Abstract: A graph calculation method of RNA similarity analysis, an apparatus, a device, and a medium are provided. The method includes: converting sequence data of a looked-up RNA into a looked-up RNA structure graph; obtain a first similarity between the looked-up RNA structure graph and a target RNA structure graph; obtaining a second similarity based on the number of base constituent structures in the looked-up RNA structure graph and the number of base constituent structures in the target RNA structure graph; reconstructing the looked-up RNA structure graph based on the base constituent structures in the looked-up RNA structure graph to generate a looked-up RNA higher-order graph; and analyzing similarity between the looked-up RNA higher-order graph and a target RNA higher-order graph to obtain a third similarity; and obtaining a final similarity between the looked-up RNA and the target RNA based on the first similarity, the second similarity, and the third similarity.

Abstract: A semiconductor structure may include a substrate having a surface, an isolation structure formed on the surface, an active region formed on the surface adjacent to the isolation structure, a gate extended over the isolation structure and the active region, and a source region formed within the active region. The source region may include a first subregion formed adjacent to a first portion of the gate and the isolation structure, the first subregion having a first doping and a second subregion formed adjacent to a second portion of the gate, wherein the second subregion has a second doping different from the first doping.

Abstract: A semiconductor structure may include a substrate, an active region formed on the substrate, a gate region extended over the active region along a first direction parallel to a surface of the substrate, and a source region formed within the active region and adjacent to the gate region in a second direction parallel to the surface and perpendicular to the first direction. The source region may comprise a first subregion and a second subregion such that the second subregion is between the first subregion and the gate region. The first subregion may comprise a first width along the first direction and the second subregion may comprise a second width along the first direction. The first width may be less than the second width.

Abstract: Embodiments receive a plurality of incoming requests, determine that the plurality of incoming requests comprise a plurality of instant requests, create a software image based on the instant requests, and pull the software image based on a deployed configuration.

Abstract: The present invention discloses a Cpf 1 protein and its use in gene editing, which relates to the field of biotechnology. The present invention provides a novel Cas12a nuclease named as the Cas12a-68 protease, which has a wider PAM recognition range than traditional nucleases and is capable of recognizing targets of various genes. When editing nucleic acid fragments, using the new nuclease of the present invention is capable of overcoming the shortcomings of the traditional Cas12a and increasing the PAM recognition range. These advantages make the present invention more suitable for editing a wider range of nucleic acid fragments and lay a foundation for gene editing applications.

Abstract: Methods, devices and computer-readable media for processing a compressed video to perform an inference task are disclosed. Processing the compressed video may include selecting a subset of frame encodings of the compressed video, or zero or more modalities (RGB, motion vectors, residuals) of a frame encoding, for further processing to perform the inference task. Pre-existing motion vector and/or residual information in frame encodings of the compressed video are leveraged to adaptively and efficiently perform the inference task. In some embodiments, the inference task is an action recognition task, such as a human action recognition task.

Abstract: A power on/off control circuit and an electronic device is provided, the circuit including: a switching element, a first semiconductor switching circuit including a first semiconductor switching element, a second semiconductor switching circuit including a second semiconductor switching element, and a holding circuit connected between a first terminal of the first semiconductor switching element and a second terminal of the second semiconductor switching element. When the switching element is closed, the first semiconductor switching element is turned on. When the first semiconductor switching element is turned on, the second semiconductor switching element is turned on, and a power on signal is output via the second terminal of the second semiconductor switching element. When the second semiconductor switching element is turned on, the holding circuit is operable to maintain the on state of the first semiconductor switching element. The control circuit can stably lock a started state.

Abstract: Systems, methods and computer-readable medium for predicting a depth for a video frame are disclosed. An example method may include steps of: receiving a plurality of training data, each comprising a set of consecutive video frames and a depth representation of a subsequent video frame to the consecutive video frames; receiving a pre-trained neural network model f? having a plurality of weights ?; while the pre-trained neural network model f? has not converged: computing a plurality of second weights ?i?, based on each set of consecutive video frames, and updating the plurality of weights ?, based on the plurality of training data and the plurality of second weights ?i?; receiving a plurality of new consecutive video frames with consecutive timestamps; and predicting a depth representation of video frame immediately subsequent to the new consecutive video frames based on the updated plurality of weights ?.

Abstract: Methods and modules for accelerating inference computations in transformer models using edge devices includes partitioning inputs for each layer and synchronizing between transformer layers. A method includes receiving a transformer input, partitioning the transformer input into two or more first-stage divisions, processing each first-stage division into a processed first-stage division, and combining the processed first-stage divisions into a first output. A module includes a computing device for partitioning a transformer input into two or more divisions, transmitting each of the divisions, and receiving processed divisions, as well as two or more transformer processing units, each for receiving a division from the computing device, processing the division into a processed division, and sending the processed division to the computing device.

Abstract: System, method and/or computer readable medium for audio visual sound source Separation that applies cross-modal meta consistency learning. Inputs include an audio spectrogram that represents first sounds second sounds, a first video of a first sound producing object, and a second video of a second sound producing object. Audio features and audio tokens are generated by applying audio encoders to the audio spectrogram. Visual tokens are generated by applying a visual encoder to the first video and the second video. Respective audio-visual features are obtained based on combining the audio token with the respective visual tokens. Based on the respective audio-visual features, first and second separated audio masks are generated by applying a decoder.

