531,465 research outputs found

    Some recent advances in ab initio calculations of nonradiative decay rates of point defects in semiconductors

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    In this short review, we discuss a few recent advances in calculating the nonradiative decay rates for point defects in semiconductors. We briefly review the debates and connections of using different formalisms to calculate the multi-phonon processes. We connect Dr. Huang's formula with Marcus theory formula in the high temperature limit, and point out that Huang's formula provide an analytical expression for the phonon induced electron coupling constant in the Marcus theory formula. We also discussed the validity of 1D formula in dealing with the electron transition processes, and practical ways to correct the anharmonic effects

    Asimov's Coming Back

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    Ever since the word ‘ROBOT’ first appeared in a science\ud fiction in 1921, scientists and engineers have been trying\ud different ways to create it. Present technologies in\ud mechanical and electrical engineering makes it possible\ud to have robots in such places as industrial manufacturing\ud and assembling lines. Although they are\ud essentially robotic arms or similarly driven by electrical\ud power and signal control, they could be treated the\ud primitive pioneers in application. Researches in the\ud laboratories go much further. Interdisciplines are\ud directing the evolution of more advanced robots. Among these are artificial\ud intelligence, computational neuroscience, mathematics and robotics. These disciplines\ud come closer as more complex problems emerge.\ud From a robot’s point of view, three basic abilities are needed. They are thinking\ud and memory, sensory perceptions, control and behaving. These are capabilities we\ud human beings have to adapt ourselves to the environment. Although\ud researches on robots, especially on intelligent thinking, progress slowly, a revolution\ud for biological inspired robotics is spreading out in the laboratories all over the world

    Temporal Relational Reasoning in Videos

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    Temporal relational reasoning, the ability to link meaningful transformations of objects or entities over time, is a fundamental property of intelligent species. In this paper, we introduce an effective and interpretable network module, the Temporal Relation Network (TRN), designed to learn and reason about temporal dependencies between video frames at multiple time scales. We evaluate TRN-equipped networks on activity recognition tasks using three recent video datasets - Something-Something, Jester, and Charades - which fundamentally depend on temporal relational reasoning. Our results demonstrate that the proposed TRN gives convolutional neural networks a remarkable capacity to discover temporal relations in videos. Through only sparsely sampled video frames, TRN-equipped networks can accurately predict human-object interactions in the Something-Something dataset and identify various human gestures on the Jester dataset with very competitive performance. TRN-equipped networks also outperform two-stream networks and 3D convolution networks in recognizing daily activities in the Charades dataset. Further analyses show that the models learn intuitive and interpretable visual common sense knowledge in videos.Comment: camera-ready version for ECCV'1

    Iterative channel equalization, channel decoding and source decoding

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    The performance of soft source decoding is evaluated over dispersive AWGN channels. By employing source codes having error-correcting capabilities, such as Reversible Variable-Length Codes (RVLCs) and Variable-Length Error-Correcting (VLEC) codes, the softin/soft-out (SISO) source decoder benefits from exchanging information with the MAP equalizer, and effectively eliminates the inter-symbol interference (ISI) after a few iterations. It was also found that the soft source decoder is capable of significantly improving the attainable performance of the turbo receiver provided that channel equalization, channel decoding and source decoding are carried out jointly and iteratively. At SER = 10-4, the performance of this three-component turbo receiver is about 2 dB better in comparison to the benchmark scheme carrying out channel equalization and channel decoding jointly, but source decoding separately. At this SER value, the performance of the proposed scheme is about 1 dB worse than that of the ½-rate convolutional coded non-dispersive AWGN channel.<br/

    Origin of sawtooth domain walls in ferroelectrics

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    Domains and domain walls are among the key factors that determine the performance of ferroelectric materials. In recent years, a unique type of domain walls, i.e., the sawtooth-shaped domain walls, has been observed in BiFeO3_{3} and PbTiO3_{3}. Here, we build a minimal model to reveal the origin of these sawtooth-shaped domain walls. Incorporating this model into Monte-Carlo simulations shows that (i) the competition between the long-range Coulomb interaction (due to bound charges) and short-range interaction (due to opposite dipoles) is responsible for the formation of these peculiar domain walls and (ii) their relative strength is critical in determining the periodicity of these sawtooth-shaped domain walls. Necessary conditions to form such domain walls are also discussed

    Genetic iterative search-centre-shifting K-best sphere detection for rank-deficient SDM-OFDM systems

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    A generic sphere-detection (SD) scheme is proposed, which substantially reduces the detection complexity by decomposing it into two stages, namely the generic iterative search-centre-update phase and the reduced-complexity search around it. This two-stage philosophy circumvents the high complexity of channel-coded soft-decision aided SDs

    Estimation of the parameters of continuous-time systems using data compression

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    This chapter provides a unified introductory account of the estimation of the parameters of continuous-time systems using data compression based on a number of previous publication
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