Surrogate analysis of volatility series from long-range correlated noise

Detrended fluctuation analysis (DFA) [C.-K. Peng, S.V. Buldyrev, A.L. Goldberger, S. Havlin, F. Sciortino, M. Simons, H.E. Stanley, Nature 356 (1992) 168] of volatility series has been proposed to identify possible nonlinear/multifractal signatures in the given empirical sample [Y. Ashkenazy, P.Ch. Ivanov, S. Havlin, C.-K. Peng, A.L. Goldberger, H.E. Stanley, Phys. Rev. Lett. 86 (2001) 1900; Y. Ashkenazy, S. Havlin, P. Ch. Ivanov, C.-K. Peng, V. Schulte-Frohlinde, H.E. Stanley, Physica A. 323 (2003) 19; T. Kalisky, Y. Ashkenazy, S. Havlin, Phys. Rev. E 72 (2005) 011913]. Long-range volatility correlation can be an outcome of static as well as dynamical nonlinearity. In order to argue in favor of dynamical nonlinearity, surrogate testing is used in conjunction with volatility analysis [Y. Ashkenazy, P.Ch. Ivanov, S. Havlin, C.-K. Peng, A.L. Goldberger, H.E. Stanley, Phys. Rev. Lett. 86 (2001) 1900; Y. Ashkenazy, S. Havlin, P. Ch. Ivanov, C.-K. Peng, V. Schulte-Frohlinde, H.E. Stanley, Physica A. 323 (2003) 19; T. Kalisky, Y. Ashkenazy, S. Havlin, Phys. Rev. E 72 (2005) 011913]. In this brief communication, surrogate testing of volatility series from long-range correlated monofractal noise and their static, invertible nonlinear transforms is investigated. Long-range correlated noise is generated from fractional auto regressive integrated moving average (FARIMA) (0, d, 0), with Gaussian and non-Gaussian innovations. We show significant deviation in the scaling behavior between the empirical sample and the surrogate counterpart at large time-scales in the case of FARIMA (0, d, 0) with non-Gaussian innovations whereas no such discrepancy was observed in the case of Gaussian innovations. The results encourage cautious interpretation of surrogate analysis of volatility series in the presence of non-Gaussian innovations.     

Condensation of a chain polymer molecule

A new expression for the expansion coefficient of a flexible chain polymer molecule can be given in such a way that its first term corresponds to the case with only a hard-sphere repulsion and the other terms represent the effects of attraction between the segments. For low and high temperatures, more convenient expressions are used to find a possible condensation into a liquid-like condensed state from a gas-like state. It is found that in the condensed state, the square average end-to-end distance varies as N^?, where N is the number of segments and the exponent ? is around 0.6 in contrast to 1.2 for the high temperature case.     

Fractional diffusion and reflective boundary condition

Scaling analysis of the magnitude series (volatile series) has been proposed recently to identify possible non-linear/multifractal signatures in the given data [Y. Ashkenazy, et al. Phys. Rev. Lett. 86 (2001) 1900; Y. Ashkenazy, et al. Physica A 323 (2003) 19; T. Kalisky, Y. Ashkenazy, S. Havlin. Phys. Rev. E 72 (2005) 011913]. In this article, correlations of volatile series generated from stationary first-order linear feedback process with Gaussian and non-Gaussian innovations are investigated. While volatile correlations corresponding to Gaussian innovations exhibited uncorrelated behavior across all time scales, those of non-Gaussian innovations showed significant deviation from uncorrelated behavior even at large time scales. The results presented raise the intriguing question whether non-Gaussian innovations can be sufficient to realize long-range volatile correlations.     

