Abstract: A layout dependent statistical leakage analyzing method includes providing pre-silicon data, acquiring an i-th pre-silicon leakage value of an i-th cell group according to the pre-silicon data, acquiring an (i,j)-th abutment possibility of a j-th cell group abutted on the i-th cell group according to physical information extracted from the pre-silicon data, acquiring an i-th scaling factor for a Silicon-to-SPICE (S2S) of the i-th cell group according to the pre-silicon data, acquiring an (i,j)-th layout dependent effect (LDE) factor between the i-th cell group and the j-th cell group according to the pre-silicon data and post-silicon data, and generating an estimated silicon leakage of a block according to N pre-silicon leakage values, N2 abutment possibilities, N scaling factors, and N2 LDE factors.

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: A method, system, and computer program product for hyper-parameter determination. In a method, a network architecture of a learning model may be determined, and the learning model may be configured for performing a computing task based on machine learning. A metric value record associated with a group of hyper-parameters may be obtained during hyper-parameter determination for the learning model. An estimation of a metric value may be obtained based on the network architecture, and the metric value record and an association relationship representing an association between network architectures and metric values for the network architectures. The group of hyper-parameters may be selected in response to the estimation of the metric value meeting a predefined criterion. With these embodiments, a group of hyper-parameters may be selected, and further the learning model may be trained based on the selected group of hyper-parameters.

Abstract: This application provides a call processing method and an apparatus, which are used in an electronic device. The electronic device that is called or initiates a call camps on a first cell of an LTE network after being redirected form a 5G network to the LTE network. The first cell belongs to a tracking area. The method includes: after tracking area update fails, searching, in response to identifying an abnormal signaling flow, for an available cell that does not belong to a tracking area, and initiating the tracking area update in the available cell to attempt to establish a call. Therefore, it is attempted to establish a call in the available cell that does not belong to the tracking area, which can improve a possibility of successfully establishing a call after the redirection from the 5G network to the LTE network.

Abstract: A method for controlling a robotic arm includes: acquiring a first depth image and a first color image of a target object; predicting first predicted values and second predicted values of respective candidate actions of the robotic arm based on the first depth image and the first color image, in which the first predicted value represents a probability of the robotic arm separating the target object from an adjacent object by executing the corresponding candidate action; and the second predicted value represents a probability of the robotic arm successfully grabbing the target object by executing the corresponding candidate action; determining a target action based on the first predicted values and the second predicted values of the candidate actions; and controlling the robotic arm to execute the target action.

Abstract: Aspects of the invention include systems and methods to obtain meta features of a dataset for training in a deep learning application. A method includes selecting an initial search space that defines a type of deep learning architecture representation that specifies hyperparameters for two or more neural network architectures. The method also includes applying a search strategy to the initial search space. One of the two or more neural network architectures are selected based on a result of an evaluation according to the search strategy. A new search space is generated with new hyperparameters using an evolutionary algorithm and a mutation type that defines one or more changes in the hyperparameters specified by the initial search space, and, based on the mutation type, the new hyperparameters are applied to the one of the two or more neural networks or the search strategy is applied to the new search space.

Abstract: An object is to provide a moving route determination device that makes it possible to accurately determine a user's moving route. A server device 100 includes a geofence evaluation unit 105 configured to store a scoring table for determining a moving route of a user terminal 200, a check-in log acquisition unit 103 configured to acquire at least one of a check-in log which is a history of position management information, such as check-in information, settlement information, or search information, indicating that the user terminal 200 is located in a predetermined range, a settlement log, and a search log as a position management log, and a scoring determination unit 106 configured to determine a moving route of the user terminal 200 on the basis of the position management log and determination information stored in the geofence evaluation unit 105 in a case where a moving means is not able to be determined from at least one of the check-in log, the settlement log, and the search log.

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: 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: 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 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: 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: 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.