Abstract: A refrigerant circuit includes a compressor that compresses refrigerant and an expansion mechanism that decompresses refrigerant. The refrigerant circuit configures a vapor compression refrigeration cycle in which the refrigerant circulates. The adsorbent adsorbs and desorbs the refrigerant circulating in a first unit. The refrigerant circuit includes a high-pressure region and a low-pressure region. The refrigeration cycle apparatus is operated under at least one of a first condition and a second condition. In the first condition, the refrigerant in the high-pressure region has a pressure lower than or equal to a critical pressure of the refrigerant, and the refrigerant in the high-pressure region has a temperature exceeding a critical temperature of the refrigerant.

Abstract: A refrigeration cycle apparatus includes a refrigerant circuit and an adsorbent. The refrigerant circuit includes a compressor that compresses a refrigerant. The refrigerant circuit configures a vapor compression refrigeration cycle in which the refrigerant circulates. The adsorbent adsorbs and desorbs the refrigerant circulating in the refrigerant circuit. The adsorbent adsorbs and desorbs the refrigerant in accordance with a change in a pressure of the refrigerant circulating in the refrigerant circuit.

Abstract: An apparatus and method are provided for rapidly ranking network nodes according to input ranking criteria. The links (i.e., first-order paths) between nodes are expressed in a first-order path matrix, which is used to generate nth-order path matrices as nth powers of the first-order path matrix and summed as a power series to generate a surrogate ranking operator (SRO) representing as a single matrix operation a sum over paths of all orders. Thus, in contrast to conventional ranking methods that require multiple recursive steps to account for the interrelatedness of linked nodes, a ranking is produced by multiplying the SRO by a state vector representing the input ranking criteria.

Abstract: An apparatus and method are provided for rapidly ranking network nodes according to input ranking criteria. The links (i.e., first-order paths) between nodes are expressed in a first-order path matrix, which is used to generate nth-order path matrices as nth powers of the first-order path matrix and summed as a power series to generate a surrogate ranking operator (SRO) representing as a single matrix operation a sum over paths of all orders. Thus, in contrast to conventional ranking methods that require multiple recursive steps to account for the interrelatedness of linked nodes, a ranking is produced by multiplying the SRO by a state vector representing the input ranking criteria.

Abstract: In a system and method for encoding data for transmission, the data may be encoded using principles of superpositioning, holography and entanglement over a distributed network. The data is encoded into a virtual tokenized state, called a holographic token or Q-Token, an exists in a superposed, holographic and distributed form throughout the network. Unique properties of the holographic token include ultra-high integrity and availability to any node authorized to acquire the holographic token, while enabling cryptographic certification and rich data processing functionality. The example system is highly efficient, low-power, anti-fragile, and resilient to attacks of various kinds. The data encoded by the method and system cannot be cloned because the encoded data exists in a distributed form over the network. Hence, an intrinsic identity and integrity of the encoded data is preserved through all operations or catastrophes even to large parts of the distributed network or persistence structures.

Abstract: An apparatus and method are provided for machine learning method using a network of agents. The agents are arranged in a network with respective links between pairs of agents, and the links enabling the exchange information. Different agents can apply different reasoning paradigms corresponding to different approaches to machine learning and artificial intelligence. These disparate approaches are seamlessly integrated to aggregate decisions and learning performed using different approaches using an economics model in which a Nash equilibrium is reached through the exchange of information. Each agent selects which other agents to exchange information with by seeking to optimize preference, utility, and objective functions, and these function include how well the agents obtain an assigned goal subject to other desirable features and characteristics (e.g., enforcing diversity).

Abstract: The Quanton virtual machine approximates solutions to NP-Hard problems in factorial spaces in polynomial time. The data representation and methods emulate quantum computing on classical hardware but also implement quantum computing if run on quantum hardware. The Quanton uses permutations indexed by Lehmer codes and permutation-operators to represent quantum gates and operations. A generating function embeds the indexes into a geometric object for efficient compressed representation. A nonlinear directional probability distribution is embedded to the manifold and at the tangent space to each index point is also a linear probability distribution. Simple vector operations on the distributions correspond to quantum gate operations. The Quanton provides features of quantum computing: superpositioning, quantization and entanglement surrogates. Populations of Quantons are evolved as local evolving gate operations solving problems or as solution candidates in an Estimation of Distribution algorithm.

