Abstract: Techniques are described for generating parallel data for real-time speech form conversion. In an embodiment, based at least in part on input speech data of an original form, a speech machine learning (ML) model generates parallel speech data. The parallel speech data includes the input speech data of the original form and temporally aligned output speech data of a target form different than the original form. Each frame of the input speech data temporally corresponds to the corresponding output speech frame of the target speech form and contains a same portion of the particular content. The techniques further include training a teacher machine learning model that is offline and is substantially larger than a student machine learning model for converting speech form. Transferring “knowledge” from the trained Teacher model for training the Production Student Model that performs the speech form conversion on an end-user computing device.

Abstract: The present application discloses a method and a device for user grouping and resource allocation in a NOMA-MEC system. The hybrid deep reinforcement learning algorithm proposed in the present application solves the hybrid problem of deep reinforcement learning that is difficult to deal with both discrete and continuous action spaces by using DDPG to optimize continuous actions and DQN to optimize discrete actions. Specifically, the algorithm determines a bandwidth allocation, an offloading decision, and a sub-channel allocation (user grouping) of the user device based on the user's channel state, in order to maximize the ratio of the computation rate to the consumed power of the system. The algorithm is well adapted to the dynamic characteristics of the environment and effectively improves the energy efficiency and spectrum resource utilization of the system.

Abstract: A system, or platform, for processing enterprise data is configured to adapt to different domains and analyze data from various data sources and provide enriched results. The platform includes a data extraction and consumption module to translate domain specific data into defined abstractions, breaking it down for consumption by a feature extraction engine. A core engine, which includes a number of machine learning modules, such as a feature extraction engine, analyzes the data stream and produces data fed back to the clients via various interfaces. A learning engine incrementally and dynamically updates the training data for the machine learning by consuming and processing validation or feedback data. The platform includes a data viewer and a services layer that exposes the enriched data results. Integrated domain modeling allows the system to adapt and scale to different domains to support a wide range of enterprises.

Abstract: A method begins by agriculture equipment collecting current on-site gathered agriculture data regarding an agriculture region and sending at least a representation of the current on-site gathered agriculture data to a host device. The method continues with the host device processing one or more of the at least a representation of the current on-site gathered agriculture data, current off-site gathered agriculture data, historical on-site gathered agriculture data, historical off-site gathered agriculture data, and historical analysis of agriculture predictions regarding the agriculture region to produce a current agriculture prediction for the agriculture region. The method continues with the host device generating an agriculture prescription regarding at least a portion of the agriculture region based on the current agriculture prediction and sending the agriculture prescription to one or more of the agriculture equipment.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a data entity that causes a processing unit to process a computational graph. In one aspect, a method includes the actions of receiving data identifying a computational graph, the computational graph including a plurality of nodes representing operations; obtaining compilation artifacts for processing the computational graph on a processing unit; and generating a data entity from the compilation artifacts, wherein the data entity, when invoked, causes the processing unit to process the computational graph by executing the operations represented by the plurality of nodes.

Abstract: A method and apparatus for generating a temporal knowledge graph, a device and a medium. An embodiment comprises: acquiring corpus including time information; performing multivariate data extraction on the corpus, multivariate data including an entity pair, an entity relationship and a target time interval of the entity relationship, the target time interval being used to indicate a valid period of the entity relationship; and generating a temporal knowledge graph based on the entity pair, the entity relationship and the target time interval of the entity relationship.

Abstract: Systems and methods for performing a quantization of artificial neural networks (ANNs) are provided. An example method may include receiving a description of an ANN and sets of inputs to neurons of the ANN, the description including sets of weights of the inputs, the weights being of a first data type, determining a first interval of the first data type to be mapped to a second interval of a second data type; performing computations of sums of products of the weights and the inputs to obtain a set of sum results, wherein the computations are performed using at least one number within the second interval, the number being a result of mapping of a number of the first interval to a number of the second interval, determining a measure of saturations in sum results, and adjusting, based on the measure of saturations, one of the first and second intervals.

Abstract: A factor estimation device is configured to receive information pertaining to objects, to extract state information from the information received, to identify a predetermined state pertaining to a first object from among the objects, to receive state information extracted that corresponds to the predetermined state and classify the predetermined state, to extract condition information from the information received, to identify the condition up until the predetermined state, and to receive condition information that is output by the condition-information extraction unit and corresponds to the condition identified and classify the condition identified. Subsequently, the factor estimation device is configured to estimate the condition that may result in the predetermined state on the basis of the result of classifying the predetermined state and the result of classifying the identified condition.

Abstract: A multi-layered knowledge base system and a processing method thereof are provided. In the system, semantic space learning of converting learning data into a semantic vector, which is a vector of semantic space, by learning a transformation function based on a plurality of learning data is performed. Then, relational knowledge learning of acquiring a relation between the semantic vectors by learning a relation function based on the semantic vectors obtained by the semantic space learning is performed. The acquired relation is converted into graph knowledge. The graph knowledge uses the relation as an edge and the semantic vector corresponding to the relation as a node.

Abstract: Comprehensive molecular profiling provides a wealth of data concerning the molecular status of patient samples. Such data can be compared to patient response to treatments to identify biomarker signatures that predict response or non-response to such treatments. This approach has been applied to identify biomarker signatures that strongly correlate with response of colorectal cancer patients to FOLFOX. Described herein are data structures, data processing, and machine learning models to predict effectiveness of a treatment for a disease or disorder of a subject having a particular set of biomarkers, as well as an exemplary application of such a model to precision medicine, e.g., to methods for selecting a treatment based on a molecular profile, e.g., a treatment comprising administration of 5-fluorouracil/leucovorin combined with oxaliplatin (FOLFOX) or with irinotecan (FOLFIRI).

