Abstract: Provided in the present application are a converter and a zero crossing point control method therefor. The zero crossing point control method comprises: first, determining whether the current power grid voltage is within a preset range of a zero crossing point; if the current power grid voltage is not within the preset range of the zero crossing point, controlling a primary-side circuit and a secondary-side circuit, which are connected to a transformer in a converter, to operate in a normal working mode; and if the current power grid voltage is within the preset range of the zero crossing point, controlling the primary-side circuit and the secondary-side circuit to operate in a preset working mode.

Abstract: Provided is a system that includes a processor to provide a first input to an autoencoder machine learning model; generate a first output of the autoencoder machine learning model based on the first input; provide the first input to a production machine learning model; provide the first output of the autoencoder machine learning model as a second input to the production machine learning model; generate a first output of the production machine learning model based on the first input; generate a second output of the production machine learning model based on the second input; determine a metric of divergence between the first output and the second output of the production machine learning model, wherein the metric of divergence comprises an indication of whether the first input is associated with an adversarial attack; and perform an action. Methods and computer program products are also provided.

Abstract: This application discloses material with dual heterogeneous structure, preparation method and application thereof. The material with dual heterogeneous structure is CdS/Bi2S3/ZnS composite material. With spherical Bi2S3 as the carrier, CdS particles and ZnS particles are uniformly dispersed on the surface of Bi2S3 to form a dual heterogeneous structure. The preparation method of the material with dual heterogeneous structure, comprising: dissolving Bi (NO3)3·5H2O in ethylene glycol solution to obtain Solution A; dissolving ZnCl2 in water to obtain solution B; dissolving CdCl2·2.5H2O in water to obtain solution C; mixing the solution A, B and C, and adding L-cysteine to the mixture. After hydrothermal reaction, washing and drying, CdS/Bi2S3/ZnS composite material is obtained. The material with dual heterogeneous structure can be applied in photocatalytic reduction of carbon dioxide, which selectively reduces carbon dioxide to CH4.

Abstract: Provided is a system for generating an inference based on real-time selection of a machine learning model using a machine learning model framework that includes at least one processor programmed or configured to receive a request for inference, wherein the request includes a payload, select a machine learning model of a plurality of machine learning models based on the request for inference, determine an aggregation of data based on the machine learning model and the payload of the request, transform the aggregation of data into inference data, wherein the inference data has a configuration that is capable of being processed by the machine learning model, and generate an inference based on the inference data using the machine learning model. Methods and computer program products are also provided.

Abstract: The present disclosure provides a display panel and an electronic device. A difference value between a size of a first optical part and a size of a first sub-pixel of the display panel is defined as a first difference value. A difference value between a size of a second optical part and a size of a second sub-pixel of the display panel is defined as a second difference value. The first difference value corresponding to a maximum light-emitting efficiency of the first sub-pixel is defined as a first preset value. The second difference corresponding to a maximum light-emitting efficiency of the second sub-pixel is defined as a second preset value. An absolute value of a difference value between the first difference value and the first preset value is less than an absolute value of a difference value between the second difference value and the second preset value.

Abstract: This application discloses Bi2S3/ZnS composites comprising S—Bi double defect and its preparation method. The preparation method includes: bismuth nitrate pentahydrate is dissolved in mannitol solution to obtain solution A; zinc chloride is dissolved in water to obtain solution B; solution A is mixed with solution B, L-cysteine is added, and then obtained by hydrothermal reaction. The method described in this application can obtain Bi2S3/ZnS composites containing both cationic Bi defects and anionic S defects. The composite material can inhibit the recombination of photogenerated electron-hole pairs while promoting the photogenerated charge transfer rate, and effectively improve the ability of photocatalytic reduction of carbon dioxide.

Abstract: The present application relates to an atomization device (100) and a counter module (114) for the same. The atomization device (100) comprises: an upper housing (102) and a lower housing (104), wherein the upper housing (102) is configured to mount a container (106) for containing liquid, and the container (106) is movable in an axial direction of the atomization device (100) between a release position and a stretch position, and wherein the stretch position is closer to a proximal end of the atomization device (100) than the release position; and the counter module (114) mounted inside the lower housing (104) and for counting the movement of the container (106) between the release position and the stretch position.

