Abstract: The present invention provides a method for predicting whether an individual has early myocardial infarction. The method includes: providing a blood sample of the individual; testing a marker in the blood sample to obtain the quantity of the marker; comparing the obtained number with a preset threshold, and judging whether the individual will suffer from myocardial infarction by the comparison, wherein the marker is any one or more nucleotide sequences of miRNA, lncRNA or circRNA.

Abstract: A device for measuring a microwave surface resistance of a dielectric conductor deposition interface includes: a test platform, a calibration component, a sealing cavity and a support plate; wherein the test platform comprises: a shielding cavity having an open bottom, a dielectric rod, an input coupling structure, an output coupling structure, and a dielectric supporter; the dielectric conductor test sample and the test platform form a TE0m(n+?) mode dielectric resonator; the calibration component and the dielectric conductor test sample are mounted on the test platform to measure corresponding quality factors, thereby calculating the microwave surface resistance of the deposition interface of the dielectric conductor test sample. The present invention requires no pre-measurement of relative permittivity and loss tangent of the dielectric conductor test sample.

Abstract: The present disclosure relates to data processing methods. In one example method, a computing device determines a first reference value based on a plurality of pieces of to-be-compressed first data, calculates a difference between each piece of first data and the first reference value, determines a code corresponding to each difference, and obtains compressed data of each corresponding piece of first data based on each code. The reference value is data with a larger quantity of occurrence times in the plurality of pieces of to-be-compressed data. When the difference between the reference value and each of the plurality of pieces of to-be-compressed data is calculated, a quantity of 0s in a plurality of obtained differences is the largest. After the difference is encoded, the data corresponding to the difference can be compressed to a smaller quantity of bits.

Abstract: An augmented reality (AR)-assisted enhanced fracture reduction fixator includes a bidirectional telescopic rod and two connecting columns, a ball groove is provided in a middle part of each of the two connecting columns, a ball is rotatably connected to an inner wall of each of the two ball grooves, and the two balls are fixedly connected to a telescopic end of the bidirectional telescopic rod. A rectangular slider is slid to a suitable needle insertion position and angle, and a bone nail is inserted into proximal and distal ends of a fracture. Then, the rectangular slider is fixed by screwing. Based on different fracture sites of a patient, two fixing frame bodies need to adapt to the fracture sites of the patient, that is, an angle between the two fixing frame bodies will change. The ball rotates in the corresponding ball groove.

Abstract: A proximal-femur intraosseous release device includes a release mechanism, wherein a primary fixing mechanism is provided at one end of the release mechanism, the primary fixing mechanism includes a first shell, and a second shell, a first triangular block and a second triangular block are arranged inside the first shell; the second shell is screwed to the first shell through threads, a bottom of the second shell is rotatably connected with the first triangular block, the first triangular block is slidably connected with the first shell, a bottom surface of the first triangular block is an inclined surface, the second triangular block is located at a bottom of the first triangular block, a top surface of the second triangular block is an inclined surface, and a through hole matched with the second triangular block is formed in the first shell.

Abstract: A hot-humid climatic wind tunnel and a multi-field coupling control system therefor. A fan (18), a variable temperature device, a variable humidity device, a salt fog generator (8), a sky background radiation board (11), a solar radiation lamp array (12) and a rain generator (13) are mounted in a wind tunnel body (29).

Abstract: The present disclosure provides a transverse connecting device and a reduction and fixation system using the same. The transverse connecting device includes a connecting rod, wherein the connecting rod is provided with clamping heads, a lower end of each of the clamping heads is provided with two clamping arms, a clamping groove is formed between the two clamping arms, one of the two clamping arms is provided with a transverse through hole, the vertical through hole is internally threaded with a screw plug, a lower end of the screw plug abuts against a side of a first sliding block away from the clamping groove, when the screw plug is screwed into the vertical through hole, the screw plug pushes the first sliding block to slide into the clamping groove along the transverse through hole.

Abstract: Techniques for monitoring particulate matter (PM) mass concentration using relatively low cost devices are described. A computer-implemented method comprises determining, by a device operatively coupled to a processor, relationships between: first PM mass data determined by a monitor station device for a first atmospheric area over a period of time; first PM count data determined by a reference PM count device for the first atmospheric area over the period of time; and first conditional information comprising first values for defined conditional parameters, wherein the first values are associated with the first atmospheric area over the period of time. The method further includes generating an initial conversion model based on the relationships, wherein the conversion model converts a PM count to a PM mass based on one or more conditional parameters of the defined conditional parameters and features for updating the conversion model.

Abstract: In some examples, a method includes receiving an artificial intelligence (AI) system scenario definition file from a user, parsing the definition file and building an application workflow graph for the AI system, and mapping the application workflow graph to an execution pipeline. In some examples, the method further includes automatically generating, from the workflow graph, application executable binary code implementing the AI system, and outputting the application executable binary code to the user. In some examples, the execution pipeline includes one or more building blocks, and the method then further includes collecting running performance of each of the building blocks of the execution pipeline in a runtime environment.

