Abstract: A resin composition is provided, which includes a first polymer and a second polymer. The first polymer is formed by a reaction of an epoxy resin modified with a first elastic molecular segment and an epoxy resin curing agent. The second polymer is formed by a polymerization of an acrylate modified with a second elastic molecular segment.

Abstract: A method for fine tuning a pre-trained machine learning model includes receiving, from a pre-trained machine learning model, at least one image embedding corresponding to first training data used to train the pre-trained machine learning model. The method also includes receiving, from the pre-trained machine learning model, at least one text embedding corresponding to the at least one image embedding. The method also includes generating at least one perturbation vector that includes the at least one image embedding, the at least one text embedding, a perturbation magnitude value, and a perturbation direction value. The method also includes generating second training data based on the at least one perturbation vector, and fine tuning the pre-trained machine learning model using the second training data.

Abstract: A fragrance lanyard includes a string, a pair of length adjusters, a clasp, a fragrance container, and an aromatic element. The string has an overlapping part, and a hanging ring is formed in a non-overlapping area, each end of the string is wrapped with a string cap, each length adjuster is provided with two lanyard holes such that the cross-section of the length adjuster is designed in an 8-shape for the string to sequentially pass through the pair of length adjusters to adjust the length of the string, the clasp is arranged at the position of the hanging ring, the fragrance container is fixed to any one of the string cap, the length adjuster, or the clasp, and the fragrance container has a containing space provided for installing the aromatic element.

Abstract: A method that includes obtaining, from one or more stations, embedding vectors that embed features of the stations, obtaining measurement vectors and associated measurement names, generating a text array of the measurement names utilizing a language model, concatenating the text array and the measurement vector at one or more cross-attention modules configured to encode one or more measurement arrays to one or more latent embedding vectors, generating one or more latent embedding vectors associated with the measurement vector and corresponding measurement names via the cross-attention module and a fixed-size station embedding vector, outputting the latent embeddings; generating a query vector; generating key vectors value vectors utilizing a latent embedding vector; decoding the latent vectors utilizing the key vector and value vector; utilizing the cross attention modules and query vectors, decoding the latent embedding vectors; and output a predication.

Abstract: A task scheduling method includes retrieving at least first data generated by monitoring a plurality of processors and second data generated by monitoring a memory subsystem, generating task type data and processor type data according to at least the first data and the second data, dynamically estimating current capacities and maximum capacities of the plurality of processors according to the task type data and the processor type data, generating prediction data according to the task type data, the processor type data, and the current capacities and the maximum capacities of the plurality of processors, scheduling a task according to the task type data, the processor type data, the prediction data, and the current capacities and the maximum capacities of the plurality of processors.

Abstract: A data processing method includes: acquiring an initial sample angiography image set; performing data expansion processing on a first sample angiography image based on physical characteristics of blood vessels at a target site to obtain a processed sample angiography image, performing label conversion processing on a first label based on the physical characteristics of the blood vessels at the target site to obtain a second label of the processed sample angiography image, and adding the processed sample angiography image and the second label to a target sample angiography image set; and training an angiography image recognition model using the initial sample angiography image set and the target sample angiography image set to obtain a trained angiography image recognition model. The performance of the trained angiography image recognition model is improved by increasing the number of samples.

Abstract: A production scheduling method including: reading work order data and production line status data of each of multiple production lines; identifying a target to-be-scheduled production line according to the production line status data; selecting one or more to-be-scheduled work orders; calculating a production cycle indicator of each of the to-be-scheduled work orders according to a production cycle days, a scheduling date, and a delivery date of the each of the to-be-scheduled work orders; sorting the one or more to-be-scheduled work orders to schedule a first-order target to-be-scheduled work order to the target to-be-scheduled production line for production according to the production cycle indicator, a production process direction, and grade transition cost.

Abstract: A magnetic positioning structure for an expanded device includes a magnetic positioning plate and at least one magnetic conductive element. The magnetic positioning plate with a fixing surface and a mounting surface fixed on a plane includes at least one first magnetic element and an anti-slip sleeve, the first magnetic element is enclosed in the anti-slip sleeve, and the magnetic conductive element is arranged in a housing of the expanded device. In this way, the magnetic conductive element can quickly and temporarily fix the expanded device to the mounting surface of the magnetic positioning plate, and the anti-slip sleeve can prevent displacement of the expanded device. The magnetic positioning structure is applicable to expanded devices such as hubs, notebook docking stations, power banks, etc. to prevent random shaking, keep the operating environment neat without causing wire entanglement or affecting connection angles, thus greatly improving the convenience of use.

Abstract: A golden path search method for manufacturing process provides a two-phase process to search for a golden path. A first phase step of the two-phase process includes preparing a search model based on a search algorithm, and selecting a plurality of key process stages of a plurality of process stages by feeding sets of final inspection values and the production paths of the workpieces into the searching model, and then generating a plurality of key paths according to the key process stages. A second phase step of the two-phase process includes building a plurality of prediction models of the key paths according to the production paths and the sets of final inspection values, and predicting a plurality of yield rates corresponding to the key paths according to the prediction models, and then searching for the golden path of the key paths according to the yield rates.

Abstract: Provided are systems and methods that invert a trained NeRF model, which stores the structure of a scene or object, to estimate the 6D pose from an image taken with a novel view. 6D pose estimation has a wide range of applications, including visual localization and object pose estimation for robot manipulation.

