Abstract: A computing system including a plurality of processing devices configured to execute a Mixture-of-Experts (MoE) layer included in an MoE model. The processing devices are configured to, in each of a plurality of iterations, at each of the processing devices, receive a respective plurality of input tokens. Executing the MoE layer further includes, at each of the processing devices, selecting one or more destination expert sub-models associated with the input tokens. Respective numbers k of expert sub-models selected differ across the iterations. At each of the processing devices, executing the MoE layer further includes conveying the input tokens to the one or more destination expert sub-models. Executing the MoE layer further includes generating one or more respective expert sub-model outputs at the one or more destination expert sub-models. Executing the MoE layer further includes generating and outputting an MoE layer output based on the one or more expert sub-model outputs.

Abstract: One or more computing devices, systems, and/or methods are provided. In an example, a first performance metric score may be determined based upon first content item text. A plurality of similarity scores associated with a plurality of sets of content item text may be determined. One or more sets of content item text may be selected from among the plurality of sets of content item text based upon the plurality of similarity scores and a plurality of performance metric scores associated with the plurality of sets of content item text. The plurality of performance metric scores may comprise one or more performance metric scores associated with the one or more sets of content item text. The one or more performance metric scores may be higher than the first performance metric score. One or more representations of the one or more sets of content item text may be displayed.

Abstract: A method for preparing a rare earth anisotropic bonded magnetic powder, comprises the following steps: (1) preparing raw powder with RTBH as the main component, wherein, R is Nd or Pr/Nd, and T is a transition metal containing Fe; (2) adding La hydride or Ce hydride and copper powder to the raw powder to form a mixture; (3) subjecting the mixture to atmosphere diffusion heat treatment to give the rare earth anisotropic bonded magnetic powder.

Abstract: A computing system is provided, including a plurality of processing devices configured to execute a Mixture-of-Experts (MoE) layer included in an MoE model. The MoE layer includes a plurality of expert sub-models that each have a respective plurality of parameter values. The MoE layer is configured to be switchable between a data parallel mode and an expert-data-model parallel mode without conveying the respective parameter values of the expert sub-models among the plurality of processing devices.

Abstract: A computing system including a plurality of processing devices configured to execute a Mixture-of-Experts (MoE) layer included in an MoE model. The processing devices are configured to execute the MoE layer at least in part by, during a first collective communication phase between the processing devices, splitting each of a plurality of first input tensors along a first dimension to obtain first output tensors. Executing the MoE layer further includes processing the first output tensors at a respective a plurality of expert sub-models to obtain a plurality of second input tensors. Executing the MoE layer further includes, during a second collective communication phase between the processing devices, receiving the second input tensors from the expert sub-models and concatenating the second input tensors along the first dimension to obtain second output tensors. Executing the MoE layer further includes outputting the second output tensors as output of the MoE layer.

Abstract: The invention discloses a composite rare earth anisotropic bonded magnet and a preparation method thereof. The composite rare earth anisotropic bonded magnet comprises a Nd—Fe—B magnetic powder, a Sm—Fe—N magnetic powder, a binder and an inorganic nano-dispersant. The preparation method comprises steps of preparing a Nd—Fe—B magnetic powder by a HDDR method, preparing a Sm—Fe—N magnetic powder by a powder metallurgy method, mixing the Nd—Fe—B magnetic powder, the Sm—Fe—N magnetic powder, the binder and the inorganic nano-dispersant at a specific ratio to finally obtain the composite rare earth anisotropic bonded magnet. The invention, by adding an inorganic nano-dispersant, enables the full dispersion of the fine Sm—Fe—N powder during the mixing process of the binder, the Nd—Fe—B magnetic powder and the Sm—Fe—N powder, and thus makes the fine Sm—Fe—N powder and the binder evenly coated on the surface of the anisotropic Nd—Fe—B magnetic powder.

