Abstract: In the information processing device, the control unit receives an input of information including the remaining price of the first vehicle in use by the user, the remaining bond of the first vehicle, the monthly insurance premium of the first vehicle, the delivery date of the second vehicle that the user desires to transfer, and the vehicle type of the third vehicle that can be used in the cancellation fee free plan, and calculates the payment amount in a case where it is assumed that the third vehicle is used until the delivery date of the second vehicle. The control unit outputs information including the calculated payment amount.

Abstract: Automatic generation of optimized aggregation metrics for usage-based insurance (UBI) includes performing a high-dimensional Bayesian optimization on a data archive, the data archive including a plurality of signals from vehicles and corresponding UBI effects, wherein the high-dimensional Bayesian optimization includes performing testing on weighted groups of the plurality of signals using one or more aggregation functions; and transmitting the one or more aggregation functions to a vehicle to cause the vehicle to provide aggregated signals for input to a UBI model to predict a UBI rate for the vehicle.

Abstract: Maintenance method and maintenance system for smart gas pipeline network are provided. The method includes: determining a preset frequency; controlling the gas metering device in the target region to obtain gas statistical data at the preset frequency; obtaining historical gas consumption data in the target region; determining target demand information of the target region at a future time point; predicting a target difference amplitude of a gas supply and demand difference at the future time point in the target region; for the target region where the target difference amplitude meets a first preset condition, determining a reason for supply and demand difference through a reason model; determining a gas supply adjustment parameter; generating a gas supply adjustment instruction, and sending the gas supply adjustment instruction to a terminal device and the gas metering device to instruct an instruction execution object to perform an adjustment on gas supply.

Abstract: Disclosed are methods, systems, and non-transitory computer readable memory for processing, analyzing, and visualizing complex digital object sets using large language models. For instance, a method may include receiving a set of digital objects; indexing the received set of digital objects to generate indexed data; generating a timeline prompt based on the indexed data; processing the timeline prompt to generate a timeline response; and outputting the timeline response as an interactive timeline of based on the set of digital objects.

Abstract: Input/output filter units for use in a graphics processing unit include a first buffer configured to store data received from, and output to, a first component of the graphics processing unit; a second buffer configured to store data received from, and output to, a second component of the graphics processing unit; a weight buffer configured to store filter weights; a filter bank configurable to perform a plurality of types of filtering on a set of input data, the plurality of types of filtering comprising texture filtering types and pixel filtering types; and control logic configured to cause the filter bank to: (i) perform one of the plurality of types of filtering on a set of data stored in one of the first and second buffers using a set of weights stored, and (ii) store the results of the filtering in one of the first and second buffers.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for efficient scaling of inputs to be processed by a machine learning model. An example method generally includes receiving, by a machine learning model, an input having a starting size in a plurality of dimensions. The method further includes scaling, by the machine learning model, the input in one or more dimensions of the plurality of dimensions to generate a scaled input, wherein the input is scaled in each respective dimension of the one or more dimensions based on a respective stride length determined based on a starting size in the respective dimension and a target size in the respective dimension, and the respective stride length associated with at least one dimension in the one or more dimensions comprises a non-integer value. The method further includes generating, by the machine learning model, an inference based on the scaled input.

Abstract: A data processing apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to output first complementary data by inputting, to a first neural network, first partial sampling data resulting from performing a partial sampling process; to obtain first corrected data, by performing a process to improve a consistency degree between the first complementary data and the first partial sampling data; to generate second partial sampling data, on the basis of the first corrected data and the first partial sampling data; and to output second complementary data, by inputting the second partial sampling data to a second neural network.

Abstract: Disclosed are systems, apparatuses, processes, and computer-readable media to capture images with subjects at different depths of fields. A method of processing image data includes obtaining a first image captured using an image sensor, the first image being associated with a first exposure: obtaining a second image captured using the image sensor, the second image being associated with a second exposure that is longer than the first exposure: modifying a first region of the first image based on a first transformation and a second region of the first image based on a second transformation to generate a modified first image; and generating a combined image at least in part by combining the modified first image and the second image.

Abstract: A method for attacking a neural network that includes receiving an input data that includes an image and ground truth label, adding a pre-determined amount of noise to the image, denoising the noisy image utilizing a diffusion model that includes a deep equilibrium root solver, determining a first gradient of the denoised image with respect to the input data including at least the image, wherein the first gradient is associated with the diffusion model, utilizing the denoised image at downstream model, outputting a predicated label associated with the denoised image, determining a loss utilizing with the predicted label and the ground truth label, determining a second gradient associated with the downstream model utilizing at least the loss, and outputting an aggregate gradient that represents an error of the neural network output utilizing the predicted label, wherein the aggregate gradient is calculated utilizing the first gradient and the second gradient.

Abstract: A target element generation method and apparatus, an electronic device, and a storage medium are provided. The method includes: displaying an editing page on which a stylized image is displayed, where the stylized image is an image generated by performing target style conversion on an initial image; and displaying, in a target region of the stylized image, a target element corresponding to a processing operation for the target region in response to the processing operation for the target region, where the target element is an element generated by re-drawing the target region based on an initial element corresponding to the processing operation.

