Provided is an artificial neural network learning apparatus for deep learning. The apparatus includes an input unit configured to acquire an input data or a training data, a memory configured to store the input data, the training data, and a deep learning artificial neural network model, and a processor configured to perform computation based on the artificial neural network model, in which the processor sets the initial weight depending on the number of nodes belonging to a first layer and the number of nodes belonging to a second layer of the artificial neural network model, and determines the initial weight by compensation by multiplying a standard deviation (σ) by a square root of a reciprocal of a probability of a normal probability distribution for a remaining section except for a section in which an output value of the activation function converges to a specific value.

Systems and method determine a fluid efficiency of a fluid that flows through a fluid power system. Characteristics of the fluid is monitored in real-time as the fluid flows through the fluid monitoring device that is coupled to the fluid power system as the fluid flows through the fluid power system. A fluid status is determined in real-time that is associated with fluid parameters of the fluid that is determined from the fluid parameters detected by the fluid monitoring device. The fluid status of the fluid is determined in real-time when the fluid status indicates that a corrective action is to be executed to increase a quality of the fluid and an assessment of the corrective action that is to be executed is generated based on the fluid parameters. Degradation of the components of the fluid power system increases without the corrective action being executed to the fluid.

The present disclosure provides methods and systems for performing non-classical computations. The methods and systems generally use a plurality of spatially distinct optical trapping sites to trap a plurality of atoms, one or more electromagnetic delivery units to apply electromagnetic energy to one or more atoms of the plurality to induce the atoms to adopt one or more superposition states of a first atomic state and a second atomic state, one or more entanglement units to quantum mechanically entangle at least a subset of the one or more atoms in the one or more superposition states with at least another atom of the plurality, and one or more readout optical units to perform measurements of the superposition states to obtain the non-classical computation.

An apparatus for providing similar contents, using a neural network, includes a memory storing instructions, and a processor configured to execute the instructions to obtain a plurality of similarity values between a user query and a plurality of images, using a similarity neural network, obtain a rank of each the obtained plurality of similarity values, and provide, as a most similar image to the user query, at least one among the plurality of images that has a respective one among the plurality of similarity values that corresponds to a highest rank among the obtained rank of each of the plurality of similarity values. The similarity neural network is trained with a divergence neural network for outputting a divergence between a first distribution of first similarity values for positive pairs, among the plurality of similarity values, and a second distribution of second similarity values for negative pairs, among the plurality of similarity values.

Embodiments for delay-induced miscommunication reduction are provided. The embodiment may include capturing data streams transmitted between participants in an A/V exchange; translating, on a sender device prior to transmission to a recipient device, an audio stream within the data streams to text; timestamping, on a sender device prior to transmission to the recipient device, each word in the translated audio stream; transmitting the audio stream and the sender-side translated and timestamped audio stream to the recipient device; translating, on the recipient device, the transmitted audio stream to text; timestamping, on the recipient device, each word in the translated audio stream; determining a lag exists in the A/V exchange based on a comparison of each timestamp for corresponding words on the sender-side translated and timestamped audio stream and the recipient-side translated and timestamped audio stream; and generating a true transcript of an intended exchange between the participants based on the comparison.

Transaction-enabled systems and methods for royalty apportionment and stacking are disclosed. An example system may include a plurality of royalty generating elements (a royalty stack) each related to a corresponding one or more of a plurality of intellectual property (IP) assets (an aggregate stack of IP). The system may further include a royalty apportionment wrapper to interpret IP licensing terms and apportion royalties to a plurality of owning entities corresponding to the aggregate stack of IP in response to the IP licensing terms and a smart contract wrapper. The smart contract wrapper is configured to access a distributed ledger, interpret an IP description value and IP addition request, to add an IP asset to the aggregate stack of IP, and to adjust the royalty stack.

The present invention relates to training predictive data-driven model for predicting an industrial time dependent process. A data driven generative model is introduced for modelling and generating complex sequential data comprising multiple modalities, by learning a joint time-dependent representation of the different modalities. The model may be configured to handle any combination of missing modalities, which enables conditional generation based on known modalities, providing a high degree of control over the properties of the generated sequences.

A technology trend prediction method and system are provided. The method comprises acquiring paper data, and further comprises following steps: processing the paper data to generate a candidate technology lexicon; screening the candidate technology lexicon based on mutual information; calculating an independent word forming probability of an OOV word; extracting missed words in a title using a bidirectional long short-term memory network and a conditional random field (BI-LSTM+CRF) model; predicting a technology trend. The technology trend prediction method and system provided analyzes relationship of technology changes in a high-dimensional space, and predicts a development of technology trend based on time by extracting technical features of papers through natural language processing and time sequence algorithms.

A client computing device and a server computing device for federated machine learning. The client computing device is configured to receive a model comprising a set of common layers and a set of client-specific layers from the server computing device. After a training at the client computing device, the set of common layers and the set of client-specific layers are both updated. The set of updated common layers is sent to the server computing device, and the set of updated client-specific layers is stored at the client computing device. The server computing device is configured to receive multiple sets of updated common layers from different client computing devices.

A method of determining a mask pattern for a target pattern to be printed on a substrate. The method includes partitioning a portion of a design layout including the target pattern into a plurality of cells with reference to a given location on the target pattern; assigning a plurality of variables within a particular cell of the plurality of cells, the particular cell including the target pattern or a portion thereof; and determining, based on values of the plurality of variables, the mask pattern for the target pattern such that a performance metric of a patterning process utilizing the mask pattern is within a desired performance range.