6765906
On the Threshold of Having a Linear Treewidth in Random Graphs
The concept of tree-width and tree-decomposition of a graph plays an important role in algorithms and graph theory. Many NP-hard problems have been shown to be polynomially sovable when restricted to the class of instances with a bounded tree-width. In this paper, we establish an improved lower bound on the threshold for a random graph to have a linear treewidth, which improves the previous result by Kloks [1].
1889757
Lightweight Deployment-Aware Scheduling for Wireless Sensor Networks
Wireless sensor networks consist of a large number of tiny sensors that have only limited energy supply. One of the major challenges in constructing such networks is to maintain long network lifetime as well as sufficient sensing areas. To achieve this goal, a broadly-used method is to turn off redundant sensors. In this paper, the problem of estimating redundant sensing areas among neighbouring wireless sensors is analysed. We present simple methods to estimate the degree of redundancy without the knowledge of location or directional information. We also provide tight upper and lower bounds on the probability of complete redundancy and on the average partial redundancy. With random sensor deployment, our analysis shows that partial redundancy is more realistic for real applications, as complete redundancy is expensive, requiring up to 11 neighbouring sensors to provide a 90 percent chance of complete redundancy. Based on the analysis, we propose a scalable Lightweight Deployment-Aware Scheduling (LDAS) algorithm, which turns off redundant sensors without using accurate location information. Simulation study demonstrates that the LDAS algorithm can reduce network energy consumption and provide desired QoS requirement effectively.
2287582
Resolution complexity of random constraint satisfaction problems: Another half of the story
Let C"n","m^2^,^k^,^t be a random constraint satisfaction problem (CSP) on n binary variables, where m constraints are selected uniformly at random from all the possible k-ary constraints each of which contains exactly t tuples of the values as its restrictions. We establish an upper bound on the constraint tightness threshold for C"n","m^2^,^k^,^t to have an exponential resolution complexity. The upper bound partly answers the open problem regarding the CSP resolution complexity with the tightness between the existing upper and lower bounds [D. Mitchell, Resolution complexity of random constraints, in: Proceedings Principles and Practices of Constraint Programming-CP 2002, Springer, Berlin, 2002, pp. 295-309].
11821573
Phase Transition of Tractability in Constraint Satisfaction and Bayesian Network Inference
There has been great interest in identifying tractable subclasses of NP complete problems and designing efficient algorithms for these tractable classes. Constraint satisfaction and Bayesian network inference are two examples of such problems that are of great importance in AI and algorithms. In this paper we study, under the frameworks of random constraint satisfaction problems and random Bayesian networks, a typical tractable subclass characterized by the treewidth of the problems. We show that the property of having a bounded treewidth for CSPs and Bayesian network inference problem has a phase transition that occurs while the underlying structures of problems are still sparse. This implies that algorithms making use of treewidth based structural knowledge only work efficiently in a limited range of random instance.
14459995
Phase Transitions of Dominating Clique Problem and Their Implications to Heuristics in Satisfiability Search
We study a monotone NP decision problem, the dominating clique problem, whose phase transition occurs at a very dense stage of the random graph evolution process. We establish the exact threshold of the phase transition and propose an efficient search algorithm that runs in super-polynomial time with high probability. Our empirical studies reveal two even more intriguing phenomena in its typical-case complexity: (1) the problem is "uniformly hard" with a tiny runtime variance on negative instances. (2) Our algorithm and its CNF-tailored implementation, outperform several SAT solvers by a huge margin on dominating cliques and some other SAT problems with similar structures.
3221081
Consistency and Random Constraint Satisfaction Models
In this paper, we study the possibility of designing non-trivial random CSP models by exploiting the intrinsic connection between structures and typical-case hardness. We show that constraint consistency, a notion that has been developed to improve the efficiency of CSP algorithms, is in fact the key to the design of random CSP models that have interesting phase transition behavior and guaranteed exponential resolution complexity without putting much restriction on the parameter of constraint tightness or the domain size of the problem. We propose a very flexible framework for constructing problem instances with interesting behavior and develop a variety of concrete methods to construct specific random CSP models that enforce different levels of constraint consistency. 
 
A series of experimental studies with interesting observations are carried out to illustrate the effectiveness of introducing structural elements in random instances, to verify the robustness of our proposal, and to investigate features of some specific models based on our framework that are highly related to the behavior of backtracking search algorithms.
