Abstract: The TGC slide switches (20) which provide the TGC control of an ultrasonic diagnostic imaging system have LEDs (10) mounted on the sliders (22) of the slide switches so that the switches can be easily visualized in a darkened room. The light emitted by the LED of a switch is changed to an indicative color or brightness when the slider of the switch is set in its default position (36) for the application of a nominal gain to echo signals received from a given depth.

Abstract: A method of operating a resource management node includes, for each of a plurality of resources available in a plurality of distributed computing systems, determining a productive spend metric based on utilization of the resource and cost associated with making the resource available in the distributed computing system. Relocation of workload of selected ones of the resources between the plurality of distributed computing systems is controlled based on the productive spend metrics determined for the selected ones of the resources. Related computer program products and resource management nodes are disclosed.

Abstract: An artificial neural network may be configured to test the impact of certain input parameters. To improve testing efficiency and to avoid test runs that may not alter system performance, the effect of input parameters on neurons or groups of neurons may be determined to classify the neurons into groups based on the impact of certain parameters on those groups. Groups may be ordered serially and/or in parallel based on the interconnected nature of the groups and whether the output of neurons in one group may affect the operation of another. Parameters not affecting group performance may be pruned as inputs to that particular group prior to running system tests, thereby conserving processing resources during testing.

Abstract: A robust method to automatically segment and identify tumor regions in medical images is extremely valuable for clinical diagnosis and disease modeling. In various embodiments, an efficient algorithm uses sparse models in feature spaces to identify pixels belonging to tumorous regions. By fusing both intensity and spatial location information of the pixels, this technique can automatically localize tumor regions without user intervention. Using a few expert-segmented training images, a sparse coding-based classifier is learned. For a new test image, the sparse code obtained from every pixel is tested with the classifier to determine if it belongs to a tumor region. Particular embodiments also provide a highly accurate, low-complexity procedure for cases when the user can provide an initial estimate of the tumor in a test image.

Abstract: According to an embodiment, a dimension reduction apparatus includes an obtaining device to obtain a first vector made up of elements; a generator to generate first partial vectors each made up of one or more elements of the elements; a first mapping controller to transform each first partial vector into a second partial vector by reducing number of elements constituting the first partial vector using first mapping corresponding to the first partial vector, concatenate the second partial vectors, and generate a second vector by reducing number of dimensions of the first vector; and a second mapping controller to apply, to the second vector, second mapping which maps the second vector into a space having number of dimensions not larger than that of the second vector, and generate a third vector by reducing the number of dimensions of the second vector to not larger than that of the second vector.

Abstract: The present invention discloses methods, systems, and tools for knowledge processing by building various data structures corresponding to uncovered informational data such as values of association strengths or significance measures and maps of ontological subjects of compositions or one or more content accompanying a request for service by a user. In one embodiment of the invention the method assigns and calculates an ontological subject association strength/value measures and spectrums to each composition or ontological subject. The resulting data, spectrums, and the adjacency matrix of the map are used to evaluate the merits of the compositions in the context of reference universes. It is also used as a research guiding tool for knowledge discovery or automatically generating high value compositions or new or less known knowledge about the ontological subjects of the universe.

Abstract: According to an embodiment, a dimension reduction apparatus includes an obtaining device to obtain a first vector made up of elements; a generator to generate first partial vectors each made up of one or more elements of the elements; a first mapping controller to transform each first partial vector into a second partial vector by reducing number of elements constituting the first partial vector using first mapping corresponding to the first partial vector, concatenate the second partial vectors, and generate a second vector by reducing number of dimensions of the first vector; and a second mapping controller to apply, to the second vector, second mapping which maps the second vector into a space having number of dimensions not larger than that of the second vector, and generate a third vector by reducing the number of dimensions of the second vector to not larger than that of the second vector.

Abstract: Technical solutions are described for predicting linepack delays. An example method includes receiving temporal sensor measurements of a first fluid-delivery pipeline network and generating a causality graph of the first fluid-delivery pipeline network. The method also includes determining a topological network of the stations based on the causality graph, where the topological network identifies a temporal delay between a pair of stations. The method also includes generating a temporal delay prediction model based on the topological network and predicting the linepack delays of a second fluid-delivery pipeline network based on the temporal delay prediction model, where a compressor station of the second fluid-delivery pipeline network compresses fluid based on the predicted linepack delays to maintain a predetermined pressure.

