18106801
Mining Protein Sequences for Motifs
We use methods from Data Mining and Knowledge Discovery to design an algorithm for detecting motifs in protein sequences. The algorithm assumes that a motif is constituted by the presence of a "good" combination of residues in appropriate locations of the motif. The algorithm attempts to compile such good combinations into a "pattern dictionary" by processing an aligned training set of protein sequences. The dictionary is subsequently used to detect motifs in new protein sequences. Statistical significance of the detection results are ensured by statistically determining the various parameters of the algorithm. Based on this approach, we have implemented a program called GYM. The Helix-Turn-Helix motif was used as a model system on which to test our program. The program was also extended to detect Homeodomain motifs. The detection results for the two motifs compare favorably with existing programs. In addition, the GYM program provides a lot of useful information about a given protein sequence.
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.
10779366
An efficient adaptive focused crawler based on ontology learning
The enormous growth of the World Wide Web has made it important to perform resource discovery efficiently. Consequently, several new ideas have been proposed; among them a key technique is focused crawling which is able to crawl particular topical portions of the World Wide Web quickly without having to explore all Web pages. In this paper, we present an intelligent focused crawler algorithm in which we embed ontology to evaluate the page's relevance to the topic. Compared with other algorithms using domain knowledge, our algorithm can evolve the ontology automatically during crawl process. Considering the instinct characteristics of the ontology, propagation has also been imported to accelerate the evolution of the ontology. We applied our approaches in several tasks and provided an empirical evaluation which has shown promising results.
46247452
Variable structure control in active queue management for TCP with ECN
It has been shown that the TCP connections through the congested routers can be modeled as a feedback dynamic system. In this paper, we design a variable structure (VS) based control scheme in active queue management (AQM) supporting explicit congestion notification (ECN). By analyzing the robustness and performance of the control scheme for nonlinear TCP/AQM model, we show that the proposed design has good performance and robustness with respect to the uncertainties of the round-trip time (RTT) and the number of active TCP sessions, which are central to the notion of AQM. Implementation issues are discussed and ns simulations are provided to validate the design and compare its performance to other peer schemes' in different scenarios. The results show that the proposed design significantly outperforms the peer AQM schemes in terms of packet loss ratio, throughput and buffer fluctuation.
14861370
On transforming statistical models for non-frontal face verification
We address the pose mismatch problem which can occur in face verification systems that have only a single (frontal) face image available for training. In the framework of a Bayesian classifier based on mixtures of gaussians, the problem is tackled through extending each frontal face model with artificially synthesized models for non-frontal views. The synthesis methods are based on several implementations of maximum likelihood linear regression (MLLR), as well as standard multi-variate linear regression (LinReg). All synthesis techniques rely on prior information and learn how face models for the frontal view are related to face models for non-frontal views. The synthesis and extension approach is evaluated by applying it to two face verification systems: a holistic system (based on PCA-derived features) and a local feature system (based on DCT-derived features). Experiments on the FERET database suggest that for the holistic system, the LinReg-based technique is more suited than the MLLR-based techniques; for the local feature system, the results show that synthesis via a new MLLR implementation obtains better performance than synthesis based on traditional MLLR. The results further suggest that extending frontal models considerably reduces errors. It is also shown that the local feature system is less affected by view changes than the holistic system; this can be attributed to the parts based representation of the face, and, due to the classifier based on mixtures of gaussians, the lack of constraints on spatial relations between the face parts, allowing for deformations and movements of face areas.
10844784
Meta-game Equilibrium for Multi-agent Reinforcement Learning
This paper proposes a multi-agent Q-learning algorithm called meta-game-Q learning that is developed from the meta-game equilibrium concept Different from Nash equilibrium, meta-game equilibrium can achieve the optimal joint action game through deliberating its preference and predicting others' policies in the general-sum game A distributed negotiation algorithm is used to solve the meta-game equilibrium problem instead of using centralized linear programming algorithms We use the repeated prisoner's dilemma example to empirically demonstrate that the algorithm converges to meta-game equilibrium.