A method, an electronic device and computer readable medium for dialogue breakdown detection are provided. The method includes obtaining a verbal input from an audio sensor. The method also includes generating a reply to the verbal input. The method additionally includes identifying a local context from the verbal input and a global context from the verbal input, additional verbal inputs previously received by the audio sensor, and previous replies generated in response to the additional verbal inputs. The method further includes identifying a dialogue breakdown in response to determining that the reply does not correspond to the local context and the global context. In addition, the method includes generating sound corresponding to the reply through a speaker when the dialogue breakdown is not identified.

The present disclosure is directed to a computer-implemented method and system for anatomical tree structure analysis. The method includes receiving model inputs for a set of positions in an anatomical tree structure. The method further includes applying, by a processor, a learning network to the model inputs. The learning network comprises a set of encoders and a neural network modeling the anatomical tree structure, wherein each encoder provides features extracted from the model input at a corresponding position. The neural network has a plurality of nodes constructed according to the anatomical tree structure and each node is configured to process the extracted features from one or more of the encoders. The method additionally includes providing an output of the learning network as an analysis result of the anatomical tree structure analysis.

A GHeT includes a bottom gate formed on a substrate; a first dielectric layer (DL) formed on the bottom gate; a monolayer film formed of an atomically thin material on the first DL; a bottom contact (BC) formed on part of the monolayer film; a second DL formed on the BC; a top contact (TC) formed on the second DL on top of the BC; a network of CNTs formed on the TC and the monolayer film, to define an overlap region with the monolayer film; a third DL formed on the CNT network, the monolayer film and the TC; and a top gate formed on the third DL and overlapping with the overlap region. Such GHeT design allows gate tunability of Gaussian peak position, height and width that define Gaussian transfer characteristic, thereby enabling simplified circuit architectures for various spiking neuron functions for emerging neuromorphic applications.

A device for implementing spike-timing-dependent plasticity is provided. The device includes a phase-change element, first and second electrodes disposed respective first and second surfaces of the phase-change element. The phase-change element includes a phase-change material with an inverse resistivity characteristic. The first electrode includes a first heater element, and a first electrical insulating layer which electrically insulates the first resistive heater element from the first electrode and the phase-change element. The second electrode includes a second resistive heater element, and a second electrical insulating layer which electrically insulates the second resistive heater element from the second electrode and the phase-change element.

There is provided a magnetoresistance effect element includes: a channel layer that extends in a first direction; a recording layer which includes a film formed from a ferromagnetic material, of which a magnetization state is changed to one of two or greater magnetization states, and which is formed on the channel layer; a non-magnetic layer that is provided on a surface of the recording layer; a reference layer which is provided on a surface of the non-magnetic layer, which includes a film formed from a ferromagnetic material, and of which a magnetization direction is fixed; a terminal pair that includes a first terminal and a second terminal which are electrically connected to the channel layer with an interval in the first direction, and to which a current pulse for bringing the recording layer to any one magnetization state with a plurality of pulses is input by flowing a current to the channel layer between the first terminal and the second terminal; and a third terminal that is electrically connected to the reference layer.

Techniques for improved neural network modeling are provided. In one embodiment, a system comprises a memory that stores computer-executable components and a processor that executes the components. The computer-executable components can comprise a loss function logic component that determines a penalty based on a training term, the training term being a function of a relationship between an output scalar value of a first neuron of a plurality of neurons of a neural network model, a plurality of input values from the first neuron, and one or more tunable weights of connections between the plurality of neurons; an optimizer component that receives the penalty from the loss function component, and changes one or more of the tunable weights based on the penalty; and an output component that generates one or more output values indicating whether a defined pattern is detected in unprocessed input values received at the neural network evaluation component.

High dynamic range, high class count, high input rate winner-take-all on neuromorphic hardware is provided. In some embodiments, a plurality of thermometer codes are received by a neurosynaptic core. The plurality of thermometer codes are split into a plurality of intervals. One of the plurality of intervals is selected. A local maximum is determined within the one of the plurality of intervals. A global maximum is determined based on the local maximum.

Systems and methods are described in relation to specific technical improvements adapted for machine learning architectures that conduct classification on numerical and/or unstructured data. In an embodiment, two neural networks are utilized in concert to generate output data sets representative of predicted future states of an entity. A second learning architecture is trained to cluster prior entities based on characteristics converted into the form of features and event occurrence such that a boundary function can be established between the clusters to form a decision boundary between decision regions. These outputs are mapped to a space defined by the boundary function, such that the mapping can be used to determine whether a future state event is likely to occur at a particular time in the future.

Methods for improving compliance with regulations pertaining to vehicle driving records are disclosed. One or more digital images from a camera mounted in a vehicle are received. Based on a determination that the vehicle has hours of service that have not been assigned to a driver, a subset of the one or more digital images corresponding to the hours of service are identified based on the timestamps. The subset of the one or more digital images are processed to identify a correspondence between a face of a person included in the one or more digital images and a face of a known person. Based on the correspondence transgressing a threshold level of correspondence, a user interface is generated for presentation on a device. The user interface includes an interactive user interface element for accepting a recommendation to assign the known person as the driver for the unassigned hours of service.

A hardware neural network conversion method, a computing device, a compiling method and a neural network software and hardware collaboration system for converting a neural network application into a hardware neural network fulfilling a hardware constraint condition are disclosed. The method comprises: obtaining a neural network connection diagram corresponding to the neural network application; splitting the neural network connection diagram into neural network basic units; converting each of the neural network basic units so as to form a network having equivalent functions thereto and formed by connecting basic module virtual entities of neural network hardware; and connecting the obtained basic unit hardware network according to the sequence of splitting so as to create a parameter file for the hardware neural network. The present disclosure provides a novel neural network and a brain-like computing software and hardware system.