Abstract: An antenna is provided in this application. The antenna includes multiple radiating units, at least two adjacent radiating units are correspondingly provided with a respective decoupling structure, the decoupling structure includes two microstrip line units, one microstrip line unit of the two microstrip line units includes at least one microstrip line, and the two microstrip line units are located on two opposite sides of two radiating units in a direction perpendicular to an arrangement direction of two adjacent radiating units. According to the antenna provided in the embodiments of the present disclosure, the decoupling structure composed of the microstrip line are disposed on two sides of the at least two adjacent radiating units, so that an indirect coupling field is formed by the decoupling structure, and the indirect coupling field counteracts a direct coupling field between adjacent radiating units.

Abstract: An image segmentation method is provided for a computing device. The method includes obtaining a general tumor image, performing tumor localization on the tumor image to obtain a candidate image indicating a position of a tumor region in the general tumor image, inputting the candidate image to a cascaded segmentation network constructed based on a machine learning model, and performing image segmentation on the general tumor region in the candidate image using a first-level segmentation network and a second-level segmentation network in the cascaded segmentation network to obtain a segmented image.

Abstract: A computing system including a plurality of processing devices configured to execute a Mixture-of-Experts (MoE) layer. The processing devices are configured to execute the MoE layer at least in part by receiving an input tensor including input tokens. Executing the MoE layer further includes computing a gating function output vector based on the input tensor and computing a sparse encoding of the input tensor and the gating function output vector. The sparse encoding indicates one or more destination expert sub-models. Executing the MoE layer further includes dispatching the input tensor for processing at the one or more destination expert sub-models, and further includes computing an expert output tensor. Executing the MoE layer further includes computing an MoE layer output at least in part by computing a sparse decoding of the expert output tensor. Executing the MoE layer further includes conveying the MoE layer output to an additional computing process.

Abstract: The disclosure relates to methods, devices, systems, and computer storage medium for performing medical image segmentation. The method includes: The method includes: obtaining, by a device, a first medical image and a second medical image with a labeled region; performing, by the device, feature extraction on the first medical image and the second medical image respectively, to obtain first feature information of the first medical image and second feature information of the second medical image; obtaining, by the device, optical flow motion information from the second medical image to the first medical image according to the first feature information and the second feature information; and segmenting, by the device, the first medical image according to the optical flow motion information and the labeled region, to obtain a segmentation result of the first medical image.

Abstract: A computer device obtains a plurality of medical images. The device generates a texture image based on image data of a region of interest in the medical images. The device extracts a local feature from the texture image using a first network model. The device extracts a global feature from the medical images using a second network model. The device fuses the extracted local feature and the extracted global feature to form a fused feature. The device performs image classification based on the fused feature.

Abstract: One or more computing devices, systems, and/or methods are provided. In an example, a first performance metric score may be determined based upon first content item text. A plurality of similarity scores associated with a plurality of sets of content item text may be determined. One or more sets of content item text may be selected from among the plurality of sets of content item text based upon the plurality of similarity scores and a plurality of performance metric scores associated with the plurality of sets of content item text. The plurality of performance metric scores may comprise one or more performance metric scores associated with the one or more sets of content item text. The one or more performance metric scores may be higher than the first performance metric score. One or more representations of the one or more sets of content item text may be displayed.

Abstract: The pure mechanical well deviation wireless measure-while-drilling and mud pulse generation device includes an outer cylinder and an inner cylinder coaxially arranged inside the outer cylinder; an inner flow channel is formed inside the inner cylinder, and an outer flow channel is formed between the inner cylinder and the outer cylinder; a flow control valve and a hydraulic turbine are arranged in the inner flow channel, and the flow control valve is located at an upstream section of the hydraulic turbine. The device further includes a signal generation base and a rotary stopper. An overflow hole is formed on the signal generation base and arranged in the annular outer flow channel, and the rotary stopper can periodically shield the overflow hole. The inclinometer includes the pure mechanical well deviation wireless measure-while-drilling and mud pulse generation device, an inclinometer outer cylinder and an eccentric rotary column.

