181464
An Analysis of Phase Transition in NK Landscapes
In this paper, we analyze the decision version of the NK landscape model from the perspective of threshold phenomena and phase transitions under two random distributions, the uniform probability model and the fixed ratio model. For the uniform probability model, we prove that the phase transition is easy in the sense that there is a polynomial algorithm that can solve a random instance of the problem with the probability asymptotic to 1 as the problem size tends to infinity. For the fixed ratio model, we establish several upper bounds for the solubility threshold, and prove that random instances with parameters above these upper bounds can be solved polynomially. This, together with our empirical study for random instances generated below and in the phase transition region, suggests that the phase transition of the fixed ratio model is also easy.
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].
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.
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.
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.
32735043
Mining Web Sequential Patterns Using Reinforcement Learning
In this paper, the problem of discovering sequential patterns from the Web log is discussed and an algorithm of sequential patterns mining is brought forward. First, the data in the Web server log file is cleaned and the temporal set about every user is constructed. After the analysis of the temporal set, Markov decision process is applied to model Web log. Then, the sequential patterns of user behaviors are mined by means of reinforcement learning technology. Finally the experiment shows that our mining algorithm is effective.
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.
24987149
LMRL: a multi-agent reinforcement learning model and algorithm
Multi-agent reinforcement learning technologies are mainly investigated from two perspectives: one is from the concurrence, and the other from the game theory. The former chiefly applies to cooperative multi-agent systems, while the latter usually applies to coordinated multi-agent systems. However, There exist some problems such as that of the credit assignment and the multiple Nash equilibriums for agents with them. In this paper, we propose a new multi-agent reinforcement learning model and algorithm LMRL from a layer perspective. LMRL model is composed of an offline training layer that employs a single agent reinforcement learning technology to acquire stationary strategy knowledge and an online interaction layer that employs a multi-agent reinforcement learning technology and the strategy knowledge that can be revised dynamically to interact with environment. An agent with LMRL can improve its generalization, adaptability and coordination ability. Experiments show that the performance of LMRL can be better than that of single agent reinforcement learning and Nash-Q.
45735125
Robust visual similarity retrieval in single model face databases
In this paper, we introduce a novel visual similarity measuring technique to retrieve face images in photo album databases for law enforcement. Though much work is being done on face similarity matching techniques, little attention is given to the design of face matching schemes suitable for visual retrieval in single model databases where accuracy, robustness to scale and environmental changes, and computational efficiency are three important issues to be considered. This paper presents a robust face retrieval approach using structural and spatial point correspondence in which the directional corner points (DCPs) are generated for efficient face coding and retrieval. A complete investigation on the proposed method is conducted, which covers face retrieval under controlled/ideal condition, scale variations, environmental changes and subject actions. The system performance is compared with the performance of the eigenface method. It is an attractive finding that the proposed DCP retrieval technique has performed superior to the eigenface method in most of the comparison experiments. This research demonstrates that the proposed DCP approach provides a new way, which is both robust to scale and environmental changes, and efficient in computation, for retrieving human faces in single model databases.