Abstract: A method for optimal design of experiments for joint model selection and parametrization determination of a symbolic mathematical model includes: determining a prediction value for a given inquiry data point, functional form and parameterization for conducting an experiment relating to a system under investigation; assuming a set of input-output data pairs as a starting point in a model discovery process relating to the system under investigation; performing discovery of symbolic models minimizing complexity for a bounded misfit, or minimizing a misfit measure, subject to bounded complexity; determining a new data point through optimal experimental design that informs best as for the underlying symbolic models; and updating a posterior distribution, given results of the experiment relating to the system under investigation for the determined new data point to enable informed assessment among a plurality of functional forms and parameterizations. An apparatus configured to perform the method is also provided.

Abstract: Techniques and a system to facilitate estimation of a quantum phase, and more specifically, to facilitate estimation of an expectation value of a quantum state, by utilizing a hybrid of quantum and classical methods are provided. In one example, a system is provided. The system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can include an encoding component and a learning component. The encoding component can encode an expectation value associated with a quantum state. The learning component can utilize stochastic inference to determine the expectation value based on an uncollapsed eigenvalue pair.

Abstract: Low-cost techniques for sensing ambient temperatures in a container or package using phase change materials are provided. In one aspect, a temperature sensor is provided. The temperature sensor includes: at least one phase change material configured to transition from an amorphous state to a crystalline state upon exposure to temperatures above a predetermined threshold temperature for a given duration. A method of monitoring temperature exposure of a consumer good using the temperature sensor is also provided.

Abstract: Techniques and a system to facilitate estimation of a quantum phase, and more specifically, to facilitate estimation of an expectation value of a quantum state, by utilizing a hybrid of quantum and classical methods are provided. In one example, a system is provided. The system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can include an encoding component and a learning component. The encoding component can encode an expectation value associated with a quantum state. The learning component can utilize stochastic inference to determine the expectation value based on an uncollapsed eigenvalue pair.

Abstract: A reinforcement learning system includes a plurality of agents, each agent having an individual reward function and one or more safety constraints that involve joint actions of the agents, wherein each agent maximizes a team-average long-term return in performing the joint actions, subject to the safety constraints, and participates in operating a physical system. A peer-to-peer communication network is configured to connect the plurality of agents. A distributed constrained Markov decision process (D-CMDP) model is implemented over the peer-to-peer communication network and is configured to perform policy optimization using a decentralized policy gradient (PG) method, wherein the participation of each agent in operating the physical system is based on the D-CMDP model.

Abstract: Embodiments herein disclose computer-implemented methods, computer program products and computer systems for authenticating a user. The computer-implemented method may include receiving biographical data corresponding to a user. A change rate may be determined based on user biographical data. The computer-implemented method may include receiving first biometric data having a time-varying characteristic from the user at a first time and receiving second biometric data having the time-varying characteristic from the user at a second time that is later in time than the first time. Further, the computer-implemented method may include determining third biometric data based at least on the first biometric data, the second time, and the time-varying characteristic, and authenticating the user if the third biometric data is within a predetermined threshold of the second biometric data at the second time.

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: Techniques regarding topological classification of complex datasets are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a quantum computing component that can encode eigenvalues of a Laplacian matrix into a phase on a quantum state of a quantum circuit. The computer executable components can also comprise a classical computing component that infers a Betti number using a Bayesian learning algorithm by measuring an ancilla state of the quantum circuit.

Abstract: A processor performing machine learning including spectral clustering can receive data from the sensor. Graph Laplacian of the data can be created and stored in a memory device. Spectral characteristic can be created by applying density of states and spectral gaps can be detected in an unsupervised manner in the spectral characteristic to determine r as number of clusters to cluster the data. A range space of a rational matrix of the graph Laplacian can be determined. K-means clustering can be performed on the range space of rational matrix of the graph Laplacian using r as the number of clusters, the K-means clustering returning r clusters of the received data.

Abstract: In one or more embodiments described herein, device, computer-implemented methods, and/or computer program products that facilitate automated survey results generation from an image are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an image capturing component that captures a first sample image. The computer executable components can further comprise an image processing component that processes the first sample image to determine a survey count, wherein the survey count indicates a number of times a survey image was identified in the first sample image. The computer executable components can further comprise an authentication component that adjusts the survey count based on detection of a discrepancy.

Abstract: Methods and systems for solving a linear system include setting resistances in an array of settable electrical resistances in accordance with values of an input matrix. A series of input vectors is applied to the array as voltages to generate a series of respective output vectors. Each input vector in the series of vectors is updated based on comparison of the respective output vectors to a target vector. A solution of a linear system is determined that includes the input matrix based on the updated input vectors.

