The present invention is a combined commodity mining method based on knowledge graph rule embedding, comprising: expressing rules, commodities, attributes, and attribute values as embeddings; splicing and inputting the embeddings of the rules and the embeddings of the attributes into a first neural network to obtain a importance scores of the attributes; splicing and inputting the rules and attributes into a second neural network to obtain the embeddings of the attribute values that the rules should take under the attributes; calculating a similarity between the value of two inputted commodities under the attribute and the embedding of the attribute value calculated by a model; after calculating scores of all attribute-attribute value pairs, summing up to obtain scores of these two commodities under this rule; then making the cross entropy loss with the real scores of these two commodities, and iteratively training based on an optimization algorithm having gradient descent; after the model is trained, parsing the embeddings of the rules in a similar way to obtain rules that can be understood by human beings.

Disclosed are systems and computer implemented methods for providing quantum computing as a service. According to one embodiment the system includes a frontend computing system storing a frontend computer program, a backend computing system, and a quantum computer, the frontend computer program being a spreadsheet application configured to receive a service request from a user, the service request comprising service request parameters and input data. The frontend computing system sends the service request to the backend computing system, which is configured to encode it to a service job in a format suitable for the quantum computer to execute, and to submit the service job to the quantum computer. The quantum computer is configured to execute the service job and to provide service job results to the backend computing system, which translates them into results data and sends them to the frontend computing system.

The operations of a computer-implemented method include obtaining a topological map of an environment including a series of waypoints and a series of edges. Each edge topologically connects a corresponding pair of adjacent waypoints. The edges represent traversable routes for a robot. The operations include determining, using the topological map and sensor data captured by the robot, one or more candidate alternate edges. Each candidate alternate edge potentially connects a corresponding pair of waypoints that are not connected by one of the edges. For each respective candidate alternate edge, the operations include determining, using the sensor data, whether the robot can traverse the respective candidate alternate edge without colliding with an obstacle and, when the robot can traverse the respective candidate alternate edge, confirming the respective candidate alternate edge as a respective alternate edge. The operations include updating, using nonlinear optimization and the confirmed alternate edges, the topological map.

A method performed by a server is provided. The method comprises sending copies of a set of parameters of a hyper network (HN) to at least one client device, receiving from each client device in the at least one client device, a corresponding set of updated parameters of the HN, and determining a next set of parameters of the HN based on the corresponding sets of updated parameters received from the at least one client device. Each client device generates the corresponding set of updated parameters based on a local model architecture of the client device.

A hardware/software co-compressed computing method for a static random access memory (SRAM) computing-in-memory-based (CIM-based) processing unit includes performing a data dividing step, a sparsity step, an address assigning step and a hardware decoding and calculating step. The data dividing step is performed to divide a plurality of kernels into a plurality of weight groups. The sparsity step includes performing a weight setting step. The weight setting step is performed to set each of the weight groups to one of a zero weight group and a non-zero weight group. The address assigning step is performed to assign a plurality of index codes to a plurality of the non-zero weight groups, respectively. The hardware decoding and calculating step is performed to execute an inner product to the non-zero weight groups and the input feature data group corresponding to the non-zero weight groups to generate the output feature data group.

The present disclosure refers to a method and a system of sudden water pollutant source detection by forward-inverse coupling, including: building an one-dimensional forward water quality simulation model of a river way according to acquired mechanical parameters and water quality parameters; according to the one-dimensional forward water quality simulation model of the river way, measuring and calculating each monitoring index by using an inverse optimization source-detection model; by constructing the one-dimensional forward water quality simulation model of the river way, using the inverse optimization source-detection model for measurement and calculation; and performing the Bayesian updating, in order to realize multi-information fusion. The present disclosure may reasonably control and use different observation information, and combine the redundancy or complementarity of multi-sourced information in space or in time to obtain consistent interpretation of the measured object, thus overcoming the uncertainty of the water environment, improving the accuracy of water pollutant source detection.

Methods and systems for measurements of semiconductor structures based on a trained parameter conditioned measurement model are described herein. The shape of a measured structure is characterized by a geometric model parameterized by one or more conditioning parameters and one or more non-conditioning parameters. A trained parameter conditioned measurement model predicts a set of values of each non-conditioning parameter based on measurement data and a corresponding set of predetermined values for each conditioning parameter. In this manner, the trained parameter conditioned measurement model predicts the shape of a measured structure. Although a parameter conditioned measurement model is trained at discrete geometric points of a structure, the trained model predicts values of non-conditioning parameters for any corresponding conditioning parameter value. In some examples, training data is augmented by interpolation of conditioning parameters and corresponding non-conditioning parameters that lie between discrete DOE points. This improves prediction accuracy of the trained model.

The disclosed embodiments relate to a system that optimizes a prognostic-surveillance system to achieve a user-selectable functional objective. During operation, the system allows a user to select a functional objective to be optimized from a set of functional objectives for the prognostic-surveillance system. Next, the system optimizes the selected functional objective by performing Monte Carlo simulations, which vary operational parameters for the prognostic-surveillance system while the prognostic-surveillance system operates on synthesized signals, to determine optimal values for the operational parameters that optimize the selected functional objective.

Systems and methods for domain adaptation for structured output via disentangled representations are provided. The system receives a ground truth of a source domain. The ground truth is used in a task loss function for a first convolutional neural network that predicts at least one output based on inputs from the source domain and a target domain. The system clusters the ground truth of the source domain into a predetermined number of clusters, and predicts, via a second convolutional neural network, a structure of label patches. The structure includes an assignment of each of the at least one output of the first convolutional neural network to the predetermined number of clusters. A cluster loss is computed for the predicted structure of label patches, and an adversarial loss function is applied to the predicted structure of label patches to align the source domain and the target domain on a structural level.

Methods and systems for federated learning using a parameterized optimization algorithm are described. A central server receives, from each of a plurality of user devices, a proximal map and feedback representing a current state of each user device. The server computes an update to optimization parameters of a parameterized optimization algorithm, using the received feedback. Model updates are computed for each user device, using the received proximal maps and the parameterized optimization algorithm having the updated optimization parameters. Each model update is transmitted to each respective client for updating the respective model.