425,246 research outputs found
Some recent advances in ab initio calculations of nonradiative decay rates of point defects in semiconductors
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
Genetic iterative search-centre-shifting K-best sphere detection for rank-deficient SDM-OFDM systems
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
Temporal Relational Reasoning in Videos
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
Asimov's Coming Back
Ever since the word āROBOTā 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 robotās 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
Iterative Equalization and Source Decoding for Vector Quantized Sources
In this contribution an iterative (turbo) channel equalization and source decoding scheme is considered. In our investigations the source is modelled as a Gaussian-Markov source, which is compressed with the aid of vector quantization. The communications channel is modelled as a time-invariant channel contaminated by intersymbol interference (ISI). Since the ISI channel can be viewed as a rate-1 encoder and since the redundancy of the source cannot be perfectly removed by source encoding, a joint channel equalization and source decoding scheme may be employed for enhancing the achievable performance. In our study the channel equalization and the source decoding are operated iteratively on a bit-by-bit basis under the maximum aposteriori (MAP) criterion. The channel equalizer accepts the a priori information provided by the source decoding and also extracts extrinsic information, which in turn acts as a priori information for improving the source decoding performance. Simulation results are presented for characterizing the achievable performance of the iterative channel equalization and source decoding scheme. Our results show that iterative channel equalization and source decoding is capable of achieving an improved performance by efficiently exploiting the residual redundancy of the vector quantization assisted source coding
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