Abstract: Techniques are disclosed for generating anomaly scores for a neuro-linguistic model of input data obtained from one or more sources. According to one embodiment, generating anomaly scores includes receiving a stream of symbols generated from an ordered stream of normalized vectors generated from input data received from one or more sensor devices during a first time period. Upon receiving the stream of symbols, generating a set of words based on an occurrence of groups of symbols from the stream of symbols, determining a number of previous occurrences of a first word of the set of words, determining a number of previous occurrences of words of a same length as the first word, and determining a first anomaly score based on the number of previous occurrences of the first word and the number of previous occurrences of words of the same length as the first word.

Abstract: An electrolyte solution for a lithium-ion cell includes at least one organic carbonate solvent, at least one lithium salt including a non-coordinating anion and at least one polyfluorinated alkoxy olefin according to the general formula I, the general formula II, or the general formula III: I: RaRbC?CH—O—R, II: RaRbC?CH—O—CHRa?Rb?, III: RaRbC?CH—O—Rc—O—CH?CRa?Rb?, wherein Ra and Ra? are each independently a fluorinated alkyl group having 1 or 2 carbon atoms, Rb and Rb? are each independently F, Cl, Br or H, and R is CnHxFy, wherein n is an integer from 1 to 6, x and y are each independently integers from 0 to 13, and x+y=2n+1 or x+y=2n?1, and Rc is CkH(2k+1), wherein k is an integer from 2 to 4.

Abstract: Techniques are disclosed for analyzing and learning behaviors based on acquired sensor data. A neuro-linguistic cognitive engine performs learning and analysis on linguistic content (e.g., identified alpha symbols, betas, and gammas) obtained by a linguistic model that clusters observations to generate the linguistic content. The neuro-linguistic cognitive engine compares new data to learned patterns stored in short and longer-term memories and determines whether to issue special event notifications indicating anomalous behavior. In one embodiment, condition(s) may be generated for new data and checked against inference nodes of an inference network. Inference nodes matching the condition(s) are executed to, e.g., compare the new data with the learned patterns, with output from the inference nodes being used to generate additional condition(s) that are again matched to inference nodes which may be executed.

Abstract: The present invention relates to glutamate dehydrogenase mutants and their application in preparation of L-phosphinothricin. The amino acid sequences of the glutamate dehydrogenase mutants are as shown in SEQ ID NO. 1-9, 11, 13, 15, 17-19 and 22. By means of molecular engineering, mutating the specific alanine in glutamate dehydrogenase substrate-binding pocket into glycine and/or mutating the specific valine in glutamate dehydrogenase substrate-binding pocket into alanine, the present invention has obtained NADPH-specific glutamate dehydrogenase mutants with high enzyme activity in catalyzing the substrate 2-oxo-4-[(hydroxy)(methyl)phosphinoyl]butyric acid or its salt for L-phosphinothricin preparation or NADH-specific glutamate dehydrogenase mutants with catalytic activity toward PPO; this has significantly improved substrate conversion, and increased the product concentration of the L-phosphinothricin preparation process.

Abstract: Techniques are disclosed to optimize feature selection in generating betas for a feature dictionary of a neuro-linguistic Cognitive AI System. A machine learning engine receives a sample vector of input data to be analyzed by the neuro-linguistic Cognitive AI System. The neuro-linguistic Cognitive AI System is configured to generate multiple betas for each of a plurality of sensors. The machine learning engine identifies a sensor specified in the sample vector and selects optimization parameters for generating betas based on the identified sensor.

Abstract: Techniques are disclosed for generating a low-dimensional representation of an image. An image driver receives an image captured by a camera. The image includes features based on pixel values in the image, and each feature describes the image in one or more image regions. The image driver generates, for each of the plurality of features, a feature vector that includes values for that feature corresponding to at least one of the image regions. Each value indicates a degree that the feature is present in the image region. The image driver generates a sample vector from each of the feature vectors. The sample vector includes each of the values included in the generated feature vectors.

Abstract: Techniques are disclosed for analyzing a scene depicted in an input stream of video frames captured by a video camera. The techniques include receiving data for an object within the scene and determining whether the object has remained substantially stationary within the scene for at least a threshold period. If the object is determined to have remained stationary for at least the threshold period, a rareness score is calculated for the object to indicate a likelihood of the object being stationary to an observed degree at an observed location. The rareness score may use a learning model to take into account previous stationary and/or non-stationary behavior of objects within the scene. In general, the learning model may be updated based on observed stationary and/or non-stationary behaviors of the objects. If the rareness score meets reporting conditions, the stationary object event may be reported.

Abstract: The invention discloses a glutamate dehydrogenase mutant and an application thereof. The mutant is one of the following: a mutation of the 402th lysine of the amino acid sequence shown in SEQ ID NO. 1 to phenylalanine or aspartic acid; a mutation of the 406th isoleucine to phenylalanine or threonine; a combined mutation of the 121th threonine and the 123th leucine; a combined mutation of the 379th alanine and the 383th leucine. In the invention, the catalytic activity of glutamate dehydrogenase derived from Pseudomonas putida to 2-carbonyl-4-(hydroxymethylphosphonoyl)butanoic acid (PPO) is significantly improved by a molecular transformation method combining directed evolution and a semi-rational design; and the issue of low glutamate dehydrogenase activity in the process of preparing L-glufosinate by reductive amination is solved.