Abstract: A matching method includes: generating a first numerical vector; generating a second numerical vector by squaring each component of the first numerical vector and a third numerical vector by cubing each component of the first numerical vector; generating first to third polynomials by executing polynomial transformation of the first to third numerical vectors; encrypting the first to third polynomials by a homomorphic encryption scheme; executing a predetermined operation while keeping data used in the predetermined operation encrypted, by using fourth to sixth polynomials obtained by the polynomial transformation and the homomorphic encryption of fourth to sixth numerical vectors, wherein the fourth numerical vector is generated by numerically vectorizing second text, the fifth numerical vector is generated by squaring each component of the fourth numerical vector, and the sixth numerical vector is generated by cubing each component of the fourth numerical vector; and decrypting a result of the predetermined

Abstract: A sequence of images depicting an object is captured, e.g., by a camera at a point-of-sale terminal in a retail store. The object is identified, such as by a barcode or watermark that is detected from one or more of the images. Once the object's identity is known, such information is used in training a classifier (e.g., a machine learning system) to recognize the object from others of the captured images, including images that may be degraded by blur, inferior lighting, etc. In another arrangement, such degraded images are processed to identify feature points useful in fingerprint-based identification of the object. Feature points extracted from such degraded imagery aid in fingerprint-based recognition of objects under real life circumstances, as contrasted with feature points extracted from pristine imagery (e.g., digital files containing label artwork for such objects).

Abstract: Techniques are provided for efficiently collecting statistics for hierarchically-organized collections of data. A database system leverages container-level modification time stamps and stored subtree-level change information to gather statistical information from only those resources in a hierarchical collection for which the statistics have changed since the last time that statistics were gathered for the collection. A lockless data structure is also described for storing the subtree-level change information in which an identifier corresponding to each subtree in a collection containing a changed resource may be stored. This data structure may be a table that is distinct from one or more tables representing the collection. In one embodiment of the invention, the immediate parent resource of a particular modified resource may be omitted from the subtree table by leveraging modification time stamps while gathering statistics based on tracked subtree-level information.

Abstract: The subject matter disclosed herein provides methods, apparatus, and articles of manufacture for neural-based processing. In one aspect, there is provided a method. The method may include reading, from a first memory, context information stored based on at least one connection value; reading, from a second memory, an activation value matching the at least one connection value; sending, by a first processor, the context information and the activation value to at least one of a plurality of microengines to configure the at least one microengine as a neuron; and generating, at the at least one microengine, a value representative of an output of the neuron. Related apparatus, systems, methods, and articles are also described.

Abstract: A system for managing a data center including a plurality of electronic components, each of which are configured to generate varying levels of heat loads under varying power level utilizations, is disclosed. The system may comprise a data collection module adapted to collect data describing heat loads generated by the plurality of electronic components; an implementation module adapted to implement a model to predict a thermal topology of the data center, wherein the model is based on thermodynamic state equations; and a control module adapted to adjust the heat load of at least one of the plurality of electronic components based on the model.

Abstract: Some embodiments relate to using an unnormalized neural network language model in connection with a speech processing application. The techniques include obtaining a language segment sequence comprising one or more language segments in a vocabulary; accessing an unnormalized neural network language model having a normalizer node and an output layer comprising a plurality of output nodes, each of the plurality of output nodes associated with a respective language segment in the vocabulary; and determining, using the unnormalized neural network language model, a first likelihood that a first language segment in the vocabulary follows the language segment sequence.

Abstract: The resent invention relates to a system for gathering and assembling information based on data received from one or several mobile terminals, the system comprising: one or several mobile terminals, and a central processing unit. The data at least relates to a position, environmental information and a tool used by a user of the mobile terminal for carrying out an activity and the central processing unit is configured to: process said data, assemble a unique information based on said data, said unique information comprising optimized data for carrying out said activity.

