Abstract: Techniques are disclosed for analyzing and learning behavior in an acquired stream of video frames. In one embodiment, a trajectory analyzer clusters trajectories of objects depicted in video frames and builds a trajectory model including the trajectory clusters, a prior probability of assigning a trajectory to each cluster, and an intra-cluster probability distribution indicating the probability that a trajectory mapping to each cluster is least various distances away from the cluster. Given a new trajectory, a score indicating how unusual the trajectory is may be computed based on the product of the probability of the trajectory mapping to a particular cluster and the intra-cluster probability of the trajectory being a computed distance from the cluster. The distance used to match the trajectory to the cluster and determine intra-cluster probability is computed using a parallel Needleman-Wunsch algorithm, with cells in antidiagonals of a matrix and connected sub-matrices being computed in parallel.

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.

Abstract: A graphene oxide membrane with a controllable interlayer spacing, a preparation method and use thereof are provided. The preparation method provides of infiltrating a graphene oxide membrane in an aqueous solution A of salt to swell, thereby obtaining the graphene oxide membrane with the controllable interlayer spacing. The aqueous solution A of salt is a solution containing metal cation, and the concentration of the metal cation in the aqueous solution A is from 0.25-2.5 mol/L. The application can precisely control the size of the interlayer spacing of the graphene oxide membrane in the range of 11˜14 ?, and the variable range of this spacing can be controlled to within 0.6˜1 ?. The graphene oxide membrane with the controllable interlayer spacing of the application has excellent mechanical strength, which remains a complete membrane state after 5 hours of infiltration.

Abstract: Methods and apparatus to migrate a mobile device from a first virtual private mobile network to a second virtual private mobile network are disclosed. An example apparatus includes a processor and a memory including instructions that cause the processor to perform operations including determining, based on a set of latency routing rules, that a communication transmitted via the first virtual private mobile network is a latency sensitive communication. In response to determining the communication is a latency sensitive communication, the mobile device that originated the latency sensitive communication is identified. The mobile device is communicating via the first virtual private mobile network. Example operations also include migrating the mobile device from the first virtual private mobile network to the second virtual private mobile network wherein the second virtual private mobile network is configured to reduce the latency of the latency sensitive communication.

Abstract: Techniques are disclosed for building a dictionary of words from combinations of symbols generated based on input data. A neuro-linguistic behavior recognition system includes a neuro-linguistic module that generates a linguistic model that describes data input from a source (e.g., video data, SCADA data, etc.). To generate words for the linguistic model, a lexical analyzer component in the neuro-linguistic module receives a stream of symbols, each symbol generated based on an ordered stream of normalized vectors generated from input data. The lexical analyzer component determines words from combinations of the symbols based on a hierarchical learning model having one or more levels. Each level indicates a length of the words to be identified at that level. Statistics are evaluated for the words identified at each level. The lexical analyzer component identifies one or more of the words having statistical significance.

Abstract: Techniques are disclosed for generating a syntax for a neuro-linguistic model of input data obtained from one or more sources. A stream of words of a dictionary built from a sequence of symbols are received. The symbols are generated from an ordered stream of normalized vectors generated from input data. Statistics for combinations of words co-occurring in the stream are evaluated. The statistics includes a frequency upon which the combinations of words co-occur. A model of combinations of words based on the evaluated statistics is updated. The model identifies statistically relevant words. A connected graph is generated. Each node in the connected graph represents one of the words in the stream. Edges connecting the nodes represent a probabilistic relationship between words in the stream. Phrases are identified based on the connected graph.

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: 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: Specialized computing systems, devices, interfaces and methods facilitate the simulation of downhole milling procedures such as wellbore departure milling procedures. Computing systems, devices, interfaces and methods enable a user to design and select milling components and procedures to be compared and simulated. Various milling parameters, such as milling tool parameters, whipstock parameters, and wellbore casing parameters may be accessed and selectably modified with milling and simulation interfaces to define and control the simulated milling procedures. Different types of output are selectably rendered to reflect various aspects of the simulated milling procedures.

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

Abstract: Techniques are disclosed for generating a sequence of symbols based on input data for a neuro-linguistic model. The model may be used by a behavior recognition system to analyze the input data. A mapper component of a neuro-linguistic module in the behavior recognition system receives one or more normalized vectors generated from the input data. The mapper component generates one or more clusters based on a statistical distribution of the normalized vectors. The mapper component evaluates statistics and identifies statistically relevant clusters. The mapper component assigns a distinct symbol to each of the identified clusters.

Abstract: Herein is reported a blood brain barrier shuttle module comprising a brain effector entity, a linker and one monovalent binding entity which binds to a blood brain barrier receptor, wherein the linker couples the effector entity to the monovalent binding entity which binds to the blood brain barrier receptor wherein the monovalent binding entity does not comprise the variable domains of the anti-transferrin receptor antibody 8D3 (SEQ ID NO: 01 and SEQ ID NO: 02) or of the variant anti-transferrin receptor antibody 8D3v (SEQ ID NO: 01 and SEQ ID NO: 03).

Abstract: Embodiments presented herein describe techniques for generating a linguistic model of input data obtained from a data source (e.g., a video camera). According to one embodiment of the present disclosure, a sequence of symbols is generated based on an ordered stream of normalized vectors generated from the input data. A dictionary of words is generated from combinations of the ordered sequence of symbols based on a frequency at which combinations of symbols appear in the ordered sequence of symbols. A plurality of phrases is generated based an ordered sequence of words from the dictionary observed in the ordered sequence of symbols based on a frequency by which combinations of words in ordered sequence of words appear relative to one another.

Abstract: Certain embodiments described herein are generally directed to processing domain objects in a distributed system using logical sharding. In some embodiments, a central control plane (CCP) node receives a domain object. In some embodiments, if the CCP node determines that the domain object is not already present in a shared data store and that the CCP node is the logical master of the domain object, the CCP node generates a status based on the domain object, and stores the status and domain object in the shared data store. In some embodiments, the shared data store notifies the plurality of CCP nodes of the stored status and domain object.

Abstract: Data processing methods, live broadcasting methods and devices are disclosed. An example data processing method may comprise converting audio and video data into broadcast data in a predetermined format, and performing speech recognition on audio data in the audio and video data, and adding the text information obtained from speech recognition into the broadcast data. In real time, text information obtained from speech recognition according to the audio data can be inserted.

Abstract: In a disclosed embodiment, a method for communication in a network includes receiving, at a first device registered to the network, a physical layer (PHY) frame that includes a PHY header and a MAC header. The PHY frame may further include a MAC payload. The PHY header includes a destination address field. The method further includes comparing a network address of the first device to the destination address field to determine whether the destination address field stores a value having the same number of bits as the network address. When the comparison indicates that the value stored by the destination address field does not have the same number of bits as the network address, the method skips decoding the MAC header and the MAC payload.

Abstract: Alert directives and focused alert directives allow a user to provide feedback to a behavioral recognition system to always or never publish an alert for certain events. Such an approach bypasses the normal publication methods of the behavioral recognition system yet does not obstruct the system's learning procedures.

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 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: Copolymers formed by copolymerization of: (1) one or more hydrofluoroolefin monomer(s) selected from the group consisting of hydrofluoroethylenes, hydrofluoropropenes, hydrofluorobutenes, hydrofluoropentenes and combinations of these; (2) one or more aromatic vinyl ester monomer(s); (3) one or more vinyl ester monomer(s); and (4) one or more vinyl ether monomer(s), wherein at least a portion of said vinyl ether monomer is a hydroxyl group-containing vinyl ether monomer.