Abstract: An LED tube includes a translucent tubular body having an inner surface, an outer surface, a first end and a second end. The LED tube further includes a first end cap with a first pair of contact pins, arranged at the first end of the tubular body and a second end cap with a second pair of contact pins, arranged at the second end of the tubular body, an LED light engine with a plurality of LEDs and a driver circuit for driving the LED light engine. The driver circuit includes a first sub-circuit with at least one electronic component and a second sub-circuit with at least one electronic component.

Abstract: Techniques are disclosed for matching a current background scene of an image received by a surveillance system with a gallery of scene presets that each represent a previously captured background scene. A quadtree decomposition analysis is used to improve the robustness of the matching operation when the scene lighting changes (including portions containing over-saturation/under-saturation) or a portion of the content changes. The current background scene is processed to generate a quadtree decomposition including a plurality of window portions. Each of the window portions is processed to generate a plurality of phase spectra. The phase spectra are then projected onto a corresponding plurality of scene preset image matrices of one or more scene preset. When a match between the current background scene and one of the scene presets is identified, the matched scene preset is updated. Otherwise a new scene preset is created based on the current background scene.

Abstract: An approach for an adaptive, performance-oriented, and compression-assisted encryption scheme implemented on a host computer to adaptively improve utilization of CPU resources is provided. The method comprises queueing a new data packet and determining a size of the new data packet. Based on historical data, a plurality of already encrypted data packets is determined. Based on information stored for the plurality of already encrypted data packets, an average ratio of compression for the plurality of already encrypted data packets is determined. Based on the average ratio of compression, a throughput of compression value and a throughput of encryption value, a prediction whether compressing the new data packet will reduce a CPU load is derived. If it is determined that compressing the new data packet will improve utilization of the CPU resources, then a compressed new data packet is generated by compressing the new data packet.

Abstract: A method for presenting and controlling a panoramic image is provided.

Abstract: This application discloses a media file presentation method, a client application, and a plug-in. The method includes: sending a media information obtaining request to a plug-in installed in the client application, so that the plug-in obtains first brief information and second brief information of a media file from a server based on the media information obtaining request; and receiving a notification message for the media file from the plug-in; and presenting the first brief information at a first presentation position according to the notification message, and then presenting the second brief information at a second presentation position, the media file being obtained and presented when an operation performed by a user on the second brief information is received.

Abstract: Techniques are disclosed for extracting micro-features at a pixel-level based on characteristics of one or more images. Importantly, the extraction is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. A micro-feature extractor that does not require training data is adaptive and self-trains while performing the extraction. The extracted micro-features are represented as a micro-feature vector that may be input to a micro-classifier which groups objects into object type clusters based on the micro-feature vectors.

Abstract: Techniques are disclosed for generating an anomaly score for a neuro-linguistic model of input data obtained from one or more sources. According to one embodiment, generating an anomaly score comprises receiving a score indicating how often a characteristic is observed in the input data. Upon receiving the score, comparing the score with an unusual score model to determine an unusualness score and comparing the unusualness score with an anomaly score model based on one or more unusual score models to generate the anomaly score indicating an overall unusualness for the input data.

Abstract: Disclosed are a high-strength multiphase steel tinned raw plate and a manufacturing method therefor, wherein the mass percentages of the components of the multiphase steel tinned raw plate are: 0.081%-0.14% of C, 0.2%-0.8% of Mn, 0.01%-0.09% of Al, 0.01%-0.03% of P, 0.002%-0.015% of N, also containing one or more than one of 0.001%-0.005% of B, 0.005%-0.05% of Cr, 0.001%-0.1% of Ti, 0.001%-0.2% of Nb, 0.005%-0.03% of Cu, 0.001%-0.008% of Mo, and the balance of Fe and other inevitable impurities; and satisfy: 0.21% <Mn+1.3 Cr+3.2 Mo+0.5 Cu <0.91%. The tinned raw plate has a structure comprising ferrite grains, pearlite, martensite and cementite particles, wherein the total volume fraction of the pearlite, martensite and cementite particles is 5%-20%, the volume fraction of the martensite is 1%-5%, and the martensite has a solid solution content of carbon of ?0.07%.

Abstract: A display screen control method and a terminal for monitoring whether a terminal satisfies a low-power running condition, where a display screen of the terminal is an organic light emitting display screen, and each pixel unit of the organic light emitting display screen includes at least one red subpixel, at least one blue subpixel, and at least one green subpixel, and decreasing a frequency of any one or two of a drive signal of the red subpixel, a drive signal of the green subpixel, and a drive signal of the blue subpixel when it is detected that the terminal satisfies the low-power running condition.

Abstract: A method for improving fault tolerance of a touchscreen determines an abnormal node, and shield the abnormal node to improve the fault tolerance of the touchscreen. The method includes detecting a capacitance value of each node in the touchscreen, comparing the detected capacitance value of each node with a preset capacitance value of each node to determine N target nodes, where N is an integer greater than or equal to zero, and the target nodes are nodes whose capacitance values vary, determining whether an abnormal node is included in the N target nodes, where the abnormal node is a target node determined when no touch operation occurs on the touchscreen, and discarding a row value, a column value, and a capacitance variation value of the abnormal node when the abnormal node is included in the N target nodes.

Abstract: An electronic communication device comprises a first transceiver capable of a bi-directional communication session on a first communication medium; a second transceiver capable of a bi-directional communication session on a second communication medium; and a control logic coupled to the first transceiver and the second transceiver and capable of implementing a convergence layer, wherein the control logic is configured to receive, from the first transceiver, a first signal; and cause, in response to the first signal, data received and transmitted by the first transceiver on the first communication medium as part of a communication session to be received and transmitted instead by the second transceiver on the second communication medium.

Abstract: A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.