State Key Laboratory of Computer Science

Beijing, China

State Key Laboratory of Computer Science

Beijing, China
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Qiu Z.,Peking University | Qiu Z.,State Key Laboratory of Computer Science | Hong A.,Peking University | Liu Y.,Peking University
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

Interface types in OO languages support polymorphism, abstraction and information hiding by separating interfaces from their implementations. The separation enhances modularity of programs, however, it causes also challenges to the formal verification. Here we present a study on interface types, and develop a specification and verification theory based on our former veriJ framework. We support multi-specifications for classes inherited from interfaces and the superclass, and keep the verification modularly without re-touching the verified code. The concepts developed in veriJ, namely the abstract specification and specification predicate, play important roles in this extension, and thus are proved widely useful and very natural in the formal proofs of OO programs. © 2012 Springer-Verlag.

Feng Y.,University of Technology, Sydney | Zhang L.,State Key Laboratory of Computer Science | Zhang L.,University of Chinese Academy of Sciences
Journal of Computer and System Sciences | Year: 2015

The model checking problem of continuous-time Markov chains with respect to continuous-time stochastic logic was introduced and shown to be decidable by Aziz et al. in 1996. Unfortunately, their proof is only constructive, but highly unpractical. Later in 2000, an efficient approximate algorithm was proposed by Baier et al. for a sublogic with binary until. In this paper, we apply transcendental number theory and classical linear algebra to bridge the gap between the precise but unpractical algorithm, and the imprecise but efficient approximate algorithm. We prove that the approximate algorithm in can be used as an off-the-shell tool for a precise model checking algorithm for binary until formulas. Further, we discuss extensions of our results to nested until and continuous-time Markov decision processes. © 2017 Elsevier Inc.

Chen P.,Jilin University | Wang L.,Jilin University | Wang L.,State Key Laboratory of Computer Science
Journal of Computational Information Systems | Year: 2015

As a new research direction in the field of database security, the technology of multilevel secure database is advancing by leaps and bounds. There are so many great multilevel secure relational models such as Bell-Lapadula model, SeaView model, Jajodia-Sandhu model and Smith-Winslett model in the last 40 years, which have solved or mitigated a lot of problems in this field with their own ways. But each of them still has one or several problems such as covert channels, semantic ambiguity, proliferation of tuples and poor expressiveness for complex situations. In this paper, we attempt to build a new model named TL model which could resolve as many problems as possible independently and retain the simpleness and expressiveness at the same time. We will clarify the TL model and present its simplicity, power and robustness. ©, 2015, Binary Information Press. All right reserved.

Wang L.,Jilin University | Wang L.,State Key Laboratory of Computer Science
BioData Mining | Year: 2015

Background: Cancer is the second leading cause of death around the world after cardiovascular diseases. Over the past decades, various data mining studies have tried to predict the outcome of cancer. However, only a few reports describe the causal relationships among clinical variables or attributes, which may provide theoretical guidance for cancer diagnosis and therapy. Different restricted Bayesian classifiers have been used to discover information from numerous domains. This research work designed a novel Bayesian learning strategy to predict cause-specific death classes and proposed a graphical structure of key attributes to clarify the implicit relationships implicated in the data set. Results: The working mechanisms of 3 classical restricted Bayesian classifiers, namely, NB, TAN and KDB, were analysed and summarised. To retain the properties of global optimisation and high-order dependency representation, the proposed learning algorithm, i.e., flexible K-dependence Bayesian network (FKBN), applies the greedy search of conditional mutual information space to identify the globally optimal ordering of the attributes and to allow the classifiers to be constructed at arbitrary points (values of K) along the attribute dependence spectrum. This method represents the relationships between different attributes by using a directed acyclic graph (DAG) model. A total of 12 data sets were selected from the SEER database and KRBM repository by 10-fold cross-validation for evaluation purposes. The findings revealed that the FKBN model outperformed NB, TAN and KDB. Conclusions: A Bayesian classifier can graphically describe the conditional dependency among attributes. The proposed algorithm offers a trade-off between probability estimation and network structure complexity. The direct and indirect relationships between the predictive attributes and class variable should be considered simultaneously to achieve global optimisation and high-order dependency representation. By analysing the DAG inferred from the breast cancer data set of the SEER database we divided the attributes into two subgroups, namely, key attributes that should be considered first for cancer diagnosis and those that are independent of each other but are closely related to key attributes. The statistical analysis results clarify some of the causal relationships implicated in the DAG. © 2015 Wang; licensee BioMed Central.

Sun X.,Microsoft | Xie G.,State Key Laboratory of Computer Science | Dong Y.,Microsoft | Lin S.,Microsoft | And 4 more authors.
ACM Transactions on Graphics | Year: 2012

We introduce a vector representation called diffusion curve textures for mapping diffusion curve images (DCI) onto arbitrary surfaces. In contrast to the original implicit representation of DCIs [Orzan et al. 2008], where determining a single texture value requires iterative computation of the entire DCI via the Poisson equation, diffusion curve textures provide an explicit representation from which the texture value at any point can be solved directly, while preserving the compactness and resolution independence of diffusion curves. This is achieved through a formulation of the DCI diffusion process in terms of Green's functions. This formulation furthermore allows the texture value of any rectangular region (e.g. pixel area) to be solved in closed form, which facilitates anti-aliasing. We develop a GPU algorithm that renders anti-aliased diffusion curve textures in real time, and demonstrate the effectiveness of this method through high quality renderings with detailed control curves and color variations. © 2012 ACM 0730-0301/2012/08-ART74.

