5161241
Protein structure alignment by deterministic annealing
MOTIVATION
Protein structure alignment is one of the most important computational problems in molecular biology and plays a key role in protein structure prediction, fold family classification, motif finding, phylogenetic tree reconstruction and so on. From the viewpoint of computational complexity, a pairwise structure alignment is also a NP-hard problem, in contrast to the polynomial time algorithm for a pairwise sequence alignment.


RESULTS
We propose a method for solving the structure alignment problem in an accurate manner at the amino acid level, based on a mean field annealing technique. We define the structure alignment as a mixed integer-programming (MIP) problem. By avoiding complicated combinatorial computation and exploiting the special structure of the continuous partial problem, we transform the MIP into a reduced non-linear continuous optimization problem (NCOP) with a much simpler form. To optimize the reduced NCOP, a mean field annealing procedure is adopted with a modified Potts model, whose solution is generally identical to that of the MIP. There is no 'soft constraint' in our mean field model and all constraints are automatically satisfied throughout the annealing process, thereby not only making the optimization more efficient but also eliminating many unnecessary parameters that depend on problems and usually require careful tuning. A number of benchmark examples are tested by the proposed method with comparisons to several existing approaches.
8972258
Designing oscillators in synthetic gene networks based on multi-scale dynamics
Multistability, oscillations, and switching exist at various levels of biological processes and organizations and have been investigated on the basis of many theoretical models, such as circadian oscillations with the period protein (PER) and the timeless protein (TIM) in Drosophila, and multistable dynamics regulated by transcriptional factors. Considerable experimental evidence suggests that cellular processes are intrinsically rhythmic or periodic. Various periodic oscillations with different time scales ranging from less than a second to more than a year, which may allow for living organisms to adapt their behaviors to a periodically varying environment, have also been observed experimentally. On the other hand, in synthetic gene networks, both toggle switch and repressilator have been theoretically proposed and further confirmed by experiments. All of these works stress the importance of feedback regulation of transcriptional factors, which is a key in giving rise to oscillatory or multistable dynamical behaviors exhibited by biological genetic systems. In addition, it should be noted that many periodic behaviors do not simply oscillate smoothly; rather, they change rapidly or jump at certain states. In gene expression systems, many different time scales characterize the gene regulatory processes. For instance, the transcription and translation processes generally evolve on a time scale that is much slower than that of phosphorylation, dimerization or binding reactions of transcription factors. In genetic networks, the time scale for expression of some genes is much slower than that of others, depending on the length of the genes. We aim to design robust periodic oscillators in synthetic gene-protein systems by simple nonlinear models and to analyze the basic mechanism of limit cycles with jumping behaviors or relaxation oscillations by exploiting multiple time-scale properties [1, 2]. We show that periodic oscillations are mainly generated by nonlinear feedback loops in gene regulatory systems and the jumping dynamics caused by time scale differences among biochemical reactions. Moreover, effects of time delay are also examined. We show that time delay generally enlarges the stability region of oscillations, thereby making the oscillations more sustainable despite parameter changes or noise [1, 2]. The dynamics of the proposed models is robust in terms of stability and period length to the parameter perturbations or environment variations. Although we mainly analyze some specific models, the mechanisms identified in this work are likely to apply to a variety of genetic regulatory systems. These simple models may actually act as basic building block in synthetic gene-protein networks, such as genetic oscillators or switches because the dynamics is robust for parameter perturbations or environment variations. Several examples are also provided to demonstrate implementation of synthetic oscillators by using genes of the /spl lambda/ phage bacteria.
32150042
Exploring Protein's Optimal Hp Configurations by Self-organizing Mapping
Self-organizing map (SOM) has been used in protein folding prediction when the HP model is employed. The existing work uses a square-like shape lattice with l = m x n points to represent the optimal compact structure of a sequence of l amino acids. In this paper, a general l'-size sequence of amino acids is self-organized in a two dimensional lattice with l (> l') points. The obtained minimum configuration then has a flexible shape, in contrast to the compact structure limited in the lattice. To fulfil this extension, a new self-organizing map (SOM) technique is proposed to deal with the difficulty of the unsymmetric input and output spaces. New competition rules in the training phase are introduced and a local search method is applied to overcome the multi-mapping phenomena. Several HP benchmark examples with up to 36 amino acids are tested to verify the effectiveness of the proposed approach in this paper.
