Abstract: A press-fit wire disposed in a magnetic device is provided. The press-fit wire includes a first wire and a second wire. The second wire is attached to the first wire. The first wire and the second wire are arranged along a first direction and are parallel circuits. The first wire and/or the second wire is a multi-strand wire.

Abstract: In an example embodiment, machine learning is used to train a machine-learned model that projects each entity, title pair into a single number, called a seniority score, to represent the career progression needed for that position. For example, company A’s “software engineer” and company B’s “senior software engineer” can be represented as two separate numbers, one being p (company A, software engineer) and the other being p (company B, senior software engineer) on the same axis. This allows a comparison to be made about the absolute levels of each title despite their potential different meanings at different entities.

Abstract: An electric energy transmission aluminum part, an aluminum connector and a copper-aluminum joint. The electric energy transmission aluminum part includes an aluminum body (1) internally provided with a conical insertion hole (11) which penetrates through front and rear ends thereof. The conical insertion hole is provided with a maximum diameter end and a minimum diameter end. Both the aluminum connector and the copper-aluminum joint include the electric energy transmission aluminum part. The electric energy transmission aluminum part, the aluminum connector and the copper-aluminum joint not only avoid an insulation layer (3) from being crimped into a lead portion and increasing a resistance of the lead portion, but also prevent an indentation from being formed on the surface of the insulation layer (3) and causing breakdown, and further reduce an interference with a mating end environment, thus achieving a wide application range.

Abstract: Methods for visualizing treatment outcomes are provided. In some embodiments, a method includes obtaining a first 2D image of a patient's teeth from a mobile device, and obtaining an arch model representing an outcome for the patient's teeth after orthodontic treatment. The method can include matching the patient's teeth in the first 2D image to corresponding teeth in the arch model, using a parametric model. The method can also include determining a target position for the patient's teeth, based on a position of the matched corresponding teeth in the arch model. The method can further include generating a second 2D image depicting the patient's teeth in the target position.

Abstract: The present disclosure is related to a display panel. The display panel may include a display substrate, a plurality of light emitting units on the display substrate, and a first organic layer covering the plurality of light emitting units. A surface of the first organic layer opposite from the light emitting units may include a plurality of raised portions and a plurality of recessed portions. The first organic layer may be directly in contact with the plurality of light emitting units.

Abstract: The present application discloses a method for acquiring a network resource, device, and system, relating to the field of communications technologies, and enabling logical use of a communication resource while improving the success ratio of communication resource requests. In the present application, a terminal device may request a communication resource from a second network device according to action indication information and/or statistical information sent by a first network device. The terminal device chooses, according to an actual status, to lower a resource request level or to implement other measures matching an actual current status of a network, and then requests a communication resource from the second network device, thereby making logical use of a communication resource while improving the success ratio of communication resource requests.

Abstract: A method, computer system and computer program product for model compression service. The method comprises determining an initial deep neural network (DNN) and an associated compression algorithm available in a compression engine, a type of target hardware and a performance requirement of target hardware. The method also comprises emulating a plurality of different compressed models of the initial DNN on target hardware of the type to obtain corresponding runtime performance data, wherein the different compressed models are defined with different configuration data. The method further comprises obtaining a runtime performance estimator of the target hardware by regression with the different configuration data and the corresponding runtime performance data. Lastly, the method comprises applying the runtime performance estimator to the compression algorithm by the compression engine to generate a compressed DNN of the initial DNN complying with the performance requirement of the type of target hardware.

Abstract: Methods for simulating orthodontic treatment are provided. In some embodiments, a method includes capturing a first representation, the first representation including a depiction of a patient's teeth. The method can include building a parametric model of the patient's teeth based on the first representation, using one or more case-specific parameters for one or more shapes associated with at least one of the patient's teeth. The method can also include simulating an outcome of a dental treatment plan for the patient's teeth to produce a simulated outcome of the dental treatment plan. The method can further include modifying the parametric model to provide a modified model representing the simulated outcome of the dental treatment plan.

Abstract: Disclosed are a rock similar material satisfying a water-induced strength degradation characteristic and a preparation method and use thereof. The rock similar material satisfying the water-induced strength degradation characteristic includes an aggregate, a cementing material, and an additive, where the aggregate includes quartz sand, barite powder, and bentonite, and the cementing material includes cement and gypsum.

Abstract: A method, an apparatus, a system, a device, and a storage medium for implementing terminal verification are provided. In this method, a transmission feature of a terminal is reconstructed to verify the terminal. For example, if a difference between the transmission feature obtained through reconstruction and the transmission feature of the terminal is relatively large, it indicates that the transmission feature of the terminal is abnormal, and the terminal is an abnormal terminal. In this case, it is determined that the verification of the terminal fails.