Fluorescence quenching kinetics in short polymer chains: Dependence on chain length

Monte Carlo simulations of chain conformations and the diffusion equation were used to analyze the fluorescence kinetics of short polymer chains labeled with a probe and a quencher at opposite ends. In simulations, three chain models were considered: an ideal chain (without volume interactions); a self-avoiding chain taking into account the exclusive volume effect; and a self-avoiding chain with limited flexibility between nearest segments. For each model, end-to-end distance distribution functions were obtained, which were different from Gaussian ones. The distribution functions were used in a diffusion equation to simulate the fluorescence kinetics of the probe affected by intramolecular end-to-end collisions of short chains. The kinetics has been numerically calculated for a representative experimental system in a nonviscous solution. The simulated time-resolved fluorescence decays were monoexponential except at very short times (<2 ns). Diffusion coefficients were calculated for different chain models and different chain lengths. The experimental data could be reproduced by assuming systematically smaller end-to-end diffusion coefficients for the shorter chains.     

Deep Learning-Based Joint CSI Feedback and Hybrid Precoding in FDD mmWave Massive MIMO Systems

In this paper, we propose an end-to-end deep learning approach to realize channel state information (CSI) feedback and hybrid precoding for millimeter wave massive multiple-input multiple-output systems in the frequency division duplexing mode. Different from conventional approaches that treat the CSI reconstruction and hybrid precoding as separate components, we propose a new end-to-end learning method bypassing the channel reconstruction phase, and design the hybrid precoders and combiners directly from the feedback codewords (a compressed version of the CSI). More specifically, we design a neural network composed of the CSI feedback and hybrid precoding. Experiment results show that our proposed network can achieve better performance than conventional hybrid precoding schemes that reserve channel reconstruction, especially when the feedback resources are limited.     

Improved gamut-constrained illuminant estimation by combining modified category correlation

In this paper we propose a new gamut-constrained illuminant estimation framework using an improved category correlation method. Firstly, we obtain a set of feasible illuminations by the original gamut mapping method. Then, the probability of each feasible illumination as the ground truth illuminant is calculated according to its ability to map the corrected image onto specific colors using the improved category correlation method. Differently from the original category correlation method, to decrease the effect of image noise and the computation complexity, instead of using an entire pixel set for estimating the probability of portable illuminant, superpixel segments of an input image are used in our improved method. And the best illuminant estimate is given on the basis of the measure of the degree to which each feasible illumination is consistent with the image data. Experiment results prove that our improved method shows better current state-of-the-art performance Gamut mapping methods.     

STUDIES OF THE FIELD DISTRIBUTION OF THE MODULATING WIDTH MULTIPOLE PERMANENT MAGNETS

In this paper,the magnetic field distribution of the modulating width multipole permanent magnets is analyzed. Only radially magnetized permanent magnet segments are used and the required magnetic field is formed by modulating the widths of the segments in this project. In 2-dimensional ideal case pure 2N-pole magnetic field can be formed. The spatial expressions of the scalar potential for the ideal and real 2-dimensional cases are given. And the methods for reducing the undesirable higher harmonics of the magnetic field in the real case are also discussed.     

单链DNA在受限环境中伸展的Monte Carlo模拟

将MonteCarlo方法和键长涨落算法相结合,模拟了受限于两平行平面间的单链DNA分子在外力作用下的伸展以及撤除外力以后弛豫的动力学过程,研究了受限程度对DNA分子的伸展长度及弛豫过程的影响.结果表明,随着受限程度的增加,DNA分子链的构象更加伸展,这主要是由于随着平面间距的减少,DNA分子不同链段之间流体力学相互作用将会被平面屏蔽所致,受限程度不同时DNA分子的弛豫过程进一步证实了这一点.DNA分子的伸展长度(即末端距)随着流速的变化关系与文献给出的实验结果及其对此所做的理论分析是基本一致的.     

Cyclization of a polymer: first-passage problem for a non-Markovian process

We discuss a closure of a polymer chain released from a state with given end-to-end distance. Considering the situation as a first passage problem for a non-Markovian process, we express the survival probability through the three-time distribution of the end-to-end distance and evaluate it numerically by solving a Volterra equation. We also show how the Wilemski-Fixmann approximation emerges within this scheme and discuss its geometrical nature.     

