Learning Image-Specific Attributes by Hyperbolic Neighborhood Graph Propagation

As a kind of semantic representation of visual object descriptions, attributes are widely used in various computer vision tasks. In most of existing attribute-based research, class-specific attributes (CSA), which are class-level annotations, are usually adopted due to its low annotation cost for each class instead of each individual image. However, class-specific attributes are usually noisy because of annotation errors and diversity of individual images. Therefore, it is desirable to obtain image-specific attributes (ISA), which are image-level annotations, from the original class-specific attributes. In this paper, we propose to learn image-specific attributes by graph-based attribute propagation. Considering the intrinsic property of hyperbolic geometry that its distance expands exponentially, hyperbolic neighborhood graph (HNG) is constructed to characterize the relationship between samples. Based on HNG, we define neighborhood consistency for each sample to identify inconsistent samples. Subsequently, inconsistent samples are refined based on their neighbors in HNG. Extensive experiments on five benchmark datasets demonstrate the significant superiority of the learned image-specific attributes over the original class-specific attributes in the zero-shot object classification task.Comment: Accepted for IJCAI 201

Positivity and vanishing theorems for ample vector bundles

In this paper, we study the Nakano-positivity and dual-Nakano-positivity of certain adjoint vector bundles associated to ample vector bundles. As applications, we get new vanishing theorems about ample vector bundles. For example, we prove that if $E$ is an ample vector bundle over a compact K\"ahler manifold $X$, S^kE\ts \det E is both Nakano-positive and dual-Nakano-positive for any $k\geq 0$. Moreover, H^{n,q}(X,S^kE\ts \det E)=H^{q,n}(X,S^kE\ts \det E)=0 for any $q\geq 1$. In particular, if $(E,h)$ is a Griffiths-positive vector bundle, the naturally induced Hermitian vector bundle (S^kE\ts \det E, S^kh\ts \det h) is both Nakano-positive and dual-Nakano-positive for any $k\geq 0$.Comment: 27 page

Crystal Field Effect Induced Topological Crystalline Insulators In Monolayer IV-VI Semiconductors

Two-dimensional (2D) topological crystalline insulators (TCIs) were recently predicted in thin films of the SnTe class of IV-VI semiconductors, which can host metallic edge states protected by mirror symmetry. As thickness decreases, quantum confinement effect will increase and surpass the inverted gap below a critical thickness, turning TCIs into normal insulators. Surprisingly, based on first-principles calculations, here we demonstrate that (001) monolayers of rocksalt IV-VI semiconductors XY (X=Ge, Sn, Pb and Y= S, Se, Te) are 2D TCIs with the fundamental band gap as large as 260 meV in monolayer PbTe, providing a materials platform for realizing two-dimensional Dirac fermion systems with tunable band gap. This unexpected nontrivial topological phase stems from the strong {\it crystal field effect} in the monolayer, which lifts the degeneracy between $p_{x,y}$ and $p_z$ orbitals and leads to band inversion between cation $p_z$ and anion $p_{x,y}$ orbitals. This crystal field effect induced topological phase offers a new strategy to find and design other atomically thin 2D topological materials.Comment: submitted in Jan. 2015 and published in Nano Let

Fast Online Clustering with Randomized Skeleton Sets

We present a new fast online clustering algorithm that reliably recovers arbitrary-shaped data clusters in high throughout data streams. Unlike the existing state-of-the-art online clustering methods based on k-means or k-medoid, it does not make any restrictive generative assumptions. In addition, in contrast to existing nonparametric clustering techniques such as DBScan or DenStream, it gives provable theoretical guarantees. To achieve fast clustering, we propose to represent each cluster by a skeleton set which is updated continuously as new data is seen. A skeleton set consists of weighted samples from the data where weights encode local densities. The size of each skeleton set is adapted according to the cluster geometry. The proposed technique automatically detects the number of clusters and is robust to outliers. The algorithm works for the infinite data stream where more than one pass over the data is not feasible. We provide theoretical guarantees on the quality of the clustering and also demonstrate its advantage over the existing state-of-the-art on several datasets

Shared quantum control via sharing operation on remote single qutrit

Two qubit operation sharing schemes [J. Phys. B 44 (2011) 165508] are generalized to qutrit ones. Operations to be shared are classified into three different classes in terms of different probabilities (i.e, 1/3, 2/3 and 1). For the latter two classes, ten and three restricted sets of operations are found out, respectively. Moreover, the two generalized schemes are amply compared from four aspects, namely, quantum and classical resource consumption, necessaryoperation complexity, success probability and efficiency. It is found that the second scheme is overall more optimal than the first one as far as three restricted sets of operations are concerned

Canonical Metrics on the Moduli Space of Riemann Surfaces I

We prove the equivalences of several classical complete metrics on the Teichm\"uller and the moduli spaces of Riemann surfaces. We use as bridge two new K\"ahler metrics, the Ricci metric and the perturbed Ricci metric and prove that the perturbed Ricci metric is a complete K\"ahler metric with bounded negative holomorphic sectional curvature and bounded bisectional and Ricci curvature. As consequences we prove that these two new metrics are equivalent to several famous classical metrics, which inlcude the Teichm\"uller metric, therefore the Kabayashi metric, the K\"ahler-Einstein metric and the McMullen metric. This also solves a conjecture of Yau in the early 80s.Comment: 42 page

