Solitary wave of the Schrodinger lattice system with nonlinear hopping

This paper is concerned with the nonlinear Schrodinger lattice with nonlinear hopping. Via variation approach and the Nehari manifold argument, we obtain two types of solution: periodic ground state and localized ground state. Moreover, we consider the convergence of periodic solutions to the solitary wave

Salient Object Detection: A Benchmark

We extensively compare, qualitatively and quantitatively, 40 state-of-the-art models (28 salient object detection, 10 fixation prediction, 1 objectness, and 1 baseline) over 6 challenging datasets for the purpose of benchmarking salient object detection and segmentation methods. From the results obtained so far, our evaluation shows a consistent rapid progress over the last few years in terms of both accuracy and running time. The top contenders in this benchmark significantly outperform the models identified as the best in the previous benchmark conducted just two years ago. We find that the models designed specifically for salient object detection generally work better than models in closely related areas, which in turn provides a precise definition and suggests an appropriate treatment of this problem that distinguishes it from other problems. In particular, we analyze the influences of center bias and scene complexity in model performance, which, along with the hard cases for state-of-the-art models, provide useful hints towards constructing more challenging large scale datasets and better saliency models. Finally, we propose probable solutions for tackling several open problems such as evaluation scores and dataset bias, which also suggest future research directions in the rapidly-growing field of salient object detection

Complete λ\lambda-surfaces in R3\mathbb R^3

The purpose of this paper is to study complete $\lambda$-surfaces in Euclidean space $\mathbb R^3$. A complete classification for 2-dimensional complete $\lambda$-surfaces in Euclidean space $\mathbb R^3$ with constant squared norm of the second fundamental form is given.Comment: 19 pages, comments are welcom

Self-Erasing Network for Integral Object Attention

Recently, adversarial erasing for weakly-supervised object attention has been deeply studied due to its capability in localizing integral object regions. However, such a strategy raises one key problem that attention regions will gradually expand to non-object regions as training iterations continue, which significantly decreases the quality of the produced attention maps. To tackle such an issue as well as promote the quality of object attention, we introduce a simple yet effective Self-Erasing Network (SeeNet) to prohibit attentions from spreading to unexpected background regions. In particular, SeeNet leverages two self-erasing strategies to encourage networks to use reliable object and background cues for learning to attention. In this way, integral object regions can be effectively highlighted without including much more background regions. To test the quality of the generated attention maps, we employ the mined object regions as heuristic cues for learning semantic segmentation models. Experiments on Pascal VOC well demonstrate the superiority of our SeeNet over other state-of-the-art methods.Comment: Accepted by NIPS201

FMtree: A fast locating algorithm of FM-indexes for genomic data

Motivation: As a fundamental task in bioinformatics, searching for massive short patterns over a long text is widely accelerated by various compressed full-text indexes. These indexes are able to provide similar searching functionalities to classical indexes, e.g., suffix trees and suffix arrays, while requiring less space. For genomic data, a well-known family of compressed full-text index, called FM-indexes, presents unmatched performance in practice. One major drawback of FM-indexes is that their locating operations, which report all occurrence positions of patterns in a given text, are particularly slow, especially for the patterns with many occurrences. Results: In this paper, we introduce a novel locating algorithm, FMtree, to fast retrieve all occurrence positions of any pattern via FM-indexes. When searching for a pattern over a given text, FMtree organizes the search space of the locating operation into a conceptual quadtree. As a result, multiple occurrence positions of this pattern can be retrieved simultaneously by traversing the quadtree. Compared with the existing locating algorithms, our tree-based algorithm reduces large numbers of redundant operations and presents better data locality. Experimental results show that FMtree is usually one order of magnitude faster than the state-of-the-art algorithms, and still memory-efficient

Low Surface Brightness Galaxy catalogue selected from the alpha.40-SDSS DR7 Survey and Tully-Fisher relation

We present a catalogue of an HI-selected sample of 1129 low surface brightness galaxies (LSBGs) searched from the alpha.40-SDSS DR7 survey. This sample, consisting of various types of galaxies in terms of luminosity and morphology, has extended the parameter space covered by the existing LSBG samples. Based on a subsample of 173 LSBGs which are selected from our entire LSBG sample to have the 2-horn shapes of the HI line profiles, minor-to-major axial ratios (b/a) less than 0.6 and signal-to-noise ratio (S/N) of HI detection greater than 6.5, we investigated the Tully-Fisher relation (TFr) of LSBGs in the optical B, g and r bands and near-infrared J, H and K bands as well. In optical bands, the LSBG subsample follows the fundamental TFr which was previously defined for normal spiral galaxies. In NIR bands, the TFrs for our LSBG subsample are slightly different from the TFrs for the normal bright galaxies. This might be due to the internal extinction issue. Furthermore, the mass-to-light ratio (M/L),disk scale length (h) and mass surface density (sigma) for our LSBG subsample were deduced from the optical TFr results. Compared with High Surface Brightness Galaxies(HSBGs), our LSBGs have higher M/L, larger h and lower sigma than HSBGs.Comment: Accepted to be published in MNRA

Sequential Defense Against Random and Intentional Attacks in Complex Networks

Network robustness against attacks is one of the most fundamental researches in network science as it is closely associated with the reliability and functionality of various networking paradigms. However, despite the study on intrinsic topological vulnerabilities to node removals, little is known on the network robustness when network defense mechanisms are implemented, especially for networked engineering systems equipped with detection capabilities. In this paper, a sequential defense mechanism is firstly proposed in complex networks for attack inference and vulnerability assessment, where the data fusion center sequentially infers the presence of an attack based on the binary attack status reported from the nodes in the network. The network robustness is evaluated in terms of the ability to identify the attack prior to network disruption under two major attack schemes, i.e., random and intentional attacks. We provide a parametric plug-in model for performance evaluation on the proposed mechanism and validate its effectiveness and reliability via canonical complex network models and real-world large-scale network topology. The results show that the sequential defense mechanism greatly improves the network robustness and mitigates the possibility of network disruption by acquiring limited attack status information from a small subset of nodes in the network.Comment: 13 pages, 14 figure

Estimates for eigenvalues of the Paneitz operator

For an $n$-dimensional compact submanifold $M^n$ in the Euclidean space $\mathbf R^{N}$, we study estimates for eigenvalues of the Paneitz operator on $M^n$. Our estimates for eigenvalues are sharp.Comment: 16 page

A nonparametric copula density estimator incorporating information on bivariate marginals

We propose a copula density estimator that can include information on bivariate marginals when the information is available. We use B-splines for copula density approximation and include information on bivariate marginals via a penalty term. Our estimator satisfies the constraints for a copula density. Under mild conditions, the proposed estimator is consistent

Factoring a quadratic operator as a product of two positive contractions

Let $T$ be a quadratic operator on a complex Hilbert space $H$. We show that $T$ can be written as a product of two positive contractions if and only if $T$ is of the form $$aI \oplus bI \oplus\begin{pmatrix} aI & P \cr 0 & bI \cr \end{pmatrix} \quad \text{on} \quad H_1\oplus H_2\oplus (H_3\oplus H_3)$$ for some $a, b\in [0,1]$ and strictly positive operator $P$ with $\|P\| \le |\sqrt{a} - \sqrt{b}|\sqrt{(1-a)(1-b)}.$ Also, we give a necessary condition for a bounded linear operator $T$ with operator matrix $\begin{pmatrix} T_1 & T_3\\ 0 & T_2\cr\end{pmatrix}$ on $H\oplus K$ that can be written as a product of two positive contractions.Comment: 9 page