Abstract: Media content is recommended based on suitability for a designated activity. A vector engine is trained using a plurality of lists, each of the lists containing metadata associated with a plurality of media objects. The vector engine includes a neural network trained with corpus data including (i) the plurality of lists (ii) a plurality of titles, each one of the titles associated with one of the lists, and (iii) the metadata associated with the plurality of media objects. Training the vector engine involves initializing, using the vector engine, a plurality of feature vectors representing each of the lists, each of the media objects, and each of a plurality of words in the titles of the lists. The training then further involves nudging, using the vector engine, the feature vectors based on a plurality of co-occurrences of the lists, the media objects, the words in the titles of the lists, or a combination thereof. A feature vector corresponding to an activity is identified among the feature vectors.

Abstract: A topic tracking platform is disclosed that includes a machine-learning model that may be trained to expose topics in a corpus in response to a training table. Because topics are exposed, rather than searched for using existing taxonomies, the sensitivity of a topic tracking platform may be increased, and emerging topic trends may be more quickly flagged. Exposed topics may be automatically labelled, increasing the specificity of the topic tracking platform by overcoming the potential for topic labelling inconsistencies currently experienced in the art. Documents may be scored for each topic using information provided at a token granularity, and the contribution that each token of each document contributes to the topic may be visually represented. In some aspects, mechanisms are provided for reviewing topics of the corpus at varying granularities, including at a topic level, document level or token level granularity.

Abstract: Under conventional techniques, wastewater treatment has many problems such as poor production conditions, serious random interference, strong nonlinear behavior, large time-varying, and serious lagging. These problems cause difficulty in detecting wastewater treatment parameters such as biochemical oxygen demand (BOD) values that are used to monitor water quality. To solve problems associated with monitoring BOD values in real-time, the present disclosure utilizes a self-organizing recurrent RBF neural network designed for intelligent detecting of BOD values. Implementations of the present disclosure build a computing model of BOD values based on the self-organizing recurrent RBF neural network to achieve real-time and more accurate detection of the BOD values (e.g., a BOD concentration). The implementations herein quickly and accurately obtain BOD concentrations and improve the quality and efficiency of wastewater treatment.

Abstract: A system and method for determining stress level of a person in real-time have been disclosed. In one aspect, the system captures physiological data associated to the person. In one embodiment, the physiological data may be captured by using a plurality of sensors attached at wrist or ankle or neck or waist or hip of the person, for a predetermined time interval. The plurality of sensors may include a wrist watch or a wristband or a textile material. The system further pre-processes the physiological data in order to extract one or more physiological parameters. In one aspect, the pre-processing may include performing an analysis on the physiological data. The system further determines the stress level of the person upon performing the statistical analysis on the one or more physiological parameters. According to another embodiment, a method for real time determination of stress level of the person has also been provided.

Abstract: Systems and methods for analyzing documents are provided herein. A plurality of documents and user input are received via a computing device. The user input includes hard coding of a subset of the plurality of documents, based on an identified subject or category. Instructions stored in memory are executed by a processor to generate an initial control set, analyze the initial control set to determine at least one seed set parameter, automatically code a first portion of the plurality of documents based on the initial control set and the seed set parameter associated with the identified subject or category, analyze the first portion of the plurality of documents by applying an adaptive identification cycle, and retrieve a second portion of the plurality of documents based on a result of the application of the adaptive identification cycle test on the first portion of the plurality of documents.

Abstract: Techniques related to a system for news classification comprising one or more non-transitory memory devices and one or more hardware processors configured to execute instructions from the one or more non-transitory memory devices to cause the system to receive an article, the article including text, extract text from the received article, store the extracted text in a database, determine a set of potential target entities based on the extracted text, determine a classification of the article for each potential target entity of the set of potential target entities for a category, valence, presence of litigation, rumor, or opinion based on the extracted text, associate the classification of the article, along with a probability of the determined classification of the article for each potential target entity, assign the classification of the article if the probability of the classification is greater than a threshold probability, and store the classification of the article and the probability.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a recurrent neural network on training sequences using backpropagation through time. In one aspect, a method includes receiving a training sequence including a respective input at each of a number of time steps; obtaining data defining an amount of memory allocated to storing forward propagation information for use during backpropagation; determining, from the number of time steps in the training sequence and from the amount of memory allocated to storing the forward propagation information, a training policy for processing the training sequence, wherein the training policy defines when to store forward propagation information during forward propagation of the training sequence; and training the recurrent neural network on the training sequence in accordance with the training policy.

Abstract: The systems, devices, articles, and methods generally relate to sampling from an available probability distribution. The samples maybe used to create a desirable probability distribution, for instance for use in computing values used in computational techniques including: Importance Sampling and Markov chain Monte Carlo systems. An analog processor may operate as a sample generator, for example by: programming the analog processor with a configuration of the number of programmable parameters for the analog processor, which corresponds to a probability distribution over qubits of the analog processor, evolving the analog processor, and reading out states for the qubits. The states for the qubits in the plurality of qubits correspond to a sample from the probability distribution. Operation of the sampling device may be summarized as including updating a set of samples to include the sample from the probability distribution, and returning the set of samples.

Abstract: Described herein is a technology that facilitates the production of and the use of automated datagens for event-based. A datagen (i.e., data-generator or data generation system) is a component, module, or subsystem of computer systems that searches, monitors, and analyzes machine data. A datagen produces events that are further processed in various ways for subsequent use (such as searching, monitoring, and analysis).

