Abstract: An apparatus may comprise a nondestructive evaluation system and a cutting system. The nondestructive evaluation system may be configured to inspect a processed portion of a structure. The nondestructive evaluation system may be configured to determine whether an inconsistency is present in the processed portion. The nondestructive evaluation system may also be configured to generate information about a location of the inconsistency. The cutting system may be configured to cut a number of parts out of the processed portion of the structure in which the inconsistency may be at least substantially excluded from the number of parts.

Abstract: A cooling system and method are provided for facilitating cooling of a liquid-cooled electronics rack. The cooling system includes a coolant flow controller, a modular cooling unit and a pressure controller. The flow controller is associated with a respective electronics rack and controls flow of coolant through that electronics rack based on its changing cooling requirements. The cooling unit includes an adjustable coolant pump for facilitating supply of coolant to the rack. The pressure controller is associated with the cooling unit for controlling pressure of coolant at an output of the cooling unit via control of pump speed of the pump. Responsive to adjusting coolant flow through the electronics rack, the pressure controller automatically adjusts pump speed of the adjustable pump to maintain pressure about a constant coolant pressure set point at an output of the cooling unit, thereby conserving power while still cooling the liquid-cooled electronics rack.

Abstract: A new type of Markovian sequence generator and generation method generates a Markovian sequence having controllable properties, notably properties that satisfy at least one control criterion which is a computable requirement holding on items in the sequence. The Markovian sequence is generated chunkwise, each chunk containing a plurality of items in the sequence. During generation of each chunk a search is performed in the space of Markovian sequences to find a chunk-sized series of items which enables the control criterion to be satisfied. The search can be performed using a generate and test approach in which chunk-sized Markovian sequences are generated then tested for compliance with the requirement(s) of the control criteria.

Abstract: Methods for setting a basal rate profile for an insulin pump with an input unit and a calculation unit are disclosed. A number of interpolation nodes for the basal rate profile are defined by means of the input unit of the insulin pump. A continuous function with respect to the interpolation nodes, which images the interpolation nodes and any previously stored basal rates, is formed using the calculation unit of the insulin pump, and a temporal sequence of basal rates to be released by the insulin pump during specific time intervals is generated from the continuous function using the calculation unit of the insulin pump.

Abstract: An energy management system manages energy consumption of managed device(s) disposed to receive energy via a meter connected to an energy supply, the energy supply. A system controller has a computer processor that instructs the managed device(s) to employ an operational mode based on the priority, the energy need, the demand state, and whether the energy consumption rate of the managed device(s) exceeds energy available from at least one of the external energy source and internal energy source.

Abstract: The present invention relates generally to deformation machining systems and methods that combine, in a single machine tool setup, the machining of thin structures and single point incremental forming (SPIF), such that novel part geometries and enhanced material properties may be obtained that are not achievable using conventional machining or forming systems and methods, individually or collectively. Important to many applications, lighter weight parts may be produced at lower cost using a conventional 3-axis computer numerically controlled (CNC) machine tool or the like, instead of the conventional 5-axis CNC machine tool or the like that is typically required.

Abstract: Discovering mixtures of models includes: initiating learning algorithms, determining, data sets including a cluster of points in a first region of a domain and a set of points distributed near a first line extending across the domain; inferencing parameters from the cluster and the set of points; creating a description of the cluster of points in the first region of the domain and computing approximations of a first learned mixture model and a second learned mixture model; determining a first and second probability, generating a confidence rating that each point of the cluster of points in the first region of the domain corresponds to the first learned mixture model and generating a confidence rating that each point of the set of points distributed near the first line correspond to the second learned mixture model, thus causing determinations of behavior of a system described by the learned mixture models.

Abstract: To suggest new connections to a user of a social networking system, the system generates a set of candidate users to whom the user has not already formed a connection. The system determines the likelihood that the user will connect to each candidate user if suggested to do so, and it also computes the value to the social networking system if the user does connect to the candidate user. Then, the system computes an expected value score for each candidate user based on the corresponding likelihood and the value. The candidate users are ranked and the suggestions are provided to the user based on the candidate users' expected value scores. The social networking system can suggest other actions to a user in addition to forming a new connection with other users.

Abstract: Supervised kernel nonnegative matrix factorization generates a descriptive part-based representation of data, based on the concept of kernel nonnegative matrix factorization (kernel NMF) aided by the discriminative concept of graph embedding. An iterative procedure that optimizes suggested formulation based on Pareto optimization is presented. The present formulation removes any dependence on combined optimization schemes.

Abstract: A smart grid for improving the management of a power utility grid is provided. The smart grid as presently disclosed includes using sensors in various portions of the power utility grid, using communications and computing technology, such as additional bus structures, to upgrade an electric power grid so that it can operate more efficiently and reliably and support additional services to consumers. Specifically, the additional bus structures may comprise multiple buses dedicated to the different types of data, such as operational/non-operational data, event processing data, grid connectivity data, and network location data. The multiple buses may comprise different segments in a single bus or may comprise separate buses. The multiple buses may be used to transport the various types of data to other smart grid processes (such as at a centrally located controller).

Abstract: An engineering analysis tool comprises a unified resource model-based (URM) objective and tool mapping capability for linking engineering analysis objectives to analysis tools. A Markov chain-based analysis plan generator (APTG) for reusing engineering analysis plans may be included in the engineering analysis tool. Further, the engineering analysis tool comprises a graphic symptom capturer (GSC) that auto-captures engineering perceived fault symptoms from engineering data analysis (EDA) tools.

