Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. User selection of simulated market scenarios generated using neural networks is obtained. A range of unfiltered simulated market factor values for each market factor is determined. Customized market factors are updated based on a user modification. A range of allowable values for each customized market factor is determined.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. An asset return metrics calculation request datastructure is obtained. The number of sessions to utilize for calculating asset return metrics data is determined.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. User selection of simulated market scenarios generated using multi-variate mixture datastructures is obtained. A range of unfiltered simulated market factor values for each market factor is determined. Customized market factors are updated based on a user modification. A range of allowable values for each customized market factor is determined.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. A portfolio return computation request configured to specify simulated market scenarios generated using neural networks and a set of filters is obtained. Constituent portfolio securities of a portfolio are determined. The simulated market scenarios are filtered based on the set of filters. Expected returns for the constituent portfolio securities are retrieved.

Abstract: A computer-program product storing instructions which, when executed by a computer, cause the computer to receive an input data from a sensor, wherein the input data includes data indicative of an image, wherein the sensor includes a video, radar, LiDAR, sound, sonar, ultrasonic, motion, or thermal imaging sensor, generate an adversarial version of the input data, utilizing a generator, in response to the input data, create a training data set utilizing the input data and the adversarial version of the input data, determine an update direction of a meta model utilizing stochastic gradient respect with respect to an adversarial loss, and determine a cross-entropy based classification loss in response to the input data and classification utilizing a classifier, and update the meta model and the classifier in response to the cross-entropy classification loss utilizing the training data set.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. A portfolio return computation request configured to specify simulated market scenarios generated using multi-variate mixture datastructures and a set of filters is obtained. Constituent portfolio securities of a portfolio are determined. The simulated market scenarios are filtered based on the set of filters. Expected returns for the constituent portfolio securities are retrieved.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. A portfolio construction request configured to include a set of optimization parameters is obtained. A set of simulated market scenarios is generated using multi-variate mixture datastructures. A set of expected returns for securities in the universe of securities for the set of simulated market scenarios is retrieved.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. A portfolio construction request configured to include a set of optimization parameters is obtained. A set of simulated market scenarios is generated using neural networks. A set of expected returns for securities in the universe of securities for the set of simulated market scenarios is retrieved.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. Neural networks are used as encoder to generate a set of latent variables. Latent variables are simulated with neural networks as decoder such that the decoded simulated market scenarios follow dynamic dependencies and volatilities of historical market risk factors.

Abstract: The present disclosure provides a neural network model splitting method and related products. The scheme provided by the present disclosure splits an operator into a plurality of smaller-scale sub-operators, so that a compute library under a single-core architecture can be called directly, which helps to avoid the extra work caused by re-implementation.

Abstract: A network performance degradation detection system is provided. The system receives Key Performance Indicator (KPI) values for devices of different device types running different software versions. The system determines baseline values of a first device type by averaging the KPI values of the different software versions running on devices of the first device type. The system compares KPI values for a first software version running on devices of the first device type with the determined baseline values to produce a set of comparison results. The system applies the set of comparison results to a classification model to determine whether the first software version running on devices of the first device type causes network performance degradation.

Abstract: A method and system for code package. A dataset is generated according to a code package. The code package includes an image file associated with a container for a tenant. The dataset includes general information related to security aspects of the image file. The image file includes two or more image layers. Generating the dataset according to the code package includes: exporting an image layer from the image file; and obtaining a configuration file of the exported image layer as the dataset. In response to the vulnerability having been identified, the image file is updated with a patch that fixes the identified vulnerability. The patch includes a new image layer added to the two or more image layers in the updated image file.

Abstract: This application discloses a data storage method and associated server, and a non-transitory computer readable storage medium, and belongs to the field of data processing technologies. The method includes: determining historical-state data of a to-be-dumped data item from a database, and determining a service requirement of the historical-state data; determining a target storage format of the historical-state data according to the service requirement; and dumping the historical-state data according to the target storage format.

Abstract: A method and system. A dataset is generated according to a code package. The code package includes an image file associated with a container for a tenant in a cloud environment. The dataset includes general information related to security aspects of the image file. The image file includes two or more image layers. A security indicator of the image file is extracted according to the dataset. A security level of the image file is determined by comparing the extracted security indicator of the image file with a security indicator of an authenticated image file. A vulnerability in the image file is identified based on the determined security level. In response to the vulnerability having been identified, the image file is updated with a patch that fixes the identified vulnerability. The patch includes a new image layer added to the two or more image layers in the updated image file.

Abstract: Embodiments facilitating performance anomaly detection are described. A computer-implemented method comprises: detecting, by a device operatively coupled to one or more processing units, based on monitoring data of a plurality of performance metrics of a monitored device, at least one trend within the monitoring data of the respective performance metrics; removing, by the device, the at least one trend from the monitoring data of the respective performance metrics to generate modified data of the respective performance metrics; and detecting, by the device, a performance anomaly based on the modified data of the respective performance metrics and a behavior clustering model comprising at least one steady state.

Abstract: Systems and methods for ticket classification and response include labeling tickets with a ticket classifier that assigns a ticket label and an associated confidence score to each ticket. Tickets are clustered according to semantic similarity to form ticket clusters. A template associated with each ticket cluster is determined. Templates and the respective ticket clusters are clustered according to semantic similarity to form one or more ticket super-clusters. Tickets that have below-threshold confidence scores are labeled according to the one or more ticket super-clusters. The tickets are automatically responded to.

Abstract: The invention relates to a V—Ni2P/g-C3N4 photocatalyst, a preparation method, and application thereof. The V—Ni2P/g-C3N4 photocatalyst is a composite material of V—Ni2P and g-C3N4, wherein V—Ni2P has the spherical structure formed by nanosheets; the mass ratio of the V—Ni2P and g-C3N4 is (0.01 to 0.2):1.

Abstract: A micro-nano incremental mechanical surface treatment method, comprising the following steps: using a modification tool having a designable end to contact a surface of a substrate material, rotating the modification tool in a local region and compressing the material surface, presetting processing parameters by means of 3D modeling software, and after the tool has processed the entire surface, enabling the tool to move downwards to the indented surface compressed previously. The process continues until the surface material is compressed to a pre-defined thickness, thereby achieving the goals of grain refinement and surface performance improvement. By means of the present method, a workpiece having a complex shape can be flexibly and designably surface modified. The method has the advantages of high bonding strength, no pollution, and low cost.

Abstract: A microfluidic channel backplane includes a base, and a plurality of microfluidic channels, a sample-adding channel and an enrichment channel that are disposed above the base. Each microfluidic channel of the plurality of microfluidic channels includes a first end and a second end. The sample-adding channel is communicated with first ends of the plurality of microfluidic channels. The enrichment channel includes a first enrichment sub-channel and a second enrichment sub-channel. The first enrichment sub-channel is communicated with second ends of the plurality of microfluidic channels, and one end of the second enrichment sub-channel is communicated with the first enrichment sub-channel.

Abstract: A motor includes a rotating shaft extending along a central axis, a stator core arranged about the rotating shaft by being centered on the central axis, a housing arranged about the stator core in a circumferential direction and including an opening on one side in an axial direction, and a support covering the opening. A functional component is mounted on at least one side of the support in the axial direction. The motor further includes a fastener disposed in the axial direction to secure the support, the stator core, and the housing.