Multicomponent bi-superHamiltonian KdV systems

It is shown that a new class of classical multicomponent super KdV equations is bi-superHamiltonian by extending the method for the verification of graded Jacobi identity. The multicomponent extension of super mKdV equations is obtained by using the super Miura transformation

Vitamin K catabolite inhibition of ovariectomy-induced bone loss: Structure–activity relationship considerations

The potential benefit of vitamin K as a therapeutic in osteoporosis is controversial and the vitamin K regimen being used clinically (45 mg/day) employs doses that are many times higher than required to ensure maximal gamma‐carboxylation of the vitamin K‐dependent bone proteins. We therefore tested the hypothesis that vitamin K catabolites, 5‐carbon (CAN5C) and 7‐carbon carboxylic acid (CAN7C) aliphatic side‐chain derivatives of the naphthoquinone moiety exert an osteotrophic role consistent with the treatment of osteoporosis

Motion-compensated prediction based algorithm for medical image sequence compression

Cataloged from PDF version of article.A method for irreversible compression of medical image sequences is described. The method relies on discrete cosine transform and motion-compensated prediction to reduce intra- and inter-frame redundancies in medical image sequences. Simulation examples are presented

An investigation on the impact fatigue characteristics of valve leaves for small hermetic reciprocating compressors in a new automated test system

This paper presents an investigation on the impact fatigue characteristics of valve leaves that are prevalently used in hermetic reciprocating compressors especially for the household type refrigerators. A unique automated impact fatigue test system has been designed and produced, which enables to carry out impact fatigue tests of the compressor valve leaves under the desired impact velocities. The test system serves investigations on the impact fatigue characteristics with the ability of crack detection and as the subsequent step of automatically terminating the test. The crack detection technique incorporates a non-contact actuation, a data acquisition system and a microphone. The investigation relates the impact fatigue lifetime of the valve leaves with the impact velocity, asymmetrical impact, operation temperature, material type (carbon strip steel, stainless strip steel and new stainless strip steel grade) and tumbling operation duration. Microscopic and metallographic observations were performed on the specimens. It was observed that the crack initiated at the edge of the valve leaves on the contact surface of valve leaf and vale plate and a particle is torn away from the edge before propagation. As the crack propagates, branching along the crack path is caused by the geometrical shape and stress waves on the valve leaves. The investigation and introduced test system guide the design optimization of valve leaves in terms of compressor performance due to energy consumption and lifetime of the valve leaf

An Inverse Optimal Control Approach to Explain Human Arm Reaching Control Based on Multiple Internal Models

Human motor control is highly efficient in generating accurate and appropriate motor behavior for a multitude of tasks. This paper examines how kinematic and dynamic properties of the musculoskeletal system are controlled to achieve such efficiency. Even though recent studies have shown that the human motor control relies on multiple models, how the central nervous system (CNS) controls this combination is not fully addressed. In this study, we utilize an Inverse Optimal Control (IOC) framework in order to find the combination of those internal models and how this combination changes for different reaching tasks. We conducted an experiment where participants executed a comprehensive set of free-space reaching motions. The results show that there is a trade-off between kinematics and dynamics based controllers depending on the reaching task. In addition, this trade-off depends on the initial and final arm configurations, which in turn affect the musculoskeletal load to be controlled. Given this insight, we further provide a discomfort metric to demonstrate its influence on the contribution of different inverse internal models. This formulation together with our analysis not only support the multiple internal models (MIMs) hypothesis but also suggest a hierarchical framework for the control of human reaching motions by the CNS

An Experimental Study of Reduced-Voltage Operation in Modern FPGAs for Neural Network Acceleration

We empirically evaluate an undervolting technique, i.e., underscaling the circuit supply voltage below the nominal level, to improve the power-efficiency of Convolutional Neural Network (CNN) accelerators mapped to Field Programmable Gate Arrays (FPGAs). Undervolting below a safe voltage level can lead to timing faults due to excessive circuit latency increase. We evaluate the reliability-power trade-off for such accelerators. Specifically, we experimentally study the reduced-voltage operation of multiple components of real FPGAs, characterize the corresponding reliability behavior of CNN accelerators, propose techniques to minimize the drawbacks of reduced-voltage operation, and combine undervolting with architectural CNN optimization techniques, i.e., quantization and pruning. We investigate the effect of environmental temperature on the reliability-power trade-off of such accelerators. We perform experiments on three identical samples of modern Xilinx ZCU102 FPGA platforms with five state-of-the-art image classification CNN benchmarks. This approach allows us to study the effects of our undervolting technique for both software and hardware variability. We achieve more than 3X power-efficiency (GOPs/W) gain via undervolting. 2.6X of this gain is the result of eliminating the voltage guardband region, i.e., the safe voltage region below the nominal level that is set by FPGA vendor to ensure correct functionality in worst-case environmental and circuit conditions. 43% of the power-efficiency gain is due to further undervolting below the guardband, which comes at the cost of accuracy loss in the CNN accelerator. We evaluate an effective frequency underscaling technique that prevents this accuracy loss, and find that it reduces the power-efficiency gain from 43% to 25%.Comment: To appear at the DSN 2020 conferenc