Abstract: An electrical connector includes: an insulating body defining a mating space; and a terminal module assembled to the insulating body and having a circuit board and plural mating terminals mounted on the circuit board, wherein: each of the mating terminals has a contact portion extending obliquely backward, a bending portion bent backward from a front end of the contact portion, a connecting portion extending rearward from a rear end of the bending portion, and a mounting portion vertically extending from a rear end of the connecting portion for mounting on the circuit board; and a front end of the circuit board extends forward into the mating space.

Abstract: A semiconductor device includes: M*1st conductors in a first layer of metallization (M*1st layer) and being aligned correspondingly along different corresponding ones of alpha tracks and representing corresponding inputs of a cell region in the semiconductor device; and M*2nd conductors in a second layer of metallization (M*2nd layer) aligned correspondingly along beta tracks, and the M*2nd conductors including at least one power grid (PG) segment and one or more of an output pin or a routing segment; and each of first and second ones of the input pins having a length sufficient to accommodate at most two access points; each of the access points of the first and second input pins being aligned to a corresponding different one of first to fourth beta tracks; and the PG segment being aligned with one of the first to fourth beta tracks.

Abstract: A multi-phase coupled inductor includes a first iron core, a second iron core, and a plurality of coil windings. The first iron core includes a first body and a plurality of first core posts. The plurality of first core posts are connected to the first body. The second iron core is opposite to the first iron core. The second iron core and the first body are spaced apart from each other by a gap. The plurality of coil windings wrap around the plurality of first core posts, respectively. Each of the coil windings has at least two coils.

Abstract: A semiconductor structure includes a first interposer; a second interposer laterally adjacent to the first interposer, where the second interposer is spaced apart from the first interposer; and a first die attached to a first side of the first interposer and attached to a first side of the second interposer, where the first side of the first interposer and the first side of the second interposer face the first die.

Abstract: A paper feeding device includes a pickup roller mechanism disposed at an entrance of a paper feeding path, and a separation mechanism disposed to a rear end of the pickup roller mechanism. The pickup roller mechanism includes a first pickup unit positioned at the entrance of the paper feeding path, a second pickup unit mounted to a rear end of the first pickup unit, and a third pickup unit disposed to a rear end of the second pickup unit. The second pickup unit includes a first driving gear. The third pickup unit includes a second driving gear, and a transmission belt looped around an outer periphery of the first driving gear and an outer periphery of the second driving gear.

Abstract: The present disclosure relates to an optical filter and a method of producing the same. In the producing method, a thermal evaporation deposition process of a sacrificial layer, and depositions process of a base layer and a dielectric stack layer are sequentially performed on a substrate having a trench with a specific width, so that the base layer and the dielectric stack layer extend outward to form a solidified structure with a specific length. Next, a fixed layer is affixed to the dielectric stack layer, and the sacrificial layer is removed using a solvent to remove the substrate. As such, structural strength and flatness of the produced optical filter are enhanced, and a volume thereof is reduced, such that the optical filter can be applied to automated processes of miniaturized elements.

Abstract: A device includes an interposer, which includes a substrate having a top surface. An interconnect structure is formed over the top surface of the substrate, wherein the interconnect structure includes at least one dielectric layer, and metal features in the at least one dielectric layer. A plurality of through-substrate vias (TSVs) is in the substrate and electrically coupled to the interconnect structure. A first die is over and bonded onto the interposer. A second die is bonded onto the interposer, wherein the second die is under the interconnect structure.

Abstract: A computer implemented method for certifying robustness of image classification in a neural network is provided. The method includes initializing a neural network model. The neural network model includes a problem space and a decision boundary. A processor receives a data set of images, image labels, and a perturbation schedule. Images are drawn from the data set in the problem space. A distance from the decision boundary is determined for the images in the problem space. A re-weighting value is applied to the images. A modified perturbation magnitude is applied to the images. A total loss function for the images in the problem space is determined using the re-weighting value. A confidence level of the classification of the images in the data set is evaluated for certifiable robustness.

Abstract: A method of making an integrated circuit includes steps of selecting a first cell and a second cell for an integrated circuit layout from a cell library in an electronic design automation (EDA) system, the first and second cells each having a cell active area, a cell gate electrode, at least one fin of a first set of fins, and a cell border region, each cell also having the active area at an exposed side, and placing the first exposed side against the second exposed side at a cell border. The method also includes operations of aligning at least one fin of the first set of fins with at least one fin of the second set of fins across a cell border.

Abstract: Provided are a database processing method and apparatus, and a computer readable storage medium. The database processing method comprises: after a lock wait is generated, writing lock wait related information into a lock wait log.