Abstract: A method, system, and computer program product for managing model updates at multiple data centers hosting a same machine learning model obtain a plurality of first feature profiles input to a first implementation of a first machine learning model and a plurality of first model states determined from processing a model input with the first implementation; determine that a first model policy associated with the first machine learning model is satisfied, based on the plurality of first model states including a set of model states defined by the first model policy and the plurality of first feature profiles including a set of feature profiles defined by the first model policy; and in response to determining that the first model policy is satisfied, provide the plurality of first model states and the plurality of first feature profiles for updating at least one second implementation of the first machine learning model.

Abstract: Embodiments of the invention are directed to systems and methods for utilizing a multi-tiered caching architecture in a multi-tenant caching system. A portion of the in-memory cache may be allocated as dedicated shares (e.g., dedicated allocations) that are each dedicated to a particular tenant, while another portion of the in-memory cache (e.g., a shared allocation) can be shared by all tenants in the system. When a threshold period of time has elapsed since data stored in a dedicated allocation has last been accessed, the data may be migrated to the shared allocation. If data is accessed from the shared allocation, it may be migrated back to the dedicated allocation. Utilizing the techniques for providing a multi-tiered approach to a multi-tenant caching system can increase performance and decrease latency with respect to conventional caching systems.

Abstract: Provided are systems for ensemble learning with machine learning models that include a processor to receive a training dataset of a plurality of data instances, wherein each data instance comprises a time series of data points, add an amount of time delay to one or more data instances to provide an augmented training dataset, select a first plurality of supervised machine learning models, select a second plurality of unsupervised machine learning models, train the first plurality of supervised machine learning models and the second plurality of unsupervised machine learning models based on the augmented training dataset, generate an ensemble machine learning model based on outputs of the supervised machine learning models and unsupervised machine learning models, and generate a runtime output of the ensemble machine learning model based on a runtime input to the ensemble machine learning model. Methods and computer program products are also provided.

Abstract: Provided is a system for generating an inference based on real-time selection of a machine learning model using a machine learning model framework that includes at least one processor programmed or configured to receive a request for inference, wherein the request includes a payload, select a machine learning model of a plurality of machine learning models based on the request for inference, determine an aggregation of data based on the machine learning model and the payload of the request, transform the aggregation of data into inference data, wherein the inference data has a configuration that is capable of being processed by the machine learning model, and generate an inference based on the inference data using the machine learning model. Methods and computer program products are also provided.

Abstract: Provided is a system that includes at least one processor programmed or configured to receive an XML data file, wherein the XML data file includes data associated with one or more input parameters of a machine learning model, generate a code generation template based on the data associated with one or more input parameters of the machine learning model included in the XML file, where the code generation template includes one or more keys associated with one or more parameters of a transaction aggregate for an account of a user, and generate a file of executable code based on the code generation template, wherein the file of executable code includes instructions that, when executed by at least one processor, causes at least one processor to retrieve transaction aggregate data associated with the transaction aggregate for the account of the user. A method and computer program product are also provided.

Abstract: An audio synthesis method, an audio synthesis model training method, an apparatus, an electronic device, a computer-readable storage medium, and a computer program product, which relate to artificial intelligence technology. The method includes: invoking an audio synthesis model based on language information and preset style information of a target text to perform following processing, the audio synthesis model including a prior encoder and a waveform decoder: generating audio features corresponding to the target text based on the language information and the preset style information by using the prior encoder; performing normalizing flow processing on the audio features by using the prior encoder, to obtain a hidden variable of the target text; and performing waveform decoding on the hidden variable of the target text by using the waveform decoder, to obtain a synthetic waveform conforming to an audio style described in the preset style information and corresponding to the target text.

Abstract: Methods and systems of predicting and processing cacheable AI calls are disclosed. One method includes determining, by a call prediction processing computer, that an AI computer is operating below a threshold processor usage. The method then includes requesting, by the call prediction processing computer, a set of cacheable requests from an orchestration service computer and receiving, from the orchestration service computer, the set of cacheable requests. Then the method includes sending, by the call prediction processing computer, a cacheable request from the set of cacheable requests to the AI computer. The method then includes receiving, by the call prediction processing computer, an output. The output is generated by the AI computer based on the cacheable request. Then the call prediction processing computer stores the output in a data repository.

