Abstract: A photothermal converter using a wavelength selective perfect absorber made of a low-loss metal material or dielectric and a heat detection sensor are combined to develop a sensor that efficiently converts light of a specific wavelength into heat and further electrically detects the heat. Here, since the wavelength selective perfect absorber of the present invention has a periodic structure, it has high directivity, and can also be used as a small motion sensor or a watching sensor using detection of thermal radiation. In addition, it can also be used as a high-precision small position sensor by being combined with a laser light source matching the resonance wavelength of the sensor.

Abstract: Implementations relate to generating multi-modal response(s) through utilization of large language model(s) (LLM(s)) and other generative model(s). Processor(s) of a system can: receive natural language (NL) based input, generate a multi-modal response that is responsive to the NL based output, and cause the multi-modal response to be rendered. In some implementations, and in generating the multi-modal response, the processor(s) can process, using a LLM, LLM input to generate LLM output, and determine, based on the LLM output, textual content and generative multimedia content for inclusion in the multi-modal response. In some implementations, the generative multimedia content can be generated by another generative model (e.g., an image generator, a video generator, an audio generator, etc.) based on generative multimedia content prompt(s) included in the LLM output and that is indicative of the generative multimedia content.

Abstract: Methods and systems for generation of shape data for a set of electronic designs include inputting a set of shape data, where the set of shape data represents a set of shapes for a device fabrication process. A convolutional neural network is used on the set of shape data to determine a set of generated shape data, where the convolutional neural network comprises a generator trained with a set of pre-determined discriminators. The set of generated shape data comprises a scanning electron microscope (SEM) image.

Abstract: A processor includes a plurality of register sets of a register file, and a plurality sets of functional units which are coupled by sets of dedicated read and write buses to allow parallel execution of instruction. The register sets and functional units are organized as odd and even sets. Shared buses may also be employed. The processor may also include a time counter and a time-resource matrix and provides a method for statically dispatching instructions.

Abstract: A method and a system for the coproduction of hydrogen, electrical power, and heat energy. An exemplary method includes desulfurizing a feed stream to form a desulfurized feed stream, reforming the desulfurized feed stream to form a methane rich gas, and providing the methane rich gas to a membrane separator. A hydrogen stream is produced in a permeate from the membrane separator. A retentate stream from the membrane separator is provided to a solid oxide fuel cell (SOFC). Electrical power is produced in the SOFC from the retentate stream.

Abstract: Systems and methods are provided that train a machine-learned language encoding model through the use of a contrastive learning task. In particular, the present disclosure describes a contrastive learning task where the encoder learns to distinguish input tokens from plausible alternatives. In some implementations, on each training example the proposed method masks out some subset (e.g., 15%) of the original input tokens, replaces the masked tokens with samples from a “generator” (e.g., which may be a small masked language model), and then trains the encoder to predict whether each token comes from the original data or is a replacement produced by the generator.

Abstract: A processor includes a time counter, a vector coprocessor, and a vector data buffer for executing vector load and store instructions. The processor handles unit, stride or indices of data elements of a vector register. The vector data buffer includes crossbar switches for coupling between a plurality of data elements of a vector register and a plurality of data banks of the vector data buffer.

Abstract: Implementations relate to managing multimedia content that is obtained by large language model(s) (LLM(s)) and/or generated by other generative model(s). Processor(s) of a system can: receive natural language (NL) based input that requests multimedia content, generate a response that is responsive to the NL based input, and cause the response to be rendered. In some implementations, and in generating the response, the processor(s) can process, using a LLM, LLM input to generate LLM output, and determine, based on the LLM output, at least multimedia content to be included in the response. Further, the processor(s) can evaluate the multimedia content to determine whether it should be included in the response. In response to determining that the multimedia content should not be included in the response, the processor(s) can cause the response, including alternative multimedia content or other textual content, to be rendered.

Abstract: A method includes obtaining multiple input image frames and generating bad pixel maps associated with different ones of the input image frames. Each bad pixel map is generated using first and second adaptive thresholds that are based on statistical properties of image data contained in a specified operation window within the associated input image frame. The method also includes generating at least one refined bad pixel map using the bad pixel maps associated with two or more of the input image frames. The method further includes using one or more coordinates of one or more bad pixels identified in the at least one refined bad pixel map to update one or more pixel values of at least one of the input image frames.

Abstract: A method of performing open world object detection includes receiving object data, that includes embeddings data corresponding to a plurality of embeddings for known objects in a first input image, projecting the embeddings into a hyperbolic embedding space that includes embeddings in a plurality of categories of objects each including one or more classes of objects, regularizing the projected embeddings within the hyperbolic embedding space by moving each of the projected embeddings closer to embeddings in a same category of the plurality of categories and further away from embeddings in different categories of the plurality of categories, receiving an unmatched query corresponding to an object in a second input image, and generating, based on the hyperbolic embedding space including the regularized embeddings, an output signal that indicates whether the object in the second input image corresponds to an unknown object in one of the classes of objects.

Abstract: The method of controlling service load selectively by session in the telecommunications charging system is designed to ensure stable system operation even when the load exceeds the system's capacity. The method consists of three steps: Step 1: the charging system determines the overload status of the system; Step 2: The charging system selectively bypass or accept request according to conditions and has a status memory; Step 3: The charging system analyzes and reprocesses the saved charging requests.

