Abstract: An apparatus for robotic surgery comprises a processor configured with instructions to receive patient data from treated patients, receive surgical robotics data for each of the plurality of treated patients, and output a treatment plan of a patient to be treated in response to the patient data and the surgical robotics data. This approach has the advantage of accommodating individual variability among patients and surgical system parameters so as to provide improved treatment outcomes.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for false detection removal using adversarial masks. The method includes performing object detection on a first image that includes a first region using a detection model determining the detection model incorrectly classified the first region of the first image; generating an adversarial mask based on the first region of the first image and the detection model; obtaining a second image that includes the first region; generating a masked image based on the second image and the adversarial mask; and performing object detection on the masked image including the first region using the detection model.

Abstract: Systems and methods for pre-fetching results from large language models (LLMs) are provided. The method includes acquiring a context of an interaction between a user and an Artificial Intelligence (AI) character; predicting, based on the context, one or more anticipated words to be uttered by the user; generating, based on the one or more anticipated words, at least one query to an LLM; providing the at least one query to the LLM; generating, based on at least one response obtained from the LLM, an anticipated reply of the AI character model to the one or more anticipated words to be pronounced by the user; receiving one or more words uttered by the user; determining that a level of a discrepancy between the one or more words and the one or more anticipated words is below a predetermined threshold; and providing the anticipated reply to the user.

Abstract: The present disclosure relates to systems and method for deploying a fiber optic network. Distribution devices are used to index fibers within the system to ensure that live fibers are provided at output locations throughout the system. In an example, fibers can be indexed in multiple directions within the system. In an example, spare ports can be providing in a forward direction and reverse direction ports can also be provided.

Abstract: A control method of an image signal processor for an artificial neural network may be configured to include a step of acquiring an image, a step of determining at least one image characteristic data corresponding to the image, and a step of determining an image correction parameter (SFR preset) for improving an inference accuracy of an artificial neural network model based on the at least one of image characteristic data and an inference accuracy profile of an artificial neural network model.

Abstract: A wrinkle detection method includes: rotating a region, in a face image, in which a wrinkle needs to be detected, to obtain a plurality of to-be-detected images; determining wrinkle points from all pixel points based on grayscale values of the pixel points in each of the to-be-detected images with different angles; determining at least one wrinkle line based on the wrinkle points; and then displaying, by the electronic device, the wrinkle line in the region in which the wrinkle needs to be detected, where each wrinkle line indicates one wrinkle in each of the to-be-detected images.

Abstract: A method for image classification includes accessing a plurality of images of at least a portion of a gastrointestinal tract (GIT) captured by a capsule endoscopy device and for each image of the plurality of images: providing a classification score for each segment of a plurality of consecutive segments of the GIT by a deep learning neural network, and providing a classification probability for each segment of the plurality of consecutive segments of the GIT based on the classification scores by a classical machine learning classifier. The method further includes determining a classification for each image to one segment of the plurality of consecutive segments of the GIT based on processing a signal corresponding to the classification probabilities of the plurality of images.

Abstract: An encoder outputs a first bit sequence having N bits. A mapper generates a first complex signal s1 and a second complex signal s2 with use of bit sequence having X+Y bits included in an input second bit sequence, where X indicates the number of bits used to generate the first complex signal s1, and Y indicates the number of bits used to generate the second complex signal s2. A bit length adjuster is provided after the encoder, and performs bit length adjustment on the first bit sequence such that the second bit sequence has a bit length that is a multiple of X+Y, and outputs the first bit sequence after the bit length adjustment as the second bit sequence. As a result, a problem between a codeword length of a block code and the number of bits necessary to perform mapping by a set of modulation schemes is solved.

Abstract: Information representing a physique of a subject is extracted from an image obtained by imaging the subject. A group in which the subject is classified is specified, using the extracted information representing the physique of the subject. Image data representing a medical image obtained by imaging the subject is input to a learned model corresponding to a specified group among learned models obtained for each group by machine learning using learning data for each group. Information representing an area extracted from the medical image is acquired, which is output from the learned model with the input.

Abstract: Provided is an image determination device. The image determination device is provided with: feature extractors which output, on the basis of an image to be examined, each piece of feature data indicating a specific feature of the image; a determiner which outputs, on the basis of the feature data output from the extractors, output data indicating the determination result pertaining to the image; and a training part which trains the determiner so as to output, output data indicating the label data associated with the training image on the basis of the feature data output when the training image is input to the extractors, wherein the training part further trains, by using new training data, the determiner so that the output data indicating the label data associated with the image is output by the determiner.

