5531222
Divide and Compose: SCC Refinement for Language Emptiness
We propose a refinement approach to symbolic SCC analysis, which performs large parts of the computation on abstracted systems, and on small subsets of the state space. For language-emptiness checking, it quickly discards uninteresting parts of the state space; for the remaining states, it adapts the model checking computation to the strength of the SCCs at hand. 
 
We present a general framework for SCC refinement, which uses a compositional approach to generate and refine overapproximations. We show that our algorithm significantly outperforms the one of Emerson and Lei.
11387042
Predicate learning and selective theory deduction for a difference logic solver
Design and verification of systems at the register-transfer (RT) or behavioral level require the ability to reason at higher levels of abstraction. Difference logic consists of an arbitrary Boolean combination of propositional variables and difference predicates and therefore provides an appropriate abstraction. In this paper, we present several new optimization techniques for efficiently deciding difference logic formulas. We use the lazy approach by combining a DPLL Boolean SAT procedure with a dedicated graph-based theory solver, which adds transitivity constraints among difference predicates on a "need-to" basis. Our new optimization techniques include flexible theory constraint propagation, selective theory deduction, and dynamic predicate learning. We have implemented these techniques in our lazy solver. We demonstrate the effectiveness of the proposed techniques on public benchmarks through a set of controlled experiments
18730116
A satisfiability-based approach to abstraction refinement in model checking
Abstract We present an abstraction refinement algorithm for model checking of safety properties that relies exclusively on a SAT solver for checking the abstract model, testing abstract counterexamples on the concrete model, and refinement. Model checking of the abstractions is based on bounded model checking extended with checks for the existence of simple paths that help in deciding passing properties. All minimum-length spurious counterexamples are eliminated in one refinement step by a procedure that combines the analysis of the conflict dependency graph produced by the SAT solver while looking for concrete counterexamples with an effective abstraction minimization procedure.
10395010
Abstraction and BDDs Complement SAT-Based BMC in DiVer
Bounded Model Checking (BMC) based on Boolean Satisfiability (SAT) procedures has recently gained popularity for finding bugs in large designs. However, due to its incompleteness, there is a need to perform deeper searches for counterexamples, or a proof by induction where possible. The DiVer verification platform uses abstraction and BDDs to complement BMC in the quest for completeness. We demonstrate the effectiveness of our approach in practice on industrial designs.
53757025
The Compositional Far Side of Image Computation
2969808
Improving Ariadne's Bundle by Following Multiple Threads in Abstraction Refinement
The authors propose a scalable abstraction-refinement method for model checking invariant properties on large sequential circuits, which is based on fine-grain abstraction and simultaneous analysis of all abstract counterexamples of the shortest length. Abstraction efficiency is introduced to measure for a given abstraction-refinement algorithm how much of the concrete model is required to make the decision. The fully automatic techniques presented in this paper can efficiently reach or come near to the maximal abstraction efficiency. First, a fine-grain abstraction approach is given to keep the abstraction granularity small by breaking down large combinational logic cones with Boolean network variables (BNVs) and then treating both state variables and BNVs as atoms in abstraction. Second, a refinement algorithm is proposed based on an improved Ariadne's bundle In the legend of Theseus, Ariadne's bundle contained one ball of thread to help Theseus navigate the labyrinth. In this paper, we work with multiple threads-hence, the "improved." of synchronous onion rings on the abstract model, through which the transitions contain all shortest abstract counterexamples. The synchronous onion rings are exploited in two distinct ways to provide global guidance to the abstraction refinement process. The scalability of our algorithm is ensured in the sense that all the analysis and computation required in our refinement algorithm are conducted on the abstract model. Finally, we derive sequential don't cares from the invisible variables and use them to constrain the behavior of the abstract model. We conducted experimental comparisons of our new method with various existing techniques. The results show that our method outperforms other counterexample-guided methods in terms of both run time and abstraction efficiency
16197603
Parallel and distributed methods for incremental frequent itemset mining
Traditional methods for data mining typically make the assumption that the data is centralized, memory-resident, and static. This assumption is no longer tenable. Such methods waste computational and input/output (I/O) resources when data is dynamic, and they impose excessive communication overhead when data is distributed. Efficient implementation of incremental data mining methods is, thus, becoming crucial for ensuring system scalability and facilitating knowledge discovery when data is dynamic and distributed. In this paper, we address this issue in the context of the important task of frequent itemset mining. We first present an efficient algorithm which dynamically maintains the required information even in the presence of data updates without examining the entire dataset. We then show how to parallelize this incremental algorithm. We also propose a distributed asynchronous algorithm, which imposes minimal communication overhead for mining distributed dynamic datasets. Our distributed approach is capable of generating local models (in which each site has a summary of its own database) as well as the global model of frequent itemsets (in which all sites have a summary of the entire database). This ability permits our approach not only to generate frequent itemsets, but also to generate high-contrast frequent itemsets, which allows one to examine how the data is skewed over different sites.
1861940
103 Graphs that are irreducible for the projective plane
33560656
Clique covering the edges of a locally cobipartite graph
Abstract We prove that a locally cobipartite graph on n vertices contains a family of at most n cliques that cover its edges. This is related to Opsut's conjecture that states the competition number of a locally cobipartite graph is at most two.
4374799
Appearance manifolds for modeling time-variant appearance of materials
We present a visual simulation technique called appearance manifolds for modeling the time-variant surface appearance of a material from data captured at a single instant in time. In modeling time-variant appearance, our method takes advantage of the key observation that concurrent variations in appearance over a surface represent different degrees of weathering. By reorganizing these various appearances in a manner that reveals their relative order with respect to weathering degree, our method infers spatial and temporal appearance properties of the material's weathering process that can be used to convincingly generate its weathered appearance at different points in time. Results with natural non-linear reflectance variations are demonstrated in applications such as visual simulation of weathering on 3D models, increasing and decreasing the weathering of real objects, and material transfer with weathering effects.
7097063
Summarizing itemset patterns using probabilistic models
In this paper, we propose a novel probabilistic approach to summarize frequent itemset patterns. Such techniques are useful for summarization, post-processing, and end-user interpretation, particularly for problems where the resulting set of patterns are huge. In our approach items in the dataset are modeled as random variables. We then construct a Markov Random Fields (MRF) on these variables based on frequent itemsets and their occurrence statistics. The summarization proceeds in a level-wise iterative fashion. Occurrence statistics of itemsets at the lowest level are used to construct an initial MRF. Statistics of itemsets at the next level can then be inferred from the model. We use those patterns whose occurrence can not be accurately inferred from the model to augment the model in an iterative manner, repeating the procedure until all frequent itemsets can be modeled. The resulting MRF model affords a concise and useful representation of the original collection of itemsets. Extensive empirical study on real datasets show that the new approach can effectively summarize a large number of itemsets and typically significantly outperforms extant approaches.