拉伸取向聚合物2H NMR谱的键涨落模型Monte Carlo模拟

采用三维键涨落模型（BFM）的Monte Carlo计算机模拟方法,模拟了两种分别具有高取向和低取向链微观结构的拉伸取向聚合物²H NMR谱. 讨论了排除体积效应与链的均方末端距分布对²H NMR谱线的影响. 结果表明,排除体积效应对高取向和低取向聚合物²H谱劈裂的影响是不同的,较小均方末端距的链决定²H谱劈裂,而较大均方末端距的链使²H谱产生长的拖尾. 采用BFM计算机模拟与²H NMR实验的结合是研究拉伸取向聚合物网络微观结构的有力手段.     

Modelling RNA folding under mechanical tension

We investigate the thermodynamics and kinetics of RNA unfolding and refolding under mechanical tension. The hierarchical nature of RNA structure and the existence of thermodynamic parameters for base pair formation based on nearest-neighbour interactions allows modelling of sequence-dependent folding dynamics for any secondary structure. We calculate experimental observables such as the transition force for unfolding, the end-to-end distribution function and its variance, as well as kinetic information, for a representative RNA sequence and for a sequence containing two homopolymer segments: A.U and G.C.     

Can analysis of word frequency distinguish between writings of different authors?

Various literature writings are compared by the “rank distance”, d, between two word frequency Zipf plots introduced by S. Havlin (Physica A 216 (1995) 148). We studied 22 books written by six authors. For this ensemble of books we find that the mean distance between books written by the same authors (〈d〉 = 15.2 ± 2.6) is considerably smaller than that between books written by different authors (〈d〉 = 21.8 ± 3.2), in good agreement with earlier results on a smaller sample of books. Our results suggest that the distribution of the rank difference of the same words in different books decays exponentially.     

予条件共轭梯度法的收敛性

本文对某些予条件共轭梯度法的收敛性进行了一些分析,如ICCG法,ILUCK(K)法、ILUCG(P)法及所谓的"TCG法",其中用了许多分裂作为对所要解的线性代数方程组的系数矩阵A进行予条件的工具,这里设A为对角优势矩阵,文中还给出了若干数值算例来说明我们分析的结果。     

Machine Learning Kinetic Energy Functional for a One-Dimensional Periodic System

Kinetic energy(KE) functional is crucial to speed up density functional theory calculation. However, deriving it accurately through traditional physics reasoning is challenging. We develop a generally applicable KE functional estimator for a one-dimensional (1D) extended system using a machine learning method. Our end-to-end solution combines the dimensionality reduction method with the Gaussian process regression, and simple scaling method to adapt to various 1D lattices. In addition to reaching chemical accuracy in KE calculation, our estimator also performs well on KE functional derivative prediction. Integrating this machine learning KE functional into the current orbital free density functional theory scheme is able to provide us with expected ground state electron density.     

Advanced Driving Assistance Based on the Fusion of Infrared and Visible Images

Obtaining key and rich visual information under sophisticated road conditions is one of the key requirements for advanced driving assistance. In this paper, a newfangled end-to-end model is proposed for advanced driving assistance based on the fusion of infrared and visible images, termed as FusionADA. In our model, we are committed to extracting and fusing the optimal texture details and salient thermal targets from the source images. To achieve this goal, our model constitutes an adversarial framework between the generator and the discriminator. Specifically, the generator aims to generate a fused image with basic intensity information together with the optimal texture details from source images, while the discriminator aims to force the fused image to restore the salient thermal targets from the source infrared image. In addition, our FusionADA is a fully end-to-end model, solving the issues of manually designing complicated activity level measurements and fusion rules existing in traditional methods. Qualitative and quantitative experiments on publicly available datasets RoadScene and TNO demonstrate the superiority of our FusionADA over the state-of-the-art approaches.     

A novel local PCA-based method for detecting activation signals in fMRI.

