282 research outputs found
薬物代謝・毒性研究のための過フッ素化エラストマー製低収着マイクロ流体デバイスの開発
京都大学新制・課程博士博士(薬科学)甲第24548号薬科博第165号新制||薬科||18(附属図書館)京都大学大学院薬学研究科薬科学専攻(主査)教授 山下 富義, 教授 髙倉 喜信, 教授 寺田 智祐学位規則第4条第1項該当Doctor of Pharmaceutical SciencesKyoto UniversityDFA
Probing Supersymmetric Black Holes with Surface Defects
It has long been conjectured that the large deconfinement phase
transition of super-Yang-Mills corresponds via
AdS/CFT to the Hawking-Page transition in which black holes dominate the
thermal ensemble, and quantitative evidence of this has come through the recent
matching of the superconformal index of -BPS states to the
supersymmetric black hole entropy. We introduce the half-BPS Gukov-Witten
surface defect as a probe of the superconformal index, which also serves as an
order parameter for the deconfinement transition. This can be studied directly
in field theory as a modification of the usual unitary matrix model or in the
dual description as a D3-brane probe in the background of a (complex)
supersymmetric black hole. Using a saddle point approximation, we determine our
defect index in the large limit as a simple function of the chemical
potentials and show independently that it is reproduced by the renormalized
action of the brane in the black hole background. Along the way, we also
comment on the Cardy limit and the thermodynamics of the D3-brane in the
generalized ensemble. The defect index sharply distinguishes between the
confining and the deconfining phases of the gauge theory and thus is a
supersymmetric non-perturbative order parameter for these large phase
transitions which deserves further investigation. Finally, our work provides an
example where the properties of a black hole coupled to an external system can
be analyzed precisely.Comment: 51 pages + appendices, 7 figure
Spiking Semantic Communication for Feature Transmission with HARQ
In Collaborative Intelligence (CI), the Artificial Intelligence (AI) model is
divided between the edge and the cloud, with intermediate features being sent
from the edge to the cloud for inference. Several deep learning-based Semantic
Communication (SC) models have been proposed to reduce feature transmission
overhead and mitigate channel noise interference. Previous research has
demonstrated that Spiking Neural Network (SNN)-based SC models exhibit greater
robustness on digital channels compared to Deep Neural Network (DNN)-based SC
models. However, the existing SNN-based SC models require fixed time steps,
resulting in fixed transmission bandwidths that cannot be adaptively adjusted
based on channel conditions. To address this issue, this paper introduces a
novel SC model called SNN-SC-HARQ, which combines the SNN-based SC model with
the Hybrid Automatic Repeat Request (HARQ) mechanism. SNN-SC-HARQ comprises an
SNN-based SC model that supports the transmission of features at varying
bandwidths, along with a policy model that determines the appropriate
bandwidth. Experimental results show that SNN-SC-HARQ can dynamically adjust
the bandwidth according to the channel conditions without performance loss
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