Abstract: A charge method for a battery, a storage medium, and a terminal are provided and include: acquiring a discharge current of the battery when the battery is in a charge state; determining whether the discharge current is smaller than a predetermined current threshold value; if yes, determining whether the discharge current is in a stable state within a predetermined time period; and selecting a target voltage from a plurality of sample voltages in a predetermined voltage set according to a result of the stable state, adopting the target voltage as a charge voltage to charge the battery, and continuing to perform the determining whether the discharge current is smaller than the predetermined current threshold value.

Abstract: A method for prognostic survival stage prediction based on machine learning includes acquiring patients' original information data within a previous preset time period and integrating the patients' original information data so as to obtain a first data set without recurrence time and a second data set with recurrence time; performing analysis based on preoperative information, postoperative information and survival status of each corresponding patient, so as to obtain a correlation degree among the preoperative information, the postoperative information and the survival status; and training in the first data set, so as to obtain a postoperative survival probability prediction model. The method includes training in the second data set, so as to obtain a survival time period prediction model if judging that a survival probability of a target patient is less than or equal to a preset value according to the postoperative survival probability prediction model.

Abstract: Two sets of data, each containing property listings, are obtained from two discrete merchant platforms. Each property listing in a set of data of a first merchant is sequentially paired with each of the property listings in a set of data of a second merchant. For each pair, each image of the property listing of the first merchant is compared to each image of the property listing of the second merchant, and images of statistically sufficient similarity are identified. The similarity of images, and in particular, of similar images likely to be rooms of the property, are considered in a determination of whether the product listings of the first and second merchant are for the same cross-listed product.

Abstract: Disclosed are a method and apparatus for detecting traffic anomaly, a device and a medium. The method includes: performing a network topology division operation on a network topology architecture to be detected, so as to obtain a plurality of security domains; acquiring a regional full traffic in each of the plurality of security domains, determining a traffic characteristic time series on the basis of regional full traffics, and analyzing the traffic characteristic time series to obtain a new traffic characteristic time series; constructing a traffic anomaly detection model, and performing distributed anomaly detection on the traffic characteristic time series by the traffic anomaly detection model, so as to obtain a detection result; and sending the detection result to an information processing center, so that the information processing center obtains anomalous asset devices and anomalous information according to the detection result.

Abstract: Two sets of data, each containing property listings, are obtained from two discrete merchant platforms. Each property listing in a set of data of a first merchant is sequentially paired with each of the property listings in a set of data of a second merchant. For each pair, each image of the property listing of the first merchant is compared to each image of the property listing of the second merchant, and images of statistically sufficient similarity are identified. The similarity of images, and in particular, of similar images likely to be rooms of the property, are considered in a determination of whether the product listings of the first and second merchant are for the same cross-listed product.

Abstract: Disclosed in the present invention is a differentiable method for mining constant rules. On the basis of defining constant operators and path operators according to a knowledge graph, a fusion attention mechanism is used to evaluate relations passed by rules by using attention values. Meanwhile, attentions are calculated by aggregating surrounding attributes and the corresponding attribute values for tail node of each hop respectively. Attentions of the attributes are used to enhance selection of relations in the rules to achieve link prediction. According to generated model parameters, high quality symbolic rules are output by parameter analysis and statistics. This method is particularly suitable for application scenarios with complex reasoning requirements that require high prediction accuracy, provide interpretations for predictions, and need to precipitate reasoning rules.

Abstract: A rotary joint includes a shaft having a first end, a second end, and a cavity. The rotary joint includes a first waveguide section having a first proximal end and a first distal end. The first proximal end of the first waveguide section is positioned within the cavity and secured to the inner surface of the shaft. The rotary joint includes a second waveguide section that includes a second proximal end and a second distal end. The second proximal end of the second waveguide section is positioned within the cavity of the shaft and unsecured to the inner surface of the shaft to form a radial gap between an outer surface of the second proximal end and a laterally adjacent portion of the inner surface of the shaft. The shaft and the first waveguide section are configured to rotate about the rotational axis and relative to the second waveguide section.

