Abstract: Aspects of embodiments pertain to systems configured to perform neuromonitoring data analysis, by employing the following: receiving patient data comprising data that are descriptive of at least one physical stimulus applied to a mammalian subject for responsively generating at least one signal in a plurality of neural structures of the subject's nervous system; and sensor data descriptive of at least one neurophysiological response signal generated in response the applied physical stimulus. The systems are further configured to determine, based on the received patient data descriptive of the at least one physical stimulus and the generated response signal, at least one characteristic with respect to at least one of the plurality of neural structures of the patient.

Abstract: During end-to-end training of a Deep Neural Network (DNN), a differentiable estimator subnetwork is operated to estimate a functionality of an external software application. Then, during inference by the trained DNN, the differentiable estimator subnetwork is replaced with the functionality of the external software application, by enabling API communication between the DNN and the external software application.

Abstract: In some examples, a system for executing natural language processing techniques can include a processor to detect text comprising a word and a number. The processor can also embed, via a word embedding model, the word into a first vector of a vector space and embed the number by converting the number into a second vector of the vector space. Additionally, the processor can train a deep neural network to execute instructions based on the first embedded vector of the word and the second embedded vector of the number. Furthermore, the processor can process an instruction based on the trained deep neural network.

Abstract: In some examples, a system for executing natural language processing techniques can include a processor to detect text comprising a word and a number. The processor can also embed, via a word embedding model, the word into a first vector of a vector space and embed the number by converting the number into a second vector of the vector space. Additionally, the processor can train a deep neural network to execute instructions based on the first embedded vector of the word and the second embedded vector of the number. Furthermore, the processor can process an instruction based on the trained deep neural network.

Abstract: An example system includes a processor to receive a plurality of object aspects of an object to be evaluated using a process, a structure of the process, a plurality of extracted facts from documents, a tree related to the plurality of object aspects and the structure, and a thesis for each leaf in the tree. The processor is also to relate the extracted facts to the theses in the tree. The processor is to generate a score for each leaf corresponding to a fact in the tree. The processor is to generate a thesis score and a thesis summary for each thesis based on the scores and the summaries of related facts for each thesis. The processor is to further generate a final score for the object based on the thesis scores.

Abstract: During end-to-end training of a Deep Neural Network (DNN), a differentiable estimator subnetwork is operated to estimate a functionality of an external software application. Then, during inference by the trained DNN, the differentiable estimator subnetwork is replaced with the functionality of the external software application, by enabling API communication between the DNN and the external software application.

Abstract: An example system includes a processor to receive a plurality of object aspects of an object to be evaluated using a process, a structure of the process, a plurality of extracted facts from documents, a tree related to the plurality of object aspects and the structure, and a thesis for each leaf in the tree. The processor is also to relate the extracted facts to the theses in the tree. The processor is to generate a score for each leaf corresponding to a fact in the tree. The processor is to generate a thesis score and a thesis summary for each thesis based on the scores and the summaries of related facts for each thesis. The processor is to further generate a final score for the object based on the thesis scores.

Abstract: A computer-implemented method performed by a computerized device, comprising: obtaining consumption data comprising readings indicating consumption of a product, the consumption data is monitored by a plurality of metering devices, wherein the metering devices are associated with a plurality of consumption entities, wherein the plurality of consumption entities comprising a consumption unit and one or more sub consumption units of the consumption unit, wherein the product is supplied serially to the one or more sub consumption units via the consumption unit; and calculating residual consumption of the plurality of consumption entities at a point in time, wherein the residual consumption is consumption of the consumption unit and which is not associated with a sub consumption unit, wherein the residual consumption is calculated based on the consumption by the plurality of consumption entities at a plurality of points in time.

Abstract: A computer-implemented method performed by a computerized device, comprising: obtaining consumption data comprising readings indicating consumption of a product, the consumption data is monitored by a plurality of metering devices, wherein the metering devices are associated with a plurality of consumption entities, wherein the plurality of consumption entities comprising a consumption unit and one or more sub consumption units of the consumption unit, wherein the product is supplied serially to the one or more sub consumption units via the consumption unit; and calculating residual consumption of the plurality of consumption entities at a point in time, wherein the residual consumption is consumption of the consumption unit and which is not associated with a sub consumption unit, wherein the residual consumption is calculated based on the consumption by the plurality of consumption entities at a plurality of points in time.