Canonical Metrics on the Moduli Space of Riemann Surfaces II

In this paper we continue our study on the canonical metrics on the Teichm\"uller and the moduli space of Riemman surfaces. We first prove the equivalence of the Bergman metric and the Carath\'eodory metric to the K\"ahler-Einstein metric, solving another old conjecture of Yau. We then prove that the Ricci curvature of the perturbed Ricci metric has negative upper and lower bounds, and it also has bounded geometry. Then we study in detail the boundary behaviors of the K\"ahler-Einstein metric and prove that it has bounded geometry, and all of the covariant derivatives of its curvature are uniformly bounded on the Teichm\"uller space. As an application of our detailed understanding of these metrics, we prove that the logarithmic cotangent bundle of the moduli space is stable in the sense of Mumford.Comment: 40 page

Complementary Attributes: A New Clue to Zero-Shot Learning

Zero-shot learning (ZSL) aims to recognize unseen objects using disjoint seen objects via sharing attributes. The generalization performance of ZSL is governed by the attributes, which transfer semantic information from seen classes to unseen classes. To take full advantage of the knowledge transferred by attributes, in this paper, we introduce the notion of complementary attributes (CA), as a supplement to the original attributes, to enhance the semantic representation ability. Theoretical analyses demonstrate that complementary attributes can improve the PAC-style generalization bound of original ZSL model. Since the proposed CA focuses on enhancing the semantic representation, CA can be easily applied to any existing attribute-based ZSL methods, including the label-embedding strategy based ZSL (LEZSL) and the probability-prediction strategy based ZSL (PPZSL). In PPZSL, there is a strong assumption that all the attributes are independent of each other, which is arguably unrealistic in practice. To solve this problem, a novel rank aggregation framework is proposed to circumvent the assumption. Extensive experiments on five ZSL benchmark datasets and the large-scale ImageNet dataset demonstrate that the proposed complementary attributes and rank aggregation can significantly and robustly improve existing ZSL methods and achieve the state-of-the-art performance.Comment: Accepted by IEEE TRANSACTIONS ON CYBERNETIC

Cumulants of Net-Proton, Net-Kaon and Net-Charge Multiplicity Distributions in Au+Au Collisions at RHIC BES Energies from UrQMD Model

Fluctuations of conserved quantities are sensitive observables to probe the signature of QCD phase transition and critical point in heavy-ion collisions. With the UrQMD model, we have studied the centrality and energy dependence of various order cumulants and cumulant ratios (up to fourth order) of net-proton,net-charge and net-kaon multiplicity distributions in Au+Au collisions at $\sqrt{s_{NN}}$= 7.7, 11.5, 19.6, 27, 39, 62.4, 200 GeV. The model results show that the production mechanism of the particles and anti-particles have significant impacts on the cumulants of net-particles multiplicity distributions and show strong energy dependence. We also made comparisons between model calculations and experimental data measured in the first phase of the beam energy scan (BES) program by the STAR experiment at RHIC. The comparisons indicate that the baryon conservation effect strongly suppress the cumulants of net-proton distributions at low energies and the non-monotonic energy dependence for the net-proton {\KV} at the most central Au+Au collisions measured by the STAR experiment can not be described by the UrQMD model. Since there has no physics of QCD phase transition and QCD critical point implemented in the UrQMD, the model results provide us baselines and qualitative estimates about the non-critical background contributions to the fluctuations observables in heavy-ion collisions.Comment: 9 pages, 10 figure

Human hexokinase: multiple mechanisms of G6P inhibition

Human hexokinase I catalyzes the first step in glycolysis, playing a key role in the energy metabolism of brain tissue. The chapters included in this thesis focus on structure-function relationships of hexokinase I. Glucose 6-phosphate (G6P), the reaction product, binds to hexokinase I with apparent negative cooperativity and inhibits catalysis (Chapter II). Both N- and C-terminal binding sites for G6P are functional, yet only one G6P molecule binds to hexokinase I.;A series of site-directed mutations were constructed in the base- and ribose-binding pocket of ATP in C-terminal half of hexokinase I G6P inhibition completely (Chapter III). The same mutations in the context of a truncated form of hexokinase I, which contains only the C-terminal half of the enzyme (mini-hexokinase), do not affect G6P inhibition. Obviously, the effects of the same mutations in full-length and mini-hexokinase I are different. On the basis of these results and structural information, we propose a model for allosteric G6P inhibition of hexokinase I. G6P binds to N-terminal half that reaches the active site of the C-terminal half. Furthermore, the results here suggest that the base- and ribose-binding pocket is the target of allosteric inhibition.;Chapter IV investigates the conformation of hexokinase I under different ligation states. The interface mutant engineered for the investigation is proved to be monomeric by small angel x-ray scattering and x-ray crystallography. The monomeric interface mutant and wild-type hexokinase I have essentially identical kinetic properties, thus evidently dimerization plays no role in hexokinase I function under conditions of in vitro. Small angle x-ray scattering data are consistent with the retention of a rod-like conformation under different ligation states, suggesting only subtle conformational changes in response to different ligands.;Chapter V reveals the ADP binding site in the C-terminal half of hexokinase I. Also, it suggests the location of allosteric interface between the N- and C-halves. Combining the structural information and kinetic results, the authors propose molecular mechanism of allosteric inhibition