Abstract: Multiple mobility sets are maintained for nodes of radio networks. The sets comprise information such as: transmit and receive point identities; cell identities; beam identities; frequency channels; channel bandwidth; and black lists. The sets may be defined at different levels, such as network and physical (PHY) level. A network mobility set, e.g., a new-radio (NR) mobility set may, be determined by the gNB, the cell, the UE, or another device. Multiple radio access network nodes and UEs may exchange mobility set information to achieve a distributed mobility solution. A UE may monitor its orientation relative to a TRP, e.g., via use of an onboard MEMS gyroscope, and alter its beamforming parameters in response to changes in orientation and/or changes in TRP connection strength. Cell selection and reselection for beam based networks may use Single Frequency Network (SFN) broadcast of initial access signals without beam sweeping.

Abstract: Provided is a radio communication device which can separate propagation paths of antenna ports and improve a channel estimation accuracy even when using virtual antennas. The device includes: a mapping unit which maps a data signal after modulation to a virtual antenna and a virtual antenna; a phase inversion unit which inverts the phase of S0 transmitted from an antenna port in synchronization with a phase inversion unit between the odd-number slot and the even-number slot; the phase inversion unit which inverts the phase of R0 transmitted from the antenna port; a phase inversion unit which inverts the phase of S1 transmitted from an antenna port in synchronization with a phase inversion unit; and the phase inversion unit which inverts the phase of R1 transmitted from an antenna port.

Abstract: Motion and video data from vehicle sensors and camera arrays attached to a vehicle collect video and sensor data along a path driven by the vehicle. A system processes such data to produce high-accuracy structured map data, as might be used to precisely locate a moving vehicle in its environment. Positions are calculated from the sensor data. The positions are updated based on the video data. Positions of features in the video are used to create or update structured map data.

Abstract: Provided is a magnetic resonance imaging apparatus with no occurrence of artifacts, even after noise removal by applying Wavelet transform to a zero-fill reconstructed image. Nuclear magnetic resonance signals acquired by the magnetic resonance imaging apparatus are processed to perform reconstruction with a reconstruction matrix extended by zero-filling an acquisition matrix, and then a zero-fill reconstructed image is produced. This reconstructed image is subjected to an iterative operation combining the Wavelet transform and L1 norm minimization to remove noise. Before the noise removal, a pre-processing is performed to change the reconstruction matrix size so that an artifact does not occur in the image after noise removal, an artifact portion appears outside the reconstruction matrix after the noise removal, or cutting out is performed so that no artifact appears after the noise removal. The matrix size is restored to its original size in the post-processing after the noise removal.

Abstract: An image determination device includes: a training model which outputs, on the basis of an image to be examined, output data indicating a determination result about the image; a training part which trains the training model to output, by using training data including a training image and label data, output data indicating the label data associated with the training image, when the training image is input to the training model; a dividing part which divides the training data into a plurality of pieces of sub-training data; a measurement part which measures accuracy of determination when the training part trains the training model by using each of the plurality of pieces of sub-training data; and selection part which selects at least any one among the plurality of pieces of sub-training data on the basis of the accuracy of determination.

Abstract: The technology disclosed relates to generating ground truth training data to train a neural network-based template generator for cluster metadata determination task. In particular, it relates to accessing sequencing images, obtaining, from a base caller, a base call classifying each subpixel in the sequencing images as one of four bases (A, C, T, and G), generating a cluster map that identifies clusters as disjointed regions of contiguous subpixels which share a substantially matching base call sequence, determining cluster metadata based on the disjointed regions in the cluster map, and using the cluster metadata to generate the ground truth training data for training the neural network-based template generator for the cluster metadata determination task.

Abstract: Improved multi-modal machine learning networks integrate computer vision systems with language models. In certain embodiments, a computer vision system analyzes at least one image to generate a computer vision output. The language model generates an output based, at least in part, on a consideration of the computer vision output. The outputs of the language model can be generated by jointly considering textual information learned by the language model and visual content extracted by the computer vision system, thereby significantly improving the accuracy, breadth, and comprehensiveness of the outputs.

Abstract: A method for video clip extraction includes: obtaining a video, and sampling the video to obtain N video frames, wherein N is a positive integer; inputting the N video frames to a pre-trained frame feature extraction model to obtain a feature vector of each video frame in N video frames; determining scores of the N video frames based on a pre-trained scoring model; and extracting target video clips from the video based on the scores of the N video frames.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating output sequences using auto-regressive decoder neural networks. In particular, during generation, adaptive early exiting is used to reduce the time required to generate the output sequence.

Abstract: An image determination device includes: a training model which outputs, on the basis of an image to be examined, output data indicating a determination result about the image; a training part which trains the training model to output, by using training data including a training image and label data, output data indicating the label data associated with the training image, when the training image is input to the training model; a dividing part which divides the training data into a plurality of pieces of sub-training data; a measurement part which measures accuracy of determination when the training part trains the training model by using each of the plurality of pieces of sub-training data; and selection part which selects at least any one among the plurality of pieces of sub-training data on the basis of the accuracy of determination.