7815226
Peak Load Forecasting Using the Self-organizing Map
This paper aims to study the short-term load forecasting of electricity by using an extended self-organizing map. We first adopt a traditional Kohonen self-organizing map (SOM) to learn time-series load data with weather information as parameters. Then, in order to improve the accuracy of the prediction, an extension of SOM algorithm based on error-correction learning rule is used, and the estimation of the peak load is achieved by averaging the output of all the neurons. Finally, as an implementation example, data of electricity demand from New York Independent System Operator (ISO) are used to verify the effectiveness of the learning and prediction for the proposed methods.
6274371
Trust building with explanation interfaces
Based on our recent work on the development of a trust model for recommender agents and a qualitative survey, we explore the potential of building users' trust with explanation interfaces. We present the major results from the survey, which provided a roadmap identifying the most promising areas for investigating design issues for trust-inducing interfaces. We then describe a set of general principles derived from an in-depth examination of various design dimensions for constructing explanation interfaces, which most contribute to trust formation. We present results of a significant-scale user study, which indicate that the organization-based explanation is highly effective in building users' trust in the recommendation interface, with the benefit of increasing users' intention to return to the agent and save cognitive effort.
6316461
Partitioned optimization algorithms for multiple sequence alignment
Multiple sequence alignment is an important and difficult problem in molecular biology and bioinformatics. In this paper, we propose a partitioning approach that significantly improves the solution time and quality by utilizing the locality structure of the problem. The algorithm solves the multiple sequence alignment in three stages. First, an automated and suboptimal partitioning strategy is used to divide the set of sequences into several subsections. Then a multiple sequence alignment algorithm based on ant colony optimization is used to align the sequences of each subsection. Finally, the alignment of original sequences can be obtained by assembling the result of each subsection. The ant colony algorithm is highly optimized in order to avoid local optimal traps and converge to global optimal efficiently. Experimental results show that the algorithm can significantly reduce the running time and improve the solution quality on large-scale multiple sequence alignment benchmarks.
43431226
Wavelet-based optical flow estimation
A new algorithm for accurate optical flow (OF) estimation using discrete wavelet approximation is proposed. The computation of OF depends on minimizing the image and smoothness constraints. The proposed method takes advantages of the nature of wavelet theory, which can efficiently and accurately approximate any function. OF vectors and image functions are represented by means of linear combinations of scaling basis functions. Based on such wavelet-based approximation, the leading coefficients of these basis functions carry global information about the approximated functions. The proposed method can successfully convert the problem of minimizing a constraint function into that of solving a linear system of a quadratic and convex function of scaling coefficients. Once all the corresponding coefficients are determined, the flow vectors can be obtained accordingly. Experiments have been conducted on both synthetic and real image sequences. In terms of accuracy, the results show that our approach outperforms the existing methods which adopted the same objective function as ours.
5124709
Embedded hardware and software self-testing methodologies for processor cores
At-speed testing of GHz processors using external testers may not be technically and economically feasible. Hence, there is an emerging need for low-cost, high-quality self-test methodologies, which can be used by processors to test themselves at-speed. Currently, Built-In Self-Test (BIST) is the primary self-test methodology available and is widely used for testing embedded memory cores. In this paper, we report our experiences in applying a commercial BIST methodology to two processor cores and analyze the problems associated with the current hardware-based BIST methodologies. We propose a new software-based self-testing methodology for processors, which uses a software tester embedded in the processor memory as a vehicle for applying structural tests. The software tester consists of programs for test generation and test application. Prior to the test, structural tests are prepared for processor components in the form of self-test signatures. During the process of self-test, the test generation program expands the self-test signatures into test sets, and the test application program applies the tests to the components-under-test at the speed of the processor. Application of the novel software-based self-test method demonstrates its significant cost/fault coverage benefits and its ability to apply at-speed test while alleviating the need for high-speed testers.
32197027
A hardware accelerator for video segmentation using programmable morphology PE array
Video segmentation is a key operation for MPEG-4 and MPEG-7. It is a computationally intensive task and cannot be afforded by general microprocessors. In this paper, we propose a hardware accelerator for video segmentation. Since most of the core operations of video segmentation algorithms can be mapped to morphological operations, the core of this accelerator is a programmable morphology PE array. With the programmable ability of each PE and the interconnection between them, this accelerator can achieve both high throughput and high flexibility. Simulation shows the proposed hardware accelerator can speed up the most important video segmentation algorithms 20 times to achieve realtime performance and has high programmability with a little hardware cost overhead.