arXiv subjects

Xin Huang

Publications and source records attributed to Xin Huang.

At least 271 records · Page 15Linked to original sources

arXiv2018

Boost Blockchain Broadcast Propagation with Tree Routing

In recent years, with the rapid development and popularization of BitCoin, the research of blockchain technology has also shown growth. It has gradually become a new generation of distributed, non-centralized and trust-based technology solution. However, the blockchain operation is expensive and transaction is delayed. Take BitCoin as an example. On the one hand, a block is produced every ten minute. On the other hand, once the new block is generated, it takes a certain time to propagate world wide. The slow speed of propagation determines that BitCoin can not use too small block interval time. Ethereum also faces similar problems, so the concept of uncle block was introduced to reduce blockchain forks. This paper introduces a new tree structure based broadcast propagation routing model, providing a novel method to organize network nodes and message propagation mechanism. In oder to avoid the single node failure problem, the tree cluster routing is proposed. The research shows that the tree based routing can accelerate broadcast convergence time and reduce redundant traffic.

cs.DC↗

arXiv2018

PP-DBLP: Modeling and Generating Attributed Public-Private Networks with DBLP

In many online social networks (e.g., Facebook, Google+, Twitter, and Instagram), users prefer to hide her/his partial or all relationships, which makes such private relationships not visible to public users or even friends. This leads to a new graph model called public-private networks, where each user has her/his own perspective of the network including the private connections. Recently, public-private network analysis has attracted significant research interest in the literature. A great deal of important graph computing problems (e.g., shortest paths, centrality, PageRank, and reachability tree) has been studied. However, due to the limited data sources and privacy concerns, proposed approaches are not tested on real-world datasets, but on synthetic datasets by randomly selecting vertices as private ones. Therefore, real-world datasets of public-private networks are essential and urgently needed for such algorithms in the evaluation of efficiency and effectiveness. In this paper, we generate four public-private networks from real-world DBLP records, called PPDBLP. We take published articles as public information and regard ongoing collaborations as the hidden information, which is only known by the authors. Our released datasets of PPDBLP offer the prospects for verifying various kinds of efficient public-private analysis algorithms in a fair way. In addition, motivated by widely existing attributed graphs, we propose an advanced model of attributed public-private graphs where vertices have not only private edges but also private attributes. We also discuss open problems on attributed public-private graphs. Preliminary experimental results on our generated real-world datasets verify the effectiveness and efficiency of public-private models and state-of-the-art algorithms.

cs.DB↗

arXiv2018

Adversarial Attack on Graph Structured Data

Deep learning on graph structures has shown exciting results in various applications. However, few attentions have been paid to the robustness of such models, in contrast to numerous research work for image or text adversarial attack and defense. In this paper, we focus on the adversarial attacks that fool the model by modifying the combinatorial structure of data. We first propose a reinforcement learning based attack method that learns the generalizable attack policy, while only requiring prediction labels from the target classifier. Also, variants of genetic algorithms and gradient methods are presented in the scenario where prediction confidence or gradients are available. We use both synthetic and real-world data to show that, a family of Graph Neural Network models are vulnerable to these attacks, in both graph-level and node-level classification tasks. We also show such attacks can be used to diagnose the learned classifiers.

cs.LG↗

arXiv2018

Deep Cross-media Knowledge Transfer

Cross-media retrieval is a research hotspot in multimedia area, which aims to perform retrieval across different media types such as image and text. The performance of existing methods usually relies on labeled data for model training. However, cross-media data is very labor consuming to collect and label, so how to transfer valuable knowledge in existing data to new data is a key problem towards application. For achieving the goal, this paper proposes deep cross-media knowledge transfer (DCKT) approach, which transfers knowledge from a large-scale cross-media dataset to promote the model training on another small-scale cross-media dataset. The main contributions of DCKT are: (1) Two-level transfer architecture is proposed to jointly minimize the media-level and correlation-level domain discrepancies, which allows two important and complementary aspects of knowledge to be transferred: intra-media semantic and inter-media correlation knowledge. It can enrich the training information and boost the retrieval accuracy. (2) Progressive transfer mechanism is proposed to iteratively select training samples with ascending transfer difficulties, via the metric of cross-media domain consistency with adaptive feedback. It can drive the transfer process to gradually reduce vast cross-media domain discrepancy, so as to enhance the robustness of model training. For verifying the effectiveness of DCKT, we take the largescale dataset XMediaNet as source domain, and 3 widelyused datasets as target domain for cross-media retrieval. Experimental results show that DCKT achieves promising improvement on retrieval accuracy.