Abstract: A system for machine training can comprise one or more data processors and a non-transitory computer-readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform operations including: accessing a dataset comprising data tracking a plurality of features; determining a series of values for a regularization parameter of a sparse support vector machine model, the series including an initial regularization value and a next regularization value; computing an initial solution to the sparse support vector machine model for the initial regularization value; identifying, using the initial solution, inactive features of the sparse support vector machine model for the next regularization value; and computing a next solution to the sparse support vector machine model for the next regularization value, wherein computing the next solution includes excluding the inactive features.

Abstract: The use of a pipelined algorithm that performs parallelized computations to train deep neural networks (DNNs) for performing data analysis may reduce training time. The DNNs may be one of context-independent DNNs or context-dependent DNNs. The training may include partitioning training data into sample batches of a specific batch size. The partitioning may be performed based on rates of data transfers between processors that execute the pipelined algorithm, considerations of accuracy and convergence, and the execution speed of each processor. Other techniques for training may include grouping layers of the DNNs for processing on a single processor, distributing a layer of the DNNs to multiple processors for processing, or modifying an execution order of steps in the pipelined algorithm.

Abstract: A facility for constructing an advertising message in connection with a web page is described. The facility obtains text associated with the web page, and uses the obtained text to generate a query. The facility uses the generated query to select a stock photography image, and generates an advertising message that incorporates the selected stock photography image.

Abstract: Calculating a value of a website visitor includes initializing a calculation model for calculating the value of the website visitor, the calculation model being a neural network model with visitor information as an input and the visitor's value as an output; training the calculation model by using a data sample and determining the calculation model; and obtaining the visitor information, and calculating the value of the visitor by using the determined calculation model.

Abstract: A method for gaining access or entry to a system. The method comprises (a) beginning a secure system act by a user; (b) beginning counting of time intervals concurrent with execution of step (a); (c) ending the secure system act by the user; (d) capturing a final time interval count concurrent with execution of step (c); (e) determining whether the secure system act matches a correct secure system act; (d) determining whether the final time interval count matches a correct final time interval count; and (e) granting the user access or entry to the system responsive affirmative results of step (d).

Abstract: A data analysis system (1) for displaying data facilitating visual analysis of communication transaction is disclosed. The system includes a transactions database (3) operable to store transaction records (5) defining communication transactions and a processing module (11) operable to determine a hierarchy having a tree structure wherein leaf nodes in the lowest level of the hierarchy correspond to sources and destinations associated with communication transactions represented by transaction records 5) stored in the transactions database (5).

Abstract: An automated reader for reading fluorescence in-situ hybridization signals includes one or more computer processors for receiving a digitized FISH image and executing the steps of converting colors within the image to a hue value, separately for each color extracting quantitative values to detect the presence of signals corresponding to spots and applying a plurality of algorithms to extract features from the signals to determine cell shapes and segment cells within the FISH image. After recombining the signals, the extracted features for the colors learning machines are used to classify the spots according to the color and separate merged signals of classified spots that are in close proximity to each other within the image. The classified spots are counted to determine relative frequency of colors and a report is generated providing the number of classified spots of each color.

Abstract: In one embodiment, an indication that a decision has been requested, selected, or applied with respect to one or more users may be obtained. After the indication that a decision that has been requested, selected, or applied is obtained, a value function may be updated, where the value function approximates an expected reward associated with the one or more users over time since the decision has been requested, selected, or applied with respect to the one or more users. The value function may be updated by performing or providing one or more updates to the value function, where a time at which each of the one or more updates is performed or provided is independent of activity of the one or more users.

Abstract: In one embodiment a method for managing event messages includes predicting an event message arrival rate, determining a current capacity of an event message repository in which received event messages are stored, and calculating a time at which to perform event message deletion relative to the predicted event message arrival rate and the determined current capacity of the event message repository.

Abstract: A system and method for computer-assisted karyotyping includes a processor which receives a digitized image of metaphase chromosomes for processing in an image processing module and a classifier module. The image processing module may include a segmenting function for extracting individual chromosome images, a bend correcting function for straightening images of chromosomes that are bent or curved and a feature selection function for distinguishing between chromosome bands. The classifier module, which may be one or more trained kernel-based learning machines, receives the processed image and generates a classification of the image as normal or abnormal.

