arXiv subjects

Bo Zhang

Publications and source records attributed to Bo Zhang.

At least 739 records · Page 41Linked to original sources

arXiv2019

AuthPDB: Query Authentication for Outsourced Probabilistic Databases

Spurred by developments such as cloud computing, there are increasing efforts for outsourcing of data management. A company (data owner) who lacks expertise and comptational resources can outsource his data to a third-party service provider (server), who provides storage and query evaluation on the outsourced data as the services. One of the security concerns of the outsourcing paradigm is the integrity of the returned query results on the outsourced data. In this paper, we consider the outsourcing of probabilistic databases, on which query evaluation is of high complexity. A dishonest server may return cheap (and incorrect) query answers, hoping that the client who has weak computational power cannot catch the incorrect results. To address this issue, we design efficient integrity verification methods for both all-answer and top-k query evaluation on outsourced probabilistic databases. Our empirical results demonstrate the effectiveness and efficiency of our verification methods.

cs.CR↗

arXiv2018

COSINE: Compressive Network Embedding on Large-scale Information Networks

There is recently a surge in approaches that learn low-dimensional embeddings of nodes in networks. As there are many large-scale real-world networks, it's inefficient for existing approaches to store amounts of parameters in memory and update them edge after edge. With the knowledge that nodes having similar neighborhood will be close to each other in embedding space, we propose COSINE (COmpresSIve NE) algorithm which reduces the memory footprint and accelerates the training process by parameters sharing among similar nodes. COSINE applies graph partitioning algorithms to networks and builds parameter sharing dependency of nodes based on the result of partitioning. With parameters sharing among similar nodes, COSINE injects prior knowledge about higher structural information into training process which makes network embedding more efficient and effective. COSINE can be applied to any embedding lookup method and learn high-quality embeddings with limited memory and shorter training time. We conduct experiments of multi-label classification and link prediction, where baselines and our model have the same memory usage. Experimental results show that COSINE gives baselines up to 23% increase on classification and up to 25% increase on link prediction. Moreover, time of all representation learning methods using COSINE decreases from 30% to 70%.

cs.SI↗

arXiv2018

The Entropy of Artificial Intelligence and a Case Study of AlphaZero from Shannon's Perspective

The recently released AlphaZero algorithm achieves superhuman performance in the games of chess, shogi and Go, which raises two open questions. Firstly, as there is a finite number of possibilities in the game, is there a quantifiable intelligence measurement for evaluating intelligent systems, e.g. AlphaZero? Secondly, AlphaZero introduces sophisticated reinforcement learning and self-play to efficiently encode the possible states, is there a simple information-theoretic model to represent the learning process and offer more insights in fostering strong AI systems? This paper explores the above two questions by proposing a simple variance of Shannon's communication model, the concept of intelligence entropy and the Unified Intelligence-Communication Model is proposed, which provide an information-theoretic metric for investigating the intelligence level and also provide an bound for intelligent agents in the form of Shannon's capacity, namely, the intelligence capacity. This paper then applies the concept and model to AlphaZero as a case study and explains the learning process of intelligent agent as turbo-like iterative decoding, so that the learning performance of AlphaZero may be quantitatively evaluated. Finally, conclusions are provided along with theoretical and practical remarks.

cs.AI↗

arXiv2018

Graphical Generative Adversarial Networks

We propose Graphical Generative Adversarial Networks (Graphical-GAN) to model structured data. Graphical-GAN conjoins the power of Bayesian networks on compactly representing the dependency structures among random variables and that of generative adversarial networks on learning expressive dependency functions. We introduce a structured recognition model to infer the posterior distribution of latent variables given observations. We generalize the Expectation Propagation (EP) algorithm to learn the generative model and recognition model jointly. Finally, we present two important instances of Graphical-GAN, i.e. Gaussian Mixture GAN (GMGAN) and State Space GAN (SSGAN), which can successfully learn the discrete and temporal structures on visual datasets, respectively.

cs.LG↗

arXiv2018

An Unified Intelligence-Communication Model for Multi-Agent System Part-I: Overview

