Abstract: Described is a system for online vehicle recognition in an autonomous driving environment. Using a learning network comprising an unsupervised learning component and a supervised learning component, images of moving vehicles extracted from videos captured in the autonomous driving environment are learned and classified. Vehicle feature data is extracted from input moving vehicle images. The extracted vehicle feature data is clustered into different vehicle classes using the unsupervised learning component. Vehicle class labels for the different vehicle classes are generated using the supervised learning component. Based on a vehicle class label for a moving vehicle in the autonomous driving environment, the system selects an action to be performed by the autonomous vehicle, and causes the selected action to be performed by the autonomous vehicle in the autonomous driving environment.

Abstract: Described is a system for predicting future social activity. The system extracts social activities from spatial-temporal social network data collected in a first time period ranging from hours to days to capture spatial structures of social activities in a graph network representation. A graph matching technique is applied over a set of spatial-temporal social network data collected in a second time period ranging from weeks to months to capture temporal structures of the social activities. A spatial-temporal structure of each social activity is represented as an activity core, where each activity core is defined as active nodes that participate in the social activity with a frequency over a predetermined threshold over the second time period. For each activity core, the system computes statistics of the social activity and uses the statistics to generate a prediction of future behaviors of the social activity.

Abstract: A system configured to identify a target in a synthetic aperture radar signal includes: a feature extractor configured to extract a plurality of features from the synthetic aperture radar signal; an input spiking neural network configured to encode the features as a first plurality of spiking signals; a multi-layer recurrent neural network configured to compute a second plurality of spiking signals based on the first plurality of spiking signals; a readout neural layer configured to compute a signal identifier based on the second plurality of spiking signals; and an output configured to output the signal identifier, the signal identifier identifying the target.

Abstract: A method of identifying a target from synthetic aperture radar (SAR) data without incurring the computational load associated with generating an SAR image. The method includes receiving SAR data collected by a radar system including RF phase history data associated with reflected RF pulses from a target in a scene, but excluding an SAR image. Range profile data is determined from the SAR data by converting the RF phase history data into a structured temporal array that can be applied as input to a classifier incorporating a recurrent neural network, such as a recurrent neural network made up of long short-term memory (LSTM) cells that are configured to recognize temporal or spatial characteristics associated with a target, and provide an identification of a target based on the recognized temporal or spatial characteristic.

Abstract: A method for eliminating pump noise by empirical mode decomposition and particle swarm optimization is provided. In the method, based on a hypothesis of the pump noise being a linear combination of a group of bases, an extracted pump noise sample is decomposed into a group of signals as bases by the empirical mode decomposition. Coefficients of the optimized linear combination of the group of bases is determined by the particle swarm optimization, thus updating the pump noise sample and improving a noise elimination effect. During a limited number of noise elimination periods, a current pump noise sample is modified by weighting, such that in a limited number of iterations, the current pump noise sample gradually converges to the pump noise waveform in the unit of a varied period, so as to be applicable to a slow variation of the pump noise during a long-time operation of the system.

Abstract: A gene chip includes a chip carrier, a plurality of DNA nanoballs assembled on the chip carrier, and a polymer film formed on the chip carrier and wrapping the DNA nanoballs. The polymer film includes at least one of a film of a positively charged polymer, a film of a positively charged polymer which is modified, a film of a zwitterionic polymer, and a composite polymer film. The composite polymer film is formed by a layer-by-layer self-assembly process of a positively charged polymer and a negatively charged polymer. The gene chip has good sequencing quality and different functions can be achieved by coating with different polymers, such as the chip surface rapidly drying out and surface non-specific adsorption. A method of preparing a gene chip is further disclosed.

Abstract: A system to detect a feature in an input image comprising a processor to evaluate a model including: four layers including: a supragranular layer, a granular layer, a first infragranular layer, and a second infragranular layer, each of the layers including a base connection structure including: an excitatory layer including a excitatory neurons arranged in a two dimensional grid; and an inhibitory layer including a inhibitory neurons arranged in a two dimensional grid; within-layer connections between the neurons of each layer in accordance with a Gaussian distribution; between-layer connections between the neurons of different layers, the probability of a neuron of a first layer of the different layers to a neuron of a second layer of the different layers in accordance with a uniform distribution; and input connections from lateral geniculate nucleus (LGN) neurons of an input LGN layer to the granular layer in accordance with a uniform distribution.

Abstract: Glucosylceramide synthase inhibitors and compositions containing the same are disclosed. Methods of using the glucosylceramide synthase inhibitors in the treatment of diseases and conditions wherein inhibition of glucosylceramide synthase provides a benefit, like Gaucher disease and Fabry disease, also are disclosed.

Abstract: A system configured to identify a target in a synthetic aperture radar signal includes: a feature extractor configured to extract a plurality of features from the synthetic aperture radar signal; a spiking neural network configured to encode the features as a plurality of spiking signals; a readout neural layer configured to compute a signal identifier based on the spiking signals; and an output configured to output the signal identifier, the signal identifier identifying the target.

