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ENTITY ANALYSIS SYSTEM

Thu, 06 Apr 2017 08:00:00 EDT

A method for building a factual database of concepts and entities that are related to the concepts through a learning process. Training content (e.g., news articles, books) and a set of entities (e.g., Bill Clinton and Barack Obama) that are related to a concept (e.g., Presidents) is received. Groups of words that co-occur frequently in the textual content in conjunction with the entities are identified as templates. Templates may also be identified by analyzing parts-of-speech patterns of the templates. Entities that co-occur frequently in the textual content in conjunction with the templates are identified as additional related entities (e.g., Ronald Reagan and Richard Nixon). To eliminate erroneous results, the identified entities may be presented to a user who removes any false positives. The entities are then stored in association with the concept.



Training Artificial Intelligence

Thu, 06 Apr 2017 08:00:00 EDT

Data is received characterizing a request for agent computation of sensor data. The request includes a required confidence and required latency for completion of the agent computation. Agents to query are determined based on the required confidence. Data is transmitted to query the determined agents to provide analysis of the sensor data. Related apparatus, systems, techniques, and articles are also described.



ASYNCHRONOUS STOCHASTIC GRADIENT DESCENT

Thu, 06 Apr 2017 08:00:00 EDT

The example computer-implemented method may comprise computing, by a generator processor on each of a plurality of learners, a gradient for a mini-batch using a current weight at each of the plurality of learners. The method may also comprise generating, by the generator processor on each of the plurality of learners, a plurality of triples, wherein each of the triples comprises the gradient, the weight index of the current weights used to compute the gradient, and a mass of the gradient. The method may further comprise performing, by a reconciler processor on each of the plurality of learners, an allreduce operation on the plurality of triples to obtain an allreduced triple sequence. Additionally, the method may comprise updating, by the reconciler processor on each of the plurality of learners, the current weight at each of the plurality of learners to a new current weight using the allreduced triple sequence.



PROBABILISTIC MESSAGE DISTRIBUTION

Thu, 06 Apr 2017 08:00:00 EDT

This disclosure relates to systems and methods that include configuring a machine learning system to train on a plurality of messages, solving, for a set of input messages, a multi-objective optimization problem to minimize a number of messages to send while satisfying one or more constraints, selecting a random value for a message in the set, setting a send constraint for the message in the set using the send threshold for the message in the set and the random value, and sending the message in the set in response to the send constraint being satisfied.



METHOD AND SYSTEM FOR PROVIDING SYNTHETIC ANSWERS TO A PERSONAL QUESTION

Thu, 06 Apr 2017 08:00:00 EDT

A method, implemented on at least one computing device each of which has at least one processor, storage, and a communication platform connected to a network for providing synthetic answers to a personal question is disclosed. A personal question is received from a person. One or more entities are extracted from the personal question. One or more relations are extracted from the personal question. A model is selected based on the personal question. One or more synthetic answers to the personal question are obtained based on the one or more entities, the one or more relations, and the selected model.



DEVICE AND METHOD FOR ESTIMATING TRAVEL SPEED

Thu, 06 Apr 2017 08:00:00 EDT

A device that estimates the travel speed of a mobile body obtains a speed similarity of a travel speed associated with a road subject to estimation and a travel speed associated with each section of a map. The device also obtains an environment similarity degree of environment information corresponding to a section including the subject road and environment information corresponding to each section. A section similar to the section including the subject road is selected based on the total similarity degree, which is calculated based on the speed similarity and the environment similarity, to set the travel speed associated with the selected section as a travel speed of a corresponding time period on the subject road.



SCHEMA AND METHOD FOR DECEPTION DETECTION

Thu, 06 Apr 2017 08:00:00 EDT

A method for predicting subject trustworthiness includes using at least one classifier to predict truthfulness of subject responses to prompts during a local or remote interview, based on subject responses and response times, as well as interviewer impressions and response times, and, in embodiments, also biometric measurements of the interviewer. Data from the subject interview is normalized and analyzed relative to an experience database previously created using data obtained from test subjects. Classifier prediction algorithms incorporate assumptions that subject response times are indicators of truthfulness, that subjects will tend to be consistently truthful or deceitful, and that conscious and subconscious impressions of the interviewer are predictive of subject trustworthiness. Data regarding interviewer impressions can be derived from interviewer response times, interviewer questionnaire answers, and/or interviewer biometric data. Appropriate actions based on trustworthiness predictions can include denial of security clearance or further investigation relevant to the subject.