Abstract: Anomalous activities on a computer network are detected from audit or sign-in activity information of a target entity as recorded in an audit or sign-in log. A baseline graph of the target entity is generated using information on activities of the target entity during a collection period. A predict graph of the target entity is generated with information on activities of the target entity during another collection period, which follows and is shorter than the earlier collection period. A residual graph that indicates nodes or edges that are in the predict graph but not in the baseline graph is generated. The residual graph is scored and the score is compared to a threshold to determine whether the target entity has performed an anomalous activity.

Abstract: Two sets of data, each containing property listings, are obtained from two discrete merchant platforms. Each property listing in a set of data of a first merchant is sequentially paired with each of the property listings in a set of data of a second merchant. For each pair, each image of the property listing of the first merchant is compared to each image of the property listing of the second merchant, and images of statistically sufficient similarity are identified. The similarity of images, and in particular, of similar images likely to be rooms of the property, are considered in a determination of whether the product listings of the first and second merchant are for the same cross-listed product.

Abstract: Two sets of data, each containing property listings, are obtained from two discrete merchant platforms. Each property listing in a set of data of a first merchant is sequentially paired with each of the property listings in a set of data of a second merchant. For each pair, each image of the property listing of the first merchant is compared to each image of the property listing of the second merchant, and images of statistically sufficient similarity are identified. The similarity of images, and in particular, of similar images likely to be rooms of the property, are considered in a determination of whether the product listings of the first and second merchant are for the same cross-listed product.

Abstract: A charge method for a battery, a storage medium, and a terminal are provided and include: acquiring a discharge current of the battery when the battery is in a charge state; determining whether the discharge current is smaller than a predetermined current threshold value; if yes, determining whether the discharge current is in a stable state within a predetermined time period; and selecting a target voltage from a plurality of sample voltages in a predetermined voltage set according to a result of the stable state, adopting the target voltage as a charge voltage to charge the battery, and continuing to perform the determining whether the discharge current is smaller than the predetermined current threshold value.

Abstract: A silicon carbide diode having a high surge current capability, and including a semiconductor base plate. The semiconductor base plate includes an N-type silicon carbide substrate and an N-type silicon carbide epitaxial layer located on the N-type silicon carbide substrate. The upper portion of the N-type silicon carbide epitaxial layer is provided with a plurality of P-type well regions. The N-type high resistance region is provided under the P-type well region or on the lower surface of the P-type well region. The resistivity of the N-type high resistance region is greater than the resistivity of the N-type silicon carbide epitaxial layer. The N-type high resistance region is provided under the P-type well region, and a plurality of grooves are provided in the P-type well region or a plurality of block-shaped P-type regions uniformly arranged at intervals are provided in the N-type high resistance region.

Abstract: Systems and methods are provided for extracting a plurality of features for a listing from a datastore comprising a plurality of listings and a plurality of features for each of the plurality of listings, determining a cluster of similar listings to the listing and generating a set of cluster features for the cluster of similar listings, analyzing the set of cluster features for the cluster of similar listings based on a booking price, using a first trained machine learning model to determine a cluster-level probability of booking the listing on the given date, analyzing the plurality of features for the listing using the booking price, using a second trained machine learning model to determine a listing-level probability of booking the listing on the given date, and generating a final probability of booking by combining the cluster-level probability of booking and the listing-level probability of booking.

Abstract: A silicon carbide diode having a high surge current capability, and including a semiconductor base plate. The semiconductor base plate includes an N-type silicon carbide substrate and an N-type silicon carbide epitaxial layer located on the N-type silicon carbide substrate. The upper portion of the N-type silicon carbide epitaxial layer is provided with a plurality of P-type well regions. The N-type high resistance region is provided under the P-type well region or on the lower surface of the P-type well region. The resistivity of the N-type high resistance region is greater than the resistivity of the N-type silicon carbide epitaxial layer. The N-type high resistance region is provided under the P-type well region, and a plurality of grooves are provided in the P-type well region or a plurality of block-shaped P-type regions uniformly arranged at intervals are provided in the N-type high resistance region.

