Abstract: Some aspects include a method of detecting change in degree of midline shift in a brain of a patient. The method comprises, while the patient remains positioned within the low-field magnetic resonance imaging device, acquiring first magnetic resonance (MR) image data and second MR image data of the patient's brain; providing the first and second MR data as input to a trained statistical classifier to obtain corresponding first and second output, identifying, from the first output, at least one initial location of at least one landmark associated with at least one midline structure of the patient's brain; identifying, from the second output, at least one updated location of the at least one landmark; and determining a degree of change in the midline shift using the at least one initial location of the at least one landmark and the at least one updated location of the at least one landmark.

Abstract: Techniques for removing artefacts, such as RF interference and/or noise, from magnetic resonance data. The techniques include: obtaining input magnetic resonance (MR) data using at least one radio-frequency (RF) coil of a magnetic resonance imaging (MRI) system; and generating an MR image from input MR data at least in part by using a neural network model to suppress at least one artefact in the input MR data.

Abstract: A magnetic resonance imaging (MRI) system, comprising: a magnetics system comprising: a B0 magnet configured to provide a B0 field for the MRI system; gradient coils configured to provide gradient fields for the MRI system; and at least one RF coil configured to detect magnetic resonance (MR) signals; and a controller configured to: control the magnetics system to acquire MR spatial frequency data using non-Cartesian sampling; and generate an MR image from the acquired MR spatial frequency data using a neural network model comprising one or more neural network blocks including a first neural network block, wherein the first neural network block is configured to perform data consistency processing using a non-uniform Fourier transformation.

Abstract: A magnetic resonance imaging (MRI) system, comprising: a magnetics system comprising: a B0 magnet configured to provide a B0 field for the MRI system; gradient coils configured to provide gradient fields for the MRI system; and at least one RF coil configured to detect magnetic resonance (MR) signals; and a controller configured to: control the magnetics system to acquire MR spatial frequency data using non-Cartesian sampling; and generate an MR image from the acquired MR spatial frequency data using a neural network model comprising one or more neural network blocks including a first neural network block, wherein the first neural network block is configured to perform data consistency processing using a non-uniform Fourier transformation.

Abstract: Some aspects include a method of determining change in size of an abnormality in a brain of a patient positioned within a low-field magnetic resonance imaging (MRI) device. The method comprises, while the patient remains positioned within the low-field MRI device, acquiring first and second magnetic resonance (MR) image data of the patient's brain; providing the first and second MR image data as input to a trained statistical classifier to obtain corresponding first and second output; identifying, using the first output, at least one initial value of at least one feature indicative of a size of the abnormality; identifying, using the second output, at least one updated value of the at least one feature; determining the change in the size of the abnormality using the at least one initial value of the at least one feature and the at least one updated value of the at least one feature.

Abstract: In one embodiment, a learning machine device initializes thresholds of a data representation of one or more data features, the thresholds specifying a first number of pre-defined bins (e.g., uniform and equidistant bins). Next, adjacent bins of the pre-defined bins having substantially similar weights may be reciprocally merged, the merging resulting in a second number of refined bins that is less than the first number. Notably, while merging, the device also learns weights of a linear decision rule associated with the one or more data features. Accordingly, a data-driven representation for a data-driven classifier may be established based on the refined bins and learned weights.

Abstract: Some aspects include a method of detecting change in degree of midline shift in a brain of a patient. The method comprises, while the patient remains positioned within the low-field magnetic resonance imaging device, acquiring first magnetic resonance (MR) image data and second MR image data of the patient's brain; providing the first and second MR data as input to a trained statistical classifier to obtain corresponding first and second output, identifying, from the first output, at least one initial location of at least one landmark associated with at least one midline structure of the patient's brain; identifying, from the second output, at least one updated location of the at least one landmark; and determining a degree of change in the midline shift using the at least one initial location of the at least one landmark and the at least one updated location of the at least one landmark.

Abstract: A computer-implemented data processing method comprises: executing a recurrent neural network (RNN) comprising nodes each implemented as a Long Short-Term Memory (LSTM) cell and comprising links between nodes that represent outputs of LSTM cells and inputs to LSTM cells, wherein each LSTM cell implements an input layer, hidden layer and output layer of the RNN; receiving network traffic data associated with networked computers; extracting feature data representing features of the network traffic data and providing the feature data to the RNN; classifying individual Uniform Resource Locators (URLs) as malicious or legitimate using LSTM cells of the input layer, wherein inputs to the LSTM cells are individual characters of the URLs, and wherein the LSTM cells generate feature representation; based on the feature representation, generating signals to a firewall device specifying either admitting or denying the URLs.

Abstract: In one embodiment, a method includes obtaining a set of samples, each of the set of samples including sample values for each of a plurality of variables in a variable space. The method includes receiving, for each of an initial subset of the set of samples, a label for the sample as being either malicious or legitimate; identifying one or more boundaries in the variable space based on the labels and sample values for each of the initial subset; selecting an incremental subset of the unlabeled samples of the set of samples, wherein the incremental subset includes at least one unlabeled sample including sample values further from any of the one or more boundaries than an unlabeled sample that is not included in the incremental subset; and receiving, for each of the incremental subset, a label for the sample as being either malicious or legitimate.

Abstract: A computer-implemented data processing method comprises: executing a recurrent neural network (RNN) comprising nodes each implemented as a Long Short-Term Memory (LSTM) cell and comprising links between nodes that represent outputs of LSTM cells and inputs to LSTM cells, wherein each LSTM cell implements an input layer, hidden layer and output layer of the RNN; receiving network traffic data associated with networked computers; extracting feature data representing features of the network traffic data and providing the feature data to the RNN; classifying individual Uniform Resource Locators (URLs) as malicious or legitimate using LSTM cells of the input layer, wherein inputs to the LSTM cells are individual characters of the URLs, and wherein the LSTM cells generate feature representation; based on the feature representation, generating signals to a firewall device specifying either admitting or denying the URLs.

