Two-State Migration of DNA in a structured Microchannel

DNA migration in topologically structured microchannels with periodic cavities is investigated experimentally and with Brownian dynamics simulations of a simple bead-spring model. The results are in very good agreement with one another. In particular, the experimentally observed migration order of Lambda- and T2-DNA molecules is reproduced by the simulations. The simulation data indicate that the mobility may depend on the chain length in a nonmonotonic way at high electric fields. This is found to be the signature of a nonequilibrium phase transition between two different migration states, a slow one and a fast one, which can also be observed experimentally under appropriate conditions.Comment: Revised edition corresponding to the comments by the referees, submitted to Physical Review

Efficiently Clustering Very Large Attributed Graphs

Attributed graphs model real networks by enriching their nodes with attributes accounting for properties. Several techniques have been proposed for partitioning these graphs into clusters that are homogeneous with respect to both semantic attributes and to the structure of the graph. However, time and space complexities of state of the art algorithms limit their scalability to medium-sized graphs. We propose SToC (for Semantic-Topological Clustering), a fast and scalable algorithm for partitioning large attributed graphs. The approach is robust, being compatible both with categorical and with quantitative attributes, and it is tailorable, allowing the user to weight the semantic and topological components. Further, the approach does not require the user to guess in advance the number of clusters. SToC relies on well known approximation techniques such as bottom-k sketches, traditional graph-theoretic concepts, and a new perspective on the composition of heterogeneous distance measures. Experimental results demonstrate its ability to efficiently compute high-quality partitions of large scale attributed graphs.Comment: This work has been published in ASONAM 2017. This version includes an appendix with validation of our attribute model and distance function, omitted in the converence version for lack of space. Please refer to the published versio

Distinct order of Gd 4f and Fe 3d moments coexisting in GdFe4Al8

Single crystals of flux-grown tetragonal GdFe4Al8 were characterized by thermodynamic, transport, and x-ray resonant magnetic scattering measurements. In addition to antiferromagnetic order at TN ~ 155 K, two low-temperature transitions at T1 ~ 21 K and T2 ~ 27 K were identified. The Fe moments order at TN with an incommensurate propagation vector (tau,tau,0) with tau varying between 0.06 and 0.14 as a function of temperature, and maintain this order over the entire T<TN range. The Gd 4f moments order below T2 with a ferromagnetic component mainly out of plane. Below T1, the ferromagnetic components are confined to the crystallographic plane. Remarkably, at low temperatures the Fe moments maintain the same modulation as at high temperatures, but the Gd 4f moments apparently do not follow this modulation. The magnetic phase diagrams for fields applied in [110] and [001] direction are presented and possible magnetic structures are discussed.Comment: v2: 14 pages, 12 figures; PRB in prin

Rate of convergence in the Smoluchowski-Kramers approximation for mean-field stochastic differential equations

In this paper we study a second-order mean-field stochastic differential systems describing the movement of a particle under the influence of a time-dependent force, a friction, a mean-field interaction and a space and time-dependent stochastic noise. Using techniques from Malliavin calculus, we establish explicit rates of convergence in the zero-mass limit (Smoluchowski-Kramers approximation) in the $L^p$-distances and in the total variation distance for the position process, the velocity process and a re-scaled velocity process to their corresponding limiting processes

Physical Layer Security: Detection of Active Eavesdropping Attacks by Support Vector Machines

This paper presents a framework for converting wireless signals into structured datasets, which can be fed into machine learning algorithms for the detection of active eavesdropping attacks at the physical layer. More specifically, a wireless communication system, which consists of K legal users, one access point (AP) and one active eavesdropper, is considered. To cope with the eavesdropper who breaks into the system during the uplink phase, we first build structured datasets based on several different features. We then apply support vector machine (SVM) classifiers and one-class SVM classifiers to those structured datasets for detecting the presence of eavesdropper. Regarding the data, we first process received signals at the AP and then define three different features (i.e., MEAN, RATIO and SUM) based on the post-processing signals. Noticeably, our three defined features are formulated such that they have relevant statistical properties. Enabling the AP to simulate the entire process of transmission, we form the so-called artificial training data (ATD) that is used for training SVM (or one-class SVM) models. While SVM is preferred in the case of having perfect channel state information (CSI) of all channels, one-class SVM is preferred in the case of having only the CSI of legal users. We also evaluate the accuracy of the trained models in relation to the choice of kernel functions, the choice of features, and the change of eavesdropper's power. Numerical results show that the accuracy is relatively sensitive to adjusting parameters. Under some settings, SVM classifiers (or even one-class SVM) can bring about the accuracy of over 90%.Comment: All versions on this site are withdrawn because of their serious mistakes. Moreover, the contributions of the co-authors were not considered carefully. Two co-authors have little contributions, which cannot constitute any main contribution. It was a mistake when the first author forgot to update the actual authors, and he hurried to upload the incomplete and flaw file

Riesz transform characterization of Hardy spaces associated with Schr\"odinger operators with compactly supported potentials

Let L=-\Delta+V be a Schr\"odinger operator on R^d, d\geq 3. We assume that V is a nonnegative, compactly supported potential that belongs to L^p(R^d), for some p>d/2. Let K_t be the semigroup generated by -L. We say that an L^1(R^d)-function f belongs to the Hardy space H_L^1 associated with L if sup_{t>0} |K_t f| belongs to L^1(R^d). We prove that f\in H_L^1 if and only if R_j f \in L^1(R^d) for j=1,...,d, where R_j= \frac{d}{dx_j} L^{-1/2} are the Riesz transforms associated with L.Comment: 6 page