Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that creates a machine learning model with k-means clustering. In some implementations, an instruction to create a model is obtained. A data set including geographic data and non-geographic data is received. The data set includes multiple data entries. Geographic centroids are determined from the geographic data. The data set is analyzed to obtain statistics of the data set. Transformed data is generated from the data set, the statistics, and the geographic centroids. A model is generated with the transformed data, the model indicating multiple data groupings.

Abstract: The present invention belongs to the field of medicinal chemistry. Specifically disclosed is a class of URAT1 inhibitors for promoting uric acid excretion, which are compounds as represented by the structure of formula (I) or pharmaceutically acceptable salts thereof. Experiments show that the compounds provided by the present invention have a very good inhibitory effect on the transport of uric acid by hURAT1 in HEK293 transfected cells, and that such compounds have a good application prospect in the treatment of hyperuricemia or gout.

Abstract: A classification machine learning model is trained to predict the likelihood that a software program is likely to have a software bug in the future. The model is based on features from different source code files having changes made to fix a software bug and source code files having changes that were not made for a bug fix. The features include a time-weighted bug density, a time-weighted addition factor, and a time-weighted deletion factor for a source code file and its dependent code, a page rank, and complexity features representing a number of different types of code elements in the source code file.

Abstract: Machine learning techniques are used to determine the viability of user data measuring the behavior of a new version of the program when compared with user data that measured the behavior of a previous version of the program. The machine learning techniques utilize statistical techniques in a non-conventional manner to train a system to learn from data obtained from the usage of both a new version of the program and a previous version that accounts for the variability in the user population, time variability of the results of the previous version, and feature coverage between the two test results in order to ensure the suitability of the user data in making estimations or predictions about the performance and reliability of the new version.

Abstract: Disclosed are a class of URAT1 inhibitor compounds and the use of such compounds. These compounds are compounds represented by the structure of formula (I) or pharmaceutically acceptable salts thereof. Experiments show that the compounds provided by the present invention have a very good inhibitory effect on hURAT1-transported uric acid in HEK293 transfected cells, and show that such compounds have a good potential for application in the treatment of hyperuricemia or gout.

Abstract: A data packet processing method comprises: receiving data packet including a key message; analyzing the key message; determining whether the data packet is a high priority data packet or a normal data packet according to a result of analyzing the first key message of the data packet; and executing an Rx high priority interrupt in response to determining that the data packet is the high priority data packet. The Rx high priority interrupt is to immediately transmit an interrupt signal to interrupt receiving of the data packets.

Abstract: A data packet processing method comprises: receiving data packet including a key message; analyzing the key message; determining whether the data packet is a high priority data packet or a normal data packet according to a result of analyzing the first key message of the data packet; and executing an Rx high priority interrupt in response to determining that the data packet is the high priority data packet. The Rx high priority interrupt is to immediately transmit an interrupt signal to interrupt receiving of the data packets.

Abstract: A classification machine learning model is trained to predict the likelihood that a software program is likely to have a software bug in the future. The model is based on features from different source code files having changes made to fix a software bug and source code files having changes that were not made for a bug fix. The features include a time-weighted bug density, a time-weighted addition factor, and a time-weighted deletion factor for a source code file and its dependent code, a page rank, and complexity features representing a number of different types of code elements in the source code file.

Abstract: The present disclosure describes methods and apparatuses for implementing virtual dynamic library loading in embedded systems. In some aspects, a determination is made that a feature is not available in firmware executed from a first memory. Based on information associated with the feature, a portion of a second memory is allocated for code of the feature. In some cases, the portion of the second memory is allocated from a data buffer or part of a shared memory. The code of the feature is loaded to the allocated portion of the second memory and address aliasing logic is configured enable execution of the feature from the second memory. By allocating and executing firmware features from the second memory, a size of the firmware executed from the first memory can be reduced to conserve memory space and other firmware features may be dynamically loaded and executed from the second memory.

Abstract: The invention discloses a class of compounds for treating or preventing hyperuricemia or gout, which is a compound shown in general formula (I) or a pharmaceutically acceptable salt. These compounds and their pharmaceutically acceptable salts in the invention are useful for the promotion of uric acid excretion to treat or prevent hyperuricemia or gout.

Abstract: Machine learning techniques are used to determine the viability of user data measuring the behavior of a new version of the program when compared with user data that measured the behavior of a previous version of the program. The machine learning techniques utilize statistical techniques in a non-conventional manner to train a system to learn from data obtained from the usage of both a new version of the program and a previous version that accounts for the variability in the user population, time variability of the results of the previous version, and feature coverage between the two test results in order to ensure the suitability of the user data in making estimations or predictions about the performance and reliability of the new version.

