Abstract: One embodiment is a method for implementing a cloud-based portable miniaturized system for performing non-invasive blood glucose level measurement in real time. The method includes using an optical source to emit optical radiations at certain wavelengths through breath in an air collection chamber; receiving the emitted optical transmissions at a photodetector; converting the received optical transmissions to digital data; accumulating the digital data for a first time period; and periodically transmitting the accumulated digital data to a cloud service for further processing.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to scalable learning of latent language structure with logical offline cycle consistency. The computer-implemented system can comprise a memory that can store computer executable components. The computer-implemented system can further comprise a processor that can execute the computer executable components stored in the memory, wherein the computer executable components can comprise a training component that can train a semantic parser to predict one or more parses for an input text using offline reinforcement learning based on parallelizable offline sampling.

Abstract: The present disclosure relates to a pipe cutting apparatus for forming a circumferentially-extending cut around a pipe having an elongate axis. The apparatus comprises a support having a track at least partially defining a pipe receiving channel having an axis, with an inner surface configured to abut a pipe within the pipe receiving channel. The apparatus further comprises a cutting tool supported by the support, wherein the cutting tool is a milling tool.

Abstract: A method, computer system, and a computer program product are provided. Inferencing is performed with a probabilistic logical neural network. The probabilistic logical neural network includes a probabilistic graphical model that includes propositional nodes, logical operational nodes, and directed edges. The directed edges indicate a direction of upward inference. The downward inference is in an opposite direction from that of the directed edges. The probabilistic logical neural network implements upward and downward inference. The propositional and logical operational nodes are coupled with respective belief bounds. Each of the logical operational nodes includes a respective activation function set to a probability-respecting generalization of the Fréchet inequalities.

Abstract: Provided are techniques for a search using an overlay graph mapping to source knowledge graphs. A plurality of overlay graphs are generated, where each overlay graph comprises entities represented by nodes and relations represented by edges, and where the entities and the relations map to a subset of entities and relations in a plurality of source knowledge graphs. A search request comprising an entity and a relation is received. An overlay graph is selected from the plurality of overlay graphs based on the entity and the relation. The search request is issued against the overlay graph, where the search request is translated to knowledge graph specific queries, and where the knowledge graph specific queries are issued against the plurality of source knowledge graphs. Search results are received from the plurality of source knowledge graphs. The search results are used to respond to the search request.

Abstract: Provided are techniques for a search using an overlay graph mapping to source knowledge graphs. A plurality of overlay graphs are generated, where each overlay graph comprises entities represented by nodes and relations represented by edges, and where the entities and the relations map to a subset of entities and relations in a plurality of source knowledge graphs. A search request comprising an entity and a relation is received. An overlay graph is selected from the plurality of overlay graphs based on the entity and the relation. The search request is issued against the overlay graph, where the search request is translated to knowledge graph specific queries, and where the knowledge graph specific queries are issued against the plurality of source knowledge graphs. Search results are received from the plurality of source knowledge graphs. The search results are used to respond to the search request.

Abstract: The exemplary embodiments disclose a method, a computer program product, and a computer system for determining that one or more model pipelines satisfy one or more constraints. The exemplary embodiments may include detecting a user uploading data, one or more constraints, and one or more model pipelines, collecting the data, the one or more constraints, and the one or more model pipelines, and determining that one or more of the model pipelines satisfies all of the one or more constraints based on applying one or more algorithms to the collected data, constraints, and model pipelines.

Abstract: A computer-implemented method for facilitating reasoning under conditions of uncertainty includes receiving input including a set of logic formulas, a set of intervals representing lower and upper bounds on the truth values of the formulas in the set of logic formulas, and a query formula. The logic formulas can be converted into a logical credal network (LCN) representation and a factor graph representation of the LCN representation can be created. The method can output a probability interval [l, u] such that l?P(q)?u, where P(q) represents a query for a given probability interval.

Abstract: A system can comprise a memory that stores computer executable components, and a processor, operably coupled to the memory, that executes the computer executable components comprising: a linking component that associates one or more unmasked elements of the logical form with one or more corresponding structured knowledge elements of a knowledge base and a prediction component that predicts the one or more masked elements based on extended context of the corresponding structured knowledge elements of the knowledge base to generate one or more predicted elements. In an embodiment, the prediction component predicts the one or more masked elements based on scores of one or more candidate elements. In an embodiment, the system can determine one or more rules that describe the natural language text segment in terms of the structured knowledge elements and associated weights of the knowledge base paths.

Abstract: A pillow has a body having a top, a bottom, and a plurality of sides. At least a portion of the body is formed by a 3 dimensional (3D) printed matrix.

