified networks to determine route information for the firewalls for the networks for the traffic, and sends instructions for routing updates for the IVNs to an IVN manager service. For compliance monitoring and remediation, the manager obtains information about new IVNs, subnets, resources and/or routes, and determines compliance by comparing the obtained information to the firewall policy. The manager generates a remediation plan for non-compliance and instructs remediation by performing programmatic calls to the IVN manager service.

Abstract: A connected vehicle has several electronic control units (ECUs). Entities running in different ECUs are constrained in corresponding security boundaries. Messages across security boundaries are transmitted over a connection that is not directly available to entities constrained in security boundaries. Messages across security boundaries between entities are inspected. A message from an entity in one security boundary to another entity in another security boundary is inspected by normalizing a content of the message, and comparing the normalized content to a baseline. A corrective action is performed against the message in response to determining that a difference between the normalized content and the baseline indicates a change that is prohibited by a security policy.

Abstract: An intrusion detection and prevention system (IDPS) has detection probes and detection hubs that are deployed onboard a connected vehicle and a detection correlation backend that is deployed on the cloud. A detection probe receives raw packets of network traffic of a communication network of the connected vehicle. The detection probe filters the raw packets in accordance with packet filter rule entries to generate packets of interest. The detection probe scans the packets of interest for data indicative of network threats and outputs corresponding unfiltered detection logs. The detection probe filters the unfiltered detection logs in accordance with detection filter rule entries to generate detection logs of interest. A detection hub aggregates detection logs of interest from one or more detection probes to generate aggregated detection logs. A detection correlation backend evaluates the aggregated detection logs to detect network threats.

Abstract: A light emitting device including an epitaxial structure and a plurality of surface microstructures is provided. The epitaxial structure has a light emitting surface and a surrounding wall surface. The surrounding wall surface surrounds and is connected to the light emitting surface. The plurality of surface microstructures are separately arranged on the light emitting surface along a plurality of directions. The plurality of directions are not perpendicular to the surrounding wall surface. A light emitting device substrate including a plurality of the light emitting device is also provided.

Abstract: According to one aspect, intersection scenario description may be implemented by receiving a video stream of a surrounding environment of an ego-vehicle, extracting tracklets and appearance features associated with dynamic objects from the surrounding environment, extracting motion features associated with dynamic objects from the surrounding environment based on the corresponding tracklets, passing the appearance features through an appearance neural network to generate an appearance model, passing the motion features through a motion neural network to generate a motion model, passing the appearance model and the motion model through a fusion network to generate a fusion output, passing the fusion output through a classifier to generate a classifier output, and passing the classifier output through a loss function to generate a multi-label classification output associated with the ego-vehicle, dynamic objects, and corresponding motion paths.

Abstract: A semiconductor device includes a semiconductor fin. The semiconductor device includes a first silicon oxide layer contacting the semiconductor fin at a first interface and including nitrogen at a first concentration. The semiconductor device includes a second silicon oxide layer contacting the first silicon oxide layer at a second interface and including nitrogen at a second concentration that is greater than the first concentration.

Abstract: A method and apparatus for minimizing silicon germanium facets in planar metal oxide semiconductor structures is disclosed. For example, a device fabricated according to the method may include a semiconductor substrate, a plurality of gate stacks formed on the substrate, a plurality of source/drain regions formed from silicon germanium, and a shallow trench isolation region positioned between two source/drain regions of the plurality of source/drain regions. Each source/drain region of the plurality of source/drain regions is positioned adjacent to at least one gate stack of the plurality of gate stacks. Moreover, the shallow trench isolation region forms a trench in the substrate without intersecting the two source/drain regions.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an image embedding model. In one aspect, a method comprises: obtaining training data comprising a plurality of training examples, wherein each training example comprises: an image pair comprising a first image and a second image; and selection data indicating one or more of: (i) a co-click rate of the image pair, and (ii) a similar-image click rate of the image pair; and using the training data to train an image embedding model having a plurality of image embedding model parameters.

Abstract: A display apparatus and an image processing method thereof are provided. The display apparatus includes a display panel and an image processing device. The image processing device receives a low-resolution image from a host. The image processing device tracks a user's gaze to define a region of interest (ROI). The image processing device performs a video super-resolution (VSR) reconstruction on an original ROI image corresponding to the ROI in the low-resolution image to generate a high-definition ROI image. The image processing device pastes the high-definition ROI image back to the ROI in the low-resolution image to generate a processed image. The image processing device controls the display panel to display the processed image.