Implementation of non-uniform FFT based Ewald summation in Dissipative Particle Dynamic method

The ENUF method, i.e., Ewald summation based on the Non-Uniform FFT technique (NFFT), is implemented in Dissipative Particle Dynamics (DPD) simulation scheme to fast and accurately calculate the electrostatic interactions at mesoscopic level. In a simple model electrolyte system, the suitable ENUF-DPD parameters, including the convergence parameter $\alpha$, the NFFT approximation parameter $p$, and the cut-offs for real and reciprocal space contributions, are carefully determined. With these optimized parameters, the ENUF-DPD method shows excellent efficiency and scales as $\mathcal{O}(N\log N)$. The ENUF-DPD method is further validated by investigating the effects of charge fraction of polyelectrolyte, ionic strength and counterion valency of added salts on polyelectrolyte conformations. The simulations in this paper, together with a separately published work of dendrimer-membrane complexes, show that the ENUF-DPD method is very robust and can be used to study charged complex systems at mesoscopic level

Heat flow of extrinsic biharmonic maps from a four dimensional manifold with boundary

Let $(M,g)$ be a four dimensional compact Riemannian manifold with boundary and $(N,h)$ be a compact Riemannian manifold without boundary. We show the existence of a unique, global weak solution of the heat flow of extrinsic biharmonic maps from $M$ to $N$ under the Dirichlet boundary condition, which is regular with the exception of at most finitely many time slices. We also discuss the behavior of solution near the singular times. As an immediate application, we prove the existence of a smooth extrinsic biharmonic map from $M$ to $N$ under any Dirichlet boundary condition.Comment: 26 pages, Journal of Elliptic and Parabolic Equations, to appea

Microstructures and Dynamics of Tetraalkylphosphonium Chloride Ionic Liquids

Atomistic simulations have been performed to investigate the effect of aliphatic chain length in tetraalkylphosphonium cations on liquid morphologies, microscopic ionic structures and dynamical properties of tetraalkylphosphonium chloride ionic liquids. The liquid morphologies are characterized by sponge-like interpenetrating polar and apolar networks in ionic liquids consisting of tetraalkylphosphonium cations with short aliphatic chains. The lengthening aliphatic chains in tetraalkylphosphonium cations leads to polar domains consisting of chloride anions and central polar groups in cations being partially or totally segregated in ionic liquid matrices due to a progressive expansion of apolar domains in between. Prominent polarity alternation peaks and adjacency correlation peaks are observed at low and high $q$ range in total X-ray scattering structural functions, respectively, and their peak positions gradually shift to lower q values with lengthening aliphatic chains in tetraalkylphosphonium cations. The charge alternation peaks registered in intermediate q range exhibit complicated tendencies due to the complete cancellations of peaks and anti-peaks in partial structural functions for ionic subcomponents. The particular microstructures and liquid morphologies in tetraalkylphosphonium chloride ionic liquids intrinsically contribute to distinct dynamics characterized by translational diffusion coefficients, van Hove correlation functions, and non-Gaussian parameters for ionic species in heterogeneous ionic environment. The increase of aliphatic chain length in tetraalkylphosphonium cations leads to concomitant shift of van Hove correlation functions and non-Gaussian parameters to larger radial distances and longer timescales, respectively, indicating the enhanced translational dynamical heterogeneities of tetraalkylphosphonium cations and the corresponding chloride anions.Comment: 28 pages, 11 figure

Phase controlled single-photon nonreciprocal transmission in a one-dimensional waveguide

We study the controllable single-photon scattering via a one-dimensional waveguide which is coupled to a two-level emitter and a single-mode cavity simultaneously. The emitter and the cavity are also coupled to each other and form a three-level system with cyclic transitions within the zero- and single-excitation subspaces. As a result, the phase of emitter-cavity coupling strength serves as a sensitive control parameter. When the emitter and cavity locate at the same point of the waveguide, we demonstrate the Rabi splitting and quasidark-state--induced perfect transmission for the incident photons. More interestingly, when they locate at different points of the waveguide, a controllable nonreciprocal transmission can be realized and the non-reciprocity is robust to the weak coupling between the system and environment. Furthermore, we demonstrate that our theoretical model is experimentally feasible with currently available technologies.Comment: 11 pages, 8 figures,Accepted by Phys. Rev.