Abstract: Some aspects include a method of determining change in size of an abnormality in a brain of a patient positioned within a low-field magnetic resonance imaging (MRI) device. The method comprises, while the patient remains positioned within the low-field MRI device, acquiring first and second magnetic resonance (MR) image data of the patient's brain; providing the first and second MR image data as input to a trained statistical classifier to obtain corresponding first and second output; identifying, using the first output, at least one initial value of at least one feature indicative of a size of the abnormality; identifying, using the second output, at least one updated value of the at least one feature; determining the change in the size of the abnormality using the at least one initial value of the at least one feature and the at least one updated value of the at least one feature.

Abstract: Some aspects include a method of detecting change in biological subject matter of a patient positioned within a low-field magnetic resonance imaging device, the method comprising: while the patient remains positioned within the low-field magnetic resonance device: acquiring first magnetic resonance image data of a portion of the patient; acquiring second magnetic resonance image data of the portion of the patient subsequent to acquiring the first magnetic resonance image data; aligning the first magnetic resonance image data and the second magnetic resonance image data; and comparing the aligned first magnetic resonance image data and second magnetic resonance image data to detect at least one change in the biological subject matter of the portion of the patient.

Abstract: Systems and methods of the present disclosure provide technology to identify when network-connected devices are likely infected with malware. Network communications are be monitored during a specific time window and a graph is created for a conditional random field (CRF) model. Vertices of the graph represent devices connected to the network and an edge between two vertices indicates that one or more network communications occurred between two devices represented by the two vertices during the time window. Network devices can report observations about network behavior during the time window and the observations can be used as input for the CRF model. The CRF model can then be used to determine infection-status values for the network devices.

Abstract: In one embodiment, a method includes receiving packet flow data at a feature extraction hierarchy comprising a plurality of levels, each of the levels comprising a set of feature extraction functions, computing a first set of feature vectors for the packet flow data at a first level of the feature extraction hierarchy, inputting the first set of feature vectors from the first level of the feature extraction hierarchy into a second level of the feature extraction hierarchy to compute a second set of feature vectors, and transmitting a final feature vector to a classifier to identify malicious traffic. An apparatus and logic are also disclosed herein.

Abstract: Techniques are presented that identify malware network communications between a computing device and a server based on a cumulative feature vector generated from a group of network traffic records associated with communications between computing devices and servers. Feature vectors are generated, each vector including features extracted from the network traffic records in the group. A self-similarity matrix is computed for each feature which is a representation of the feature that is invariant to an increase or a decrease of feature values across all feature vectors in the group. Each self-similarity matrix is transformed into corresponding histograms to be invariant to a number of network traffic records in the group. The cumulative feature vector is a cumulative representation of the predefined set of features of all network traffic records included in the at least one group of network traffic records and is generated based on the corresponding histograms.

Abstract: Some aspects include a method of detecting change in degree of midline shift in a brain of a patient. The method comprises, while the patient remains positioned within the low-field magnetic resonance imaging device, acquiring first magnetic resonance (MR) image data and second MR image data of the patient's brain; providing the first and second MR data as input to a trained statistical classifier to obtain corresponding first and second output, identifying, from the first output, at least one initial location of at least one landmark associated with at least one midline structure of the patient's brain; identifying, from the second output, at least one updated location of the at least one landmark; and determining a degree of change in the midline shift using the at least one initial location of the at least one landmark and the at least one updated location of the at least one landmark.

Abstract: In one embodiment, a security device identifies, from monitored network traffic of one or more users, one or more suspicious domain names as candidate domains, the one or more suspicious domain names identified based on an occurrence of linguistic units used in discovered domain names within the monitored network traffic. The security device may then determine one or more features of the candidate domains, and confirms certain domains of the candidate domains as malicious domains using a parameterized classifier against the one or more features.

Abstract: A computer-implemented data processing method comprises: executing a recurrent neural network (RNN) comprising nodes each implemented as a Long Short-Term Memory (LSTM) cell and comprising links between nodes that represent outputs of LSTM cells and inputs to LSTM cells, wherein each LSTM cell implements an input layer, hidden layer and output layer of the RNN; receiving network traffic data associated with networked computers; extracting feature data representing features of the network traffic data and providing the feature data to the RNN; classifying individual Uniform Resource Locators (URLs) as malicious or legitimate using LSTM cells of the input layer, wherein inputs to the LSTM cells are individual characters of the URLs, and wherein the LSTM cells generate feature representation; based on the feature representation, generating signals to a firewall device specifying either admitting or denying the URLs.

Abstract: An anatomical structure is detected (110) in a volume of ultrasound data by identifying (150) the anatomical structure in another volume of ultrasound data and generating (155) an image of the anatomical structure and an anatomical landmark. A group of images are generated (130) of the original volume and compared (140) to the image of the other volume. An image of the group of images is selected (150) as including the anatomical structure based on the comparison.

Abstract: In one embodiment, a method includes receiving at a security analysis device a plurality of indicators of compromise (IOCs) associated with an entity, sorting at the security analysis device, the IOCs based on a time of occurrence of each of the IOCs, creating a representation of transitions between the IOCs at the security analysis device, and generating at the security analysis device, a feature vector based on the representation of transitions. The feature vector is configured for use by a classifier in identifying malicious entities. An apparatus and logic are also disclosed herein.