Abstract: A system for configuring and using a logical neural network including a graph syntax tree of formulae in a represented knowledgebase connected to each other via nodes representing each proposition. One neuron exists for each logical connective occurring in each formula and, additionally, one neuron for each unique proposition occurring in any formula. All neurons return pairs of values representing upper and lower bounds on truth values of their corresponding subformulae and propositions. Neurons corresponding to logical connectives accept as input the output of neurons corresponding to their operands and have activation functions configured to match the connectives' truth functions. Neurons corresponding to propositions accept as input the output of neurons established as proofs of bounds on the propositions' truth values and have activation functions configured to aggregate the tightest such bounds.

Abstract: Methods, systems, and computer program products for zero-shot entity linking based on symbolic information are provided herein. A computer-implemented method includes obtaining a knowledge graph comprising a set of entities and a training dataset comprising text samples for at least a subset of the entities in the knowledge graph; training a machine learning model to map an entity mention substring of a given sample of text to one corresponding entity in the set of entities, wherein the machine learning model is trained using a multi-task machine learning framework using symbolic information extracted from the knowledge graph; and mapping an entity mention substring of a new sample of text to one of the entities in the set using the trained machine learning model.

Abstract: According to one embodiment, a method, computer system, and computer program product for probabilistic inference from imprecise knowledge is provided. The embodiment may include identifying a knowledge base of one or more statements and first probability distributions corresponding to each of the one or more statements. The embodiment may also include identifying one or more queries. The embodiment may further include determining logical inferences about and second probability distributions for queries from the one or more queries or statements from the one or more statements based on information in the knowledge base.

Abstract: One or more computer processors parse a received natural language question into an abstract meaning representation (AMR) graph. The one or more computer processors enrich the AMR graph into an extended AMR graph. The one or more computer processors transform the extended AMR graph into a query graph utilizing a path-based approach, wherein the query graph is a directed edge-labeled graph. The one or more computer processors generate one or more answers to the natural language question through one or more queries created utilizing the query graph.

Abstract: Methods, systems, and computer program products for zero-shot entity linking based on symbolic information are provided herein. A computer-implemented method includes obtaining a knowledge graph comprising a set of entities and a training dataset comprising text samples for at least a subset of the entities in the knowledge graph; training a machine learning model to map an entity mention substring of a given sample of text to one corresponding entity in the set of entities, wherein the machine learning model is trained using a multi-task machine learning framework using symbolic information extracted from the knowledge graph; and mapping an entity mention substring of a new sample of text to one of the entities in the set using the trained machine learning model.

Abstract: A pillow component includes a material layer having a top layer, a bottom layer and one or more sides separating the top layer from the bottom layer, and an interior spaced defined between the top layer and the bottom layer, the interior space configured to receive a filler. The material layer is formed by seamless knitting and is seamless about an entirety of a periphery extending in a first direction around the top layer, the bottom layer and the one or more sides. The characteristics of the knit material layer or fabric can be widely varied. A pillow includes the pillow component and a filler in the interior space.

Abstract: Word sense disambiguation using a glossary layer embedded in a deep neural network includes receiving, by one or more processors, input sentences including a plurality of words. At least two words in the plurality of words are homonyms. The one or more processors convert the plurality of words associated with each input sentence into a first vector including possible senses for the at least two words. The first vector is then combined with a second vector including a domain-specific contextual vector associated with the at least two words. The combination of the first vector with the second vector is fed into a recurrent deep logico-neural network model to generate a third vector that includes word senses for the at least two words. A threshold is set for the third vector to generate a fourth vector including a final word sense vector for the at least two words.

Abstract: A processor may include a set of primitive operators, receive a set of data-driven operators, at least one of the set of data-driven operators including a machine learning model, and receive an input-output data pair set. Based on a grammar specifying rules for linking the set of primitive operators and the set of data-driven operators, the processor may search among the set of primitive operators and the set of data-driven operators to find a symbolic model that fits the input-output data set.

Abstract: One or more computer processors parse a received natural language question into an abstract meaning representation (AMR) graph. The one or more computer processors enrich the AMR graph into an extended AMR graph. The one or more computer processors transform the extended AMR graph into a query graph utilizing a path-based approach, wherein the query graph is a directed edge-labeled graph. The one or more computer processors generate one or more answers to the natural language question through one or more queries created utilizing the query graph.

Abstract: A first neural network can be trained to approximate a state-action value function to estimate an expected cumulative return for an agent to perform an action in a given state, the agent being an autonomous reinforcement learning agent running on the processor. A second neural network can be trained to generate a simulated experience, the second network trained to predict a simulated state at a next time step after performing a given action, the second neural network being trained using real experience in a real environment. The first neural network is trained based on the simulated experience and a real experience from a real environment. A selected action selected by the second neural network given a current state of the real environment can be performed. The agent can explore an action space by uniformly sampling an action from all possible remaining action-state space combinations and performing the sampled action.