Abstract: Sensory encoder may be implemented. Visual encoder apparatus may comprise spiking neuron network configured to receive photodetector input. Excitability of neurons may be adjusted and output spike may be generated based on the input. When neurons generate spiking response, spiking threshold may be dynamically adapted to produce desired output rate. The encoder may dynamically adapt its input range to match statistics of the input and to produce output spikes at an appropriate rate and/or latency. Adaptive input range adjustment and/or spiking threshold adjustment collaborate to enable recognition of features in sensory input of varying dynamic range.

Abstract: A control system for a distributed power grid includes a simulation module operative to directly interface with operational control of distributed energy resources (DER) to develop and when necessary dynamically modify control inputs of the distributed power grid. By conducting a decentralized and distributed simulation of DER topology (components and their surrounding infrastructure) each distributed control module can simulate control response characteristics of a plurality of DER to determine a control methodology necessary to achieve a desired target. Once developed the same control inputs can be directly applied to physical DER and thereafter monitored to validate performance. Once validated, operational control of the DER is established while ongoing modifications of the control inputs continues in parallel to maintain desired performance.

Abstract: The AKOS (Artificial Knowledge Object System) of the invention is a software processing engine that relates incoming information to pre-existing stored knowledge in the form of a world model and, through a process analogous to human learning and comprehension, updates or extends the knowledge contained in the model, based on the content of the new information. Incoming information can come from sensors, computer to computer communication, or natural human language in the form of text messages. The software creates as an output. Intelligent action is defined as an output to the real-world accompanied by an alteration to the internal world model which accurately reflects an expected, specified outcome from the action. These actions may be control signals across any standard electronic computer interface or may be direct communications to a human in natural language.

Abstract: Various methods and systems are provided for related to adaptive systems using correntropy. In one embodiment, a signal processing device includes a processing unit and a memory storing an adaptive system executable in the at least one processing unit. The adaptive system includes modules that, when executed by the processing unit, cause the signal processing device to adaptively filter a desired signal using a correntropy cost function. In another embodiment, a method includes adjusting a coefficient of an adaptive filter based at least in part on a correntropy cost function signal, providing an adaptive filter output signal based at least in part on the adjusted coefficient and a reference signal, and determining an error signal based at least in part on a received signal and the adaptive filter output signal.

Abstract: A system and method are provided for receiving extracted data from a transaction database. Extracted data is transformed into a predefined structure and used to populate a database. A set of measures are then provided for interrogating the database and these may be displayed to the user with dimensions which maybe applied to filter the data. Data presented to a user is relevant to his or her area of activity. The data is periodically refreshed and signals are presented in the data relating to issues requiring further investigation.

Abstract: Disclosed is an approach for implementing a flexible parser for a networking system. A micro-core parser is implemented to process packets in a networking system. The micro-cores of the parser read the packet headers, and perform any suitably programmed tasks upon those packets and packet headers. One or more caches may be associated with the micro-cores to hold the packet headers. A dependency list is used to maintain proper ordering of packets being processed by the micro-cores.

Abstract: A method and system is used in managing organization based security risks. Organization based information is analyzed for information that is suitable for use in attacking a user in the organization. The organization based information comprises user access information. Publicly available information is analyzed for information that is suitable for use in identifying the user within the organization. Based on the analyzes, there is derived a risk score for the user.

Abstract: Discriminative pretraining technique embodiments are presented that pretrain the hidden layers of a Deep Neural Network (DNN). In general, a one-hidden-layer neural network is trained first using labels discriminatively with error back-propagation (BP). Then, after discarding an output layer in the previous one-hidden-layer neural network, another randomly initialized hidden layer is added on top of the previously trained hidden layer along with a new output layer that represents the targets for classification or recognition. The resulting multiple-hidden-layer DNN is then discriminatively trained using the same strategy, and so on until the desired number of hidden layers is reached. This produces a pretrained DNN. The discriminative pretraining technique embodiments have the advantage of bringing the DNN layer weights close to a good local optimum, while still leaving them in a range with a high gradient so that they can be fine-tuned effectively.