Abstract: The present invention provides a method for 3D waveform mapping of full-parallel structure, first, a 3D waveform mapping database is created according to the size of a 3D waveform image, the number of bits of probability value and the ADC's resolution of data acquisition module, then the 3D waveform mapping database is divided into Mt×Ma independent mapping storage areas along the time axis and the amplitude axis, and each independent mapping storage area is assigned a RAM, then RAMs are selected and addresses are calculated based on the sampling values and the structure of created 3D waveform mapping database, finally, parallel mappings are performed simultaneously on the time axis and the amplitude axis according to the selected RAMs and calculated addresses. Thus, the mapping time are shorten, especially in vector mapping mode, several RAMs are used for mapping, so the WCR of DSO is improved.

Abstract: Systems and methods are provided for extracting a plurality of features for a listing from a datastore comprising a plurality of listings and a plurality of features for each of the plurality of listings, determining a cluster of similar listings to the listing and generating a set of cluster features for the cluster of similar listings, analyzing the set of cluster features for the cluster of similar listings based on a booking price, using a first trained machine learning model to determine a cluster-level probability of booking the listing on the given date, analyzing the plurality of features for the listing using the booking price, using a second trained machine learning model to determine a listing-level probability of booking the listing on the given date, and generating a final probability of booking by combining the cluster-level probability of booking and the listing-level probability of booking.

Abstract: A method for navigating an automated guided vehicle is provided. The method includes: generating an environment map for an automated guided vehicle and setting a target position, wherein the environment map at least includes an open region and a barrier region; generating a first navigation path on the environment map according to the target position; optimizing the first navigation path by using a dichotomy method to generate a second navigation path; orthogonalizing the second navigation path to generate a third navigation path, wherein every two adjacent line segments in the third navigation path are orthogonal to each other; and navigating the automated guided vehicle to the target position using the third navigation path. The method for navigating the automated guided vehicle according to the invention optimizes navigation paths.

Abstract: A method for oscilloscope 3D mapping in scan mode. The input signal is acquired using a real-time sampling rate which is Dr times higher, thus more sampling points, i.e. Dr acquired data can be obtained during the time interval between two consecutive horizontal pixels. The Dr acquired data are mapped into a same column of the screen to implement fluorescent waveform display. In addition, to realize the scanning display, a flag X is introduced into the three-dimensional database, when the screen refresh signal arrives, the first Ds acquired data are read out from the unread acquired data in FIFO memory. The three-dimensional database is updated from the flag X, which make the leftmost waveform always be the oldest waveform, the rightmost waveform always be the newest waveform. Thus the 3D mapping is realized in scan mode, letting the DSO have a fluorescent waveform display at slow time-base.

Abstract: This disclosure includes systems for regression-tree-modified feature vector machine learning models for utilization prediction in time-expiring inventory. An online computing system receives a feature vector for a listing and inputs the feature vector and modified feature vectors into a demand function to generate demand estimates. The system inputs the demand estimates into a likelihood model to generate a set of request likelihoods, each request likelihood representing a likelihood that the time-expiring inventory will receive a transaction request at each of a set of test price and test times to expiration. The system further trains a regression tree model based on a set of training data comprising each of the request likelihoods from the set and the test price and test time period to expiration used to generate the demand estimate that was used to generate the request likelihood.

Abstract: This disclosure includes systems for regression-tree-modified feature vector machine learning models for utilization prediction in time-expiring inventory. An online computing system receives a feature vector for a listing and inputs the feature vector and modified feature vectors into a demand function to generate demand estimates. The system inputs the demand estimates into a likelihood model to generate a set of request likelihoods, each request likelihood representing a likelihood that the time-expiring inventory will receive a transaction request at each of a set of test price and test times to expiration. The system further trains a regression tree model based on a set of training data comprising each of the request likelihoods from the set and the test price and test time period to expiration used to generate the demand estimate that was used to generate the request likelihood.