Abstract: Techniques for generating and managing, including simulating and training, deep tensor neural networks are presented. A deep tensor neural network comprises a graph of nodes connected via weighted edges. A network management component (NMC) extracts features from tensor-formatted input data based on tensor-formatted parameters. NMC evolves tensor-formatted input data based on a defined tensor-tensor layer evolution rule, the network generating output data based on evolution of the tensor-formatted input data. The network is activated by non-linear activation functions, wherein the weighted edges and non-linear activation functions operate, based on tensor-tensor functions, to evolve tensor-formatted input data. NMC trains the network based on tensor-formatted training data, comparing output training data output from the network to simulated output data, based on a defined loss function, to determine an update.

Abstract: A computer-implemented method is presented for performing matrix sketching by employing an analog crossbar architecture. The method includes low rank updating a first matrix for a first period of time, copying the first matrix into a dynamic correction computing device, switching to a second matrix to low rank update the second matrix for a second period of time, as the second matrix is low rank updated, feeding the first matrix with first stochastic pulses to reset the first matrix back to a first matrix symmetry point, copying the second matrix into the dynamic correction computing device, switching back to the first matrix to low rank update the first matrix for a third period of time, and as the first matrix is low rank updated, feeding the second matrix with second stochastic pulses to reset the second matrix back to a second matrix symmetry point.

Abstract: A system, method, and computer program product are disclosed. The method includes loading a first set of data as an initial matrix and determining a truncated singular value decomposition (SVD) of the initial matrix. The method also includes loading a second set of data as a new matrix, generating a first projection matrix, which approximates k leading left singular vectors of the updated matrix, and generating a second projection matrix, which approximates k leading right singular vectors of the updated matrix. Further, the method includes determining based on the initial matrix, the new matrix, the SVD of the existing matrix, and the first or second projection matrix, an approximate truncated SVD of the updated matrix.

Abstract: An illustrative embodiment includes a method for analyzing unstructured multidimensional data with a neural network. The method includes designing the neural network at least in part by defining differential operators conforming with dimensions of the data. The method also includes performing forward propagation at a given convolution layer of the neural network at least in part by: obtaining one or more convolved values at least in part by performing convolution over an object within the data, processing respective convolved values to obtain output, and updating the object based at least in part on the output.

Abstract: Techniques are provided to implement hardware accelerated computing of eigenpairs of a matrix. For example, a system includes a processor, and a resistive processing unit coupled to the processor. The resistive processing unit includes an array of cells which include respective resistive devices, wherein at least a portion of the resistive devices are tunable to encode values of a given matrix which is storable in the array of cells. When the given matrix is stored in the array of cells, the processor is configured to determine an eigenvector of the stored matrix by executing a process which includes performing analog matrix-vector multiplication operations on the stored matrix to converge an initial vector to an estimate of the eigenvector of the stored matrix.

Abstract: A method is presented for computing an equilibrium distribution of Markov processes. The method includes storing weight values in an analog crossbar array of transition probability matrices, where the analog crossbar array of transition probability matrices represents a weight matrix with m rows and n columns, computing an eigenvector associated with a real eigenvalue of modulus one for each of the transition probability matrices, applying a gradient-based eigenvalue solver to converge to a dominant eigenpair, and determining a probability of changing from one state to another state in a stochastic entity based on outcomes of the gradient-based eigenvalue solver.

Abstract: A method of performing Principal Component Analysis is provided. The method includes receiving, by a computing device, evolving data for processing/visualization. The method further includes, by the computing device, a dimensionality for reducing of the evolving data using the PCA, wherein the PCA is performed on analog crossbar hardware. The method also includes using, by the computing device, the evolving data for visualization having the dimensionality thereof reduced by the principal component analysis for a further application.

Abstract: A method of detecting cliques in a graph includes determining, based on a number of nodes in the graph, a number of qubits to be included in a quantum processor. The method includes assigning to each node in the graph, a qubit of the quantum processor. The method includes operating on the qubits with a preparation circuit to create a quantum state in the qubits that corresponds to the graph. The method includes operating on the quantum state with a random walk circuit, and measuring the qubits of the quantum processor to detect cliques in the graph. The preparation circuit comprises a plurality of single- and two-qubit operators, wherein, for each pair of adjacent nodes in the graph, an operator of the plurality of two-qubit operators acts on a pair of qubits corresponding to the pair of adjacent nodes to create the quantum state.

Abstract: Techniques for determining a count of triangles (tr) in a graph data structure using a crosspoint array is described. An adjacency matrix (a) representing the graph is mapped to the crosspoint array by configuring resistance values of crosspoint devices in the array. The count of triangles is initialized to zero (tr=0), and iteratively updated. The updating includes generating a first vector (x1) stochastically to include digital values in a predetermined range, which are converted into the voltage values. A multiplication of the adjacency matrix and the first vector (ax1) is computed using the crosspoint array. A second voltage vector (z1=ax1) is generated that includes voltage values representing the multiplication result. The adjacency matrix and the second voltage vector (z2=az1) are multiplied using the crosspoint array. The computer updates the number of triangles in the graph data structure as tr=tr+Z1T.