Motivated by Shannon's model and recent rehabilitation of self-supervised artificial intelligence having a "World Model", this paper propose an unified intelligence-communication (UIC) model for describing a single agent and any multi-agent system. Firstly, the environment is modelled as the generic communication channel between agents. Secondly, the UIC model adopts a learning-agent model for unifying several well-adopted agent architecture, e.g. rule-based agent model in complex adaptive systems, layered model for describing human-level intelligence, world-model based agent model. The model may also provide an unified approach to investigate a multi-agent system (MAS) having multiple action-perception modalities, e.g. explicitly information transfer and implicit information transfer. This treatise would be divided into three parts, and this first part provides an overview of the UIC model without introducing cumbersome mathematical analysis and optimizations. In the second part of this treatise, case studies with quantitative analysis driven by the UIC model would be provided, exemplifying the adoption of the UIC model in multi-agent system. Specifically, two representative cases would be studied, namely the analysis of a natural multi-agent system, as well as the co-design of communication, perception and action in an artificial multi-agent system. In the third part of this treatise, the paper provides further insights and future research directions motivated by the UIC model, such as unification of single intelligence and collective intelligence, a possible explanation of intelligence emergence and a dual model for agent-environment intelligence hypothesis. Notes: This paper is a Previewed Version, the extended full-version would be released after being accepted.

cs.AI↗

arXiv2018

Semi-crowdsourced Clustering with Deep Generative Models

We consider the semi-supervised clustering problem where crowdsourcing provides noisy information about the pairwise comparisons on a small subset of data, i.e., whether a sample pair is in the same cluster. We propose a new approach that includes a deep generative model (DGM) to characterize low-level features of the data, and a statistical relational model for noisy pairwise annotations on its subset. The two parts share the latent variables. To make the model automatically trade-off between its complexity and fitting data, we also develop its fully Bayesian variant. The challenge of inference is addressed by fast (natural-gradient) stochastic variational inference algorithms, where we effectively combine variational message passing for the relational part and amortized learning of the DGM under a unified framework. Empirical results on synthetic and real-world datasets show that our model outperforms previous crowdsourced clustering methods.

stat.ML↗

arXiv2018

Determining Carrier-Envelope Phase of Relativistic Laser Pulses via Electron Momentum Distribution

The impacts of the carrier-envelope phase (CEP) of a long relativistic tightly-focused laser pulse on the dynamics of a counter-propagating electron beam have been investigated in the, so-called, electron reflection regime, requiring the Lorentz factor of the electron $γ$ to be approximately two orders of magnitudes lower than the dimensionless laser field parameter $ξ$. The electrons are reflected at the rising edge of the laser pulse due to the ponderomotive force of the focused laser beam, and an asymmetric electron angular distribution emerges along the laser polarization direction, which sensitively depends on the CEP of the driving laser pulse for weak radiative stochastic effects. The CEP siganatures are observable at laser intensities of the order or larger than $10^{19}$ W/cm$^2$ and the pulse duration up to 10 cycles. The CEP detection resolution is proportional to the electron beam density and can achieve approximately $0.1^{\circ}$ at an electron density of about $10^{15}$ cm$^{-3}$. The method is applicable for currently available ultraintense laser facilities with the laser peak power from tens of terawatt to multi-petawatt region.

physics.optics↗

arXiv2018

V1224 Cas - an EL CVn-type Eclipsing Binary Consisting of a Helium White Dwarf Precursor and a Delta Scuti Pulsator

We report the discovery of a new eclipsing EL CVn-type binary, consisting of a $δ$ Sct-type pulsator and a thermally bloated low-mass pre-He white dwarf (WD). Spectroscopy and time-series $BV$ photometry of V1224 Cas were carried out. The spectroscopy reveals a spectral type of A3 for the star. Light-curve modelling indicate that V1224 Cas is a short-period detached system containing a possible low-mass WD with an effective temperature of about 9516 K. Based on the effective temperature and the surface gravity of the A-type primary star from the spectroscopic results, the absolute parameters of the components were estimated as: $M_{P}$=2.16$\pm$0.22$M_{\sun}$, $R_{P}$=3.54$\pm$0.12$R_{\sun}$, $L_{P}$=55.9$\pm$6.9$L_{\sun}$, $M_{S}$=0.19$\pm$0.02$M_{\sun}$, $R_{S}$=0.97$\pm$0.04$R_{\sun}$, and $L_{S}$=6.9$\pm$0.6$L_{\sun}$. We therefore introduce V1224 Cas as a new EL CVn-type binary candidate. The light curves in both filters all show multi-periodic pulsations, superimposed on binary effects. We performed a preliminary frequency analysis of the light residuals after removing the synthetic eclipsing curve from the original observational data. The results suggest that the rapid light variations among the light curves could be attributed to the $δ$ Sct-type primary component. We therefore conclude that V1224 Cas is very likely a WD+$δ$ Sct binary.

astro-ph.SR↗

arXiv2018

Uniqueness and direct imaging method for inverse scattering by locally rough surfaces with phaseless near-field data

