Abstract: Systems and methods for time series prediction are described. The systems and methods include encoding driving series into encoded hidden states, the encoding including adaptively prioritizing driving series at each timestamp using input attention, the driving series including data sequences collected from sensors. The systems and methods further includes decoding the encoded hidden states to generate a predicting model, the decoding including adaptively prioritizing encoded hidden states using temporal attention. The systems and methods further include generating predictions of future events using the predicting model based on the data sequences. The systems and methods further include generating signals for initiating an action to devices based on the predictions.

Abstract: Systems and devices including an imaging sensor to capture video sequences in an environment having safety concerns therein. The systems and devices further including a processor to generate driving series based on observations from the video sequences, and generate predictions of future events based on the observations using a dual-stage attention-based recurrent neural network (DA-RNN). The DA-RNN includes an input attention mechanism to extract relevant driving series, an encoder to encode the extracted relevant driving series into hidden states, a temporal attention mechanism to extract relevant hidden states, and a decoder to decode the relevant hidden states. The processor further generates a signal for initiating an action to machines to mitigate harm to items.

Abstract: Methods and systems for detecting anomalies include determining a predictive model for each pair of a set of time series, each time series being associated with a component of a system. New values of each pair of time series are compared to values predicted by the respective predictive model to determine if the respective predictive model is broken. A number of broken predictive models is determined. An anomaly alert is generated if the number of broken predictive models exceeds a threshold.

Abstract: A power generator system with anomaly detection and methods for detecting anomalies include a power generator that includes one or more physical components configured to provide electrical power. Sensors are configured to make measurements of a state of respective physical components, outputting respective time series of said measurements. A monitoring system includes a fitting module configured to determine a predictive model for each pair of a set of time series, an anomaly detection module configured to compare new values of each pair of time series to values predicted by the respective predictive model to determine if the respective predictive model is broken and to determine a number of broken predictive model, and an alert module configured to generate an anomaly alert if the number of broken predictive models exceeds a threshold.

Abstract: Methods and systems for detecting security intrusions include detecting alerts in monitored system data. Temporal dependencies are determined between the alerts based on a prefix tree formed from the detected alerts. Content dependencies between the alerts are determined based on a distance between alerts in a graph representation of the detected alerts. The alerts are ranked based on an optimization problem that includes the temporal dependencies and the content dependencies. A security management action is performed based on the ranked alerts.

Abstract: The present teachings disclose nucleic acid cassettes for expressing in an insect cell a plurality of polypeptides encoded by a gene comprising overlapping open reading frames (ORFs). A cassette comprises, in 5? to 3? order, a) a first insect cell-operable promoter, b) a 5? portion of a gene comprising a first ORF of the gene, c) an intron comprising a second insect cell-operable promoter, and d) a 3? portion of the gene comprising at least one additional ORF. Vectors and insect cells comprising the cassettes are also disclosed, as well as methods for production of recombinant adeno-associated virus in insect cells using the cassettes.

Abstract: The present teachings disclose nucleic acid cassettes for expressing in an insect cell a plurality of polypeptides encoded by a gene comprising overlapping open reading frames (ORFs). A cassette comprises, in 5? to 3? order, a) a first insect cell-operable promoter, b) a 5? portion of a gene comprising a first ORF of the gene, c) an intron comprising a second insect cell-operable promoter, and d) a 3? portion of the gene comprising at least one additional ORF. Vectors and insect cells comprising the cassettes are also disclosed, as well as methods for production of recombinant adeno-associated virus in insect cells using the cassettes.

Abstract: A method of purifying a viral vector is provided. The method includes loading a nonionic density gradient into a continuous flow centrifuge rotor; loading a material including the viral vector into the continuous flow centrifuge rotor; rotating the continuous flow centrifuge rotor in a manner sufficient to separate the viral vector and a predictable contaminate from the material in the nonionic density gradient; and unloading the separated viral vector and the predicable contaminant from the continuous flow centrifuge rotor.

Abstract: An image quality enhancing apparatus which make a learning-type image quality enhancing method utilizing a sparse expression practical are provided. The apparatus calculates, from the feature quantity of an image, coefficients of low-image-quality base vectors expressing a feature quantity with a linear sum and generates the image with the image quality enhanced by calculating a linear sum of high-image-quality base vectors using the calculated coefficient. When calculating the coefficient, the number of base vectors with non-zero coefficients is determined, the determined number of base vectors is selected, and a solution of a coefficient matrix is calculated by assuming the coefficients of the base vectors other than the selected base vectors are zero. The amount of processes necessary for obtaining a sparse solution of a coefficient matrix can be reduced by adjusting the number of base vectors with non-zero coefficients, and a practical image quality enhancing apparatus can be realized.

Abstract: An image quality enhancing apparatus, an image display apparatus, an image quality enhancing method, and a computer readable storage medium which make a learning-type image quality enhancing method utilizing a sparse expression practical are provided. The image quality enhancing apparatus calculates, from the feature quantity of an image, coefficients of low-image-quality base vectors expressing a feature quantity with a linear sum and generates the image with the enhanced image quality by calculating a linear sum of high-image-quality base vectors using the calculated coefficient. When calculating the coefficient, T base vectors highly influential on the feature quantity are selected from among a plurality of base vectors and an analytic solution making L2 norm of a coefficient matrix ? as small as possible is calculated. A sparse solution of the coefficients can be obtained without using the iteration method and a practical image quality enhancing apparatus can be realized.