Abstract: Systems and methods are provided for depth map estimation using normalized displacement of image pairs. In one embodiment, an image manipulation application identifies image pairs from an input image. Each of the image pairs includes an image portion from a first perspective and the image portion from a second perspective. The image manipulation generates displacement vectors for the image pairs. Each of the displacement vectors represents at least one of a horizontal displacement and a vertical displacement of the image portion from the first perspective to the second perspective. The image manipulation application generates normalized displacement vectors corresponding to the displacement vectors. Each of the normalized displacement vectors is generated by transforming a respective orientation of a corresponding one of the displacement vectors to a common reference direction. The image manipulation application generates a depth map based on the normalized displacement vectors.

Abstract: An improved technique involves adjusting the operation of a KBA system based on facts that may contain information known to an adversary. Along these lines, the KBA system may receive an alert concerning an adversary that may know the answers to some of the KBA questions used by the KBA system in authenticating users. In response to alert, the KBA system may alter operations in order to account for the adversary. Subsequently, when a user requests authentication, the KBA system selects KBA questions based on adjustments made to the KBA system in order to avoid presenting the adversary with KBA questions derived from facts (s)he knows.

Abstract: A method and system for dynamically tracking the location of mobile nodes (104, 106, 108, 110n) in a wireless network (102) is disclosed. The method comprises: for each mobile node, dynamically measuring the range between the mobile node and at least one neighboring node (step 202); and executing a Bayesian tracking algorithm for each mobile node (step 204). The algorithm has the measured range as an input, exchanges data with tracking algorithms for neighboring mobile nodes, and utilizes a statistical model of error in measured range and a statistical model of node motion to dynamically determine location.

Abstract: In certain embodiments, a method includes formulating an optimization problem to determine a plurality of model parameters of a system to be modeled. The method also includes solving the optimization problem to define an empirical model of the system. The method further includes training the empirical model using training data. The empirical model is constrained via general constraints relating to first-principles information and process knowledge of the system.

Abstract: The OculoKinetic Device is used to test an individual to evaluate brain function including, but not limited to, identifying the presence of a traumatic brain injury or central nervous system disease which manifests itself through abnormal ocular responses to stimuli by using the high-speed tracking of an individual's eye movements (monocular or binocular, and either conjugate or disconjugate in horizontal, vertical, or torsional directions or combinations thereof), pupil size and reactivity, eyelid position, and blink parameters, and optionally along with other ocular elements, i.e., eyeball pressure, temperature, blood flow, etc. The eye movement stimulus protocol uses a target that moves in any direction of a two- or three-dimensional plane and may use a color display or geometric shapes. In addition, the stimuli can be used in conjunction with cognitive testing, balance assessment, and other non-eye tests.

Abstract: A time series classifying memory includes an enumerated group of synapses. Each synapse of the group has a junction including pre-synaptic emitters communicating with post synaptic receptors. A single pathway is input to each synapse of the group, and when innervated, stimulates the junctions. Each successive synapse has successively more post synaptic receptors according to a fixed ratio. At each junction, quanta of neurotransmitter emitted from the emitters bind with available receptors. When all the receptors at a junction have been bound, the junction goes refractory. The synapse adjacent to the last junction to go refractory is marked. During a training mode the mark is a Long Term Potentiation (LTP) mark. During a live mode the mark is a Short Term Potentiation (STP) mark. The input is classified upon successful correlation of the LTP mark with the STP mark. A clock operable with synaptic matrix models this process.

Abstract: A method, apparatus and product useful for classification reliability prediction. Utilize a separate classifier tool, referred to as a reliability classifier tool or RCT, in order to predict reliability of a prediction made by a classifier tool or CT by introducing additional features that were not used by the classifier tool itself.