This paper is concerned with inverse scattering of plane waves by a locally perturbed infinite plane (which is called a locally rough surface) with the modulus of the total-field data (also called the phaseless near-field data) at a fixed frequency in two dimensions. We consider the case where a Dirichlet boundary condition is imposed on the locally rough surface. This problem models inverse scattering of plane acoustic waves by a one-dimensional sound-soft, locally rough surface; it also models inverse scattering of plane electromagnetic waves by a locally perturbed, perfectly reflecting, infinite plane in the TE polarization case. We prove that the locally rough surface is uniquely determined by the phaseless near-field data generated by a countably infinite number of plane waves and measured on an open domain above the locally rough surface. Further, a direct imaging method is proposed to reconstruct the locally rough surface from the phaseless near-field data generated by plane waves and measured on the upper part of the circle with a sufficiently large radius. Theoretical analysis of the imaging algorithm is derived by making use of properties of the scattering solution and results from the theory of oscillatory integrals (especially the method of stationary phase). Moreover, as a by-product of the theoretical analysis, a similar direct imaging method with full far-field data is also proposed to reconstruct the locally rough surface. Finally, numerical experiments are carried out to demonstrate that the imaging algorithm with phaseless near-field data and full far-field data are fast, accurate and very robust with respect to noise in the data.

math.NA↗

arXiv2018

Antenna Array Based Positional Modulation with a Two-Ray Multi-Path Model

Traditional directional modulation (DM) designs are based on the assumption that there is no multi-path effect between transmitters and receivers. One problem with these designs is that the resultant systems will be vulnerable to eavesdroppers which are aligned with or very close to the desired directions, as the received modulation pattern at these positions is similar to the given one. To solve the problem, a two-ray multi-path model is studied for positional modulation and the coefficients design problem for a given array geometry and a location-optimised antenna array is solved, where the multi-path effect is exploited to generate a given modulation pattern at desired positions, with scrambled values at positions around them.

eess.SP↗

arXiv2018

Integrity Authentication for SQL Query Evaluation on Outsourced Databases: A Survey

Spurred by the development of cloud computing, there has been considerable recent interest in the Database-as-a-Service (DaaS) paradigm. Users lacking in expertise or computational resources can outsource their data and database management needs to a third-party service provider. Outsourcing, however, raises an important issue of result integrity: how can the client verify with lightweight overhead that the query results returned by the service provider are correct (i.e., the same as the results of query execution locally)? This survey focuses on categorizing and reviewing the progress on the current approaches for result integrity of SQL query evaluation in the DaaS model. The survey also includes some potential future research directions for result integrity verification of the outsourced computations.

cs.CR↗

arXiv2018

Constraints on a Spin-Dependent Exotic Interaction between Electrons with Single Electron Spin Quantum Sensors

A new laboratory bound on the axial-vector mediated interaction between electron spins at micrometer scale is established with single nitrogen-vacancy (NV) centers in diamond. A single crystal of $p$-terphenyl doped pentacene-$d_{14}$ under laser pumping provides the source of polarized electron spins. Based on the measurement of polarization signal via nitrogen-vacancy centers, we set a constraint for the exotic electron-electron coupling $g_A^eg_A^e$, within the force range from 10 to 900 $μ$m. The obtained upper bound of the coupling at 500 $μ$m is $|g_A^eg_A^e / 4π\hbar c |\leq 1.8\times 10^{-19} $, which is one order of magnitude more stringent than a previous experiment. Our result shows that the NV center can be a promising platform for searching for new particles predicted by theories beyond the standard model.

hep-ex↗

arXiv2018

Target reconstruction with a reference point scatterer using phaseless far field patterns

An important property of the phaseless far field patterns with incident plane waves is the translation invariance. Thus it is impossible to reconstruct the location of the underlying scatterers. By adding a reference point scatterer into the model, we design a novel direct sampling method using the phaseless data directly. The reference point technique not only overcomes the translation invariance, but also brings a practical phase retrieval algorithm. Based on this, we propose a hybrid method combining the novel phase retrieval algorithm and the classical direct sampling methods. Numerical examples in two dimensions are presented to demonstrate their effectiveness and robustness.

math.AP↗

arXiv2018

Phaseless inverse source scattering problem: phase retrieval, uniqueness and direct sampling methods