Abstract: Disclosed herein is a technique for implementing a framework that enables application developers to enhance their applications with dynamic adjustment capabilities. Specifically, the framework, when utilized by an application on a mobile computing device that implements the framework, can enable the application to establish predictive models that can be used to identify meaningful behavioral patterns of an individual who uses the application. In turn, the predictive models can be used to preempt the individual's actions and provide an enhanced overall user experience. The framework is configured to interface with other software entities on the mobile computing device that conduct various analyses to identify appropriate times for the application to manage and update its predictive models. Such appropriate times can include, for example, identified periods of time where the individual is not operating the mobile computing device, as well as recognized conditions where power consumption is not a concern.

Abstract: The present disclosure relates to a method for classifying a query information element using the similarity between the query information element and a set of support information elements. A resulting set of similarity scores is transformed using a sharpening function such that the transformed scores are decreasing as negative similarity scores increase and the transformed scores are increasing as positive similarity scores increase. A class of the query information element is determined based on the transformed similarity scores.

Abstract: An apparatus and method for generating a solution, the apparatus including a user interface configured to receive user data, at least a processor communicatively connected to the use interface and a memory communicatively connected to the processor, the memory containing instructions configuring the processor to analyze a user interaction received through the user interface based on the user data, identify a problem as a function of the user interaction, generate a solution based on the user interaction and user data received from the user interface, wherein the solution includes a plurality of resources for addressing the problem, wherein generating a solution includes, training a web crawler configured to retrieve and index a plurality of resources, and track user progress with a solution.

Abstract: Systems and method are provided for data flattening. A corpus of data is extracted from at least one data source and stored at a data warehousing platform. A workflow is applied to the extracted corpus of data to provide a transformed corpus of data. The workflow includes a sequence of atomic functions selected from a library of atomic functions to perform an associated task on the corpus of data. The transformed corpus of data is provided from the data warehousing platform to a machine learning model as a set of training data.

Abstract: A hardware implementation of a neural network and a method of processing data in such a hardware implementation are disclosed. Input data for a plurality of layers of the network is processed in blocks, to generate respective blocks of output data. The processing proceeds depth-wise through the plurality of layers, evaluating all layers of the plurality of layers for a given block, before proceeding to the next block.

Abstract: Systems and methods include obtaining network data including first data of devices and services in the network, Performance Monitoring (PM) data associated with the devices and services and with associated timestamps, and second data including any of tickets, alarms, and events affecting some of the devices and services and with associated timestamps; obtaining one or more target events from the second data based on associated operational impact in the network; determining the PM data that is statistically correlated with the one or more target events; determining the statistically correlated PM data over a corresponding time based on the associated timestamps of the PM data and the one or more target events; and providing labels for the determined statistically correlated PM data with an associated label based on the associated target event of the one or more target events.

Abstract: Embodiments may relate to a system to be used in an oscillating neural network (ONN). The system may include a control node and a plurality of nodes wirelessly communicatively coupled with a control node. A node of the plurality of nodes may be configured to identify an oscillation frequency of the node based on a weight W and an input X. The node may further be configured to transmit a wireless signal to the control node, wherein a frequency of the wireless signal oscillates based on the identified oscillation frequency. Other embodiments may be described or claimed.

Abstract: Systems and methods are described for training a machine learning model using intelligently selected multiclass vectors. According to an embodiment, a processing resource of a computing system receives a first set of un-labeled feature vectors. The first set feature vectors are homomorphically translated using a T-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm to obtain a second set of feature vectors with reduced dimensionality. The second set of feature vectors are clustered to obtain an initial set of clusters using centroid-based clustering. An optimal set of clusters is identified among the initial set of clusters by performing a convex optimization process on the initial set of clusters. For each cluster of the optimal set of clusters, a representative vector from the cluster is selected for labeling.

Abstract: A second problem Hamiltonian may replace a first problem Hamiltonian during evolution of an analog processor (e.g., quantum processor) during a first iteration in solving a first problem. This may be repeated during a second, or further successive iterations on the first problem, following re-initialization of the analog processor. An analog processor may evolve under a first non-monotonic evolution schedule during a first iteration, and second non-monotonic evolution schedule under second, or additional non-monotonic evolution schedule under even further iterations. A first graph and second graph may each be processed to extract final states versus a plurality of evolution schedules, and a determination made as to whether the first graph is isomorphic with respect to the second graph. An analog processor may evolve by decreasing a temperature of, and a set of quantum fluctuations, within the analog processor until the analog processor reaches a state preferred by a problem Hamiltonian.

Abstract: A recommendation method includes determining one or more aspects of a first item based on at least one descriptive text of the first item. The recommendation method also includes updating a knowledge graph containing nodes that represent multiple items, multiple users, and multiple aspects. Updating the knowledge graph includes linking one or more nodes representing the one or more aspects of the first item to a node representing the first item with one or more first edges. Each of the one or more first edges identifies weights associated with (i) user sentiment about the associated aspect of the first item and (ii) an importance of the associated aspect to the first item. In addition, the recommendation method includes recommending a second item for a user with an explanation based on at least one aspect linked to the second item in the knowledge graph.