Abstract: A method of setting up a quantum circuit for computational basis state shift. For the quantum circuit, a first quantum register, a second quantum register and a first ancilla register are set up. The first quantum register includes four qubits and the second quantum register includes N?4 qubits. To the first quantum register, the second quantum register and the first ancilla register are applied in a sequential order: (i) an initial step including three CX gates and a first X gate; (ii) a first segment including seven multi-controlled gates; (iii) a second segment comprising a first set of CX gates, a second set of CX gates and a cascade of gates arranged between the first set and the second set; and (iv) a third segment comprising two CX gates and a second X gate.

Abstract: A quantum state classifier includes a reservoir computing circuit for post-processing a quantum bit to obtain a readout signal, and a readout circuit, coupled to the reservoir computing circuit, for discriminating a quantum state of the quantum bit from the readout signal from among multiple possible quantum states. The readout circuit is trained in a calibration process respectively activated by a specific one of each of the multiple quantum states such that weights within the linear readout circuit are updated by minibatch learning for each of multiple measurement sequences of the calibration process. The readout circuit generates a binary output after the multiple measurement sequences in a post-calibration classification process for a test quantum bit. The quantum state classifier further includes a controller, coupled to the readout circuit, selectively triggerable to output a control pulse responsive to the quantum state of the test quantum bit indicated by the binary output.

Abstract: Exemplary embodiments relate to a protein engineering pipeline configured to optimize or improve proteins for specified functions. The problem space of such a task can grow quickly based on the sequence of the protein being optimized and the functions for which the protein is being designed. The solutions described herein allow the problem space to be efficiently searched by applying a combination of a protein design pipeline and an evaluation procedure performed on a quantum computer. As a result, single or multiple amino acid substitutions at a site of interest may be predicted in order to generate optimized protein variants.

Abstract: Systems and methods are provided for implementing a hardware accelerator. The hardware accelerator emulates a neural network, and includes a memristor crossbar array, and a non-linear filter. The memristor crossbar array can be programmed to calculate node values of the neural network. The nodes values can be calculated in accordance with rules to reduce an energy function associated with the neural network. The non-linear filter is coupled to the memristor crossbar array and programmed to harness noise signals that may be present in analog circuitry of the hardware accelerator. The noise signals can be harnessed such that the energy function associated with the neural network converges towards a global minimum and modifies the calculated node values. In some embodiments, the non-liner filter is implemented as a Schmidt trigger comparator.

Abstract: Described is a method for predicting multiple components' content in a case that rare earth ions with and without color feature coexist, and relates to component content prediction in rare earth extraction process. It is difficult to quickly/accurately detect component's content in rare earth extraction process. Because of relatively large difference between images' color features of CePr/Nd mixed solution with colorless Ce ions and Pr/Nd solution, detecting content method of single rare earth element based on color feature is no longer applicable.

Abstract: Apparatuses and methods for implementing artificial synapses utilizing SSM cells. A leaky-integrate-and-fire circuit can provide a feedback signal to an SSM cell responsive to a threshold quantity of pulses being applied to the gate from the signal line. A resulting state of the SSM cell can be dependent on a time difference between a latest of the threshold quantity of pulses and an initial pulse of the feedback signal.

Abstract: A system for generating a longevity element and an instruction set for a longevity element plan, the system including at least a computing device, wherein the computing device is designed and configured to receive, from a user, at least an element of user-reported data, determine, using the at least an element of user-reported data and a first machine-learning process, a longevity element, calculate, using a longevity element and at least a second element of data, a compensatory supplement, and generate, using the at least an element of user-reported data and at least a longevity element, an instruction set for a longevity element plan.

Abstract: Machine learning is enhanced by efficiently updating a weight that is represented as a conductivity of a resistive processing unit (RPU) that is connected between a row wire and a column wire. The weight is updated by the RPU interacting with bit streams carried on the row and column wires. Efficiency of the update is enhanced by calculating a bit length for the bit streams as a function of factors that include learning rate ?, maximum activity xmax, maximum error differential ?max, and minimum weight update increment ?wmin.

Abstract: In a method of training a DNN, a weight matrix (W) is provided as a linear combination of matrices/arrays A and C. In a forward cycle, an input vector x is transmitted through arrays A and C and output vector y is read. In a backward cycle, an error signal ? is transmitted through arrays A and C and output vector z is read. Array A is updated by transmitting input vector x and error signal ? through array A. In a forward cycle, an input vector ei is transmitted through array A and output vector y? is read. ƒ(y?) is calculated using y?. Array C is updated by transmitting input vector ei and ƒ(y?) through array C. A DNN is also provided.

Abstract: Systems and methods for customizing an output based on user data are described herein. An example method for customizing an output based on user data may commence with continuously capturing, by at least one sensor, the user data. The method may continue with analyzing, by at least one computing resource, the user data received from the at least one sensor and determining dependencies between the user data and output data. The method may further include determining, based on predetermined criteria, that an amount of the user data and the dependencies is sufficient to customize the output data. The method may continue with continuously customizing, by an adaptive interface, the output data using at least one machine learning technique based on the analysis of the user data. The customized output data may be intended to elicit a personalized change.