Similar to the obstacle or medium scattering problems, an important property of the phaseless far field patterns for source scattering problems is the translation invariance. Thus it is impossible to reconstruct the location of the underlying sources. Furthermore, the phaseless far field pattern is also invariant if the source is multiplied by any complex number with modulus one. Therefore, the source can not be uniquely determined, even the multifrequency phaseless far field patterns are considered. By adding a reference point source into the model, we propose a simple and stable phase retrieval method and establish several uniqueness results with phaseless far field data. We proceed to introduce a novel direct sampling method for shape and location reconstruction of the source by using broadband sparse phaseless data directly. We also propose a combination method with the novel phase retrieval algorithm and the classical direct sampling methods with phased data. Numerical examples in two dimensions are also presented to demonstrate their feasibility and effectiveness.

math.AP↗

arXiv2018

Deep Structured Generative Models

Deep generative models have shown promising results in generating realistic images, but it is still non-trivial to generate images with complicated structures. The main reason is that most of the current generative models fail to explore the structures in the images including spatial layout and semantic relations between objects. To address this issue, we propose a novel deep structured generative model which boosts generative adversarial networks (GANs) with the aid of structure information. In particular, the layout or structure of the scene is encoded by a stochastic and-or graph (sAOG), in which the terminal nodes represent single objects and edges represent relations between objects. With the sAOG appropriately harnessed, our model can successfully capture the intrinsic structure in the scenes and generate images of complicated scenes accordingly. Furthermore, a detection network is introduced to infer scene structures from a image. Experimental results demonstrate the effectiveness of our proposed method on both modeling the intrinsic structures, and generating realistic images.

cs.LG↗

arXiv2018

Collaborative Filtering with User-Item Co-Autoregressive Models

Deep neural networks have shown promise in collaborative filtering (CF). However, existing neural approaches are either user-based or item-based, which cannot leverage all the underlying information explicitly. We propose CF-UIcA, a neural co-autoregressive model for CF tasks, which exploits the structural correlation in the domains of both users and items. The co-autoregression allows extra desired properties to be incorporated for different tasks. Furthermore, we develop an efficient stochastic learning algorithm to handle large scale datasets. We evaluate CF-UIcA on two popular benchmarks: MovieLens 1M and Netflix, and achieve state-of-the-art performance in both rating prediction and top-N recommendation tasks, which demonstrates the effectiveness of CF-UIcA.

cs.LG↗

arXiv2018

A Unified Framework for Community Detection and Network Representation Learning

Network representation learning (NRL) aims to learn low-dimensional vectors for vertices in a network. Most existing NRL methods focus on learning representations from local context of vertices (such as their neighbors). Nevertheless, vertices in many complex networks also exhibit significant global patterns widely known as communities. It's intuitive that vertices in the same community tend to connect densely and share common attributes. These patterns are expected to improve NRL and benefit relevant evaluation tasks, such as link prediction and vertex classification. Inspired by the analogy between network representation learning and text modeling, we propose a unified NRL framework by introducing community information of vertices, named as Community-enhanced Network Representation Learning (CNRL). CNRL simultaneously detects community distribution of each vertex and learns embeddings of both vertices and communities. Moreover, the proposed community enhancement mechanism can be applied to various existing NRL models. In experiments, we evaluate our model on vertex classification, link prediction, and community detection using several real-world datasets. The results demonstrate that CNRL significantly and consistently outperforms other state-of-the-art methods while verifying our assumptions on the correlations between vertices and communities.

cs.SI↗

arXiv2018

Uniqueness in inverse scattering problems with phaseless far-field data at a fixed frequency. II

This paper is concerned with uniqueness in inverse acoustic scattering with phaseless far-field data at a fixed frequency. In our previous work ({\em SIAM J. Appl. Math. \bf78} (2018), 1737-1753), by utilizing spectral properties of the far-field operator we proved for the first time that the obstacle and the index of refraction of an inhomogeneous medium can be uniquely determined by the phaseless far-field patterns generated by infinitely many sets of superpositions of two plane waves with different directions at a fixed frequency under the a priori assumption that the obstacle is known to be a sound-soft or non-absorbing impedance obstacle and the index of refraction $n$ of the inhomogeneous medium is real-valued and satisfies that either $n-1\ge c_1$ or $n-1\le-c_1$ in the support of $n-1$ for some positive constant $c_1$. In this paper, we remove the a priori assumption on the obstacle and the index of refraction of the inhomogeneous medium by adding a reference ball to the scattering system together with a simpler method of using Rellich's lemma and Green's representation formula for the scattering solutions. Further, our new method is also used to prove uniqueness in determining a locally rough surface from the phaseless far-field patterns corresponding to infinitely many sets of superpositions of two plane waves with different directions as the incident fields at a fixed frequency.

math.AP↗