Abstract: A single-stage multi-path direct current access converter and a control method thereof are provided. The single-stage multi-path direct current access converter includes an AC-side conversion circuit; at least two DC-side conversion circuits; and transformers corresponding to the at least two DC-side conversion circuits. AC sides of the at least two DC-side conversion circuits are respectively connected to primary windings of the corresponding transformers; secondary windings of the transformers are connected in parallel to a first side of the AC-side conversion circuit; each of the at least two DC-side conversion circuits is configured to invert DC power received by the DC-side conversion circuit, or to rectify AC power received by the DC-side conversion circuit; and the AC-side conversion circuit is configured to perform AC frequency conversion.

Abstract: The present disclosure relates to a method and system for optimizing aerodynamic characteristics of a flow equalization air ring structure in a medium-speed coal mill based on Fluent simulation. The method comprises establishing and optimizing a model of the flow equalization air ring structure, meshing the model using Ansys ICEM, importing meshes, arranging an MRF rotating region on a dynamic ring structure, selecting an energy equation model, setting a DPM and a boundary condition, and adopting a modified mixture gas model based on empirical formulas for boundary conditions at an inlet and outlet, initializing computation information of a mesh node, setting a solver and performing iterative solving, after a computation result converges, performing post-processing on the computation result, storing the computation result, modifying structural parameters, and repeating the above steps to obtain a plurality of simulation results to obtain parameters of an optimal flow equalization air ring structure.

Abstract: Methods, systems, and computer program products are provided for implementing a model agnostic framework to provide Shapley values associated with a machine learning model. A method may include receiving an executable file for a neural network machine learning model, converting a format of the executable file for the neural network machine learning model to an agnostic model format to provide an agnostic model format file for the neural network machine learning model, parsing the agnostic model format file, to provide a forward symbolic graph associated with the neural network machine learning model and a backward symbolic graph associated with the neural network machine learning model, receiving a real-time inference request, and determining an output of the neural network machine learning model associated with the real-time inference request and one or more Shapley values associated with the output of the neural network machine learning model.

Abstract: Embodiments of the invention are directed to systems and methods for utilizing a multi-tiered caching architecture in a multi-tenant caching system. A portion of the in-memory cache may be allocated as dedicated shares (e.g., dedicated allocations) that are each dedicated to a particular tenant, while another portion of the in-memory cache (e.g., a shared allocation) can be shared by all tenants in the system. When a threshold period of time has elapsed since data stored in a dedicated allocation has last been accessed, the data may be migrated to the shared allocation. If data is accessed from the shared allocation, it may be migrated back to the dedicated allocation. Utilizing the techniques for providing a multi-tiered approach to a multi-tenant caching system can increase performance and decrease latency with respect to conventional caching systems.

Abstract: A micro inverter, a photovoltaic system and a method for controlling a micro inverter are provided. The micro inverter includes a controller, a primary H-bridge, a transformer, a bi-directional switch arm and a capacitor arm. The controller is configured to: determine a reactive current based on a grid voltage, an equivalent capacitance of an output terminal of the inverter and a grid angle; add the reactive current to a given current to generate an internal phase-shift angle and an external phase-shift angle from a new given current obtained through the addition, where the internal phase-shift angle refers to a phase-shift angle between two arms of the primary H-bridge and the external phase-shift angle refers to a phase-shift angle between the primary H-bridge and the bi-directional switch arm.

Abstract: A display panel is provided, in which an inorganic layer is formed on the driving structure layer, a recessed portion is provided on the inorganic layer. A limiting opening of the pixel definition layer exposes an anode. An angle between a side of the recessed portion and a plane where the inorganic layer is located is a first angle, which is less than 90 degrees. An angle between a side wall of the limiting opening and a plane where the pixel definition layer is located is a second angle, which is less than 90 degrees and less than the first angle.