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 disclosure relates to methods, systems, and computer program products for implementing a deep neural network in a field-programmable gate array (FPGA). In response to receiving a network model describing a deep neural network, a plurality of layers associated with the deep neural network may be determined. With respect to a layer in the plurality of layers, a parallelism factor for processing operations associated with the layer simultaneously by processing elements in an FPGA may be determined based on a workload associated with the layer and a configuration of the FPGA.

Abstract: A method for configuring a set of hardware accelerators to process a CNN. In an embodiment, the method includes one or more computer processors determining a set of parameters related to a feature map to analyze at a respective layer of the CNN, the set of parameters include quantization value and respective values that describe a shape of the feature map. The method further includes configuring a set of hardware accelerators for the respective layer of the CNN. The method further includes receiving a portion of the feature map to the configured set of hardware accelerators for the respective layer of the CNN, wherein the received portion of the feature map includes a group of sequential data slices. The method further includes analyzing the group of sequential data slices among the configured set of hardware accelerators.

Abstract: Techniques for unattended object detection using machine learning are disclosed. A machine learning policy, for use in identifying unattended objects in a captured image depicting one or more objects, is generated. The generating includes determining a level of occlusion in the captured image relating to the objects, and determining the machine learning policy based on the determined level of occlusion. A machine learning model is selected, from among a plurality of pre-defined machine learning models, based on the generated machine learning policy. An unattended object is detected in the captured image using the selected machine learning model.

Abstract: According to one embodiment, a method, computer system, and computer program product for generating anchors and selecting feature maps for a multi-scale object detection program based on analysis of the dataset is provided. The present invention may include generating a scale distribution of one or more scales of ground-truth objects, and, based on the scale distribution, dividing the effective scale range into a number of anchors greater than zero; furthermore, the invention may include generating a ratio distribution of ratios of the ground-truth objects; based on the ratio distribution, generating a ratio for at least one of the number of anchors; determining a template scale of one or more feature maps; and assigning the number of anchors to the feature maps based on the relative size of a scale of an anchor matching the relative size of a template scale of a feature map.

Abstract: A method for sending early warning information, a storage medium and a terminal are provided. The method includes: calculating a target similarity between a first face image and a target face image when the first face image captured by an imaging device is acquired; and generating and sending early warning information corresponding to the first face image if the target similarity reaches a similarity threshold at the current moment; wherein, the similarity threshold is determined by a fluctuation degree value of a plurality of similarities in a similarity sample, the similarity in the similarity sample is a target similarity corresponding to a generated warning information, and the similarity sample is updated over. As a result, the accuracy in early warning may be improved.

Abstract: In an approach for detecting queuing information, a processor analyzes a video monitoring a queue area. A processor detects a queue barrier in the queue area using an instance segmentation technique based on the video. A processor identifies a queue in the queue area using a heuristic technique. A processor recognizes a number of people in the queue. A processor provides an estimation of a wait time for the queue.

Abstract: A method, system, and computer program product for detecting multi-scale objects. The method may include receiving a sample dataset including multi-scale objects associated with a specific environment, where the sample dataset has an existing resolution. The method may also include inputting the sample dataset into a trained neural network, where the trained neural network has a plurality of scale regions. The method may also include processing the sample dataset for detecting the multi-scale objects by means of the trained neural network. The method may also include calculating a distribution of contribution degree in a course of processing the sample dataset, where the contribution degree is associated with each of the plurality of scale regions. The method may also include generating a set of configuration parameters associated with the specific environment for the trained neural network based at least in part on the distribution of contribution degree.

Abstract: Techniques for unattended object detection using machine learning are disclosed. A machine learning policy, for use in identifying unattended objects in a captured image depicting one or more objects, is generated. The generating includes determining a level of occlusion in the captured image relating to the objects, and determining the machine learning policy based on the determined level of occlusion. A machine learning model is selected, from among a plurality of pre-defined machine learning models, based on the generated machine learning policy. An unattended object is detected in the captured image using the selected machine learning model.

Abstract: According to one embodiment, a method, computer system, and computer program product for generating anchors and selecting feature maps for a multi-scale object detection program based on analysis of the dataset is provided. The present invention may include generating a scale distribution of one or more scales of ground-truth objects, and, based on the scale distribution, dividing the effective scale range into a number of anchors greater than zero; furthermore, the invention may include generating a ratio distribution of ratios of the ground-truth objects; based on the ratio distribution, generating a ratio for at least one of the number of anchors; determining a template scale of one or more feature maps; and assigning the number of anchors to the feature maps based on the relative size of a scale of an anchor matching the relative size of a template scale of a feature map.

Abstract: A method, computer system, and computer program product for model selection for training a new dataset is provided. The present invention may include choosing a model from a set of models to be evaluated for training the new dataset, selecting a sample input from a subset of the new dataset, calculating a model activation score for each of the sample inputs in the chosen model, calculating an accumulated model activation score for the chosen model, depending on the model activation score of each of the sample inputs in the chosen model, calculating an accumulated model activation score for each model from the set of models to be evaluated for training the new dataset, and selecting the model for training the new dataset with the highest accumulated model activation score.