Abstract: Neuromorphic processing apparatus is provided. The present invention may include a spiking neural network comprising a set of input spiking neurons each connected to each of a set of output spiking neurons via a respective synapse for storing a synaptic weight which is adjusted for that synapse in dependence on network operation in a learning mode of the apparatus, and each synapse is operable to provide a post-synaptic signal, dependent on its synaptic weight, to its respective output neuron. The present invention may further include a pre-processor unit adapted to process input data, defining a pattern of data points, to produce a first set of input spike signals which encode values representing respective data points, and a second set of input spike signals which encode values complementary to respective said values representing data points, and to supply the input spike signals to respective predetermined input neurons of the network.

Abstract: A neuromorphic device includes a pre-synaptic neuron, a synapse electrically coupled to the pre-synaptic neuron through a row line, and a post-synaptic neuron electrically coupled to the synapse through a column line. The post-synaptic neuron includes an integrator, a comparator, and an error corrector including an error detector and a correction signal generator. The comparator and the error corrector receive an output of the integrator.

Abstract: Implementations of the present specification disclose methods, devices, and apparatuses for determining a feature interpretation of a predicted label value of a user generated by a GBDT model. In one aspect, the method includes separately obtaining, from each of a predetermined quantity of decision trees ranked among top decision trees, a leaf node and a score of the leaf node; determining a respective prediction path of each leaf node; obtaining, for each parent node on each prediction path, a split feature and a score of the parent node; determining, for each child node on each prediction path, a feature corresponding to the child node and a local increment of the feature on the child node; obtaining a collection of features respectively corresponding to the child nodes; and obtaining a respective measure of relevance between the feature corresponding to the at least one child node and the predicted label value.

Abstract: A system and method for predicting search term popularity is disclosed herein. A database system may comprise a first database cluster H and a second database cluster L. A machine learning algorithm is trained to create a predictive model. Thereafter, for each record in a database system, the predictive model is used to calculate a probability of the record being accessed. If the calculated probability of the record being accessed is greater than a threshold value, then the record in the first database cluster H; otherwise, the record is placed in the second database cluster L. Training the machine learning algorithm comprises inputting a training feature vector associated with the record into the machine learning algorithm, inputting a cost vector into the machine learning algorithm, and iteratively operating the machine learning algorithm on each record in the set of records to create a predictive model. Other embodiments are also disclosed herein.

Abstract: Disclosed aspects relate to entity model establishment using an infinite mixture topic modeling (IMTM) technique. A set of event data which corresponds to a set of events may be detected. Using the IMTM technique, the set of event data which corresponds to the set of events may be analyzed. Based on analyzing the set of event data using the IMTM technique, a set of entity models for the set of events may be determined. Based on the set of entity models for the set of events, a subset of the set of entity models for the set of events may be established.

Abstract: According to an embodiment of the present invention, a system designates each document in a collection of documents as a member of a first group containing known subjects for a concept of interest or as a member of a second group containing candidate subjects for the concept of interest and determines a subset of documents for at least one subject. The system generates a classifier based on the documents in the first and second groups and applies the classifier to a set of documents for the at least one subject to determine whether each document belong to the first and/or second group. The system generates a score for the at least one subject based on a quantity of documents for that subject assigned to the first group of documents relative to a total quantity of documents for that subject and ranks that subject based on the determined score for each subject.

Abstract: Systems and methods for quantifying temporal indeterminacy of timelines are provided. Systems and methods can rely on solving temporal constraint problems to extract timelines and can calculate the temporal relation loss during timeline transformation and then identify the temporal indeterminate sections of extracted timelines from both timelines and temporal graphs to measure the total temporal information loss.

Abstract: The invention provides a fuzzy curve analysis based soft sensor modeling method using time difference Gaussian process regression, it is suitable for application in chemical process with time delay characteristics. This method can extract stable delay information from the historical database of process and introduce more relevant modeling data sequence to the dominant variable sequence. First of all, the method of fuzzy curve analysis (FCA) can intuitively judge the importance of the input sequence to the output sequence, estimate the time-delay parameters of process, and such offline time-delay parameter set can be utilized to restructure the modeling data. For the new input data, based on the historical variable value before a certain time, the current dominant value can be predicted by time difference Gaussian Process Regression (TDGPR) model. This method does not encounter the problem of model updating and can effectively track the drift between input and output data.

Abstract: Graph partitioning and placement for multi-chip neurosynaptic networks. According to various embodiments, a neural network description is read. The neural network description describes a plurality of neurons. The plurality of neurons has a mapping from an input domain of the neural network. The plurality of neurons is labeled based on the mapping from the input domain. The plurality of neurons is grouped into a plurality of groups according to the labeling. Each of the plurality of groups is continuous within the input domain. Each of the plurality of groups is assigned to at least one neurosynaptic core.

Abstract: A configurable spiking neural network based accelerator system is provided. The accelerator system may be executed on an expansion card which may be a printed circuit board. The system includes one or more application specific integrated circuits comprising at least one spiking neural processing unit and a programmable logic device mounted on the printed circuit board. The spiking neural processing unit includes digital neuron circuits and digital, dynamic synaptic circuits. The programmable logic device is compatible with a local system bus. The spiking neural processing units contain digital circuits comprises a Spiking Neural Network that handles all of the neural processing. The Spiking Neural Network requires no software programming, but can be configured to perform a specific task via the Signal Coupling device and software executing on the host computer.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for receiving, by a computational graph system, a request to process a computational graph; obtaining data representing a subgraph of the computational graph, the computational graph comprising a plurality of nodes and directed edges, wherein each node represents a respective operation, wherein each directed edge connects a respective first node to a respective second node, the subgraph assigned to a first device by a placer in the computational graph system; determining that the first device comprises a hardware accelerator having a plurality of streams; in response to determining, generating instructions that when executed by the first device cause the first device to: assign the operation represented by each node in the subgraph to a respective stream; and perform the operations represented by the nodes in the subgraph in accordance with the assignment.