Abstract: Supervised nonnegative matrix factorization (SNMF) generates a descriptive part-based representation of data, based on the concept of nonnegative matrix factorization (NMF) aided by the discriminative concept of graph embedding. An iterative procedure that optimizes suggested formulation based on Pareto optimization is presented. The present formulation removes any dependence on combined optimization schemes. Analytical and empirical evidence is presented to show that SNMF has advantages over popular subspace learning techniques as well as current state-of-the-art techniques.

Abstract: A method and system for reducing a number of links in a Bayesian network. A first modified Bayesian network based on a primary Bayesian network that has a plurality of links is generated, wherein the first modified Bayesian network does not include a first subset of the plurality of links. A second modified Bayesian network based on the primary Bayesian network is generated, wherein the second modified Bayesian network does not include a second subset of the plurality of links. The first modified Bayesian network and the second modified Bayesian network are validated using a first data set to generate a first validation result and a second validation result. The primary Bayesian network is substituted with the first modified Bayesian network or the second modified Bayesian network based on the first validation result and the second validation result.

Abstract: A string matching unit 110 specifies a category of an input document 801 by string matching of the input document 801 and a classifying keyword shown by matching condition information 109. Learning data 209 shows statistic information of each category. A classifying unit 220 specifies the category of the input document 801 based on a correspondence ratio of the input document 801 and the statistic information shown by the learning data 209. A correct answer comparing unit 120 compares the category specified by the string matching unit 110 and a category of correct answer information 803. A learning unit 210 compares the category specified by the classifying unit 220 and the category of the correct answer information 803. An error rate calculating unit 310 calculates a classifying error rate of a string matching filter unit 100 and a learning filter unit 200 based on the comparison result of the correct answer comparing unit 120 and the comparison result of the learning unit 210.

Abstract: A system, method, and computer program product for making a recommendation to a user of a social network to associate an existing tag with a social media entity instance are provided. The method includes generating a random walk model that includes the social media entity instance for at least a portion of the social network, determining weighted values for the random walk model, generating a weighted random walk model based on the random walk model and the weighted values, performing a random walk on the weighted random walk model starting at the social media entity instance, and recommending an existing tag to the user based on the random walk.

Abstract: Method and device for computer-aided prediction of intended movements from neuronal signals of a brain, wherein the neuronal signals are each associated in the brain with intended movements, wherein neuronal signals are recorded and the most probable movements are determined from these, specifically using a predetermined model in which a recorded neuronal signal and a determined movement are assigned to each other, and, for the probability with which a recorded neuronal signal corresponds to a respective predetermined movement, a predetermined distribution is assumed that is defined by specific characteristic values, wherein an adaptation of the neuronal signal is included in the predetermined model.

Abstract: There is proposed a control system and the like that can realize a CPG (Central Pattern Generator) network having high controllability. A control system (CPG network) includes a plurality of oscillation control devices (CPG) and one target signal generating device (rhythm generator (RG)). Each CPG and the RG are described by the Van der Pol equation, and an amplitude and a period of an output waveform are substantially independently controlled by an external signal. In order to control a phase difference between the CPGs, the period of each CPG is temporarily controlled through connections that are only conjunctions between the each CPG and the RG.

Abstract: Apparatus, systems, and methods can operate to provide efficient data clustering, data classification, and data compression. A method comprises training set of training instances can be processed to select a subset of size-1 patterns, initialize a weight of each size-1 pattern, include the size-1 patterns in classes in a model associated with the training set, and then include a set of top-k size-2 patterns in a way that provides an effective balance between local, class, and global significance patterns. A method comprises processing a dataset to compute an overall significance value of each size-2 pattern in each instance in the dataset, sort the size-2 patterns, and select the top-k size-2 patterns to be represented in clusters, which can be refined into a clustered hierarchy. A method comprises creating an uncompressed bitmap, reordering the bitmap, and compressing the bitmap. Additional apparatus, systems, and methods are disclosed.

Abstract: A cutting device is provided which can simultaneously achieve a shorter machining time, lower machining resistance during cutting, and high-precision machining. To be specific, the cutting device of the present invention includes a three-axis tool unit including a first actuator operating in a u direction, a second actuator operating in a v direction orthogonal to the u direction, a third actuator operating in a w direction orthogonal to the u direction and the v direction, a tool holder disposed at the intersection of the u direction, the v direction, and the w direction, a tool mounted in the tool holder, and three sensors for measuring the displacements of the first, second, and third actuators, respectively and feeding back displacement signals representing the measured displacements, wherein the first, second, and third actuators of the three-axis tool unit vibrate so as to perform vibration cutting on a work material.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for classifying objects. Object (e.g., Website) classification can be performed using multiple models that are implemented in a distributed processing environment. In response to receiving a request to classify an object based on attribute values that characterize the object models that are configured to classify objects based on the attribute values are identified and the attribute values are provided to the identified models. Based on classification data received from selected models is used to generate a final classification for the object. The final classification can specify or correspond to a presentation availability status of the object that can be used to control availability of the object. For example, a Website having a final classification of “blacklisted” can be made unavailable for presentation based on the classification result.