Abstract: Embodiments may relate to identifying, by a baseband processor, an amount of data in a data radio bearer (DRB) buffer of the baseband processor. Embodiments further relate to determining, by the baseband processor based on an indication from an application processor of the electronic device, an amount of data in at least one buffer of the application processor. Embodiments further relate to generating, by the baseband processor based at least in part on the amount of data in the DRB buffer and the amount of data in the at least one buffer of the application processor, a buffer status report (BSR). Other embodiments may be described or claimed.

Abstract: A computer-implemented process for training a neural network having a plurality of transform layers includes the following operations. Input data for one transform layer of the plurality of transform layers is transformed by the one transform layer into output data. A neural-backed decision tree is generated for the transform layer, a neural-backed decision tree. The transforming and the generating are repeated for each of the plurality of transform layers. A neural-backed decision tree map for a particular one of the plurality of transform layers maps output data of the particular one of the plurality of transform layers into a list of interpretable words from a generative search domain of facts and evidence.

Abstract: Disclosed is a method for training an image processing model. The method includes acquiring a plurality of original image sets; cropping the plurality of original images in the plurality of original image sets to acquire a training sample set, wherein each of the training samples represents one original image, or a subimage acquired by cropping one original image; determining a plurality of positive sample pairs and a plurality of negative sample pairs from the training sample set, wherein each of the positive sample pairs comprises two training samples acquired based on different said original images in the same original image set, and each of the negative sample pairs comprises two training samples acquired based on the original images in different said original image sets; and training the image processing model based on the plurality of positive sample pairs and the plurality of negative sample pairs.

Abstract: A method produces tantalum powder by reducing tantalum oxide with an alkaline earth metal. The method includes (1) mixing tantalum oxide with an excessive alkaline earth metal reducing agent, simultaneously mixing at least one alkali metal and/or alkaline earth metal halide accounting for 10-200% of the weight of the tantalum oxide, heating the mixture to a temperature of 700-1200° C. in a furnace filled with inert gas, and soaking so that the tantalum oxide and reducing agent are subjected to a reduction reaction; (2) at the end of soaking, lowering the temperature to 600-800° C., vacuumizing the interior of the furnace to 10 Pa or less, and soaking under the negative pressure so that the excessive magnesium and tantalum powder mixture are separated; (3) thereafter, raising the temperature of the furnace to 750-1200° C.

Abstract: Analysis of edge closures of metal surface particles based on a graph structure.

Abstract: The monitoring device of the disclosure includes a carrying unit having at least one carrying area for carrying a monitored object, at least part of the carrying area is provided with a corresponding image capturing unit and a corresponding optical path adjusting unit; the image capturing unit captures a monitoring image of the carrying area, each image capturing unit has a preset field of view, each image capturing unit forms the monitoring image according to light incident from the field of view; at least part of the carrying area is an extension area, the extension area is positioned outside the field of view of the image capturing unit; the optical path adjusting unit enables the light emitted from the extension area of the carrying area corresponding thereto to enter the image capturing unit from the field of view of the image capturing unit corresponding to the carrying area.

Abstract: An X-ray tube bearing assembly may include a bearing housing including an accommodating cavity; and a bearing core accommodated in the accommodating cavity. The bearing housing may include first and second portions. The second portion surrounds and forms a portion of the accommodating cavity to accommodate the bearing core. The first portion extends out of the second portion and surrounds and forms another portion of the accommodating cavity, and the other portion of the accommodating cavity forms an accommodating hollow cavity. The first portion may have a different structural rigidity to that of the second portion.

Abstract: A method, computer system, and a computer program product for enhancing deployed machine learning (ML) models using prompt learning is provided. The present invention may include receiving an input for a prediction task using a first ML model. The present invention may also include adding a feature to the received input using a second ML model, where the added feature is recognized by the first ML model. The present invention may further include predicting, using the first ML model, an output for the received input based on the added feature, wherein the predicted output by the first ML model based on the added feature includes an improved accuracy relative to another predicted output by the first ML model without considering the added feature.

Abstract: A computer-implemented method, according to one embodiment, includes: receiving an image having characters that correspond to a language, and using a text recognition algorithm to determine a first language believed to correspond to the characters. A first confidence level associated with the first language is also computed, and a determination is made as to whether the first confidence level associated with the first language is outside a predetermined range. In response to determining that the first confidence level associated with the first language is not outside the predetermined range, the first language is output as the given language. The text recognition algorithm is trained using a simple shallow neural network and a generated mixed language corpus. The generated mixed language corpus is formed by: randomly sampling libraries having vocabulary and/or characters therein, and combining the randomly sampled vocabulary and/or characters to form the generated mixed language corpus.

Abstract: Analyzing expression of protein-coding variants in cells is provided herein. A method may include replacing a protein coding-region of the DNA in a cell with a donor vector including a variant of the protein-coding region and a first barcode identifying that variant. The cell may generate mRNA including an expression of the variant and an expression of the first barcode. A second barcode corresponding to the cell may be coupled to the mRNA. The mRNA. having the second barcode coupled thereto, may be reverse transcribed into complementary cDNA. The cDNA may be sequenced. The donor vector or cDNA may be sequenced using amplicon sequencing. The donor vector sequence and the cDNA sequence may be correlated to identify the variant and the cell's expression of the variant.

Abstract: A titanium alloy blade tip with a wear-resistant protective coating and a preparation method thereof are provided. The method includes: (1) spraying MCrAlY alloy powder on a surface of a titanium alloy blade tip by high velocity oxygen fuel (HVOF) spraying at a spraying distance of 300-400 mm to obtain the titanium alloy blade tip with a MCrAlY layer on the surface; where M is one of Ni and NiCo; (2) pre-plating Ni at a current density of 4-10 A/dm2; (3) placing the pre-plated titanium alloy blade tip in a Watt solution, covering a surface of the pre-plated titanium alloy blade tip obtained in the step (2) with abrasive particles, and then performing composite electroplating at a current density of 0.5-2 A/dm2. In the method, the wear-resistant protective coating with the high adhesion strength is prepared on the titanium alloy blade tip, and the wear-resistant protective coating has good wear resistance.

