Abstract: A distributed security system includes instances of a compute engine that can receive an event stream comprising event data associated with an occurrence of one or more events on one or more client computing devices and generate new event data based on the event data in the event stream. A predictions engine coupled in communication with the compute engine(s) receives the new event data and applies at least a portion of the received new event data to one or more machine learning models of the distributed security system based to the received new event data. The one or more machine learning models generate a prediction result that indicates whether the occurrence of the one or more events from which the new event data was generated represents one or more target behaviors, based on the applying of at least the portion of the received new event data to the one or more machine learning models according to the received new event data.

Abstract: Example techniques described herein determine a validation dataset, determine a computational model using the validation dataset, or determine a signature or classification of a data stream such as a file. The classification can indicate whether the data stream is associated with malware. A processing unit can determine signatures of individual training data streams. The processing unit can determine, based at least in part on the signatures and a predetermined difference criterion, a training set and a validation set of the training data streams. The processing unit can determine a computational model based at least in part on the training set. The processing unit can then operate the computational model based at least in part on a trial data stream to provide a trial model output. Some examples include determining the validation set based at least in part on the training set and the predetermined criterion for difference between data streams.

Abstract: Techniques and systems for a security service system configured with a sensor component including a machine learning (ML) malware classifier to perform behavioral detection on host devices. The security service system may deploy a sensor component to monitor behavioral events on a host device. The sensor component may generate events data corresponding to monitored operations targeted by malware. The system may map individual events from events data onto a behavioral activity pattern and generate process trees. The system may extract behavioral artifacts to build a feature vector used for malware classification and generate a machine learning (ML) malware classifier. The sensor component may use the ML malware classifier to perform asynchronous behavioral detection on a host device and process system events for malware detection.

Abstract: A non-transitory computer-readable medium encoded with a computer-readable program, which, when executed by a processor, will cause a computer to execute a method of processing an image, wherein the method includes receiving a 2-D color Doppler image. The method additionally includes extracting a single component velocity field of a 2-D plane from the 2-D color Doppler image. Further, the method includes receiving a geometrical boundary of a region of interest within the 2-D color Doppler image. Moreover, the method includes applying a plurality of boundary conditions to the geometrical boundary, an at least one inlet, and an at least one outlet, of the single component velocity field of a 2-D plane. The method additionally includes solving a streamfunction vorticity formulation to reconstruct a transverse velocity component. Further, the method includes outputting a reconstructed 2-D 2-component velocity based on the transverse velocity component.

Abstract: Video processing methods and the associated system architecture for measuring transformation in objects, including pupils, entail the following steps: 1. Motion correction; 2. Object (eye) detection; 3. Image correction; and 4. Fourier-based analysis for item (in some embodiments the item is a pupil) motion estimation.

Abstract: Example techniques described herein determine a validation dataset, determine a computational model using the validation dataset, or determine a signature or classification of a data stream such as a file. The classification can indicate whether the data stream is associated with malware. A processing unit can determine signatures of individual training data streams. The processing unit can determine, based at least in part on the signatures and a predetermined difference criterion, a training set and a validation set of the training data streams. The processing unit can determine a computational model based at least in part on the training set. The processing unit can then operate the computational model based at least in part on a trial data stream to provide a trial model output. Some examples include determining the validation set based at least in part on the training set and the predetermined criterion for difference between data streams.

Abstract: Example techniques described herein determine a signature or classification of a data stream such as a file. The classification can indicate whether the data stream is associated with malware. A processor can locate training analysis regions of training data streams based on predetermined structure data, and determining training model inputs based on the training analysis regions. The processor can determine a computational model based on the training model inputs. The computational model can receive an input vector and provide a corresponding feature vector. The processor can then locate a trial analysis region of a trial data stream based on the predetermined structure data and determine a trial model input. The processor can operate the computational model based on the trial model input to provide a trial feature vector, e.g., a signature. The processor can operate a second computational model to provide a classification based on the signature.

