Using Combinatorial Optimization Methods for Quantification Scheduling
Model checking is the process of verifying whether a model of a concurrent system satisfies a specified temporal property. Symbolic algorithms based on Binary Decision Diagrams (BDDs) have significantly increased the size of the models that can be verified. The main problem in symbolic model checking is the image computation problem, i.e., efficiently computing the successors or predecessors of a set of states. This paper is an in-depth study of the image computation problem. We analyze and evaluate several newheuristics, metrics, and algorithms for this problem. The algorithms use combinatorial optimization techniques such as hill climbing, simulated annealing, and ordering by recursive partitioning to obtain better results than was previously the case. Theoretical analysis and systematic experimentation are used to evaluate the algorithms.

title: Automated Abstraction Refinement for Model Checking Large State Spaces Using SAT Based Conflict Analysis
abstract: We introduce a SAT based automatic abstraction refinement framework for model checking systems with several thousand state variables in the cone of influence of the specification. The abstract model is constructed by designating a large number of state variables as invisible. In contrast to previous work where invisible variables were treated as free inputs we describe a computationally more advantageous approach in which the abstract transition relation is approximated by pre-quantifying invisible variables during image computation. The abstract counterexamples obtained from model-checking the abstract model are symbolically simulated on the concrete system using a state-of-the-art SAT checker. If no concrete counterexample is found, a subset of the invisible variables is reintroduced into the system and the process is repeated. The main contribution of this paper are two new algorithms for identifying the relevant variables to be reintroduced. These algorithms monitor the SAT checking phase in order to analyze the impact of individual variables. Our method is complete for safety properties (AGp) in the sense that - performance permitting - a property is either verified or disproved by a concrete counterexample. Experimental results are given to demonstrate the power of our method on real-world designs.

title: High level verification of control intensive systems using predicate abstraction
abstract: Predicate abstraction has been widely used for model checking hardware/software systems. However, for control intensive systems, existing predicate abstraction techniques can potentially result in a blowup of the size of the abstract model. We deal with this problem by retaining important control variables in the abstract model. By this method we avoid having to introduce an unreasonable number of predicates to simulate the behavior of the control variables. We also show how to improve predicate abstraction by extracting useful information from a high level representation of hardware/software systems. This technique works by first extracting relevant branch conditions. These branch conditions are used to invalidate spurious abstract counterexamples through a new counterexample-based lazy refinement algorithm. Experimental results are included to demonstrate the effectiveness of our methods.

title: Nonlinear quantification scheduling in image computation
abstract: Computing the set of states reachable in one step from a given set of states, i.e. image computation, is a crucial step in several symbolic verification algorithms, including model checking and reachability analysis. So far, the best methods for quantification scheduling in image computation, with a conjunctively partitioned transition relation, have been restricted to a linear schedule. This results in a loss of flexibility during image computation. We view image computation as a problem of constructing an optimal parse tree for the image set. The optimality of a parse tree is defined by the largest BDD that is encountered during the computation of the tree. We present dynamic and static versions of a new algorithm, VarScore, which exploits the flexibility offered by the parse tree approach to the image computation. We show by extensive experimentation that our techniques outperform the best known techniques so far.

title: Formal property verification by abstraction refinement with formal, simulation and hybrid engines
abstract: We present RFN, a formal property verification tool based on abstraction refinement. Abstraction refinement is a strategy for property verification. It iteratively refines an abstract model to better approximate the behavior of the original design in the hope that the abstract model alone will provide enough evidence to prove or disprove the property. However, previous work on abstraction refinement was only demonstrated on designs with up to 500 registers. We developed RFN to verify real-world designs that may contain thousands of registers. RFN differs from the previous work in several ways. First, instead of relying on a single engine, RFN employs multiple formal verification engines, including a BDD-ATPG hybrid engine and a conventional BDD-based fixpoint engine, for finding error traces or proving properties on the abstract model. Second, RFN uses a novel two-phase process involving 3-valued simulation and sequential ATPG to determine how to refine the abstract model. Third, RFN avoids the weakness of other abstraction-refinement algorithms-finding error traces on the original design, by utilizing the error trace of the abstract model to guide sequential ATPG to find an error trace on the original design. We implemented and applied a prototype of RFN to verify various properties of real-world RTL designs containing approximately 5,000 registers, which represents an order of magnitude improvement over previous results. On these designs, we successfully proved a few properties and discovered a design violation.

title: Monaural Speech Separation
abstract: Monaural speech separation has been studied in previous systems that incorporate auditory scene analysis principles. A major problem for these systems is their inability to deal with speech in the high-frequency range. Psychoacoustic evidence suggests that different perceptual mechanisms are involved in handling resolved and unresolved harmonics. Motivated by this, we propose a model for monaural separation that deals with low-frequency and high-frequency signals differently. For resolved harmonics, our model generates segments based on temporal continuity and cross-channel correlation, and groups them according to periodicity. For unresolved harmonics, the model generates segments based on amplitude modulation (AM) in addition to temporal continuity and groups them according to AM repetition rates derived from sinusoidal modeling. Underlying the separation process is a pitch contour obtained according to psychoacoustic constraints. Our model is systematically evaluated, and it yields substantially better performance than previous systems, especially in the high-frequency range.

title: Semantics modeling based image retrieval system using neural networks
abstract: Semantics based image retrieval techniques show a promising direction to the development of CBIR systems. To design such systems, semantics modeling is one of the most difficult tasks. This paper aims to develop a semantics modeling approach using neural networks. In our work, a neural network is utilized to memorize the semantic patterns within the images. An intelligent image retrieval system is designed based on this model. User's relevance feedback is used for enhancing the retrieval performance. Experimental results from the prototype system demonstrate the effectiveness of the proposed approach.

title: Using the Semantic Web to Integrate Ecoinformatics Resources
abstract: We demonstrate an end-to-end use case of the semantic web's utility for synthesizing ecological and environmental data. ELVIS (the Ecosystem Location Visualization and Information System) is a suite of tools for constructing food webs for a given location. ELVIS functionality is exposed as a collection of web services, and all input and output data is expressed in OWL, thereby enabling its integration with other semantic web resources. In particular, we describe using a Triple Shop application to answer SPARQL queries from a collection of semantic web documents.

title: Depth-First Search as a Combinatorial Correspondence
abstract: A depth first search algorithm is used to establish the connection between labeled connected graphs and inversions of trees.

title: Theoretical Foundation for Nonlinear Edge-Preserving Regularized Learning Image Restoration
abstract: Image restoration is an important issue in image processing, which helps recovering of degraded images caused by various factors in different circumstances. Neural networks models have been successfully applied in handling image restoration problems and some progress and promising results have been reported in the past. A popular neural networks model for image restoration is the Hopfield network due to its ability on dealing with optimization problems. A degraded image may have multiple corresponding solutions, i.e., the restored images, and the obtained solution can be sub-optimal which is related to a local minimum point in the weight space of the Hopfield network. This paper gives an algebraic characterization on images and shows that images with lower complexity can be restored uniquely from their degraded images and edge information. The obtained result in this paper establishes a mathematical basis for employing the mapping neural networks to realize a learning based image restoration scheme. The linear image restoration model is firstly generalized to a nonlinear one to broaden the scope of application. Secondly, we view the image restoration task as a set of approximation problems in a high dimensional space, and a mapping relationship between the degraded images with edge information and the source images is then built using feed-forward neural networks and the well trained neural network can be used to restore the degraded images in realtime. Computer simulations demonstrate the effectiveness of the learning image restoration techniques proposed in this paper.