Sun X.,Microsoft | Zhou K.,Zhejiang University | Guo J.,Nanjing University | Xie G.,State Key Laboratory of Computer Science | And 3 more authors.
ACM Transactions on Graphics | Year: 2013

Line segment sampling has recently been adopted in many rendering algorithms for better handling of a wide range of effects such as motion blur, defocus blur and scattering media. A question naturally raised is how to generate line segment samples with good properties that can effectively reduce variance and aliasing artifacts observed in the rendering results. This paper studies this problem and presents a frequency analysis of line segment sampling. The analysis shows that the frequency content of a line segment sample is equivalent to the weighted frequency content of a point sample. The weight introduces anisotropy that smoothly changes among point samples, line segment samples and line samples according to the lengths of the samples. Line segment sampling thus makes it possible to achieve a balance between noise (point sampling) and aliasing (line sampling) under the same sampling rate. Based on the analysis, we propose a line segment sampling scheme to preserve blue-noise properties of samples which can significantly reduce noise and aliasing artifacts in reconstruction results. We demonstrate that our sampling scheme improves the quality of depth-offield rendering, motion blur rendering, and temporal light field reconstruction. Copyright © ACM. Copyright © ACM 2013.

Xie G.,University of Montréal | Xie G.,State Key Laboratory of Computer Science | Sun X.,Microsoft | Tong X.,Microsoft | Nowrouzezahrai D.,University of Montréal
ACM Transactions on Graphics | Year: 2014

Diffusion curve primitives are a compact and powerful representation for vector images. While several vector image authoring tools leverage these representations, automatically and accurately vectorizing arbitrary raster images using diffusion curves remains a difficult problem. We automatically generate sparse diffusion curve vectorizations of raster images by fitting curves in the Laplacian domain. Our approach is fast, combines Laplacian and bilaplacian diffusion curve representations, and generates a hierarchical representation that accurately reconstructs both vector art and natural images. The key idea of our method is to trace curves in the Laplacian domain, which captures both sharp and smooth image features, across scales, more robustly than previous image- and gradient-domain fitting strategies. The sparse set of curves generated by our method accurately reconstructs images and often closely matches tediously hand-authored curve data. Also, our hierarchical curves are readily usable in all existing editing frameworks. We validate our method on a broad class of images, including natural images, synthesized images with turbulent multi-scale details, and traditional vector-art, as well as illustrating simple multi-scale abstraction and color editing results. Copyright © ACM.

Ma F.,CAS Institute of Software | Yan J.,CAS Institute of Software | Zhang J.,CAS Institute of Software | Zhang J.,State Key Laboratory of Computer Science
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

In a classical constrained optimization problem, the logical relationship among the constraints is normally the logical conjunction. However, in many real applications, the relationship among the constraints might be more complex. This paper investigates a generalized class of optimization problems whose constraints are connected by various kinds of logical operators in addition to conjunction. Such optimization problems have been rarely studied in literature in contrast to the classical ones. A framework which integrates classical optimization procedures into the DPLL(T) architecture for solving Satisfiability Modulo Theories (SMT) problems is proposed. Two novel techniques for improving the solving efficiency w.r.t. linear arithmetic theory are also presented. Experiments show that the proposed techniques are quite effective. © 2012 Springer-Verlag.

Li J.,East China Normal University | Yao Y.,East China Normal University | Pu G.,East China Normal University | Zhang L.,State Key Laboratory of Computer Science | He J.,East China Normal University
Proceedings of the ACM SIGSOFT Symposium on the Foundations of Software Engineering | Year: 2014

Linear Temporal Logic (LTL) is widely used nowadays in verification and AI. Checking satisfiability of LTL formulas is a fundamental step in removing possible errors in LTL assertions. We present in this paper Aalta, a new LTL satisfiability checker, which supports satisfiability checking for LTL over both infinite and finite traces. Aalta leverages the power of modern SAT solvers. We have conducted a comprehensive comparison between Aalta and other LTL satisfiability checkers, and the experimental results show that Aalta is very competitive. The tool is available at Copyright 2014 ACM.

Yan R.,State Key Laboratory of Computer Science | Huang K.,Zhejiang University | Yu M.,Zhejiang University | Zhang X.,Zhejiang University
Proceedings - 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2013 | Year: 2013

Automatic multi-threaded code generation is one of the key techniques to improve MPSoC-based programming efficiency. Besides the saving on programming effort, system performance is also an important ant issue to be considered. As thread communication is frequent in multi-threaded code, the whole performance will be improved by reducing communication cost. We present two techniques to improve communication related performance during multi-threaded code generation. One is communication pipeline technique that applies distributed memory server for parallel execution between message passing and functional tasks to reduce the cost caused by communication between different threads. The other technique is to allocate more buffers to communication channel to reduce thread switching. The two techniques can be applied to communicated threads in acyclic topologies. To maximize the application of these techniques, we also propose a technique to search for cyclic techniques and decompose some of the threads to avoid cyclic topologies. © 2013 IEEE.

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