Abstract: Example techniques described herein determine a validation dataset, determine a computational model using the validation dataset, or determine a signature or classification of a data stream such as a file. The classification can indicate whether the data stream is associated with malware. A processing unit can determine signatures of individual training data streams. The processing unit can determine, based at least in part on the signatures and a predetermined difference criterion, a training set and a validation set of the training data streams. The processing unit can determine a computational model based at least in part on the training set. The processing unit can then operate the computational model based at least in part on a trial data stream to provide a trial model output. Some examples include determining the validation set based at least in part on the training set and the predetermined criterion for difference between data streams.

Abstract: A virtual force controlled collapse chip connection (C4) pad placement optimization frame-work for 2D power delivery grids is proposed. The present optimization framework regards power pads as mobile “positive charged particles” and current resources as a “negative charged back-ground.” The virtual electrostatic force is calculated from voltage gradients. This optimization framework optimizes pad locations by moving pads according to the virtual forces exerted on them by other pads and current sources in the system. Within this framework, three algorithms are proposed to meet various requirements of optimization quality and speed. These algorithms minimize resistive voltage drop (IR drop), the maximum current density, and power distribution network metal power dissipation at the same time.

Abstract: A non-transitory computer-readable medium encoded with a computer-readable program, which, when executed by a processor, will cause a computer to execute a method of processing an image, wherein the method includes receiving a 2-D color Doppler image. The method additionally includes extracting a single component velocity field of a 2-D plane from the 2-D color Doppler image. Further, the method includes receiving a geometrical boundary of a region of interest within the 2-D color Doppler image. Moreover, the method includes applying a plurality of boundary conditions to the geometrical boundary, an at least one inlet, and an at least one outlet, of the single component velocity field of a 2-D plane. The method additionally includes solving a streamfunction vorticity formulation to reconstruct a transverse velocity component. Further, the method includes outputting a reconstructed 2-D 2-component velocity based on the transverse velocity component.

Abstract: Systems and methods for identifying at least one neural network suitable for a given application are provided. A candidate set of neural network parameters associated with a candidate neural network is selected. At least one performance characteristic of the candidate neural network is predicted. The at least one performance characteristic of the candidate neural network is compared against a current performance baseline. When the at least one performance characteristic exceeds the current performance baseline, using a predetermined training dataset is used to train and test the candidate neural network for identifying the at least one suitable neural network.

Abstract: Example techniques described herein determine a validation dataset, determine a computational model using the validation dataset, or determine a signature or classification of a data stream such as a file. The classification can indicate whether the data stream is associated with malware. A processing unit can determine signatures of individual training data streams. The processing unit can determine, based at least in part on the signatures and a predetermined difference criterion, a training set and a validation set of the training data streams. The processing unit can determine a computational model based at least in part on the training set. The processing unit can then operate the computational model based at least in part on a trial data stream to provide a trial model output. Some examples include determining the validation set based at least in part on the training set and the predetermined criterion for difference between data streams.

Abstract: Example techniques described herein determine a signature or classification of a data stream such as a file. The classification can indicate whether the data stream is associated with malware. A processor can locate training analysis regions of training data streams based on predetermined structure data, and determining training model inputs based on the training analysis regions. The processor can determine a computational model based on the training model inputs. The computational model can receive an input vector and provide a corresponding feature vector. The processor can then locate a trial analysis region of a trial data stream based on the predetermined structure data and determine a trial model input. The processor can operate the computational model based on the trial model input to provide a trial feature vector, e.g., a signature. The processor can operate a second computational model to provide a classification based on the signature.

Abstract: A virtual force controlled collapse chip connection (C4) pad placement optimization frame-work for 2D power delivery grids is proposed. The present optimization framework regards power pads as mobile “positive charged particles” and current resources as a “negative charged back-ground.” The virtual electrostatic force is calculated from voltage gradients. This optimization framework optimizes pad locations by moving pads according to the virtual forces exerted on them by other pads and current sources in the system. Within this framework, three algorithms are proposed to meet various requirements of optimization quality and speed. These algorithms minimize resistive voltage drop (IR drop), the maximum current density, and power distribution network metal power dissipation at the same time.