Conformation-networks of two-dimensional lattice homopolymers

The effect of different Monte Carlo move sets on the the folding kinetics of lattice polymer chains is studied from the geometry of the conformation-network. The networks have the characteristics of small- world. The Monte Carlo move, rigid rotation, has drastic effect on the geometric properties of the network. The move not only change the connections but also reduce greatly the shortest path length between conformations. The networks are as robust as random network

Macroscopic loop amplitudes in the multi-cut two-matrix models

Multi-cut critical points and their macroscopic loop amplitudes are studied in the multi-cut two-matrix models, based on an extension of the prescription developed by Daul, Kazakov and Kostov. After identifying possible critical points and potentials in the multi-cut matrix models, we calculate the macroscopic loop amplitudes in the Z_k symmetric background. With a natural large N ansatz for the matrix Lax operators, a sequence of new solutions for the amplitudes in the Z_k symmetric k-cut two-matrix models are obtained, which are realized by the Jacobi polynomials.Comment: 46 pages, 3 figures; v2: 51 pages, 7 figures, notations changed, explanations in Section 2.4 extended, figures for topology of the curves added, Appendix E added, final version to appear in Nucl. Phys.

Search for the Supersymmetric Partner of the Top-Quark in ppˉp \bar{p} Collisions at s=1.8TeV\sqrt{s} = 1.8 {\rm TeV}

We report on a search for the supersymmetric partner of the top quark (stop) produced in $t \bar{t}$ events using $110 {\rm pb}^{-1}$ of $p \bar{p}$ collisions at $\sqrt{s} = 1.8 {\rm TeV}$ recorded with the Collider Detector at Fermilab. In the case of a light stop squark, the decay of the top quark into stop plus the lightest supersymmetric particle (LSP) could have a significant branching ratio. The observed events are consistent with Standard Model $t \bar{t}$ production and decay. Hence, we set limits on the branching ratio of the top quark decaying into stop plus LSP, excluding branching ratios above 45% for a LSP mass up to 40 {\rm GeV/c}$^{2}$.Comment: 11 pages, 4 figure

Observation of electron-antineutrino disappearance at Daya Bay

The Daya Bay Reactor Neutrino Experiment has measured a non-zero value for the neutrino mixing angle $\theta_{13}$ with a significance of 5.2 standard deviations. Antineutrinos from six 2.9 GW$_{\rm th}$ reactors were detected in six antineutrino detectors deployed in two near (flux-weighted baseline 470 m and 576 m) and one far (1648 m) underground experimental halls. With a 43,000 ton-GW_{\rm th}-day livetime exposure in 55 days, 10416 (80376) electron antineutrino candidates were detected at the far hall (near halls). The ratio of the observed to expected number of antineutrinos at the far hall is $R=0.940\pm 0.011({\rm stat}) \pm 0.004({\rm syst})$. A rate-only analysis finds $\sin^22\theta_{13}=0.092\pm 0.016({\rm stat})\pm0.005({\rm syst})$ in a three-neutrino framework.Comment: 5 figures. Version to appear in Phys. Rev. Let

PreFallKD: Pre-Impact Fall Detection via CNN-ViT Knowledge Distillation

Fall accidents are critical issues in an aging and aged society. Recently, many researchers developed pre-impact fall detection systems using deep learning to support wearable-based fall protection systems for preventing severe injuries. However, most works only employed simple neural network models instead of complex models considering the usability in resource-constrained mobile devices and strict latency requirements. In this work, we propose a novel pre-impact fall detection via CNN-ViT knowledge distillation, namely PreFallKD, to strike a balance between detection performance and computational complexity. The proposed PreFallKD transfers the detection knowledge from the pre-trained teacher model (vision transformer) to the student model (lightweight convolutional neural networks). Additionally, we apply data augmentation techniques to tackle issues of data imbalance. We conduct the experiment on the KFall public dataset and compare PreFallKD with other state-of-the-art models. The experiment results show that PreFallKD could boost the student model during the testing phase and achieves reliable F1-score (92.66%) and lead time (551.3 ms)