Abstract: Described herein is a method of enhancing an image includes determining a level of environmental artifacts at a plurality of positions on an image frame of image data. The method also includes adjusting local area processing of the image frame, to generate an adjusted image frame of image data, based on the level of environmental artifacts at each position of the plurality of positions. The method includes displaying the adjusted image frame.

Abstract: The present invention discloses a hand-held autonomous visual acuity measurement apparatus and a visual acuity measuring method. The invention comprises a corneal curvature measurement module comprising a first light source for emitting a first light beam and a first image collector for collecting an image formed from the first light beam after it has been reflected by cornea, the first image collector being movably provided in the corneal curvature measurement optical path of the corneal curvature measurement module, and the corneal curvature measurement module further comprises a first motor for driving the first image collector to move along the corneal curvature measurement optical path so that the first image collector moves to the imaging position.

Abstract: A method, system, and computer program product for predicting abnormal operation of at least one component of a machine is provided. Real time monitoring data from an operating machine is received and monitoring features that are informative of likely abnormal operation are extracted and/or calculated. The monitoring features are applied to a prediction matrix that outputs probabilities of abnormal operation within one or more prediction time horizons. If the output probabilities exceed a threshold probability, then an alert can be output. Maintenance can be automatically scheduled in response to the alert.

Abstract: Described is a system for neural decoding of neural activity. Using at least one neural feature extraction method, neural data that is correlated with a set of behavioral data is transformed into sparse neural representations. Semantic features are extracted from a set of semantic data. Using a combination of distinct classification modes, the set of semantic data is mapped to the sparse neural representations, and new input neural data can be interpreted.

Abstract: A method and apparatus for maintaining an aircraft. Real-time event information indicating faults in systems on the aircraft and aircraft condition monitoring system data indicating conditions of the systems on the aircraft are stored during a plurality of legs of flights of the aircraft. A feature table comprising the real-time event information and the aircraft condition monitoring system data is built. Feature vectors are extracted from the feature table. A machine learning algorithm is applied to the extracted feature vectors to generate a predicted maintenance event message that identifies a predicted maintenance event. The predicted maintenance event message is used to perform a maintenance operation on the aircraft.

Abstract: This invention relates to benzodiazepine derivatives, compositions comprising therapeutically effective amounts of those derivatives and methods of using those derivatives or compositions in treating cognitive impairment associated with CNS disorders. It also relates to the use of an ?5-containing GABAA receptor agonist (e.g., an ?5-containing GABAA receptor positive allosteric modulator) in treating cognitive impairment associated with CNS disorders in a subject in need or at risk thereof, including age-related cognitive impairment, Mild Cognitive Impairment (MCI), amnestic MCI, Age-Associated Memory Impairment, Age Related Cognitive Decline, dementia, Alzheimer's Disease (AD), prodromal AD, PTSD, schizophrenia, bipolar disorder, ALS, cancer-therapy-related cognitive impairment, mental retardation, Parkinson's disease, autism spectrum disorders, fragile X disorder, Rett syndrome, compulsive behavior, and substance addiction. It also relates to the use of an ?5-containing GABAA receptor agonist (e.g.

Abstract: A method, system, and computer program product for predicting abnormal operation of at least one component of a machine is provided. Real time monitoring data from an operating machine is received and monitoring features that are informative of likely abnormal operation are extracted and/or calculated. The monitoring features are applied to a prediction matrix that outputs probabilities of abnormal operation within one or more prediction time horizons. If the output probabilities exceed a threshold probability, then an alert can be output. Maintenance can be automatically scheduled in response to the alert.

Abstract: Described herein is a method of displaying an object that includes detecting a first location of the object on a first image frame of image data. The method also includes determining a second location of the object on a second image frame of the image data on which the object is not detectable due to an obstruction. The method includes displaying a representation of the object on the second image frame.

Abstract: Described is a system for online vehicle recognition in an autonomous driving environment. Using a learning network comprising an unsupervised learning component and a supervised learning component, images of moving vehicles extracted from videos captured in the autonomous driving environment are learned and classified. Vehicle feature data is extracted from input moving vehicle images. The extracted vehicle feature data is clustered into different vehicle classes using the unsupervised learning component. Vehicle class labels for the different vehicle classes are generated using the supervised learning component. Based on a vehicle class label for a moving vehicle in the autonomous driving environment, the system selects an action to be performed by the autonomous vehicle, and causes the selected action to be performed by the autonomous vehicle in the autonomous driving environment.

Abstract: Described is a system for identifying communication behavior patterns in communication activity time series. For each pair of variables in the communication activity time series, the system determines a transfer entropy measure, an effective transfer entropy measure from a randomly reordered version of the communication activity time series, and a partial effective transfer entropy measure. A dependency matrix is generated using pair-wised effective transfer entropy measures and partial effective transfer entropy measures, where each element in the matrix represents a total influence of a communication activity time series on another communication activity time series in the future. The dependency matrix is compared with dependency matrices generated from a predefined set of communication patterns to identify the communication behavior pattern.