Abstract: Described herein is a method and system of geometrically encoding data including partitioning data into a plurality of semantic classes based on a dissimilarity metric, generating a subspace formed by first and second data elements, the first and second data elements being included in first and second numbers of partitioned semantic classes, encoding the first data element with respect to the second data element such that the generated subspace formed by the first data element and the second data element is orthogonal, computing a weight distribution of the first data element with respect to the second data element, the weight distribution being performed for each of the first number of semantic classes and the second number of semantic classes, and determining a dominant semantic class corresponding to an ordered sequence of the first data element and the second data element, the dominant semantic class having a maximum weight distribution.

Abstract: A method and apparatus is provided for implementing combinatorial hypermaps (CHYMAPS) and/or generalized combinatorial maps (G-Maps) based data representations and operations, comprising: mapping term-algebras to tree-based numbers using a fast algorithm and representing a graph of the mapping structure as a CHYMAPS using reversible numeric encoding and decoding; generating a representation of CHYMAPS in a form optimized for sub-map (sub-graph) to map (graph) isomorphism and partial matching with a general matching process; performing operations on the CHYMAPS as operations on respective numerical representations; performing compression and decompression using a three bit self-delimiting binary code; and storing and retrieving codes.

Abstract: The present disclosure provides a fast approximate as well as exact hierarchical network storage and retrieval system and method for encoding and indexing graphs or networks as well as for identifying substructure matches or analogs within graph data. Cognitive Memory encodes graphs via generalized combinatorial maps and a new quantum-inspired Q-Hashing algorithm to summarize local structures of the graph along with a contraction and graph property calculation to build an index data structure called the Cognitive Signature for property based, analog based or structure or sub-structure based search. The system and method of the present invention is ideally suited to store and index all or parts or substructures or analogs of graphs as well as dynamically changing graphs such as traffic graphs or flows and motion picture sequences of graphs.

Abstract: The Quanton virtual machine approximates solutions to NP-Hard problems in factorial spaces in polynomial time. The data representation and methods emulate quantum computing on classical hardware but also implement quantum computing if run on quantum hardware. The Quanton uses permutations indexed by Lehmer codes and permutation-operators to represent quantum gates and operations. A generating function embeds the indexes into a geometric object for efficient compressed representation. A nonlinear directional probability distribution is embedded to the manifold and at the tangent space to each index point is also a linear probability distribution. Simple vector operations on the distributions correspond to quantum gate operations. The Quanton provides features of quantum computing: superpositioning, quantization and entanglement surrogates. Populations of Quantons are evolved as local evolving gate operations solving problems or as solution candidates in an Estimation of Distribution algorithm.

Abstract: An apparatus and method are provided for rapidly ranking network nodes according to input ranking criteria. The links (i.e., first-order paths) between nodes are expressed in a first-order path matrix, which is used to generate nth-order path matrices as nth powers of the first-order path matrix and summed as a power series to generate a surrogate ranking operator (SRO) representing as a single matrix operation a sum over paths of all orders. Thus, in contrast to conventional ranking methods that require multiple recursive steps to account for the interrelatedness of linked nodes, a ranking is produced by multiplying the SRO by a state vector representing the input ranking criteria.

Abstract: The Quanton virtual machine approximates solutions to NP-Hard problems in factorial spaces in polynomial time. The data representation and methods emulate quantum computing on classical hardware but also implement quantum computing if run on quantum hardware. The Quanton uses permutations indexed by Lehmer codes and permutation-operators to represent quantum gates and operations. A generating function embeds the indexes into a geometric object for efficient compressed representation. A nonlinear directional probability distribution is embedded to the manifold and at the tangent space to each index point is also a linear probability distribution. Simple vector operations on the distributions correspond to quantum gate operations. The Quanton provides features of quantum computing: superpositioning, quantization and entanglement surrogates. Populations of Quantons are evolved as local evolving gate operations solving problems or as solution candidates in an Estimation of Distribution algorithm.