Abstract: A classification machine learning model is trained to predict the likelihood that a software program is likely to have a software bug in the future. The model is based on features from different source code files having changes made to fix a software bug and source code files having changes that were not made for a bug fix. The features include a time-weighted bug density, a time-weighted addition factor, and a time-weighted deletion factor for a source code file and its dependent code, a page rank, and complexity features representing a number of different types of code elements in the source code file.

Abstract: The invention discloses a class of compounds for treating or preventing hyperuricemia or gout, which is a compound shown in general formula (I) or a pharmaceutically acceptable salt. These compounds and their pharmaceutically acceptable salts in the invention are useful for the promotion of uric acid excretion to treat or prevent hyperuricemia or gout.

Abstract: It discloses compounds for the treatment and prevention of breast cancer, which are specifically 2-phenyl benzoselenazole compounds, pharmaceutically acceptable salts thereof and prodrugs thereof. The present invention further discloses pharmaceutical compositions containing the compounds and applications of the compounds in preparing medicines for the treatment and prevention of breast cancer in mammals. The compounds of the present invention can effectively inhibit or reduce the growth or proliferation of breast cancer cells in mammals, with no inhibition effect on the growth of part of the tested cell lines except for the breast cancer cell lines, and are highly selective.

Abstract: It discloses compounds for the treatment and prevention of breast cancer, which are specifically 2-phenyl benzoselenazole compounds, pharmaceutically acceptable salts thereof and prodrugs thereof. The present invention further discloses pharmaceutical compositions containing the compounds and applications of the compounds in preparing medicines for the treatment and prevention of breast cancer in mammals. The compounds of the present invention can effectively inhibit or reduce the growth or proliferation of breast cancer cells in mammals, with no inhibition effect on the growth of part of the tested cell lines except for the breast cancer cell lines, and are highly selective.

Abstract: The present embodiments relate to machine learning for multimodal image data. By way of introduction, the present embodiments described below include apparatuses and methods for learning a similarity metric using deep learning based techniques for multimodal medical images. A novel similarity metric for multi-modal images is provided using the corresponding states of pairs of image patches to generate a classification setting for each pair. The classification settings are used to train a deep neural network via supervised learning. A multi-modal stacked denoising auto encoder (SDAE) is used to pre-train the neural network. A continuous and smooth similarity metric is constructed based on the output of the neural network before activation in the last layer. The trained similarity metric may be used to improve the results of image fusion.

Abstract: A computer-implemented technique includes receiving, at a server, a first request from a computing device to translate a web page from a source language to a target language. The technique includes generating, at the server, an image for each of a plurality of messages from the web page to obtain a plurality of images in response to the first request. A specific image (i) represents at least a specific portion of the web page and (ii) includes at least a specific message of the plurality of messages. The technique includes grouping, at the server, the plurality of messages based on contexts of their images to obtain one or more groups of messages. A translation service provider then translates the one or more groups of messages to obtain a plurality of translated messages. The technique also includes transmitting, from the server, the plurality of translated messages to the computing device.

Abstract: The present embodiments relate to machine learning for multimodal image data. By way of introduction, the present embodiments described below include apparatuses and methods for learning a similarity metric using deep learning based techniques for multimodal medical images. A novel similarity metric for multi-modal images is provided using the corresponding states of pairs of image patches to generate a classification setting for each pair. The classification settings are used to train a deep neural network via supervised learning. A multi-modal stacked denoising auto encoder (SDAE) is used to pre-train the neural network. A continuous and smooth similarity metric is constructed based on the output of the neural network before activation in the last layer. The trained similarity metric may be used to improve the results of image fusion.

Abstract: Methods and systems for detecting radiation for example, terahertz radiation, with the aid of acoustic signal generation and detection include: directing an optical beam into a volume of gas; ionizing at least a portion of the volume of gas with the optical beam to produce a plasma; and detecting an acoustic signal produced from an interaction of a radiation wave with the plasma. The methods and systems are particularly adapted for remote detection of chemicals, biological substances, and explosives, among others. The capability of the methods and systems can be enhanced by employing multi-color laser excitation to produce the plasma and varying the time delay between the multi-color pulses.

Abstract: Methods and apparatus for detecting variations in electromagnetic fields, in particular, terahertz (THz) electromagnetic fields, are provided. The methods and apparatus employ polarization detection devices and controllers to maintain or vary the polarization of modulated signals as desired. The methods and apparatus are provided to characterize electromagnetic fields by directing the electromagnetic field and a probe beam upon an electro-crystal and detecting the modulation of the resulting probe beam. Detection of the modulation of the probe beam is practiced by detecting and comparing the polarization components of the modulated probe beam. Aspects of the invention may be used to analyze or detect explosives, explosive related compounds, and pharmaceuticals, among other substances. A compact apparatus, modular optical devices for use with the apparatus, sample holders, and radiation source mounts are also disclosed.