13799231
Improving molecular cancer class discovery through sparse non-negative matrix factorization
MOTIVATION
Identifying different cancer classes or subclasses with similar morphological appearances presents a challenging problem and has important implication in cancer diagnosis and treatment. Clustering based on gene-expression data has been shown to be a powerful method in cancer class discovery. Non-negative matrix factorization is one such method and was shown to be advantageous over other clustering techniques, such as hierarchical clustering or self-organizing maps. In this paper, we investigate the benefit of explicitly enforcing sparseness in the factorization process.


RESULTS
We report an improved unsupervised method for cancer classification by the use of gene-expression profile via sparse non-negative matrix factorization. We demonstrate the improvement by direct comparison with classic non-negative matrix factorization on the three well-studied datasets. In addition, we illustrate how to identify a small subset of co-expressed genes that may be directly involved in cancer.
16609037
DELPHI: A pattern-based method for detecting sequence similarity
We describe DELPHI, a new computational tool for identifying sequence similarity between a query sequence and a database of proteins. Use is made of a set of patterns obtained from the underlying database through a one-time computation. The patterns are subsequently matched against every query sequence presented to the system. A pattern matched by a region of the query pinpoints a potential local similarity between that region and all of the database sequences also matching that pattern. In a final step, all such local similarities are examined more closely by aligning and scoring the corresponding query and database regions. By prudently choosing a set of patterns, the method can be used to discover weak but biologically important similarities. We provide a number of examples using both classified and unclassified proteins that corroborate this claim.
16604799
Face Recognition Using Line Edge Map
The automatic recognition of human faces presents a significant challenge to the pattern recognition research community. Typically, human faces are very similar in structure with minor differences from person to person. They are actually within one class of "human face". Furthermore, lighting conditions change, while facial expressions and pose variations further complicate the face recognition task as one of the difficult problems in pattern analysis. This paper proposes a novel concept: namely, that faces can be recognized using a line edge map (LEM). The LEM, a compact face feature, is generated for face coding and recognition. A thorough investigation of the proposed concept is conducted which covers all aspects of human face recognition, i.e. face recognition under (1) controlled/ideal conditions and size variations, (2) varying lighting conditions, (3) varying facial expressions, and (4) varying pose. The system performance is also compared with the eigenface method, one of the best face recognition techniques, and with reported experimental results of other methods. A face pre-filtering technique is proposed to speed up the search process. It is a very encouraging to find that the proposed face recognition technique has performed better than the eigenface method in most of the comparison experiments. This research demonstrates that the LEM, together with the proposed generic line-segment Hausdorff distance measure, provides a new method for face coding and recognition.
2438036
A case for exploiting self-similarity of network traffic in TCP congestion control
Analytical and empirical studies have shown that self-similar traffic can have a detrimental impact on network performance including amplified queuing delay and packet loss ratio. On the flip side, the ubiquity of scale-invariant burstiness observed across diverse networking contexts can be exploited to design better resource control algorithms. We explore the issue of exploiting the self-similar characteristics of network traffic in TCP congestion control. We show that the correlation structure present in long-range dependent traffic can be detected on-line and used to predict future traffic. We then devise an novel scheme, called TCP with traffic prediction (TCP-TP), that exploits the prediction result to infer, in the context of AIMD (additive increase, multiplicative decrease) steady-state dynamics, the optimal operational point for a TCP connection. Through analytical reasoning, we show that the impact of prediction errors on fairness is minimal. We also conduct ns-2 simulation and FreeBSD 4.1-based implementation studies to validate the design and to demonstrate the performance improvement in terms of packet loss ratio and throughput attained by connections.
28841129
Believability Based Iterated Belief Revision
Classical iterated belief revision methods rarely take into account the impact of the uncertain information. In this paper, an approach of believability based iterated belief revision(BIBR) is presented. BIBR relates the belief revision in the multi-agent system to the believability of information, which plays an important role in the revision process. Based on the Dempster-Shafer theory of evidence and believability function formalism, the believability of information can be obtained. The revised belief set by BIBR is dependent on the history of revision, namely, on the information received prior to the current belief set. It is proved that the BIBR operation meets the AGM postulates for belief revision and the Darwiche and Pearl postulates for iterated belief revision.