Abstract: A method for providing shuffle templates and using the shuffle templates to implement shuffling of data among workers comprising compute resources in a data center includes providing, by a shuffle manager, an application programming interface (API) through which applications can select shuffle templates and specify data to be processed by the workers in the data center using the shuffle templates to distribute the data as messages transmitted among the workers. The shuffle manager receives a call for a shuffle template including a shuffle template identifier source and destination identifiers for processing by the workers. The shuffle manager selects the shuffle template identified by the call for the shuffle template and provides the shuffle template to the workers. Workers use the shuffle template to generate a shuffle plan and use the shuffle plan to shuffle the messages among the workers between the sources and the destinations.

Abstract: A method comprises: generating input data for inputting to a neural network model based on coordinate data of dispersion curve data and preset values of geometric parameters with respect to a reference model; generating simulated dispersion curve data associated with the preset values of the geometric parameters based on the neural network model trained via a plurality of samples; obtaining measured dispersion curve data in relation to an object calculating a distance of the measured dispersion curve data from the simulated dispersion curve data to determine whether the distance meets a predetermined condition; and in response to determining the distance does not meet the predetermined condition, determining a gradient for updating the preset values of the geometric parameters with respect to the reference model based on the distance, to regenerate simulated dispersion curve data via the neural network model based on the updated preset values to recalculate the distance.

Abstract: Embodiments of the present disclosure relate to a metrology method and system for critical dimensions based on a dispersion relation in momentum space.

Abstract: The disclosure relates to processes, articles of manufacture, devices, and systems involving biomedical monitoring measurement and analysis. More particularly, respiratory measurement, monitoring, analysis, or related systems involving wearable sensors and receiving monitors for respiratory behavior tracking and analysis.

Abstract: Embodiments relate to detecting and mitigating network intrusions. Packets are inspected at their source/destination hosts to identify packet trends local to the hosts. The local packet trends are combined to identify network-wide packet trends. The network-wide packet trends are used to detect anomalies or attacks, which in turn informs mitigation actions. The local inspection may be performed by reconfigurable/reprogrammable “smart” network interfaces (NICs) at each of the hosts. Local inspection involves identifying potentially suspect packet features based on statistical prevalence of recurring commonalities among the packets; pre-defined threat patterns are not required. For network-wide coherence, each host/NIC uses the same packet-identifying and occurrence-measuring algorithms. An overlay or control server collects and combines the local occurrence-measures to derive the network-wide occurrence-measures.

Abstract: Embodiments relate to detecting and mitigating network intrusions. Packets are inspected at their source/destination hosts to identify packet trends local to the hosts. The local packet trends are combined to identify network-wide packet trends. The network-wide packet trends are used to detect anomalies or attacks, which in turn informs mitigation actions. The local inspection may be performed by reconfigurable/reprogrammable “smart” network interfaces (NICs) at each of the hosts. Local inspection involves identifying potentially suspect packet features based on statistical prevalence of recurring commonalities among the packets; pre-defined threat patterns are not required. For network-wide coherence, each host/NIC uses the same packet-identifying and occurrence-measuring algorithms. An overlay or control server collects and combines the local occurrence-measures to derive the network-wide occurrence-measures.

Abstract: A mechanism called time-deterministic replay (TOR) that can reproduce the execution of a program, including its precise timing. Without TOR, reproducing the timing of an execution is difficult because there are many sources of timing variability. TOR uses a combination of techniques to either mitigate or eliminate most of these sources of variability. Using a prototype implementation of TOR in a Java Virtual Machine, we show it is possible to reproduce the timing to within 1.85% of the original execution. A study of one of the applications of TOR is described: the detection of a covert timing channel. Timing channels can be used to exfiltrate information from a compromised machine by subtly varying timing of the machine's outputs, TOR can detect this variation. Unlike prior solutions, which generally look for a specific type of timing channel, our approach can detect a wide variety of channels with high accuracy.