Abstract: A method of orthopedic treatment includes steps of: by using a computer aided design (CAD) tool based on profile data that is related to a to-be-treated part of a bone of a patient, obtaining a model of a preliminary instrument that substantially fits the to-be-treated part; by using the CAD tool, obtaining a model of a patient specific instrument (PSI) based on the model of the preliminary instrument; producing the PSI based on the model of the PSI, the PSI being adjustable; performing medical operation on the to-be-treated part, and then attaching the PSI to the to-be-treated part; after attaching the PSI to the to-be-treated part, adjusting the PSI such that the PSI is adapted to real conditions of the to-be-treated part.

Abstract: An adversarial robustness testing method, system, and computer program product include testing, via an accelerator, a robustness of a black-box system under different access settings, where the testing includes tearing down the robustness testing to a subtask of a predetermined size.

Abstract: A computer vision temporal action localization (TAL) computing tool and operations are provided. The TAL computing tool receives a coarse temporal bounding box, having a first start point and a first end point, for an action in the input video data, and a first set of logits, where each logit corresponds to a potential classification of the action in the input video data. The TAL computing tool executes a first engine on the coarse temporal bounding box to generate a second set of logits, and a second engine on the first set of logits to generate a refined temporal bounding box having a second start point and a second end point. The TAL computing tool performs the computer vision temporal action localization operation based on the second set of logits and the refined temporal bounding box to specify a temporal segment of the input video data corresponding to an action represented in the input video data, and a corresponding classification of the action represented in the temporal segment.

Abstract: A processor training a reinforcement learning model can include receiving a first dataset representing an observable state in reinforcement learning to train a machine to perform an action. The processor receives a second dataset. Using the second dataset, the processor trains a machine learning classifier to make a prediction about an entity related to the action. The processor extracts an embedding from the trained machine learning classifier, and augments the observable state with the embedding to create an augmented state. Based on the augmented state, the processor trains a reinforcement learning model to learn a policy for performing the action, the policy including a mapping from state space to action space.

Abstract: A method, a device, and a computer readable storage medium for improving the physical performance of the user are provided. The method includes: determining a reference value of the physical performance of the user in a first reference time point of a physical activity duration and a final target value of the physical performance corresponding to a final target time; determining a plurality of middle target values of the physical performance arranged in increasing order in the physical activity duration based on the reference value, the final target value, and the final target time, wherein the plurality of middle target values of the physical performance respectively corresponds to a plurality middle target times; and providing a physical activity suggestion to gradually meet the target values of the physical performance.

Abstract: Techniques that facilitate graph similarity analytics are provided. In one example, a system includes an information component and a similarity component. The information component generates a first information index indicative of a first entropy measure for a first graph-structured dataset associated with a machine learning system. The information component also generates a second information index indicative of a second entropy measure for a second graph-structured dataset associated with the machine learning system. The similarity component determines similarity between the first graph-structured dataset and the second graph-structured dataset based on a graph similarity computation associated with the first information index and the second information index.

Abstract: Generate, for each of the words of a common vocabulary of first and second text corpora, a first word embedding vector in the first text corpus and a second word embedding vector in the second text corpus. Generate, for each word in a random sample of non-landmark words, an artificially shifted word embedding vector by modifying the first word embedding vector for that word. Train a machine learning classifier to predict whether an artificial shift has been injected for a given word, based on the artificially shifted word embedding vector and the second word embedding vector for the given word. Predict semantic shifts for at least a plurality of the words of the common vocabulary by providing the first word embedding vectors and the second word embedding vectors for at least the plurality of the words of the common vocabulary as input to the trained machine learning classifier.

Abstract: A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include training a neural network and predicting structural feature sets with the neural network. The operations may include producing predicted structures with the neural network using the structural feature sets, converting the predicted structures into predicted graphs with predicted edges, and comparing predicted graphs to training graphs and predicted edges to training edges to obtain a comparison. The operations may include training a model with the comparison, constructing a graph with the neural network using a node feature set, and reducing missing edges in the graph with the model.

Abstract: An output layer is removed from a pre-trained neural network model and a neural capacitance probe unit with multiple layers is incorporated on top of one or more bottom layers of the pre-trained neural network model. The neural capacitance probe unit is randomly initialized and a modified neural network model is trained by fine-tuning the one or more bottom layers on a target dataset for a maximum number of epochs, the modified neural network model comprising the neural capacitance probe unit incorporated with multiple layers on top of the one or more bottom layers of the pre-trained neural network model. An adjacency matrix is obtained from the initialized neural capacitance probe unit and a neural capacitance metric is computed using the adjacency matrix. An active model is selected using the neural capacitance metric and a machine learning system is configured using the active model.

Abstract: The present invention discloses a method for determining a maximum value of a heart rate data of a user performing a physical activity. Acquire first heart rate data in a first duration of the physical activity performed by the user. Acquire motion data in the first duration of the physical activity performed by the user. Calculate second heart rate data based on the motion data in the first duration of the physical activity performed by the user by a mathematical model and estimate the maximum value of the heart rate data of the user based on a comparison between the first first heart rate data and the second heart rate data.