cs.MM↗

arXiv2017

Exchange striction driven magnetodielectric effect and potential photovoltaic effect in polar CaOFeS

CaOFeS is a semiconducting oxysulfide with polar layered triangular structure. Here a comprehensive theoretical study has been performed to reveal its physical properties, including magnetism, electronic structure, phase transition, magnetodielectric effect, as well as optical absorption. Our calculations confirm the Ising-like G-type antiferromagnetic ground state driven by the next-nearest neighbor exchanges, which breaks the trigonal symmetry and is responsible for the magnetodielectric effect driven by exchange striction. In addition, a large coefficient of visible light absorption is predicted, which leads to promising photovoltaic effect with the maximum light-to-electricity energy conversion efficiency up to 24.2%.

cond-mat.mtrl-sci↗

arXiv2017

CCL: Cross-modal Correlation Learning with Multi-grained Fusion by Hierarchical Network

Cross-modal retrieval has become a highlighted research topic for retrieval across multimedia data such as image and text. A two-stage learning framework is widely adopted by most existing methods based on Deep Neural Network (DNN): The first learning stage is to generate separate representation for each modality, and the second learning stage is to get the cross-modal common representation. However, the existing methods have three limitations: (1) In the first learning stage, they only model intra-modality correlation, but ignore inter-modality correlation with rich complementary context. (2) In the second learning stage, they only adopt shallow networks with single-loss regularization, but ignore the intrinsic relevance of intra-modality and inter-modality correlation. (3) Only original instances are considered while the complementary fine-grained clues provided by their patches are ignored. For addressing the above problems, this paper proposes a cross-modal correlation learning (CCL) approach with multi-grained fusion by hierarchical network, and the contributions are as follows: (1) In the first learning stage, CCL exploits multi-level association with joint optimization to preserve the complementary context from intra-modality and inter-modality correlation simultaneously. (2) In the second learning stage, a multi-task learning strategy is designed to adaptively balance the intra-modality semantic category constraints and inter-modality pairwise similarity constraints. (3) CCL adopts multi-grained modeling, which fuses the coarse-grained instances and fine-grained patches to make cross-modal correlation more precise. Comparing with 13 state-of-the-art methods on 6 widely-used cross-modal datasets, the experimental results show our CCL approach achieves the best performance.

cs.MM↗

arXiv2017

MHTN: Modal-adversarial Hybrid Transfer Network for Cross-modal Retrieval

Cross-modal retrieval has drawn wide interest for retrieval across different modalities of data. However, existing methods based on DNN face the challenge of insufficient cross-modal training data, which limits the training effectiveness and easily leads to overfitting. Transfer learning is for relieving the problem of insufficient training data, but it mainly focuses on knowledge transfer only from large-scale datasets as single-modal source domain to single-modal target domain. Such large-scale single-modal datasets also contain rich modal-independent semantic knowledge that can be shared across different modalities. Besides, large-scale cross-modal datasets are very labor-consuming to collect and label, so it is significant to fully exploit the knowledge in single-modal datasets for boosting cross-modal retrieval. This paper proposes modal-adversarial hybrid transfer network (MHTN), which to the best of our knowledge is the first work to realize knowledge transfer from single-modal source domain to cross-modal target domain, and learn cross-modal common representation. It is an end-to-end architecture with two subnetworks: (1) Modal-sharing knowledge transfer subnetwork is proposed to jointly transfer knowledge from a large-scale single-modal dataset in source domain to all modalities in target domain with a star network structure, which distills modal-independent supplementary knowledge for promoting cross-modal common representation learning. (2) Modal-adversarial semantic learning subnetwork is proposed to construct an adversarial training mechanism between common representation generator and modality discriminator, making the common representation discriminative for semantics but indiscriminative for modalities to enhance cross-modal semantic consistency during transfer process. Comprehensive experiments on 4 widely-used datasets show its effectiveness and generality.

cs.MM↗

arXiv2017

An Overview of Cross-media Retrieval: Concepts, Methodologies, Benchmarks and Challenges