Abstract: Embodiments relate to detecting and mitigating network intrusions. Packets are inspected at their source/destination hosts to identify packet trends local to the hosts. The local packet trends are combined to identify network-wide packet trends. The network-wide packet trends are used to detect anomalies or attacks, which in turn informs mitigation actions. The local inspection may be performed by reconfigurable/reprogrammable “smart” network interfaces (NICs) at each of the hosts. Local inspection involves identifying potentially suspect packet features based on statistical prevalence of recurring commonalities among the packets; pre-defined threat patterns are not required. For network-wide coherence, each host/NIC uses the same packet-identifying and occurrence-measuring algorithms. An overlay or control server collects and combines the local occurrence-measures to derive the network-wide occurrence-measures.

Abstract: Embodiments relate to detecting and mitigating network intrusions. Packets are inspected at their source/destination hosts to identify packet trends local to the hosts. The local packet trends are combined to identify network-wide packet trends. The network-wide packet trends are used to detect anomalies or attacks, which in turn informs mitigation actions. The local inspection may be performed by reconfigurable/reprogrammable “smart” network interfaces (NICs) at each of the hosts. Local inspection involves identifying potentially suspect packet features based on statistical prevalence of recurring commonalities among the packets; pre-defined threat patterns are not required. For network-wide coherence, each host/NIC uses the same packet-identifying and occurrence-measuring algorithms. An overlay or control server collects and combines the local occurrence-measures to derive the network-wide occurrence-measures.

Abstract: Embodiments relate to detecting and mitigating network intrusions. Packets are inspected at their source/destination hosts to identify packet trends local to the hosts. The local packet trends are combined to identify network-wide packet trends. The network-wide packet trends are used to detect anomalies or attacks, which in turn informs mitigation actions. The local inspection may be performed by reconfigurable/reprogrammable “smart” network interfaces (NICs) at each of the hosts. Local inspection involves identifying potentially suspect packet features based on statistical prevalence of recurring commonalities among the packets; pre-defined threat patterns are not required. For network-wide coherence, each host/NIC uses the same packet-identifying and occurrence-measuring algorithms. An overlay or control server collects and combines the local occurrence-measures to derive the network-wide occurrence-measures.

Abstract: Embodiments relate to detecting and mitigating network intrusions. Packets are inspected at their source/destination hosts to identify packet trends local to the hosts. The local packet trends are combined to identify network-wide packet trends. The network-wide packet trends are used to detect anomalies or attacks, which in turn informs mitigation actions. The local inspection may be performed by reconfigurable/reprogrammable “smart” network interfaces (NICs) at each of the hosts. Local inspection involves identifying potentially suspect packet features based on statistical prevalence of recurring commonalities among the packets; pre-defined threat patterns are not required. For network-wide coherence, each host/NIC uses the same packet-identifying and occurrence-measuring algorithms. An overlay or control server collects and combines the local occurrence-measures to derive the network-wide occurrence-measures.

Abstract: A mechanism called time-deterministic replay (TOR) that can reproduce the execution of a program, including its precise timing. Without TOR, reproducing the timing of an execution is difficult because there are many sources of timing variability. TOR uses a combination of techniques to either mitigate or eliminate most of these sources of variability. Using a prototype implementation of TOR in a Java Virtual Machine, we show it is possible to reproduce the timing to within 1.85% of the original execution. A study of one of the applications of TOR is described: the detection of a covert timing channel. Timing channels can be used to exfiltrate information from a compromised machine by subtly varying timing of the machine's outputs, TOR can detect this variation. Unlike prior solutions, which generally look for a specific type of timing channel, our approach can detect a wide variety of channels with high accuracy.

Abstract: A ceramic composition comprising a binary system solid solution represented by the formulae: (1-x)(Sr1-yBiy)TiO3+x(Na0.5Bi0.5)TiO3 and (1-x)(Sr1-1.5yBiy)TiO3+x(Na0.5Bi0.5)TiO3, wherein 0<x<1 and 0<y?0.2.

Abstract: A ceramic composition comprising a binary system solid solution represented by the formulae: (1-x)(Sr1-yBiy)TiO3+x(Na0.5Bi0.5)TiO3 and (1-x)(Sr1-1.5yBiy)TiO3+x(Na0.5Bi0.5)TiO3, wherein 0<x<1 and 0<y?0.2.