Abstract: An algorithm for selecting a new suitable AP for STAs in a mesh network and a method for exchanging data between APs built according to the IEEE 1905.1 standard. The active search helps reduce the latency when STAs move in the coverage area of the mesh network, ensuring service performance and maintaining stable connections. The vender message are specifically defined to ensure that the roaming process is not interrupted and that the service remains stable during the transition.

Abstract: A method of performing data pre-selection for an object detection system includes receiving a first dataset that includes unlabeled data corresponding to one or more images, providing the first dataset and a plurality of learnable prompt vectors to a pre-training model. The learnable prompt vectors include text inputs. The method further includes generating, using the pre-training model, an unsupervised learning prompt based on the first dataset and the plurality of learnable prompt vectors. The unsupervised learning prompt corresponds to a multi-modal feature of the one or more images of the first dataset. The method further includes extracting features from either of the first dataset and a second dataset based on the unsupervised learning prompt, selecting and labeling a subset of instances of the extracted features, and generating and outputting a labeled dataset based on the labeled subset of instances.

Abstract: Devices, systems and methods for selectively providing live sporting event video at sports gaming devices may include decrypting a first encrypted video signal is decrypted into a first decrypted video signal that includes a first encoded video stream of a first live sporting event. The first encoded video stream may be decoded into a first decoded video stream that includes a first video of the first live sporting event, which may be displayed by a display device along with first wager information to facilitate a user placing a wager on the first live sporting event. In response to receiving an instruction to display a second video of a second live sporting event, a second encrypted video signal may be decrypted into a second decrypted video signal that includes a second encoded video stream of the second live sporting event. The second encoded video stream may be decoded into a second decoded video stream that includes the second video of the second live sporting event.

Abstract: Methods and apparatus configured to obtain a decision-point value, and send, for a logical channel group (LCG) having a quantity of data pending an uplink transmission, a long buffer status report in response to the decision-point value exceeding a threshold value, or a short buffer status report in response to the decision-point value being equal to or less than the threshold value are disclosed. The decision-point value may be a buffer status report-type determinative value, which may be based on a peak power envelope of a wireless communication device, a data transmission rate historically obtained by the wireless communication device, a number of component carriers available for a complete upload of a buffer holding data, a cost function, an amount of data associated with the LCG that is pending the uplink transmission, a type of the wireless communication device, or a latency of communications of the wireless communication device.

Abstract: Systems and methods for routing in mixture-of-expert models. In some aspects of the technology, a transformer may have at least one Mixture-of-Experts (“MoE”) layer in each of its encoder and decoder, with the at least one MoE layer of the encoder having a learned gating function configured to route each token of a task to two or more selected expert feed-forward networks, and the at least one MoE layer of the decoder having a learned gating function configured to route each task to two or more selected expert feed-forward networks.

Abstract: A method includes obtaining multiple image frames. The method also includes selecting, using at least one processing device of an electronic device, an asymmetrical image pair from the multiple image frames. The asymmetrical image pair includes a first image frame and a second image frame, where the first image frame has a shorter exposure than the second image frame. The method further includes identifying, using the at least one processing device, one or more features based on the asymmetrical image pair. The method also includes determining, using the at least one processing device, whether the first image frame contains flicker based on the one or more features. In addition, the method includes enabling or disabling, using the at least one processing device, the first image frame as a reference candidate based on the determination whether the first image frame contains flicker.

Abstract: Implementations relate to generating multi-modal response(s) through utilization of large language model(s) (LLM(s)). Processor(s) of a system can: receive natural language (NL) based input, generate a multi-modal response that is responsive to the NL based output, and cause the multi-modal response to be rendered. In some implementations, and in generating the multi-modal response, the processor(s) can process, using a LLM, LLM input (e.g., that includes at least the NL based input) to generate LLM output, and determine, based on the LLM output, textual content for inclusion in the multi-modal response and multimedia content for inclusion in the multi-modal response. In some implementations, the multimedia content can be obtained based on a multimedia content tag that is included in the LLM output and that is indicative of the multimedia content. In various implementations, the multimedia content can be interleaved between segments of the textual content.

Abstract: A method of operation of a compute system includes: qualifying a patient image for analyzing a suspected skin condition; detecting a skin area in the patient image; segmenting the skin area into a segmented image including the suspected skin condition; cropping the segmented image to form a cropped image including the suspected skin condition at a center of the cropped image; analyzing the suspected skin condition to identify a skin disease result and a disease subclass from the cropped image; and assembling a disease identification display including the patient image, a skin disease indication, an image match score, and the disease subclass for displaying on a device.

Abstract: Systems and methods are provided that train a machine-learned language encoding model through the use of a contrastive learning task. In particular, the present disclosure describes a contrastive learning task where the encoder learns to distinguish input tokens from plausible alternatives. In some implementations, on each training example the proposed method masks out some subset (e.g., 15%) of the original input tokens, replaces the masked tokens with samples from a “generator” (e.g., which may be a small masked language model), and then trains the encoder to predict whether each token comes from the original data or is a replacement produced by the generator.