The hard-disk fluid revisited

The hard-disk model plays a role of touchstone for testing and developing the transport theory. By large scale molecular dynamics simulations of this model, three important autocorrelation functions, and as a result the corresponding transport coefficients, i.e., the diffusion constant, the thermal conductivity and the shear viscosity, are found to deviate significantly from the predictions of the conventional transport theory beyond the dilute limit. To improve the theory, we consider both the kinetic process and the hydrodynamic process in the whole time range, rather than each process in a seperated time scale as the conventional transport theory does. With this consideration, a unified and coherent expression free of any fitting parameters is derived succesfully in the case of the velocity autocorrelation function, and its superiority to the conventional `piecewise' formula is shown. This expression applies to the whole time range and up to moderate densities, and thus bridges the kinetics and hydrodynamics approaches in a self-consistent manner.Comment: 5 pages, 4 figure

Brownian motion: from kinetics to hydrodynamics

Brownian motion has served as a pilot of studies in diffusion and other transport phenomena for over a century. The foundation of Brownian motion, laid by Einstein, has generally been accepted to be far from being complete since the late 1960s, because it fails to take important hydrodynamic effects into account. The hydrodynamic effects yield a time dependence of the diffusion coefficient, and this extends the ordinary hydrodynamics. However, the time profile of the diffusion coefficient across the kinetic and hydrodynamic regions is still absent, which prohibits a complete description of Brownian motion in the entire course of time. Here we close this gap. We manage to separate the diffusion process into two parts: a kinetic process governed by the kinetics based on molecular chaos approximation and a hydrodynamics process described by linear hydrodynamics. We find the analytical solution of vortex backflow of hydrodynamic modes triggered by a tagged particle. Coupling it to the kinetic process we obtain explicit expressions of the velocity autocorrelation function and the time profile of diffusion coefficient. This leads to an accurate account of both kinetic and hydrodynamic effects. Our theory is applicable for fluid and Brownian particles, even of irregular-shaped objects, in very general environments ranging from dilute gases to dense liquids. The analytical results are in excellent agreement with numerical experiments.Comment: 8pages,3figure

Modified Stokes-Einstein Relation for Small Brownian Particles

The Stokes-Einstein (SE) relation has been widely applied to quantitatively describe the Brownian motion. Notwithstanding, here we show that even for a simple fluid, the SE relation may not be completely applicable. Namely, although the SE relation could be a good approximation for a large enough Brownian particle, we find that it induces significant error for a smaller Brownian particle, and the error increases with the decrease of the Brownian particle's size, till the SE relation fails completely when the size of Brownian particle is comparable with that of a fluid molecule. The cause is rooted in the fact that the kinetic and the hydrodynamic effects depend on the size of the Brownian particle differently. By excluding the kinetic contribution to the diffusion coefficient, we propose a revised Stokes-Einstein relation and show that it expands significantly the applicable range.Comment: 3 figure

NormalNet: Learning-based Normal Filtering for Mesh Denoising

Mesh denoising is a critical technology in geometry processing that aims to recover high-fidelity 3D mesh models of objects from their noise-corrupted versions. In this work, we propose a learning-based normal filtering scheme for mesh denoising called NormalNet, which maps the guided normal filtering (GNF) into a deep network. The scheme follows the iterative framework of filtering-based mesh denoising. During each iteration, first, the voxelization strategy is applied on each face in a mesh to transform the irregular local structure into the regular volumetric representation, therefore, both the structure and face normal information are preserved and the convolution operations in CNN(Convolutional Neural Network) can be easily performed. Second, instead of the guidance normal generation and the guided filtering in GNF, a deep CNN is designed, which takes the volumetric representation as input, and outputs the learned filtered normals. At last, the vertex positions are updated according to the filtered normals. Specifically, the iterative training framework is proposed, in which the generation of training data and the network training are alternately performed, whereas the ground truth normals are taken as the guidance normals in GNF to get the target normals. Compared to state-of-the-art works, NormalNet can effectively remove noise while preserving the original features and avoiding pseudo-features