Abstract: An analytical method and apparatus is provided for analyzing and interpreting signals from unstructured data to identify and reason about underlying concepts. The method and apparatus include functions of generating qualitative and quantitative representations of explicit semantic concepts and implicit related or associated concepts, and defining a Semantic Boundary Index used for real-time processing of unstructured data fields or streams in a manner that characterizes, stores, measures, monitors, enables transactional updates or analyses of implicit and explicit information or evidence to identify explicit and implicit or hidden semantic concept, the semantic boundary index being produced by dynamic partitioning through semiotic-based signal processing. The semiotic-based signal processing occurs through agent-based dynamic sensing, characterizing, storing, monitoring, reasoning about and partitioning of unstructured data into core semantic elements.

Abstract: A method and apparatus for mapping concepts and attributes to distance fields via rvachev-functions. The steps including generating, for a plurality of objects, equations representing boundaries of attributes for each respective object, converting, for a plurality of objects, the equations into greater than or equal to zero type inequalities, generating, for a plurality of objects, a logical expression combining regions of space defined by the inequalities into a semantic entity, and substituting, for a plurality of objects, the logical expression with a corresponding rvachev-function such that the resulting rvachev-function is equal to 0 on a boundary of the semantic entity, greater then 0 inside a region of the semantic entity, and less then 0 outside the region of the semantic entity. Also included is the step of generating a composite rvachev-function representing logical statements corresponding to the plurality of objects using the respective rvachev-functions of the objects.

Abstract: Described herein is a method and system of geometrically encoding data including partitioning data into a plurality of semantic classes based on a dissimilarity metric, generating a subspace formed by first and second data elements, the first and second data elements being included in first and second numbers of partitioned semantic classes, encoding the first data element with respect to the second data element such that the generated subspace formed by the first data element and the second data element is orthogonal, computing a weight distribution of the first data element with respect to the second data element, the weight distribution being performed for each of the first number of semantic classes and the second number of semantic classes, and determining a dominant semantic class corresponding to an ordered sequence of the first data element and the second data element, the dominant semantic class having a maximum weight distribution.

Abstract: Described herein is a method and system of geometrically encoding data including partitioning data into a plurality of semantic classes based on a dissimilarity metric, generating a subspace formed by first and second data elements, the first and second data elements being included in first and second numbers of partitioned semantic classes, encoding the first data element with respect to the second data element such that the generated subspace formed by the first data element and the second data element is orthogonal, computing a weight distribution of the first data element with respect to the second data element, the weight distribution being performed for each of the first number of semantic classes and the second number of semantic classes, and determining a dominant semantic class corresponding to an ordered sequence of the first data element and the second data element, the dominant semantic class having a maximum weight distribution.

Abstract: An apparatus and method are provided for rapidly ranking network nodes according to input ranking criteria. The links (i.e., first-order paths) between nodes are expressed in a first-order path matrix, which is used to generate nth-order path matrices as nth powers of the first-order path matrix and summed as a power series to generate a surrogate ranking operator (SRO) representing as a single matrix operation a sum over paths of all orders. Thus, in contrast to conventional ranking methods that require multiple recursive steps to account for the interrelatedness of linked nodes, a ranking is produced by multiplying the SRO by a state vector representing the input ranking criteria.

Abstract: An apparatus and method are provided for machine learning method using a network of agents. The agents are arranged in a network with respective links between pairs of agents, and the links enabling the exchange information. Different agents can apply different reasoning paradigms corresponding to different approaches to machine learning and artificial intelligence. These disparate approaches are seamlessly integrated to aggregate decisions and learning performed using different approaches using an economics model in which a Nash equilibrium is reached through the exchange of information. Each agent selects which other agents to exchange information with by seeking to optimize preference, utility, and objective functions, and these function include how well the agents obtain an assigned goal subject to other desirable features and characteristics (e.g., enforcing diversity).

Abstract: The present disclosure provides a fast approximate as well as exact hierarchical network storage and retrieval system and method for encoding and indexing graphs or networks as well as for identifying substructure matches or analogs within graph data. Cognitive Memory encodes graphs via generalized combinatorial maps and a new quantum-inspired Q-Hashing algorithm to summarize local structures of the graph along with a contraction and graph property calculation to build an index data structure called the Cognitive Signature for property based, analog based or structure or sub-structure based search. The system and method of the present invention is ideally suited to store and index all or parts or substructures or analogs of graphs as well as dynamically changing graphs such as traffic graphs or flows and motion picture sequences of graphs.