A novel local principal component analysis (LPCA) technique is presented for activation signal detection in functional magnetic resonance imaging (fMRI) without explicit knowledge about the shape of the model activation signal. Unlike the traditional PCA methods, our LPCA algorithm is based on a measure of separation between two clusters formed by the signal segments in active periods and inactive periods, which is computed in an eigen-subspace. In addition, we only applied PCA to the temporal sequence of each individual voxel instead of applying PCA to the fMRI data set. In our algorithm, we first applied a linear regression procedure to alleviate the baseline drift artifact. Then, the baseline-corrected temporal signals were partitioned into active and inactive segments according to the paradigm used for the fMRI data acquisition. Principal components were computed from all these segments for each voxel by PCA. By projecting the segments of each voxel onto a linear subspace formed by the corresponding most dominant principal components, two separate clusters were formed from active and inactive segments. An activation measure was defined based on the degree of separation between these two clusters in the projection space. We show experimental results on the activation signal detection from various sets of fMRI data with different types of stimulation by using the proposed LPCA algorithm and the standard t-test method for comparison. Our experiments indicate that the LPCA algorithm in general provides substantial signal-to-noise ratio improvement over the t-test method.     

On Continuous-Time Self-Avoiding Random Walk in Dimension Four

We study the distribution of the end-to-end distance of continuous-time self-avoiding random walks (CTRW) in dimension four from two viewpoints. From a real-space renormalization-group map on probabilities, we conjecture the asymptotic behavior of the end-to-end distance of a weakly self-avoiding random walk (SARW) that penalizes two-body interactions of random walks in dimension four on a hierarchical lattice. Then we perform the Monte Carlo computer simulations of CTRW on the four-dimensional integer lattice, paying special attention to the difference in statistical behavior of the CTRW compared with the discrete-time random walks. In this framework, we verify the result already predicted by the renormalization-group method and provide new results related to enumeration of self-avoiding random walks and calculation of the mean square end-to-end distance and gyration radius of continous-time self-avoiding random walks.     

ADAPTIVE CONTROL AND IDENTIFICATION OF CHAOTIC SYSTEMS

A novel adaptive control and identification on-line method is proposed for a class of chaotic system with uncertain parameters. We prove that, using the presented method, a controller and identifier is developed which can remove chaos in nonlinear systems and make the system asymptotically stabilizing to an arbitrarily desired smooth orbit. And at the same time, estimates to uncertain parameters converge to their true values. The advantage of our method over the existing result is that the controller and identifier is directly constructed by analytic formula without knowing unknown bounds about uncertain parameters in advance. A computer simulation example is given to validate the proposed approach.     

腔靶X光空间特性研究

本文介绍了腔靶X光空间成像原理和方法。在国内,首次将MCP(微通道板)光电成像器件应用于激光聚变实验,将X射线测量能区扩展到亚千电子伏范围。实验中利用各种黑体腔靶,通过测量腔靶X光空间发射图象,得到了腔靶激光注入孔和腔内都存在着等离子体会聚机制,以及腔内X光发射以腔壁为主等重要信息,观察到腔靶X光输运通道存在着“堵口”现象,对测量结果进行了物理解释。     

Deep-learning-based cryptanalysis of two types of nonlinear optical cryptosystems

The two types of nonlinear optical cryptosystems (NOCs) that are respectively based on amplitude-phase retrieval algorithm (APRA) and phase retrieval algorithm (PRA) have attracted a lot of attention due to their unique mechanism of encryption process and remarkable ability to resist common attacks. In this paper, the securities of the two types of NOCs are evaluated by using a deep-learning (DL) method, where an end-to-end densely connected convolutional network (DenseNet) model for cryptanalysis is developed. The proposed DL-based method is able to retrieve unknown plaintexts from the given ciphertexts by using the trained DenseNet model without prior knowledge of any public or private key. The results of numerical experiments with the DenseNet model clearly demonstrate the validity and good performance of the proposed the DL-based attack on NOCs.