Abstract: Examples disclosed herein may involve a computing system that is operable to (i) receive image data captured from one or more devices, (ii) based on the image data, generate at least two batches of data corresponding to an area of a global map, wherein each batch of data comprises (a) a respective group of images from the received image data, and (b) one or more common images comprising one or more common visual features, (iii) generate a respective reconstruction of the area of the global map for each of the at least two batches of data, and (iv) fuse the respective reconstructions of the area of the global map using the one or more common visual features from the one or more common images.

Abstract: Inspection systems and methods of inspection are provided that include an image sensor to acquire inspection data characterizing a video of at least a portion of an asset being inspected, wherein the asset includes one or more components, and a computing system communicatively coupled to the image sensor. The computing system includes a user interface display, a processor and a memory storing instructions which, when executed by the processor causes the processor to perform operations including: receiving, from the image sensor, the inspection data, identifying, automatically, at least a first component of the one or more components, generating a graphical user interface (GUI) including the inspection data, generating a first dynamic identifier within the GUI, wherein the first dynamic identifier corresponds to the first component and is arranged to follow the first component as it moves within the GUI, and providing the GUI to the user interface display.

Abstract: Provided is a method for detecting a surface defect of a copper-clad laminate based on multi-scale gridding. The method includes: S1, collecting a photographed image of the copper-clad laminate through a line-scan camera; S2, segmenting out a copper-clad region and a non-copper-clad region, and rotating the copper-clad region upright; S3, dividing the copper-clad region of the image of the copper-clad laminate, and performing a pyramid operation on a divided original grid image; S4, determining averages of pixel values at all levels; S5, fusing the average at all levels to obtain a final background average; and S6, searching for a final defect according to a difference between a background and a foreground.

Abstract: Systems and methods are provided for. One embodiment is a system that includes an interface configured to receive an image of media printed on with print data, and memory configured to store defect reference data of nozzles belonging to printheads of a printer. The system also includes a print defect controller configured to detect a nozzle defect in the image based on a comparison of the image with the print data, and to determine a type of the nozzle defect based on a comparison of the nozzle defect with the defect reference data.

Abstract: A computer is caused to perform processing of: acquiring a plurality of medical images generated based on signals detected by a catheter inserted into a lumen organ while the catheter is moving a sensor along a longitudinal direction of the lumen organ, the lumen organ including a main trunk, a side branch branched from the main trunk, and a bifurcated portion of the main trunk and the side branch; and recognizing a main trunk cross-section, a side branch cross-section, and a bifurcated portion cross-section by inputting the acquired medical images into a learning model configured to recognize the main trunk cross-section, the side branch cross-section, and the bifurcated portion cross-section.

Abstract: The present disclosure relates to a method for generating a learning model, a learned model, a program, and a controller of a bladder endoscope in which the program or the model is recorded. The method including: acquiring, as teaching data, endoscope image data on a Hunner lesion in a bladder; and generating the learning model by using the teaching data such that a bladder endoscope image serves as an input and the position indication of a Hunner lesion in the bladder endoscope image serves as an output. The program causing a computer to perform acquiring endoscope image data on a Hunner lesion in a bladder, inputting a target bladder endoscope image to a learning model in which a bladder endoscope image serves as an input and position-indication data on a Hunner lesion in an endoscope image serves as an output, and outputting the position indication of the Hunner lesion.

Abstract: An unsupervised region-growing network (RGN) is trained to perform object segmentation on video data degraded by atmospheric turbulence. The method includes obtaining input data containing turbulence-degraded video, extracting a video frame sequence, and training the RGN using a selected algorithm incorporating a region-growing algorithm and a grouping loss function. A bidirectional optical flow sequence is computed for multiple reference frames within the video sequence. Pixel-level masks are generated for detected moving objects, followed by applying the region-growing algorithm to create coarse masks. A grouping loss function refines these masks to ensure consistency across consecutive frames. The trained RGN outputs refined masks as object segmentation data for the received video, improving segmentation accuracy in turbulent environments.

Abstract: A method of object tracking includes detecting a dynamic object in a scene, sampling key points of the dynamic object, extracting short term features of the key points, combining long-term key point features read from a key point features database into combined key point features, applying attention processing to hash the combined key point features, applying a plurality of transformer layers on top of attention processing to update the combined key point features using the interactions of the key points to form the long-term key point features and storing the long-term key point features in the key point features database, and predicting an updated 3D box for the dynamic object from the updated combined key point features, the updated 3D box including a tracklet representing motion of the dynamic object in the scene over time.

Abstract: A video of medical scan images associated with an anatomical structure may be arranged into multiple image pairs. The multiple image pairs may be provided to a machine learning (ML) model successively and the ML model may determine respective first sets of image features associated with the multiple image pairs and, for each of the multiple image pairs, refine the first set of image features associated with the image pair based on the respective first sets of image features associated with one or more other image pairs. A motion field associated with the image pair may be determined based at least on the refined first set of image features associated with the image pair and a task may be performed based on the respective motion fields.

Abstract: Methods and systems are described herein for inspection of a workpiece, such as a honeycomb body. The methods and systems include collecting a plurality of images of the honeycomb body, extracting measurement data from each of the plurality of images, converting the measurement data extracted from each image into a common frame of reference, and combining the measurement data together.