Abstract: Embodiments are directed towards a configurable accelerator framework device that includes a stream switch and a plurality of convolution accelerators. The stream switch has a plurality of input ports and a plurality of output ports. Each of the input ports is configurable at run time to unidirectionally pass data to any one or more of the output ports via a stream link. Each one of the plurality of convolution accelerators is configurable at run time to unidirectionally receive input data via at least two of the plurality of stream switch output ports, and each one of the plurality of convolution accelerators is further configurable at run time to unidirectionally communicate output data via an input port of the stream switch.

Abstract: Structured documents are processed using convolutional neural networks. For example, the processing can include receiving a rendered form of a structured document; mapping a grid of cells to the rendered form; assigning a respective numeric embedding to each cell in the grid, comprising, for each cell: identifying content in the structured document that corresponds to a portion of the rendered form that is mapped to the cell, mapping the identified content to a numeric embedding for the identified content, and assigning the numeric embedding for the identified content to the cell; generating a matrix representation of the structured document from the numeric embeddings assigned to the cells of the grids; and generating neural network features of the structured document by processing the matrix representation of the structured document through a subnetwork comprising one or more convolutional neural network layers.

Abstract: Staged neural networks and methods are described herein. In some embodiments, the methods may identify a plurality of second NP hard/complete problems that are similar to the first NP hard/complete problem and identify solutions to the second NP hard/complete problems. The methods may train a deep neural network with the second NP hard/complete problems and the solutions. The methods may provide the first NP hard/complete problem to the trained deep neural network to generate a preliminary solution to the first NP hard/complete problem and provide the preliminary solution to a recursive neural network configured to execute an energy minimization search. The recursive neural network may generate a final solution to the problem based on the preliminary solution.

Abstract: An artificial neuron device according to an embodiment of the present disclosure includes a first resistor connected between an input terminal and a first node; a capacitor connected between the first node and a ground terminal; a threshold switch connected between the first node and a second node; and a second resistor connected between the second node and the ground terminal, wherein, when an input voltage of a certain level is applied to the input terminal by time, a membrane potential occurs at the first node and a spike current flows through the second node. According to present disclosure, the artificial neuron device expresses the Integrate-and-Fire function, the rate coding ability, the SFA characteristics, and the chaotic activity of the biological neuron, and therefore may be widely used for the artificial neuron network device, the large-scale brain-inspired computing system, and the artificial intelligence (AI) system.

Abstract: Methods are taught for creating training data for a learning algorithm, training the learning algorithm with the training data and using the trained learning algorithm to suggest domain names to users. A domain name registrar may store activities of a user on a registrar website. Preferably, domain name searches, selected suggested domain names and domain names registered to the user are stored as the training data in a training database. The training data may be stored so that earlier activities act as inputs to the learning algorithm while later activities are the expected outputs of the learning algorithm. Once trained, the learning algorithm may receive activities of other users and suggest domain names to the other users based on their activities.

Abstract: A neuromorphic neuron apparatus includes an accumulation block and an output generation block. The apparatus has a current state variable corresponding to previously received one or more signals. The output generation block is configured to use an activation function for generating a current output value based on the current state variable. The accumulation block is configured to repeatedly: compute an adjustment of the current state variable using the current output value and a correction function indicative of a decay behaviour of a time constant of the apparatus; receive a current signal; update the current state variable using the computed adjustment and the received signal, the updated state variable becoming the current state variable; and cause the output generation block to generate a current output value based on the current state variable.

Abstract: A system for automatically automatic workflow triggering using real-time analytics, comprising an analytics server that receives and analyzes interaction information and a workflow server that produces workflow events based on the analysis, sends workflow events to handlers for processing, retrieves workflow-related data, and produces workflow reports for review, and a method for automatically automatic workflow triggering using real-time analytics.

Abstract: Embodiments of the invention provide a method and system for machine failure prediction. The method comprises: identifying a plurality of basic memory depth values based on a composite sequence of machine failure history; ascertaining weight values for at least one of the identified basic memory depth values according to a pre-stored table which includes a plurality of mappings wherein each mapping relates a basic memory depth value to one set of weight values; and predicting a future failure using a Back Propagation Through Time (BPTT) trained Recurrent Neural Network (RNN) based on the ascertained weight values, wherein weight values related to a first basic memory depth value in the pre-stored table is ascertained based on a second set of weight values related to a second basic memory depth value which is less than the first basic memory depth value by a predetermined value.

Abstract: System and methods for relational time-series learning are provided. Unlike traditional time series forecasting techniques, which assume either complete time series independence or complete dependence, the disclosed system and method allow time series forecasting that can be performed on multivariate time series represented as vertices in graphs with arbitrary structures and predicting a future classification for data items represented by one of nodes in the graph. The system and methods also utilize non-relational, relational, temporal data for classification, and allow using fast and parallel classification techniques with linear speedups. The system and methods are well-suited for processing data in a streaming or online setting and naturally handle training data with skewed or unbalanced class labels.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable storage media for utilizing a virtual assistant as part of a communication session. One or more of the participant users can select to utilize a virtual assistant to assist the participant users with tasks during the communication session. A user can use a communication application to enter a message directed to the virtual assistant. The virtual assistant can analyze the entered message and determine that the message was directed to the virtual assistant rather than to the other participants of the communication session. As a result, the message will not be transmitted to the other participants of the communication session and the virtual assistant will assist the user with the identified task. A virtual assistant can assist a user with a variety of different tasks. For example, the virtual assistant can gather requested data for the user from one or more web servers, such as weather data, traffic data, etc.