The Role of Chaos in One-Dimensional Heat Conductivity

We investigate the heat conduction in a quasi 1-D gas model with various degree of chaos. Our calculations indicate that the heat conductivity $\kappa$ is independent of system size when the chaos of the channel is strong enough. The different diffusion behaviors for the cases of chaotic and non-chaotic channels are also studied. The numerical results of divergent exponent $\alpha$ of heat conduction and diffusion exponent $\beta$ are in consistent with the formula $\alpha=2-2/\beta$. We explore the temperature profiles numerically and analytically, which show that the temperature jump is primarily attributed to superdiffusion for both non-chaotic and chaotic cases, and for the latter case of superdiffusion the finite-size affects the value of $\beta$ remarkably.Comment: 6 pages, 7 figure

Convolutional Visual Prompt for Robust Visual Perception

Vision models are often vulnerable to out-of-distribution (OOD) samples without adapting. While visual prompts offer a lightweight method of input-space adaptation for large-scale vision models, they rely on a high-dimensional additive vector and labeled data. This leads to overfitting when adapting models in a self-supervised test-time setting without labels. We introduce convolutional visual prompts (CVP) for label-free test-time adaptation for robust visual perception. The structured nature of CVP demands fewer trainable parameters, less than 1\% compared to standard visual prompts, combating overfitting. Extensive experiments and analysis on a wide variety of OOD visual perception tasks show that our approach is effective, improving robustness by up to 5.87% over several large-scale models

Modeling Distances in Large-Scale Networks by Matrix Factorization

In this paper, we propose a model for representing and predicting distances in large-scale networks by matrix factorization. The model is useful for network distance sensitive applications, such as content distribution networks, topology-aware overlays, and server selections. Our approach overcomes several limitations of previous coordinates-based mechanisms, which cannot model sub-optimal routing or asymmetric routing policies. We describe two algorithms -- singular value decomposition (SVD) and nonnegative matrix factorization (NMF) -- for representing a matrix of network distances as the product of two smaller matrices. With such a representation, we build a scalable system -- Internet Distance Estimation Service (IDES) -- that predicts large numbers of network distances from limited numbers of measurements. Extensive simulations on real-world data sets show that IDES leads to more accurate, efficient and robust predictions of latencies in large-scale networks than previous approaches

A statistical classification of the unassociated gamma-ray sources in the second Fermi Large Area Telescope Catalog

With assistance of the identified/associated sources in the second Fermi Large Area Telescope (LAT) catalog, we analyze and resolve the spatial distribution and the distributions of the gamma-ray spectral and variability indices of the remaining 575 unassociated Fermi LAT sources. Consequently, it is suggested that the unassociated sources could statistically consist of Galactic supernova remnants/pulsar wind nebulae, BL Lacertae objects, flat spectrum radio quasars, and other types of active galaxies with fractions of 25%, 29%, 41%, and 5%, respectively.Comment: 8 pages, 7 figures, accepted for publication in Research in Astronomy and Astrophysics (RAA

Analysis the Present Situation of Chinese Electronic Government Based on Fundamental Data

Electronic government can improve service delivery, transparency, and encourage citizens to participate government affairs which could promote government efficiency. This paper gives a full and statistics analysis on the data which collect from questionnaires by e-mail survey. Finally, the purpose of this paper is to offer some practical and effective advices for the future successful e-government development in China

Efficient interpolation of molecular properties across chemical compound space with low-dimensional descriptors

We demonstrate accurate data-starved models of molecular properties for interpolation in chemical compound spaces with low-dimensional descriptors. Our starting point is based on three-dimensional, universal, physical descriptors derived from the properties of the distributions of the eigenvalues of Coulomb matrices. To account for the shape and composition of molecules, we combine these descriptors with six-dimensional features informed by the Gershgorin circle theorem. We use the nine-dimensional descriptors thus obtained for Gaussian process regression based on kernels with variable functional form, leading to extremely efficient, low-dimensional interpolation models. The resulting models trained with 100 molecules are able to predict the product of entropy and temperature ($S \times T$) and zero point vibrational energy (ZPVE) with the absolute error under 1 kcal mol$^{-1}$ for $> 78$ \% and under 1.3 kcal mol$^{-1}$ for $> 92$ \% of molecules in the test data. The test data comprises 20,000 molecules with complexity varying from three atoms to 29 atoms and the ranges of $S \times T$ and ZPVE covering 36 kcal mol$^{-1}$ and 161 kcal mol$^{-1}$, respectively. We also illustrate that the descriptors based on the Gershgorin circle theorem yield more accurate models of molecular entropy than those based on graph neural networks that explicitly account for the atomic connectivity of molecules.Comment: 29 pages, 12 figures, submitted to Machine Learning: Science and Technology November 22nd, 202

Medium effects on the selection of sequences folding into stable proteins in a simple model

We study the medium effects on the selection of sequences in protein folding by taking account of the surface potential in HP-model. Our analysis on the proportion of H and P monomers in the sequences gives a direct interpretation that the lowly designable structures possess small average gap. The numerical calculation by means of our model exhibits that the surface potential enhances the average gap of highly designable structures. It also shows that a most stable structure may be no longer the most stable one if the medium parameters changed.Comment: 4 pages, 4 figure