Abstract: A system includes a controller configured to generate an original patch utilizing Bayesian optimization, output the original patch at a display at a scene and determine if the original patch does not meet a success criteria of the machine-learning model, in response to the original patch not meeting the success criteria, upscaling the patch, decompose the upscaled patch into o components, for each of the components, utilize Bayesian optimization to update one of the components of the upscaled patch and freezing the other components to generate an updated patch, in response to the updated patch meeting the success criteria, output the updated upscaled patch, and in response to the updated upscaled patch not meeting the success criteria, iteratively update the unfrozen components and determine if the success criteria is met and if not met, unfreeze the frozen components and iteratively update the unfrozen components until the success criteria is met.

Abstract: A computer-implemented method for attacking a machine-learning model, comprising establishing a connection between a processor that is utilizing the machine-learning model, wherein the processor is in communication with a sensor located in a physical scene, outputting on a display device in the physical scene, an adversarial pattern, wherein the display device including the adversarial pattern is located in a sensor range of the sensor, obtaining, from the machine-learning model, a classification associated with the physical scene that includes the adversarial pattern, determining if a target classification has been met with a classification output from the machine-learning model, and in response to the target classification not being met, output additional adversarial patterns at the display device and repeat steps until the target classification has been met.

Abstract: A system includes a machine learning network input interface configured to receive input data from a sensor, one or more processors collectively programmed to receive an input data from the sensor, wherein the input data is indicative of image of a scene that includes a perturbation from a black-box attack with a physical perturbation at the scene, display an adversarial pattern at the scene, determine an objective function utilizing at least the adversarial pattern and a target classification of the machine-learning network, randomly select a plurality of data points associated with the adversarial pattern and the objective function, wherein the data points are associated with a number of queries of the objective function, obtain a machine-learning model output utilizing the data points displayed in the scene, and in response to meeting a criteria associated with the adversarial pattern and model output, identify a successful attack pattern.

Abstract: Systems, methods, and computer programs products are described for optimizing circuit synthesis for implementation on an integrated circuit. A register transfer level code description of logic behavior of a circuit. The register transfer level code description is converted into structurally defined circuit designs for multiple types of components and feature size technologies. A floor plan of each structurally defined circuit design is generated. A physically simulated circuit is created for each floor plan. A range of operating conditions is swept over to analyze power, performance, and area of each physically simulated circuit.

Abstract: A circuit board module capable of suppressing common mode noise includes a circuit board, and a first circuit and a second circuit that are disposed on the circuit board. The circuit board includes an insulating substrate, and a plurality of metal layers. The metal layers are disposed in the insulating substrate, are spaced apart from each other, and include a first metal layer and a second metal layer. The metal layers are formed to include a plurality of conducting lines. The conducting lines cooperatively form a common mode choke structure in which the conducting lines extend in a spiral pattern, so that each of the conducting lines has a spiral winding segment in the spiral pattern. In the spiral pattern, a mutual inductance coupling coefficient between the spiral winding segments of two adjacent ones of the conducting lines is greater than 0.3.

Abstract: A current driver having an input channel and an output channel includes a current source, a current mirror, an output enable switch and a control circuit. The current source is deployed in the input channel. The current mirror is deployed between the input channel and the output channel and coupled to the current source. The output enable switch is deployed in the output channel and coupled to the current mirror. The control circuit is coupled between the input channel and the output channel, to form a feedback loop through the input channel.

Abstract: An electronic device includes a display and an input device. The input device includes a keyboard set, a touchpad, and a touch frame bar. The touch frame bar includes a cover plate, a touch circuit board, a haptic feedback module, and a light source module. The cover plate has symbolic patterns. The touch circuit board is opaque and disposed under the cover plate. The haptic feedback module is disposed on one side of the touch circuit board. The light source module is disposed between the cover plate and the touch circuit board. The input device has an outer side edge extending in a width direction. The outer side edge and the touch frame bar have a first width and a second width respectively in the width direction. A ratio of the second width to the first width is less than 20%.

Abstract: A cover module of a LIDAR system includes a first glass, a heating unit, and a second glass. The heating unit is disposed on an outer side of the first glass. The second glass is disposed on an outer side of the heating unit, wherein a deviation between a transmittance of a major wavelength range of the cover module and a transmittance of a secondary wavelength range of the cover module is less than 2%. A wavelength of the secondary wavelength range is calculated by the following formula: (wavelength of the major wavelength range)+[?T+(Tfog?T0)]×?, in which the T0 is an initial temperature of the light source unit, Tfog is a temperature of dew point, ?T is a defogging temperature deviation, and ? is a wavelength shift with temperature coefficient of the light source unit.

Abstract: A computer-implemented system and method include performing neural style transfer augmentations using at least a content image, a first style image, and a second style image. A first augmented image is generated based at least on content of the content image and a first style of the first style image. A second augmented image is generated based at least on the content of the content image and a second style of the second style image. The machine learning system is trained with training data that includes at least the content image, the first augmented image, and the second augmented image. A loss output is computed for the machine learning system. The loss output includes at least a consistency loss that accounts for a predicted label provided by the machine learning system with respect to each of the content image, the first augmented image, and the second augmented image.

Abstract: A substrate for a semiconductor package includes an array of bonding pads on a first surface of the substrate, and a plurality of raised structures adjacent to at least some of the bonding pads on the first surface of the substrate. The raised structures may be configured to control the height of solder balls contacting the array of bonding pads when the package substrate is mounted onto a support substrate. The raised structures may compensate for a deformation of the package substrate so that the co-planarity of the solder balls may be improved, thereby providing an improved solder connection between the package substrate and the support substrate.