Method and Apparatus for Establishing and Using User Recommendation Model in Social Network

Thu, 06 Apr 2017 08:00:00 EDT

A method and an apparatus for establishing and using a user recommendation model in a social network. The method includes obtaining training data from the social network, performing heterogeneous data transfer learning on the training data to learn a semanteme of the training data, establishing an association between a user and the image data by using the text data as a medium, establishing a semantic association relationship between the image data and the user according to the semanteme of the training data and the association between the user and the image data, and establishing a user recommendation model according to the semantic association relationship, where the user recommendation model includes the semantic association relationship between the image data and the user.



TECHNIQUES FOR RESOLVING ENTITIES IN RECEIVED QUESTIONS

Thu, 06 Apr 2017 08:00:00 EDT

A technique for resolving entities provided in a question includes creating respective entity context vectors (ECVs) for respective entities in an applicable knowledge graph (KG). A question is received from a user. A first entity is identified in the question. The first entity is associated with a matching one of the entities in the KG. An ECV for the matching one of the entities in the KG is modified. An answer to the question is generated based on the modified ECV.



Framework for Augmented Machine Decision Making

Thu, 06 Apr 2017 08:00:00 EDT

Sensor data is received. The sensor data is classified into one of two or more classes by at least requesting processing of a machine computational component, receiving a result of the machine computation component, requesting processing of an agent computation component, and receiving a result of the agent computation component. The agent computation component includes a platform to query an agent. The result from the agent computation component or the result from the machine computation component is provided. Related apparatus, systems, techniques, and articles are also described.



Augmented Machine Decision Making

Thu, 06 Apr 2017 08:00:00 EDT

Sensor data is received. The sensor data is classified into one of two or more classes by at least requesting processing of a machine computational component, receiving a result of the machine computation component, requesting processing of an agent computation component, and receiving a result of the agent computation component. The agent computation component includes a platform to query an agent. The result from the agent computation component or the result from the machine computation component is provided. Related apparatus, systems, techniques, and articles are also described.



METHOD AND SYSTEM FOR BUILDING DOMAIN INTELLIGENT SOLUTION

Thu, 06 Apr 2017 08:00:00 EDT

A method and system is provided for building domain intelligent solution. The present application provides a method and system for building a domain intelligent solution, comprises of utilizing a language existing as a generic model for capturing intrinsic knowledge pertaining to a technical domain; creating a domain intelligent solution for said technical domain using said language or vocabulary; translating the domain intelligent solution into required wrappers for them to be integrated with a third party technology or tool; and integrating said created domain intelligent solution with the third party technology or tool for providing system engineering capabilities to make them domain intelligent.



ACTION SUGGESTIONS FOR USER-SELECTED CONTENT

Thu, 06 Apr 2017 08:00:00 EDT

Systems and methods are provided for suggesting actions for selected text based on content displayed on a mobile device. An example method can include converting a selection made via a display device into a query, providing the query to an action suggestion model that is trained to predict an action given a query, each action being associated with a mobile application, receiving one or more predicted actions, and initiating display of the one or more predicted actions on the display device. Another example method can include identifying, from search records, queries where a website is highly ranked, the website being one of a plurality of websites in a mapping of websites to mobile applications. The method can also include generating positive training examples for an action suggestion model from the identified queries, and training the action suggestion model using the positive training examples.



SYSTEMS AND METHODS FOR A COMPUTER UNDERSTANDING MULTI MODAL DATA STREAMS

Thu, 06 Apr 2017 08:00:00 EDT

Systems and methods for understanding (imputing meaning to) multi modal data streams may be used in intelligent surveillance and allow a) real-time integration of streaming data from video, audio, infrared and other sensors; b) processing of the results of such integration to obtain understanding of the situation as it unfolds; c) assessing the level of threat inherent in the situation; and d) generating of warning advisories delivered to appropriate recipients as necessary for mitigating the threat. The system generates understanding of the system by creating and manipulating models of the situation as it unfolds. The creation and manipulation involve “neuronal packets” formed in mutually constraining associative networks of four basic types. The process is thermodynamically driven, striving to produce a minimal number of maximally stable models. Obtaining such models is experienced as grasping, or understanding the input stream (objects, their relations and the flow of changes).



METHODS AND SYSTEMS FOR EVENT REPORTING

Thu, 06 Apr 2017 08:00:00 EDT

An automaton is implemented in a state machine engine. The automaton is configured to observe data from a beginning of an input data stream until a point when an end of data (EOD) signal is seen. Additionally the automaton is configured to report an event only when one and only one occurrence of a target symbol is seen in the input data stream.



