New constructions of quaternary bent functions

In this paper, a new construction of quaternary bent functions from quaternary quadratic forms over Galois rings of characteristic 4 is proposed. Based on this construction, several new classes of quaternary bent functions are obtained, and as a consequence, several new classes of quadratic binary bent and semi-bent functions in polynomial forms are derived. This work generalizes the recent work of N. Li, X. Tang and T. Helleseth

Complete permutation polynomials induced from complete permutations of subfields

We propose several techniques to construct complete permutation polynomials of finite fields by virtue of complete permutations of subfields. In some special cases, any complete permutation polynomials over a finite field can be used to construct complete permutations of certain extension fields with these techniques. The results generalize some recent work of several authors

A Survey of Dynamical Matrices Theory

In this note, we survey some elementary theorems and proofs concerning dynamical matrices theory. Some mathematical concepts and results involved in quantum information theory are reviewed. A little new result on the matrix representation of quantum operation are obtained. And best separable approximation for quantum operations is presented.Comment: 22 pages, LaTe

Novel Magnetic Quantization of sp3^{3} Bonding in Monolayer Tinene

A generalized tight-binding model, which is based on the subenvelope functions of the different sublattices, is developed to explore the novel magnetic quantization in monolayer gray tin. The effects due to the $sp^{3}$ bonding, the spin-orbital coupling, the magnetic field and the electric field are simultaneously taken into consideration. The unique magneto-electronic properties lie in two groups of low-lying Landau levels, with different orbital components, localization centers, state degeneracy, spin configurations, and magnetic- and electric-field dependences. The first and second groups mainly come from the $5p_{z}$ and ($5p_{x}$,$5p_{y}$) orbitals, respectively. Their Landau-level splittings are, respectively, induced by the electric field and spin-orbital interactions. The intragroup anti-crossings are only revealed in the former. The unique tinene Landau levels are absent in graphene, silicene and germanene.Comment: 6 figure

Where to Focus: Deep Attention-based Spatially Recurrent Bilinear Networks for Fine-Grained Visual Recognition

Fine-grained visual recognition typically depends on modeling subtle difference from object parts. However, these parts often exhibit dramatic visual variations such as occlusions, viewpoints, and spatial transformations, making it hard to detect. In this paper, we present a novel attention-based model to automatically, selectively and accurately focus on critical object regions with higher importance against appearance variations. Given an image, two different Convolutional Neural Networks (CNNs) are constructed, where the outputs of two CNNs are correlated through bilinear pooling to simultaneously focus on discriminative regions and extract relevant features. To capture spatial distributions among the local regions with visual attention, soft attention based spatial Long-Short Term Memory units (LSTMs) are incorporated to realize spatially recurrent yet visually selective over local input patterns. All the above intuitions equip our network with the following novel model: two-stream CNN layers, bilinear pooling layer, spatial recurrent layer with location attention are jointly trained via an end-to-end fashion to serve as the part detector and feature extractor, whereby relevant features are localized and extracted attentively. We show the significance of our network against two well-known visual recognition tasks: fine-grained image classification and person re-identification.Comment: 8 page

Finding Modes by Probabilistic Hypergraphs Shifting

In this paper, we develop a novel paradigm, namely hypergraph shift, to find robust graph modes by probabilistic voting strategy, which are semantically sound besides the self-cohesiveness requirement in forming graph modes. Unlike the existing techniques to seek graph modes by shifting vertices based on pair-wise edges (i.e, an edge with $2$ ends), our paradigm is based on shifting high-order edges (hyperedges) to deliver graph modes. Specifically, we convert the problem of seeking graph modes as the problem of seeking maximizers of a novel objective function with the aim to generate good graph modes based on sifting edges in hypergraphs. As a result, the generated graph modes based on dense subhypergraphs may more accurately capture the object semantics besides the self-cohesiveness requirement. We also formally prove that our technique is always convergent. Extensive empirical studies on synthetic and real world data sets are conducted on clustering and graph matching. They demonstrate that our techniques significantly outperform the existing techniques.Comment: Fixing some minor issues in PAKDD 201

Configuration- and concentration-dependent electronic properties of hydrogenated graphene

The electronic properties of hydrogenated graphenes are investigated with the first-principles calculations. Geometric structures, energy bands, charge distributions, and density of states (DOS) strongly depend on the different configurations and concentrations of hydrogen adatoms. Among three types of optimized periodical configurations, only in the zigzag systems the band gaps can be remarkably modulated by H-concentrations. There exist middle-gap semiconductors, narrow-gap semiconductors, and gapless systems. The band structures exhibit the rich features, including the destruction or recovery of the Dirac-cone structure, newly formed critical points, weakly dispersive bands, and (C,H)-related partially flat bands. The orbital-projected DOS are evidenced by the low-energy prominent peaks, delta-function-like peaks, discontinuous shoulders, and logarithmically divergent peaks. The DOS and spatial charge distributions clearly indicate that the critical bondings in C-C and C-H is responsible for the diversified properties

Conjugate-Computation Variational Inference : Converting Variational Inference in Non-Conjugate Models to Inferences in Conjugate Models

Variational inference is computationally challenging in models that contain both conjugate and non-conjugate terms. Methods specifically designed for conjugate models, even though computationally efficient, find it difficult to deal with non-conjugate terms. On the other hand, stochastic-gradient methods can handle the non-conjugate terms but they usually ignore the conjugate structure of the model which might result in slow convergence. In this paper, we propose a new algorithm called Conjugate-computation Variational Inference (CVI) which brings the best of the two worlds together -- it uses conjugate computations for the conjugate terms and employs stochastic gradients for the rest. We derive this algorithm by using a stochastic mirror-descent method in the mean-parameter space, and then expressing each gradient step as a variational inference in a conjugate model. We demonstrate our algorithm's applicability to a large class of models and establish its convergence. Our experimental results show that our method converges much faster than the methods that ignore the conjugate structure of the model.Comment: Published in AI-Stats 2017. Fixed some typos. This version contains a short paragraph in the conclusions section which we could not add in the conference version due to space constraint

Anelastic Approximation of the Gross-Pitaevskii equation for General Initial Data

We perform a rigorous analysis of the anelastic approximation for the Gross-Pitaevskii equation with $x$-dependent chemical potential. For general initial data and periodic boundary condition, we show that as \eps\to 0, equivalently the Planck constant tends to zero, the density |\psi^{\eps}|^{2} converges toward the chemical potential $\rho_{0}(x)$ and the velocity field converges to the anelastic system. When the chemical potential is a constant, the anelastic system will reduce to the incompressible Euler equations. The resonant effects the singular limit process and it can be overcome because of oscillation-cancelation

Exact Safety Verification of Interval Hybrid Systems Based on Symbolic-Numeric Computation

In this paper, we address the problem of safety verification of interval hybrid systems in which the coefficients are intervals instead of explicit numbers. A hybrid symbolic-numeric method, based on SOS relaxation and interval arithmetic certification, is proposed to generate exact inequality invariants for safety verification of interval hybrid systems. As an application, an approach is provided to verify safety properties of non-polynomial hybrid systems. Experiments on the benchmark hybrid systems are given to illustrate the efficiency of our method