Abstract: Systems and methods for anomaly detection in complex physical systems, including extracting features representative of a temporal evolution of the complex physical system, and analyzing the extracted features by deriving vector trajectories using sliding window segmentation of time series, applying a linear test to determine whether the vector trajectories are linear, and performing subspace decomposition on the vector trajectory based on the linear test. A system evolution model is generated from an ensemble of models, and a fitness score is determined by analyzing different data properties of the system based on specific data dependency relationships. An alarm is generated if the fitness score exceeds a predetermined number of threshold violations for the different data properties.

Abstract: Methods are provided for both single modal and multimodal fault diagnosis. In a method, a fault fingerprint is constructed based on a fault event using an invariant model. A similarity matrix between the fault fingerprint and one or more historical representative fingerprints are derived using dynamic time warping and at least one convolution. A feature vector in a feature subspace for the fault fingerprint is generated. The feature vector includes at least one status of at least one system component during the fault event. A corrective action correlated to the fault fingerprint is determined. The corrective action is initiated on a hardware device to mitigate expected harm to at least one item selected from the group consisting of the hardware device, another hardware device related to the hardware device, and a person related to the hardware device.

Abstract: A system and method are provided. The system includes a processor. The processor is configured to receive a plurality of events from network devices, the plurality of events including entities that are involved in the plurality of events. The processor is further configured to embed the entities into a common latent space based on co-occurrence of the entities in the plurality of events and model respective pairs of the entities for compatibility according to the embedding of the entities to form a pairwise interaction for the respective pairs of the entities. The processor is additionally configured to weigh the pairwise interaction of different ones of the respective pairs of the entities based on one or more compatibility criterion to generate a probability of an occurrence of an anomaly and alter the configuration of one or more of the network devices based on the probability of the occurrence of the anomaly.

Abstract: Methods and systems for reporting anomalous events include building a process graph that models states of process-level events in a network. A topology graph is built that models source and destination relationships between connection events in the network. A set of alerts is clustered based on the process graph and the topology graph. Clustered alerts that exceed a threshold level of trustworthiness are reported.

Abstract: Methods and systems for reporting anomalous events include intra-host clustering a set of alerts based on a process graph that models states of process-level events in a network. Hidden relationship clustering is performed on the intra-host clustered alerts based on hidden relationships between alerts in respective clusters. Inter-host clustering is performed on the hidden relationship clustered alerts based on a topology graph that models source and destination relationships between connection events in the network. Inter-host clustered alerts that exceed a threshold level of trustworthiness are reported.

Abstract: A computer-implemented method for real-time detecting of abnormal network connections is presented. The computer-implemented method includes collecting network connection events from at least one agent connected to a network, recording, via a topology graph, normal states of network connections among hosts in the network, and recording, via a port graph, relationships established between host and destination ports of all network connections.

Abstract: A method is provided for root cause anomaly detection in an invariant network having a plurality of nodes that generate time series data. The method includes modeling anomaly propagation in the network. The method includes reconstructing broken invariant links in an invariant graph based on causal anomaly ranking vectors. Each broken invariant link involves a respective node pair formed from the plurality of nodes such that one of the nodes in the respective node pair has an anomaly. Each causal anomaly ranking vector is for indicating a respective node anomaly status for a given one of the plurality of nodes when paired. The method includes calculating a sparse penalty of the casual anomaly ranking vectors to obtain a set of time-dependent anomaly rankings. The method includes performing temporal smoothing of the set of rankings, and controlling an anomaly-initiating one of the plurality of nodes based on the set of rankings.

Abstract: Methods and systems for detecting anomalous network activity include determining whether a network event exists within an existing topology graph and port graph. A connection probability for the network event is determined if the network does not exist within the existing topology graph and port graph. The network event is identified as abnormal if the connection probability is below a threshold.

Abstract: A method and system are provided. The method includes extracting shapelets from each of a plurality of time series dimensions of multi-dimensional time series data. The method further includes building a plurality of decision-tree classifiers, one for each time series dimension, responsive to the shapelets extracted therefrom. The method also includes generating a pairwise similarity matrix between respective different ones of the plurality of time series dimensions using the shapelets as intermediaries for determining similarity. The method additionally includes applying a feature selection technique to the matrix to determine respective feature weights for each of shapelet features of the shapelets and respective classifier weights for each of the decision-tree classifiers that uses the shapelet features.

Abstract: A magnification factor is estimated for a magnified image. A generating means generates an image by removing a high frequency component from an input image. A first calculating means calculates a high frequency component greater than or equal to a prescribed frequency component in the spatial frequency component of the input image, as a spatial frequency component characteristic value. A second calculating means calculates the spatial frequency component of the input image, as a second spatial frequency component characteristic value. A determining means determines whether each pixel is block noise. A magnification factor estimating means removes pixels determined to be block noise by the determining means from summation target pixels and, estimates the magnification factor of the input image to be higher when the difference between the first spatial frequency component characteristic values and second spatial frequency component characteristic values of the remaining pixels is smaller.