Multimedia retrieval plays an indispensable role in big data utilization. Past efforts mainly focused on single-media retrieval. However, the requirements of users are highly flexible, such as retrieving the relevant audio clips with one query of image. So challenges stemming from the "media gap", which means that representations of different media types are inconsistent, have attracted increasing attention. Cross-media retrieval is designed for the scenarios where the queries and retrieval results are of different media types. As a relatively new research topic, its concepts, methodologies and benchmarks are still not clear in the literatures. To address these issues, we review more than 100 references, give an overview including the concepts, methodologies, major challenges and open issues, as well as build up the benchmarks including datasets and experimental results. Researchers can directly adopt the benchmarks to promptly evaluate their proposed methods. This will help them to focus on algorithm design, rather than the time-consuming compared methods and results. It is noted that we have constructed a new dataset XMedia, which is the first publicly available dataset with up to five media types (text, image, video, audio and 3D model). We believe this overview will attract more researchers to focus on cross-media retrieval and be helpful to them.

cs.MM↗

arXiv2017

Cross-modal Common Representation Learning by Hybrid Transfer Network

DNN-based cross-modal retrieval is a research hotspot to retrieve across different modalities as image and text, but existing methods often face the challenge of insufficient cross-modal training data. In single-modal scenario, similar problem is usually relieved by transferring knowledge from large-scale auxiliary datasets (as ImageNet). Knowledge from such single-modal datasets is also very useful for cross-modal retrieval, which can provide rich general semantic information that can be shared across different modalities. However, it is challenging to transfer useful knowledge from single-modal (as image) source domain to cross-modal (as image/text) target domain. Knowledge in source domain cannot be directly transferred to both two different modalities in target domain, and the inherent cross-modal correlation contained in target domain provides key hints for cross-modal retrieval which should be preserved during transfer process. This paper proposes Cross-modal Hybrid Transfer Network (CHTN) with two subnetworks: Modal-sharing transfer subnetwork utilizes the modality in both source and target domains as a bridge, for transferring knowledge to both two modalities simultaneously; Layer-sharing correlation subnetwork preserves the inherent cross-modal semantic correlation to further adapt to cross-modal retrieval task. Cross-modal data can be converted to common representation by CHTN for retrieval, and comprehensive experiment on 3 datasets shows its effectiveness.

cs.MM↗

arXiv2017

Cross-media Similarity Metric Learning with Unified Deep Networks

As a highlighting research topic in the multimedia area, cross-media retrieval aims to capture the complex correlations among multiple media types. Learning better shared representation and distance metric for multimedia data is important to boost the cross-media retrieval. Motivated by the strong ability of deep neural network in feature representation and comparison functions learning, we propose the Unified Network for Cross-media Similarity Metric (UNCSM) to associate cross-media shared representation learning with distance metric in a unified framework. First, we design a two-pathway deep network pretrained with contrastive loss, and employ double triplet similarity loss for fine-tuning to learn the shared representation for each media type by modeling the relative semantic similarity. Second, the metric network is designed for effectively calculating the cross-media similarity of the shared representation, by modeling the pairwise similar and dissimilar constraints. Compared to the existing methods which mostly ignore the dissimilar constraints and only use sample distance metric as Euclidean distance separately, our UNCSM approach unifies the representation learning and distance metric to preserve the relative similarity as well as embrace more complex similarity functions for further improving the cross-media retrieval accuracy. The experimental results show that our UNCSM approach outperforms 8 state-of-the-art methods on 4 widely-used cross-media datasets.

cs.MM↗

arXiv2017

Cross-modal Deep Metric Learning with Multi-task Regularization

DNN-based cross-modal retrieval has become a research hotspot, by which users can search results across various modalities like image and text. However, existing methods mainly focus on the pairwise correlation and reconstruction error of labeled data. They ignore the semantically similar and dissimilar constraints between different modalities, and cannot take advantage of unlabeled data. This paper proposes Cross-modal Deep Metric Learning with Multi-task Regularization (CDMLMR), which integrates quadruplet ranking loss and semi-supervised contrastive loss for modeling cross-modal semantic similarity in a unified multi-task learning architecture. The quadruplet ranking loss can model the semantically similar and dissimilar constraints to preserve cross-modal relative similarity ranking information. The semi-supervised contrastive loss is able to maximize the semantic similarity on both labeled and unlabeled data. Compared to the existing methods, CDMLMR exploits not only the similarity ranking information but also unlabeled cross-modal data, and thus boosts cross-modal retrieval accuracy.