The 2-adic valuations of differences of Stirling numbers of the second kind

Let $m, n, k$ and $c$ be positive integers. Let $\nu_2(k)$ be the 2-adic valuation of $k$. By $S(n,k)$ we denote the Stirling numbers of the second kind. In this paper, we first establish a convolution identity of the Stirling numbers of the second kind and provide a detailed 2-adic analysis to the Stirling numbers of the second kind. Consequently, we show that if $2\le m\le n$ and $c$ is odd, then $\nu_2(S(c2^{n+1},2^m-1)-S(c2^n, 2^m-1))=n+1$ except when $n=m=2$ and $c=1$, in which case $\nu_2(S(8,3)-S(4,3))=6$. This solves a conjecture of Lengyel proposed in 2009.Comment: 20 page

Divisibility by 2 of Stirling numbers of the second kind and their differences

Let $n,k,a$ and $c$ be positive integers and $b$ be a nonnegative integer. Let $\nu_2(k)$ and $s_2(k)$ be the 2-adic valuation of $k$ and the sum of binary digits of $k$, respectively. Let $S(n,k)$ be the Stirling number of the second kind. It is shown that $\nu_2(S(c2^n,b2^{n+1}+a))\geq s_2(a)-1,$ where $0<a<2^{n+1}$ and $2\nmid c$. Furthermore, one gets that $\nu_2(S(c2^{n},(c-1)2^{n}+a))=s_2(a)-1$, where $n\geq 2$, $1\leq a\leq 2^n$ and $2\nmid c$. Finally, it is proved that if $3\leq k\leq 2^n$ and $k$ is not a power of 2 minus 1, then $\nu_2(S(a2^{n},k)-S(b2^{n},k))=n+\nu_2(a-b)-\lceil\log_2k\rceil +s_2(k)+\delta(k),$ where $\delta(4)=2$, $\delta(k)=1$ if $k>4$ is a power of 2, and $\delta(k)=0$ otherwise. This confirms a conjecture of Lengyel raised in 2009 except when $k$ is a power of 2 minus 1.Comment: 23 pages. To appear in Journal of Number Theor

An Algorithm of Parking Planning for Smart Parking System

There are so many vehicles in the world and the number of vehicles is increasing rapidly. To alleviate the parking problems caused by that, the smart parking system has been developed. The parking planning is one of the most important parts of it. An effective parking planning strategy makes the better use of parking resources possible. In this paper, we present a feasible method to do parking planning. We transform the parking planning problem into a kind of linear assignment problem. We take vehicles as jobs and parking spaces as agents. We take distances between vehicles and parking spaces as costs for agents doing jobs. Then we design an algorithm for this particular assignment problem and solve the parking planning problem. The method proposed can give timely and efficient guide information to vehicles for a real time smart parking system. Finally, we show the effectiveness of the method with experiments over some data, which can simulate the situation of doing parking planning in the real world.Comment: Proceeding of the 11th World Congress on Intelligent Control and Automation (WCICA

The 2-adic valuations of Stirling numbers of the second kind

In this paper, we investigate the 2-adic valuations of the Stirling numbers $S(n, k)$ of the second kind. We show that $v_2(S(4i, 5))=v_2(S(4i+3, 5))$ if and only if $i\not\equiv 7\pmod {32}$. This confirms a conjecture of Amdeberhan, Manna and Moll raised in 2008. We show also that $v_2(S(2^n+1, k+1))= s_2(n)-1$ for any positive integer $n$, where $s_2(n)$ is the sum of binary digits of $n$. It proves another conjecture of Amdeberhan, Manna and Moll.Comment: 9 pages. To appear in International Journal of Number Theor

The universal Kummer congruences

Let $p$ be a prime. In this paper, we present a detailed $p$-adic analysis to factorials and double factorials and their congruences. We give good bounds for the $p$-adic sizes of the coefficients of the divided universal Bernoulli number ${{\hat B_n}\over n}$ when $n$ is divisible by $p-1$. Using these we then establish the universal Kummer congruences modulo powers of a prime $p$ for the divided universal Bernoulli numbers ${{\hat B_n}\over n}$ when $n$ is divisible by $p-1$.Comment: 20 pages. To appear in Journal of the Australian Mathematical Societ

Classification of entities via their descriptive sentences

Hypernym identification of open-domain entities is crucial for taxonomy construction as well as many higher-level applications. Current methods suffer from either low precision or low recall. To decrease the difficulty of this problem, we adopt a classification-based method. We pre-define a concept taxonomy and classify an entity to one of its leaf concept, based on the name and description information of the entity. A convolutional neural network classifier and a K-means clustering module are adopted for classification. We applied this system to 2.1 million Baidu Baike entities, and 1.1 million of them were successfully identified with a precision of 99.36%