Abstract: A flexible display panel includes: a stretchable base film; an adhesive layer disposed on a surface of the stretchable base film; a flexible substrate including a plurality of display units and a plurality of connection units, every two adjacent display units being connected by at least one connection unit; and a plurality of first release agent films disposed between the plurality of connection units and the adhesive layer, the plurality of first release agent films and the plurality of connection units being in one-to-one correspondence. An orthographic projection of each connection unit on the stretchable base film is within an orthographic projection of a corresponding first release agent film on the stretchable base film. The plurality of display units are bonded to the adhesive layer.

Abstract: The present disclosure relates to a circuit for performing a hash algorithm, computing chip, data processing device and method. A circuit includes: operation stages in a pipeline structure each including 0th to 15th expansion registers; expansion data operation logic modules each disposed between two adjacent operation stages including a first operation stage and its subsequent second operation stage, and including a first sub-module configured to compute data in a 0th expansion register of the second operation stage based on data in a 1st expansion register of the first operation stage and a second sub-module configured to compute data in a 15th expansion register of the second operation stage based on data in a 0th expansion register of the first operation stage: data in an (i?1)th expansion register of the second operation stage is data in an ith expansion register of the first operation stage.

Abstract: An intelligent wearable product is provided which includes a housing, a power supply circuit, a functional component, and an electrochromic film. A transparent portion is disposed on the housing, and the functional component and the power supply circuit are located inside the housing. The functional component is configured to collect an external parameter by using the transparent portion. The electrochromic film is disposed on an inner surface of the housing and shields the transparent portion. The power supply circuit is configured to supply power to the electrochromic film and the functional component, so that the electrochromic film is transparent when the functional component works. When the intelligent wearable product is not worn on a human body, the electrochromic film and the housing jointly present an integrated visual effect. According to the application, quality, competitiveness and appearance experience of the intelligent wearable product are improved.

Abstract: Methods and systems for training a machine learning model include training a machine learning model using training data. A status of the machine learning model's training is determined based on an accuracy curve of the machine learning model over the course of the training. Parameters of the training are adjusted based on the status. Training of the machine learning model is completed using the adjusted parameters.

Abstract: Using a training portion of a dataset, a set of component parameters comprising parameters of a component of an object detection model are trained. Using the trained set of component parameters, a set of backbone component weights comprising weights of component types in a backbone portion of the object detection model are trained. Using the trained set of component parameters, a set of backbone link weights comprising weights of links within the backbone portion are trained. Using the trained set of component parameters, a set of head component weights comprising weights of component types in a head portion of the object detection model are trained. Using the trained sets of component parameters, backbone component weights, backbone link weights, and head component weights, a trained object detection model is configured and trained to perform object detection.

Abstract: A model parallel training technique for neural architecture search including the following operations: (i) receiving a plurality of ML (machine learning) models that can be substantially interchangeably applied to a computing task; (ii) for each given ML model of the plurality of ML models: (a) determining how the given ML model should be split for model parallel processing operations, and (b) computing a model parallelism score (MPS) for the given ML model, with the MPS being based on an assumption that the split for the given ML model will be used at runtime; and (iii) selecting a selected ML model based, at least in part, on the MPS scores of the ML models of the plurality of ML models.

Abstract: A one-shot neural architecture search method referred to as MergeNAS by merging different types of convolutions into a single operation. This mergence approach reduces the search cost to roughly half a GPU-day as well as alleviates the over-fitting problem caused by a traditional differentiable architecture search (DARTS) approach by reducing the number of redundant parameters.

Abstract: Multi-level objectives improve efficiency of multi-objective automated machine learning. A hyperband framework is established with a kernel density estimator to shrink the search space based on evaluation of lower-level objectives. A Gaussian prior assumption directly shrinks the search space to find a main objective.

Abstract: Methods, computer program products, and/or systems are provided that perform the following operations: obtaining a target neural network structure and constraints for a target neural network; generating a meta learning network having an associated quantization function based, at least in part, on the target neural network structure; training the meta learning network based, at least in part, on providing a hybrid quantization vector as input to the meta learning network and providing a training dataset to the target neural network; obtaining a plurality of hybrid quantization vectors; determining a new hybrid quantization vector from the plurality of hybrid quantization vectors; and retraining the trained meta learning network based, at least in part, on providing the new hybrid quantization vector as input to the trained meta learning network.