MEMRISTIVE NANOFIBER NEURAL NETWORKS

Thu, 06 Apr 2017 08:00:00 EDT

Disclosed are various embodiments of memristive neural networks comprising neural nodes. Memristive nanofibers are used to form artificial synapses in the neural networks. Each memristive nanofiber may couple one or more neural nodes to one or more other neural nodes. In one case, a memristive neural network includes a first neural node, a second neural node, and a memristive fiber that couples the first neural node to the second neural node. The memristive fiber comprises a conductive core and a memristive shell, where the conductive core forms a communications path between the first neural node and the second neural node and the memristive shell forms a memristor synapse between the first neural node and the second neural node.



SELF-ORGANIZED SOLID-STATE SYNTHETIC NEURONAL STRUCTURE

Thu, 06 Apr 2017 08:00:00 EDT

A synthetic neuronal structure makes use of a semiconductor-metal phase transition material having material regions separated by discontinuities. The discontinuities represent interfaces such that different phases in two adjacent regions result in a metal-semiconductor interface. The interface supports a charge accumulation and a discharge of accumulated charge when an activation energy provided, for example, by electrical current, localized heating or optical energy, reaches a threshold necessary for breakdown of a potential barrier presented by the interface, and thus mimics a leaky integrate-and-fire neuron. With many such interfaces distributed through the structure, the local inputs to a neuron become a weighted sum of energy from neighboring neurons. Thus, different combinations of signals at one or more inputs connected to the structure will favor different neural pathways through the structure, thereby resulting in a neural network.



METHODS AND SYSTEMS FOR CREATING NETWORKS

Thu, 06 Apr 2017 08:00:00 EDT

The Automata Processor Workbench (AP Workbench) is an application for creating and editing designs of AP networks (e.g., one or more portions of the state machine engine, one or more portions of the FSM lattice, or the like) based on, for example, an Automata Network Markup Language (ANML). For instance, the application may include a tangible, non-transitory computer-readable medium configured to store instructions executable by a processor of an electronic device, wherein the instructions include instructions to represent an automata network as a graph.



INTELLIGENT IMAGE CAPTIONING

Thu, 06 Apr 2017 08:00:00 EDT

Presented herein are embodiments of a multimodal Recurrent Neural Network (m-RNN) model for generating novel image captions. In embodiments, it directly models the probability distribution of generating a word given a previous word or words and an image, and image captions are generated according to this distribution. In embodiments, the model comprises two sub-networks: a deep recurrent neural network for sentences and a deep convolutional network for images. In embodiments, these two sub-networks interact with each other in a multimodal layer to form the whole m-RNN model. The effectiveness of an embodiment of model was validated on four benchmark datasets, and it outperformed the state-of-the-art methods. In embodiments, the m-RNN model may also be applied to retrieval tasks for retrieving images or captions.



MODIFYING AT LEAST ONE ATTRIBUTE OF AN IMAGE WITH AT LEAST ONE ATTRIBUTE EXTRACTED FROM ANOTHER IMAGE

Thu, 06 Apr 2017 08:00:00 EDT

In various implementations, one or more specific attributes found in an image can be modified utilizing one or more specific attributes found in another image. Machine learning, deep neural networks, and other computer vision techniques can be utilized to extract attributes of images, such as color, composition, font, style, and texture from one or more images. A user may modify at least one of these attributes in a first image based on the attribute(s) of another image and initiate a visual-based search using the modified image.



SYSTEMS AND METHODS FOR USER AUTHENTICATION

Thu, 06 Apr 2017 08:00:00 EDT

Systems, methods, and non-transitory computer-readable media can determine at least one operation that causes a challenge-response test to be activated for authenticating a user. A first set of content items that each have a threshold similarity to a query content item can be determined. A second set of content items that each have a threshold dissimilarity to the query content item can be determined. The challenge-response test can be provided for display to the user. The challenge-response test presents a group of content items including the first set of content items and the second set of content items.



METHOD AND SYSTEM FOR EXPLORING THE ASSOCIATIONS BETWEEN DRUG SIDE-EFFECTS AND THERAPEUTIC INDICATIONS

Thu, 06 Apr 2017 08:00:00 EDT

A system and method for analyzing chemical data including a processor and one or more classifiers, stored in memory and coupled to the processor, which further includes an indication predictive module configured to predict whether a given chemical treats a particular indication or not and a side effect predictive module configured to predict whether a given chemical causes a side-effect or not. A correlation engine is configured to determine one or more correlations between one or more indications and one or more side effects for the given chemical and a visualization tool is configured to analyze the one or more correlations and to output results of the analysis.



