Abstract: The present invention relates to use of naphthenieNaphthenic imidazoline can be used in inhibiting formation of natural gas hydrate, and a A composition containing the same. The composition comprisescontains the naphthenic imidazoline and modified polysaccharide. The modified polysaccharide may be cationic amylose.

Abstract: A tunnel supporting force adjusting device, comprising a fixed component, a movable supporting component and a driving component, wherein the fixed component is connected with section steel; the movable supporting component is connected with the fixed component and used for connecting with a tunnel trunk, and the movable supporting component, the fixed component and the section steel are fixedly connected with the tunnel trunk by pouring; and the driving component is detachably connected with the fixed component and the movable supporting component respectively to adjust the supporting force of the movable supporting component on the tunnel trunk. The present invention also discloses a tunnel supporting force adjusting method, in which adjusting devices are arranged in different positions of the section steel of the arch section of a tunnel to achieve fixed-point adjustment of the force in each position so as to form optimum supporting force and supporting effect.

Abstract: Disclosed are aspects of interference-aware virtual machine assignment for systems that include graphics processing units (GPUs) that are virtual GPU (vGPU) enabled. In some examples, an interference function is used to predict interference for assignment of a workload to a graphics processing unit (GPU). The interference function outputs a predicted interference to place the workload on the GPU. The workload is assigned to the GPU based on a comparison of the predicted interference to a plurality of predicted interferences for the workload on various GPUs.

Abstract: System for detecting intention of movements by a subject comprises sensors and a computing device. The sensors are configured to be engaged to the subject. The sensors measure electromyogram signals from the subject. The computing device receives the electromyogram signals from the sensors. Features are extracted using electromyogram signals from one or more of the sensors. One or more of the extracted features are compared with their respective threshold corresponding to a first movement among the movements. Intention of making the first movement is registered, prior to the onset of the first movement, based on the comparison.

Abstract: A user-wearable system for communicating with a computer system includes a hand wearable device, a sensor array on the hand wearable device and configured to capture myoelectric signals from the user's hand and a means for converting the myoelectric signals into computer-readable signals corresponding to a computer instruction executed by the computer system.

Abstract: Disclosed are aspects of interference-aware virtual machine assignment for systems that include graphics processing units (GPUs) that are virtual GPU (vGPU) enabled. In some examples, an interference function is used to predict interference for assignment of a workload to a graphics processing unit (GPU). The interference function outputs a predicted interference to place the workload on the GPU. The workload is assigned to the GPU based on a comparison of the predicted interference to a plurality of predicted interferences for the workload on various GPUs.

Abstract: Disclosed are aspects of interference-aware virtual machine assignment for systems that include graphics processing units (GPUs) that are virtual GPU (vGPU) enabled. In some examples, a plurality of workloads are executed alone and co-located with other workloads in a virtual graphics processing unit (vGPU)-enabled system to determine baseline parameters and measured interferences. A machine learning model is trained to predict interference based on the measured interferences and the baseline parameters. A workload is assigned and executed on a particular GPU associated with a minimum predicted interference with the workload based on currently-assigned workloads of the particular GPU.

Abstract: Disclosed are aspects of interference-aware virtual machine assignment for systems that include graphics processing units (GPUs) that are virtual GPU (vGPU) enabled. In some examples, a plurality of workloads are executed alone and co-located with other workloads in a virtual graphics processing unit (vGPU)-enabled system to determine baseline parameters and measured interferences. A machine learning model is trained to predict interference based on the measured interferences and the baseline parameters. A workload is assigned and executed on a particular GPU associated with a minimum predicted interference with the workload based on currently-assigned workloads of the particular GPU.