Abstract: The present teaching relates to method, system, medium, and implementations for integrating heterogeneous experts. A nonlinear integration model is characterized by a plurality of parameters and is configured for combining individual expert predictions to generate an integrated expert prediction Values of the plurality of parameters are learned based on training data as well as outputs from the respective plurality of experts generated based on the training data. When provided an input, individual expert predictions from different experts generated based on a given input are combined, using the nonlinear integration model, to derive an integrated expert prediction in response to the given input.

Abstract: The present teaching relates to method, system, medium, and implementations for integrated targeting. An expert hierarchy is constructed with an initial expert layer with multiple initial experts and one or more augmented expert layers with each augmented expert therein augments, via machine learning, experts at any lower layer of the expert hierarchy. A nonlinear integration model is obtained, via machine learning, for combining expert predictions from experts in the expert hierarch based on an input to generate an integrated expert prediction in response to the input.

Abstract: The present teaching relates to method, system, medium, and implementations for predicting user segment. An expert hierarchy is created with an initial expert layer with multiple initial experts and at least one augmented expert layer. Each augmented expert layer has one or more augmented experts that are derived via machine training to augment at least the initial experts. When an input is received by the expert hierarchy, each of the experts, including initial and augmented, generates an expert prediction based on the input.

Abstract: The pure mechanical well deviation wireless measure-while-drilling and mud pulse generation device includes an outer cylinder and an inner cylinder coaxially arranged inside the outer cylinder; an inner flow channel is formed inside the inner cylinder, and an outer flow channel is formed between the inner cylinder and the outer cylinder; a flow control valve and a hydraulic turbine are arranged in the inner flow channel, and the flow control valve is located at an upstream section of the hydraulic turbine. The device further includes a signal generation base and a rotary stopper. An overflow hole is formed on the signal generation base and arranged in the annular outer flow channel, and the rotary stopper can periodically shield the overflow hole. The inclinometer includes the pure mechanical well deviation wireless measure-while-drilling and mud pulse generation device, an inclinometer outer cylinder and an eccentric rotary column.

Abstract: The disclosure discloses an anti-lesion storage method and storage system for cells, tissues and organs, and relates to the technical field of storage of cells, tissues or organs. The method includes: adjusting a partial pressure of carbon dioxide in a suspension medium containing cells, tissues or organs to be stored to 30-50 mmHg. The inventors found that the regulation of the partial pressure of carbon dioxide in the suspension medium is advantageous for improving anti-lesions of cells, tissues or organs. In particular, the regulation of the partial pressure of carbon dioxide in the suspension medium can prolong the in vivo survival of platelets after transfusion of a platelet concentrate and protect the hemostatic function of platelets whiling effectively alleviating PSLs.

Abstract: The disclosed systems and methods provide a framework for a proactive prediction of the toxic propensity of an article. Prior to the publication and/or reception of comments to online content, the disclosed framework determines the toxic propensity of the content's context and/or specific words, sentences, sentiments, tone or other messages receivable from consumption of the content. Thus, disclosed framework performs proactive forecasting of the content's toxicity propensity”, which quantifies how likely the content is prone to incur or attract toxic comments. The framework can function and/or be configured to operate in a manner that can perform specifically adherent moderation actions that correspond to the content and control how the content can be interacted with, based on the toxic propensity determination, prior to the content's publication in an effort to thwart, prevent or stop toxic environments surrounding or stemming from the content from coming into existence.

Abstract: The present teaching relates to method, system, medium, and implementations for text processing. Upon receiving input data including an original token and a ground truth token label for the original token, a manipulation is applied to the original token to generate a manipulated token based on which to generate manipulated input data. Training data is generated based on the manipulated input data, the ground truth token label, and a ground truth action that, when applied to the manipulated token, yields the original token with the ground truth token label. A text moderation model is trained based on the training data.