Abstract: This disclosure provides for methods and system for meta few-shot class incremental learning. According to an aspect a method is provided. The method includes obtaining at least one weight attention map of a first network and updating weights of a second network using the at least one weight attention map, where the second network is a modulatory network. The method further includes generating at least one feature attention map of the second network based on the at least one weight attention map of the first network and a set of input images of at least one class. The method further includes generating at least one feature map of the first network based on the set of input images of the at least one class, and updating the at least one feature map of the first network based on the feature attention map of the second network.

Abstract: A workforce management system and methods for managing a workload of an agent include receiving a first written interaction from a first customer; routing the first written interaction to an agent; determining a readability score of the first written interaction; aggregating the readability score with a plurality of past readability scores of written interactions assigned to the agent; creating a readability score scale based on the aggregated readability score with the plurality of past readability scores; creating a concurrency level scale based on a minimum concurrency level and a maximum concurrency level of the agent; correlating readability scores and concurrency levels using the readability score scale and the concurrency level scale; and adjusting the maximum concurrency level of the agent based on the correlation.

Abstract: A method has the steps of obtaining a set of training samples from one or more domains, using the set of training samples to query a plurality of artificial-intelligence (AI) models, combining the outputs of the queried AI models, and adapting a target AI model via knowledge distillation using the combined outputs.

Abstract: Methods and systems for determining a surface color of a target surface under an environment with an environmental light source. A plurality of images of the target surface are captured as the target surface is illuminated with a variable intensity, constant color light source and a constant intensity, constant color environmental light source, wherein the intensity of the light source on the target surface is varied by a known amount between the capturing of the images. A color feature tensor, independent of the environmental light source, is extracted from the image data, and used to infer a surface color of the target surface.

Abstract: A method, device and computer-readable medium for generating a super-resolution version of a compressed video stream. By leveraging the motion information and residual information in compressed video streams, described examples are able to skip the time-consuming motion-estimation step for most frames and make the most use of the SR results of key frames. A key frame SR module generates SR versions of I-frames and other key frames of a compressed video stream using techniques similar to existing multi-frame approaches to VSR. A non-key frame SR module generates SR version of the non-key inter frames between these key frames by making use of motion information and residual information used to encode the inter frames in the compressed video stream.

Abstract: The present disclosure discloses a method and apparatus for scheduling a resource for a deep learning framework. The method can comprise: querying statuses of all deep learning job objects from a Kubernetes platform at a predetermined interval; and submitting, in response to finding from the queried deep learning job objects a deep learning job object having a status conforming to a resource request submission status, a resource request to the Kubernetes platform to schedule a physical machine where the Kubernetes platform is located to initiate a deep learning training task. The method can completely automate the allocation and release on the resource of the deep learning training task.

Abstract: Methods and systems for image deblurring are described. The weights of a deblurring network are first trained by meta-training the deblurring network on both a primary deblurring task and an auxiliary reconstruction task. Application-time training of the deblurring network is then performed, using an application-time blurry input image, to obtain values of application-time trained weights. Application-time training includes performing the auxiliary reconstruction task on the application-time blurry input image, and updating the weights of the deblurring network based on an auxiliary loss computed from the auxiliary reconstruction task. A deblurred output image is generated from the application-time blurry input image, using the application-time trained weights in the deblurring network.

Abstract: Devices and methods for extracting motion vector data during decoding compressed of video data are described. At a video decoder, an encoded video data for a frame of video from an input buffer is obtained. The encoded video data is decoded to obtain decoded image data for a decoded frame, where the decoding includes extracting corresponding motion vector data for the decoded frame. The decoded image data is stored in a temporary storage indexed with a given index, and the corresponding motion vector data is stored in a same or different temporary storage indexed with the given index. An output buffer indexed with the given index is filled with the decoded image data and the corresponding motion vector data stored in the respective temporary storage indexed with the given index.

Abstract: Devices and methods for extracting motion vector data during decoding compressed of video data are described. At a video decoder, an encoded video data for a frame of video from an input buffer is obtained. The encoded video data is decoded to obtain decoded image data for a decoded frame, where the decoding includes extracting corresponding motion vector data for the decoded frame. The decoded image data is stored in a temporary storage indexed with a given index, and the corresponding motion vector data is stored in a same or different temporary storage indexed with the given index. An output buffer indexed with the given index is filled with the decoded image data and the corresponding motion vector data stored in the respective temporary storage indexed with the given index.

Abstract: Methods, devices and computer-readable media for processing a compressed video to perform an inference task are disclosed. Processing the compressed video may include selecting a subset of frame encodings of the compressed video, or zero or more modalities (RGB, motion vectors, residuals) of a frame encoding, for further processing to perform the inference task. Pre-existing motion vector and/or residual information in frame encodings of the compressed video are leveraged to adaptively and efficiently perform the inference task. In some embodiments, the inference task is an action recognition task, such as a human action recognition task.