Abstract: Aspects of the subject matter described herein relate to predicting and using search engine switching behavior. In aspects, switching components receive a representation of user interactions with at least one browser. The switching components derive information from the representation that is useful in predicting whether a user will switch search engines. The derived information and information about a user's current interaction with a browser is then used by a switch predictor to predict whether the user will switch search engines. This prediction may be used in a variety of ways examples of which are given herein.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a deep neural network. One of the methods includes training a deep neural network with a first training set by adjusting values for each of a plurality of weights included in the neural network, and training the deep neural network to determine a probability that data received by the deep neural network has features similar to key features of one or more keywords or key phrases, the training comprising providing the deep neural network with a second training set and adjusting the values for a first subset of the plurality of weights, wherein the second training set includes data representing the key features of the one or more keywords or key phrases.

Abstract: One embodiment of the present invention provides a system for predicting a personality trait. During operation, the system initially obtains personality data associated with users. The system collects sample data associated with the users. Next, the system trains a predictor with the collected sample data and the personality data. Then, the system collects data associated with a particular user, and generates a personality trait score for the particular user by using the predictor to analyze the particular user's collected data.

Abstract: A method and apparatus for constructing a neuroscience-inspired artificial neural network (NIDA) or a dynamic adaptive neural network array (DANNA) or combinations of substructures thereof comprises one of constructing a substructure of an artificial neural network for performing a subtask of the task of the artificial neural network or extracting a useful substructure based on one of activity, causality path, behavior and inputs and outputs. The method includes identifying useful substructures in artificial neural networks that may be either successful at performing a subtask or unsuccessful at performing a subtask. Successful substructures may be implanted in an artificial neural network and unsuccessful substructures may be extracted from the artificial neural network for performing the task.

Abstract: A method and apparatus for providing personalized configuration of physical supports for the human body, comprising accepting input including an individual's demographic information, neurological attributes, physical history, operational environment, and outcome or use objectives, processing user input employing an artificial intelligence engine, and then returning guidance and/or control parameters directed to seating adjustment and positioning, including incline angles for wheelchair tilt and recline.

Abstract: Provided is an abnormality detecting device for a construction machine that can estimate an abnormality occurring to a component (engine, pump, etc.) of the construction machine based on the relationship among a plurality of pieces of sensor information and thereby prevent machine failure. A correlation coefficient calculation unit 102 calculates correlation coefficients between time-series sensor values acquired by a plurality of sensors 101. A correlation coefficient comparison unit 103 compares the correlation coefficients and calculates the degree of difference between each correlation coefficient and other correlation coefficients. An abnormality judgment unit 104 judges that an abnormality has occurred to a part related to a sensor when the degree of difference calculated in regard to the sensor exceeds a preset value.

Abstract: A system, method and apparatus for automatically adapting power grid usage by controlling internal and/or external power-related assets of one or more users in response to power regulation and/or frequency regulation functions in a manner beneficial to both the power grid itself and the users of the power grid.

Abstract: Techniques are described for displaying help articles, for a web page, that are related to the context of the web page. A help article provider has access to a database of help articles, each of which are associated with target-context data that may include content identifiers for the help article and/or user profile attributes for users that the help article targets. The help article provider identifies content identifiers for a viewed web page based on one or more of: metadata and a URL for the web page. The help article provider searches the help article database using the identified content identifiers. The help article provider may filter the help articles based on an authenticated user's attributes. An ordered list of the identified help articles is displayed, with the most relevant or important help articles displayed at or near the top of the list.

Abstract: Various embodiments are disclosed for generating an image from a Compositional Pattern Producing Network (CPPN). One such method includes receiving, in the CPPN, a series of polar coordinates {r,?}; outputting, by the CPPN, a series of pixel values, each of the pixel values corresponding to one of the polar coordinates; and displaying the pixel values at the corresponding polar coordinates to produce the image.

Abstract: The invention provides a computer-implemented chunking process (named “OP”) to assign and physically associate unique spatial representations (“chunk codes”) in one representational (coding) space (“chunk coding space”) with unique temporal sequences of spatial codes (“items”) in another coding space (“item coding space”). OP differs from all prior chunking processes in that, for any particular item sequence, it activates a unique chunk code for that sequence during presentation of that sequence's first item, even if other sequences from the input domain may begin with the same item.