cs.LG↗

arXiv2017

Attribute Truss Community Search

Recently, community search over graphs has attracted significant attention and many algorithms have been developed for finding dense subgraphs from large graphs that contain given query nodes. In applications such as analysis of protein protein interaction (PPI) networks, citation graphs, and collaboration networks, nodes tend to have attributes. Unfortunately, previously developed community search algorithms ignore these attributes and result in communities with poor cohesion w.r.t. their node attributes. In this paper, we study the problem of attribute-driven community search, that is, given an undirected graph $G$ where nodes are associated with attributes, and an input query $Q$ consisting of nodes $V_q$ and attributes $W_q$, find the communities containing $V_q$, in which most community members are densely inter-connected and have similar attributes. We formulate our problem of finding attributed truss communities (ATC), as finding all connected and close k-truss subgraphs containing $V_q$, that are locally maximal and have the largest attribute relevance score among such subgraphs. We design a novel attribute relevance score function and establish its desirable properties. The problem is shown to be NP-hard. However, we develop an efficient greedy algorithmic framework, which finds a maximal $k$-truss containing $V_q$, and then iteratively removes the nodes with the least popular attributes and shrinks the graph so as to satisfy community constraints. We also build an elegant index to maintain the known $k$-truss structure and attribute information, and propose efficient query processing algorithms. Extensive experiments on large real-world networks with ground-truth communities shows the efficiency and effectiveness of our proposed methods.

cs.DB↗

arXiv2017

Information-theoretic interpretation of tuning curves for multiple motion directions

We have developed an efficient information-maximization method for computing the optimal shapes of tuning curves of sensory neurons by optimizing the parameters of the underlying feedforward network model. When applied to the problem of population coding of visual motion with multiple directions, our method yields several types of tuning curves with both symmetric and asymmetric shapes that resemble what have been found in the visual cortex. Our result suggests that the diversity or heterogeneity of tuning curve shapes as observed in neurophysiological experiment might actually constitute an optimal population representation of visual motions with multiple components.

cs.IT↗

arXiv2016

Electronic Structure and Stability of the CH3NH3PbBr3 (001) Surface

The energetics and the electronic structure of methylammonium lead bromine (CH3NH3PbBr3) perovskite (001) surfaces are studied based on density functional theory. By examining the surface grand potential, we predict that the CH3NH3Br-terminated (001) surface is energetically more favorable than the PbBr2-terminated (001) surface, under thermodynamic equilibrium conditions of bulk CH3NH3PbBr3. The electronic structure of each of these two different surface terminations retains some of the characteristics of the bulk, while new surface states are found near band edges which may affect the photovoltaic performance in the solar cells based on CH3NH3PbBr3. The calculated electron affinity of CH3NH3PbBr3 reveals a sizable difference for the two surface terminations, indicating a possibility of tuning the band offset between the halide perovskite and adjacent electrode with proper interface engineering.

cond-mat.mtrl-sci↗

arXiv2015

Hexagonal Rare-Earth Manganites as Promising Photovoltaics and Light Polarizers

Ferroelectric materials possess a spontaneous electric polarization and may be utilized in various technological applications ranging from non-volatile memories to solar cells and light polarizers. Recently, hexagonal rare-earth manganites, h-RMnO$_3$ (R is a rare-earth ion) have attracted considerable interest due to their intricate multiferroic properties and improper ferroelectricity characterized by a sizable remnant polarization and high Curie temperature. Here, we demonstrate that these compounds can serve as very efficient photovoltaic materials and, in addition, possess remarkable optical anisotropy properties. Using first-principles methods based on density-functional theory and considering h-TbMnO$_3$ as a representative manganite, we predict a strong light absorption of this material in the solar spectrum range, resulting in the maximum light-to-electricity energy conversion efficiency up to 33%. We also predict an extraordinary optical linear dichroism and linear birefringence properties of h-TbMnO$_3$ in a broad range of optical frequencies. These results uncover the unexplored potential of hexagonal rare-earth manganites to serve as photovoltaics in solar cells and as absorptive and birefringent light polarizers.