Abstract: Provided are techniques for identifying and correcting model focus drift during model training. A model is trained using an original dataset with data, and the is classified into clusters. A representation is assigned to each of the clusters. A first visualization dashboard with visualizations is generated, where each visualization represents a first data distribution of an associated cluster using the representation assigned to that cluster. The model is fine-tuned using a fine-tune dataset. A second visualization dashboard is generated by updating each visualization, where each visualization represents a second data distribution of the associated cluster. It is determined that a cluster of the clusters has focus drift based on changes between the first data distribution and the second data distribution. The focus drift is corrected by: adding the data of the cluster to the fine-tune dataset to form a combined dataset and fine-tuning the model using the combined dataset.

Abstract: A parking detection method, a system, a processing device and a storage medium are provided. The parking detection method includes: acquiring an image to be detected; acquiring a projection area of a vehicle on the ground according to vanishing points and a vanishing line in the image to be detected in combination with outline key points of the vehicle in the image to be detected; judging whether the vehicle is parked regularly.

Abstract: A method produces tantalum powder. The method includes: (1) uniformly mixing a tantalum powder raw material, metal magnesium and at least one alkali metal and/or alkaline earth metal halide, loading the mixture into a container, and putting the container in a furnace; (2) raising the temperature of the furnace to 600-1200° C. in the presence of inert gas, and soaking; (3) at the end of soaking, lowering the temperature of the furnace to 600-800° C., vacuumizing the interior of the furnace to 10 Pa or less, soaking under the negative pressure so that the excessive metal is separated; (4) thereafter, raising the temperature of the furnace to 750-1200° C. in the presence of inert gas, and soaking so that the tantalum powder is sintered in the molten salt after oxygen reduction; (5) then cooling to room temperature and passivating to obtain a mixed material containing halide and tantalum powder; and (6) separating the tantalum powder from the resulting mixture.

Abstract: Embodiments may relate to identifying, by a baseband processor, an amount of data in a data radio bearer (DRB) buffer of the baseband processor. Embodiments further relate to determining, by the baseband processor based on an indication from an application processor of the electronic device, an amount of data in at least one buffer of the application processor. Embodiments further relate to generating, by the baseband processor based at least in part on the amount of data in the DRB buffer and the amount of data in the at least one buffer of the application processor, a buffer status report (BSR). Other embodiments may be described or claimed.

Abstract: The present disclosure provides an image processing method, an apparatus, a computing device, and a medium, which relates to deep learning technology. In the present disclosure, after acquiring the to-be-processed target image, the first similarities between the target image and the first images of categories are determined based on the first feature vector corresponding to the target image and the second feature vectors respectively corresponding to the plurality of first images. Moreover, based on the first color distribution information of the target region in the target image and the second color distribution information of the target regions in the plurality of first images, the second similarities between the target image and the plurality first images are determined, so that the image category to which the target image belongs can be determined jointly based on the first similarities and the second similarities.

Abstract: A low-carbon, high-purity tantalum pentoxide powder has a carbon content of no greater than 15 ppm. A preparation method for the powder includes: (1) adding a fluotantalic acid (H2TaF7) solution into a reaction kettle, controlling the temperature of the reaction kettle at 30-60° C., adding a precipitator until the pH of the reaction solution is 8-10, then stopping introducing ammonia, and aging to obtain a tantalum hydroxide slurry; (2) filtering and washing the slurry obtained in step (1), and then carrying out solid-liquid separation to obtain a tantalum hydroxide filter cake; (3) drying the filter cake obtained in step (2) to obtain white tantalum hydroxide powder; (4) calcining the tantalum hydroxide powder obtained in step (3), crushing and screening the calcined sample to obtain tantalum pentoxide powder; and (5) subjecting the tantalum pentoxide powder obtained in step (4) to heat treatment at a temperature of 1000-1500° C. to obtain the powder.

Abstract: A first set of features associated with a neural network are parameterized. A decision tree is generated from the first set of features. One or more adjustments for the neural network are received at the decision tree. A second set of features associated with the adjustments at the decision tree are parameterized. The parameterized first and second set of features are combined into a plurality of parameters. From the plurality, an adjusted neural network is generated.

Abstract: Techniques are described for identifying a genetic variant in a test sample by comparing sequences reads obtained from the test sample to unique k-mers that are representative of a target genomic region. In one particular aspect, a method is described that includes generating a dictionary of a target genomic region having a set of unique k-mers by: accessing a sequence of the target genomic region, determining a set of k-mers for the target genomic region, comparing the set of k-mers for the target genomic region with one or more sets of k-mers for non-target genomic regions, and selecting the unique k-mers that do not appear in the one or more sets of k-mers for non-target genomic regions. The dictionary can then be used to identify a genetic variant in a test sample by comparing sequences reads obtained from the test sample to the unique k-mers in the dictionary.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to a process to facilitate abnormal document self-discovery. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise an object detection component that can generate a knowledge graph with vectors corresponding with nodes representative of a page layout of a document. Additionally, the computer executable components can comprise an evaluation component that can compare the vectors to identify whether an edge is present between corresponding vectors of the knowledge graph; an encoder component that can re-code the knowledge graph; and a comparison component that can compare a structure of the knowledge graph with one or more other knowledge graphs corresponding to one or more other documents to determine if the document is abnormal.