METHOD AND SYSTEM FOR EXPLORING THE ASSOCIATIONS BETWEEN DRUG SIDE-EFFECTS AND THERAPEUTIC INDICATIONS

Thu, 06 Apr 2017 08:00:00 EDT

A system and method for analyzing chemical data including a processor and one or more classifiers, stored in memory and coupled to the processor, which further includes an indication predictive module configured to predict whether a given chemical treats a particular indication or not and a side effect predictive module configured to predict whether a given chemical causes a side-effect or not. A correlation engine is configured to determine one or more correlations between one or more indications and one or more side effects for the given chemical and a visualization tool is configured to analyze the one or more correlations and to output results of the analysis.



CONSTRUCTING CUSTOM KNOWLEDGEBASES AND SEQUENCE DATASETS WITH PUBLICATIONS

Thu, 06 Apr 2017 08:00:00 EDT

Illustrative embodiments of custom knowledgebases and sequence datasets, as well as related methods, are disclosed. In one illustrative embodiment, one or more computer-readable media may comprise a custom knowledgebase and an associated sequence dataset. The custom knowledgebase may comprise a plurality of assertions that have been automatically extracted from a plurality of publications, where each of the plurality of assertions encodes a relationship between a subject and an object. The sequence dataset may comprise a plurality of called biological sequences, where each of the plurality of called biological sequences is associated with one or more of the plurality of assertions of the custom knowledgebase.



SYSTEM AND METHOD FOR GENERATING DETECTION OF HIDDEN RELATEDNESS BETWEEN PROTEINS VIA A PROTEIN CONNECTIVITY NETWORK

Thu, 06 Apr 2017 08:00:00 EDT

Systems and methods are for generating a weighted relatedness protein network. The method includes steps of obtaining a protein network; generating training data; generating a weighting function derived from the training data values; and applying the weighting function to a protein network, thereby generating a weighted relatedness protein network. The protein network may be applied for prediction of protein properties by detection of relatedness with annotated sequences.



SYSTEMS AND METHODS FOR EVENT TRACKING USING TIME-WINDOWED COUNTERS

Thu, 06 Apr 2017 08:00:00 EDT

Some embodiments include tracking events and classifying assets within a computer system. A time series of occurrences of an event type associated with at least one asset is generated. A first signal value and a second signal value is determined based on the time series. The at least one asset can be classified based on comparison of the first signal value and the second signal value. The time series can be based on at least one time window including time intervals. Counters to determine a number of occurrences of an event type can be associated with the time intervals. Each of the counters can be incremented upon occurrence of the event type associated with the at least one asset during an associated time interval.



TOPIC MINING METHOD AND APPARATUS

Thu, 06 Apr 2017 08:00:00 EDT

A topic mining method and apparatus are disclosed. When an iterative process is executed each time, an object message vector is determined from a message vector according to a residual of the message vector, so that a current document-topic matrix and a current term-topic matrix are updated according to only the object message vector, and then calculation is performed, according to the current document-topic matrix and the current term-topic matrix, on only an object element that is in the term-document matrix and that corresponds to the object message vector, thereby avoiding that in each iterative process, calculation needs to be performed on all non-zero elements in the term-document matrix, and avoiding that the current document-topic matrix and the current term-topic matrix are updated according to all message vectors, which greatly reduces an operation amount, increases a speed of topic mining, and increases efficiency of topic mining.



COMPLETION DESIGN OPTIMIZATION USING MACHINE LEARNING AND BIG DATA SOLUTIONS

Thu, 06 Apr 2017 08:00:00 EDT

Systems and methods for generating and storing completion design models in a central data repository of modems for completion design, well bore (such as fracturing or drilling) or other operations is shown. In one embodiment, the methods comprise identifying parameters of a hydraulic fracturing operation within a subterranean formation; generating a completion design model based on the parameters of the hydraulic fracturing operation; storing the completion design model in a central data repository of models; generating the central data repository of models; wherein the data repository is based on previously generated models for one or more other subterranean formations having varying levels of uncertainty for expected output; reducing the level of uncertainty for the expected output based on completion parameters, wherein the completion parameters are used to update the central data repository of models; accessing the central data repository of models to predict results expected for a data set based at least in part on the central data repository of models, wherein the results comprise a prediction as to a level of output for the dataset, further wherein the prediction comparison results in an identification of the optimized completion design for the dataset