cond-mat.mtrl-sci↗

arXiv2015

Topological magnetic phase in LaMnO$_3$ (111) bilayer

Candidates for correlated topological insulators, originated from the spin-orbit coupling as well as Hubbard type correlation, are expected in the ($111$) bilayer of perovskite-structural transition-metal oxides. Based on the first-principles calculation and tight-binding model, the electronic structure of a LaMnO$_3$ ($111$) bilayer sandwiched in LaScO$_3$ barriers has been investigated. For the ideal undistorted perovskite structure, the Fermi energy of LaMnO$_3$ ($111$) bilayer just stays at the Dirac point, rendering a semi-metal (graphene-like) which is also a half-metal (different from graphene nor previous studied LaNiO$_3$ ($111$) bilayer). The Dirac cone can be opened by the spin-orbit coupling, giving rise to nontrivial topological bands corresponding to the (quantized) anomalous Hall effect. For the realistic orthorhombic distorted lattice, the Dirac point moves with increasing Hubbard repulsion (or equivalent Jahn-Teller distortion). Finally, a Mott gap opens, establishing a phase boundary between the Mott insulator and topological magnetic insulator. Our calculation finds that the gap opened by spin-orbit coupling is much smaller in the orthorhombic distorted lattice ($\sim$$1.7$ meV) than the undistorted one ($\sim$$11$ meV). Therefore, to suppress the lattice distortion can be helpful to enhance the robustness of topological phase in perovskite ($111$) bilayers.

cond-mat.mtrl-sci↗

arXiv2015

Approximate Closest Community Search in Networks

Recently, there has been significant interest in the study of the community search problem in social and information networks: given one or more query nodes, find densely connected communities containing the query nodes. However, most existing studies do not address the "free rider" issue, that is, nodes far away from query nodes and irrelevant to them are included in the detected community. Some state-of-the-art models have attempted to address this issue, but not only are their formulated problems NP-hard, they do not admit any approximations without restrictive assumptions, which may not always hold in practice. In this paper, given an undirected graph G and a set of query nodes Q, we study community search using the k-truss based community model. We formulate our problem of finding a closest truss community (CTC), as finding a connected k-truss subgraph with the largest k that contains Q, and has the minimum diameter among such subgraphs. We prove this problem is NP-hard. Furthermore, it is NP-hard to approximate the problem within a factor $(2-\varepsilon)$, for any $\varepsilon >0 $. However, we develop a greedy algorithmic framework, which first finds a CTC containing Q, and then iteratively removes the furthest nodes from Q, from the graph. The method achieves 2-approximation to the optimal solution. To further improve the efficiency, we make use of a compact truss index and develop efficient algorithms for k-truss identification and maintenance as nodes get eliminated. In addition, using bulk deletion optimization and local exploration strategies, we propose two more efficient algorithms. One of them trades some approximation quality for efficiency while the other is a very efficient heuristic. Extensive experiments on 6 real-world networks show the effectiveness and efficiency of our community model and search algorithms.

cs.SI↗

arXiv2015

Structure of the photo-catalytically active surface of SrTiO3

A major goal of energy research is to use visible light to cleave water directly, without an applied voltage, into hydrogen and oxygen. Since the initial reports of the ultraviolet (UV) activity of TiO2 and SrTiO3 in the 1970s, researchers have pursued a fundamental understanding of the mechanistic and molecular-level phenomena involved in photo-catalysis. Although it requires UV light, after four decades SrTiO3 is still the gold standard for splitting water. It is chemically stable and catalyzes both the hydrogen and the oxygen reactions without applied bias. While ultrahigh vacuum (UHV) surface science techniques have provided useful insights, we still know relatively little about the structure of electrodes in contact with electrolytes under operating conditions. Here, we report the surface structure evolution of a SrTiO3 electrode during water splitting, before and after training with a positive bias. Operando high-energy X-ray reflectivity measurements demonstrate that training the electrode irreversibly reorders the surface. Scanning electrochemical microscopy (SECM) at open circuit correlates this training with a tripling of the activity toward photo-induced water splitting. A novel first-principles joint density-functional theory (JDFT) simulation constrained to the X-ray data via a generalized penalty function identifies an anatase-like structure for the more active, trained surface.

physics.chem-ph↗