Understand Scene Categories by Objects: A Semantic Regularized Scene Classifier Using Convolutional Neural Networks

Scene classification is a fundamental perception task for environmental understanding in today's robotics. In this paper, we have attempted to exploit the use of popular machine learning technique of deep learning to enhance scene understanding, particularly in robotics applications. As scene images have larger diversity than the iconic object images, it is more challenging for deep learning methods to automatically learn features from scene images with less samples. Inspired by human scene understanding based on object knowledge, we address the problem of scene classification by encouraging deep neural networks to incorporate object-level information. This is implemented with a regularization of semantic segmentation. With only 5 thousand training images, as opposed to 2.5 million images, we show the proposed deep architecture achieves superior scene classification results to the state-of-the-art on a publicly available SUN RGB-D dataset. In addition, performance of semantic segmentation, the regularizer, also reaches a new record with refinement derived from predicted scene labels. Finally, we apply our SUN RGB-D dataset trained model to a mobile robot captured images to classify scenes in our university demonstrating the generalization ability of the proposed algorithm

Place classification with a graph regularized deep neural network model

Place classification is a fundamental ability that a robot should possess to carry out effective human-robot interactions. It is a nontrivial classification problem which has attracted many research. In recent years, there is a high exploitation of Artificial Intelligent algorithms in robotics applications. Inspired by the recent successes of deep learning methods, we propose an end-to-end learning approach for the place classification problem. With the deep architectures, this methodology automatically discovers features and contributes in general to higher classification accuracies. The pipeline of our approach is composed of three parts. Firstly, we construct multiple layers of laser range data to represent the environment information in different levels of granularity. Secondly, each layer of data is fed into a deep neural network model for classification, where a graph regularization is imposed to the deep architecture for keeping local consistency between adjacent samples. Finally, the predicted labels obtained from all the layers are fused based on confidence trees to maximize the overall confidence. Experimental results validate the effective- ness of our end-to-end place classification framework in which both the multi-layer structure and the graph regularization promote the classification performance. Furthermore, results show that the features automatically learned from the raw input range data can achieve competitive results to the features constructed based on statistical and geometrical information

Energy Conditions and Stability in generalized f(R)f(R) gravity with arbitrary coupling between matter and geometry

The energy conditions and the Dolgov-Kawasaki criterion in generalized $f(R)$ gravity with arbitrary coupling between matter and geometry are derived in this paper, which are quite general and can degenerate to the well-known energy conditions in GR and $f(R)$ gravity with non-minimal coupling and non-coupling as special cases. In order to get some insight on the meaning of these energy conditions and the Dolgov- Kawasaki criterion, we apply them to a class of models in the FRW cosmology and give some corresponding results.Comment: 12 pages. arXiv admin note: substantial text overlap with arXiv:1203.5593, arXiv:1212.465

Some New Symmetric Relations and the Prediction of Left and Right Handed Neutrino Masses using Koide's Relation

Masses of the three generations of charged leptons are known to completely satisfy the Koide's mass relation. But the question remains if such a relation exists for neutrinos? In this paper, by considering SeeSaw mechanism as the mechanism generating tiny neutrino masses, we show how neutrinos satisfy the Koide's mass relation, on the basis of which we systematically give exact values of not only left but also right handed neutrino masses

Phoenix Cloud: Consolidating Different Computing Loads on Shared Cluster System for Large Organization

Different departments of a large organization often run dedicated cluster systems for different computing loads, like HPC (high performance computing) jobs or Web service applications. In this paper, we have designed and implemented a cloud management system software Phoenix Cloud to consolidate heterogeneous workloads from different departments affiliated to the same organization on the shared cluster system. We have also proposed cooperative resource provisioning and management policies for a large organization and its affiliated departments, running HPC jobs and Web service applications, to share the consolidated cluster system. The experiments show that in comparison with the case that each department operates its dedicated cluster system, Phoenix Cloud significantly decreases the scale of the required cluster system for a large organization, improves the benefit of the scientific computing department, and at the same time provisions enough resources to the other department running Web services with varying loads.Comment: 5 page, 8 figures, The First Workshop of Cloud Computing and its Application, The modified version. The original version is on the web site of http://www.cca08.org/, which is dated from August 13, 200

Study on Estimating Quantum Discord by Neural Network with Prior Knowledge

Machine learning has achieved success in many areas because of its powerful fitting ability, so we hope it can help us to solve some significant physical quantitative problems, such as quantum correlation. In this research we will use neural networks to predict the value of quantum discord. Quantum discord is a measure of quantum correlation which is defined as the difference between quantum mutual information and classical correlation for a bipartite system. Since the definition contains an optimization term, it makes analytically solving hard. For some special cases and small systems, such as two-qubit systems and some X-states, the explicit solutions have been calculated. However, for general cases, we still know very little. Therefore, we study the feasibility of estimating quantum discord by machine learning method on two-qubit systems. In order to get an interpretable and high performance model, we modify the ordinary neural network by introducing some prior knowledge which come from the analysis about quantum discord. Our results show that prior knowledge actually improve the performance of neural network