Comments on `parallel algorithms for finding a near-maximumindependent set of a circle graph'' [with reply]

The authors refers to the work of Y. Takefuji et al. (see ibid., vol.1, pp. 263-267, Sept. (1990)), which is concerned with the problem of RNA secondary structure prediction, and draws the reader's attention to his own model and experiments in training the neural networks on small tRNA subsequences. The author admits that Takefuji et al. outline an elegant way to map the problem onto neural architectures, but suggests that such mappings can be augmented with empirical knowledge (e.g., free energy values of base pairs and substructures) and the ability to learn. In their reply, Y. Takefuji and K.-C. Lee hold that the necessity of the learning capability for the RNA secondary structure prediction is questionable. They believe that the task is to build a robust parallel algorithm considering more thermodynamic properties in the model.     

A bayesian statistical algorithm for RNA secondary structure prediction.

A Bayesian approach for predicting RNA secondary structure that addresses the following three open issues is described: (1) the need for a representation of the full ensemble of probable structures; (2) the need to specify a fixed set of energy parameters; (3) the desire to make statistical inferences on all variables in the problem. It has recently been shown that Bayesian inference can be employed to relax or eliminate the need to specify the parameters of bioinformatics recursive algorithms and to give a statistical representation of the full ensemble of probable solutions with the incorporation of uncertainty in parameter values. In this paper, we make an initial exploration of these potential advantages of the Bayesian approach. We present a Bayesian algorithm that is based on stacking energy rules but relaxes the need to specify the parameters. The algorithm returns the exact posterior distribution of the number of destabilizing loops, stacking energy matrices, and secondary structures. The algorithm generates statistically representative structures from the full ensemble of probable secondary structures in exact proportion to the posterior probabilities. Once the forward recursions for the algorithm are completed, the backward recursive sampling executes in O(n) time, providing a very efficient approach for generating representative structures. We demonstrate the utility of the Bayesian approach with several tRNA sequences. The potential of the approach for predicting RNA secondary structures and presenting alternative structures is illustrated with applications to the Escherichia coli tRNA(Ala) sequence and the Xenopus laevis oocyte 5S rRNA sequence.     

Dynamically stable motion planning of wheeled robots for heavy object manipulation

Heavy object manipulation by wheeled mobile manipulators (WMM) may lead to serious consequences such as postural instability, and this necessitates dynamically stable planning based on systematic analysis to better predict and eliminate the possibility of toppling down. Although the problem of stable planning has been extensively examined in the context of humanoid robotics, fewer research has been devoted to that in the field of WMMs. In the present study, this challenging issue is investigated for WMMs during heavy object manipulation tasks. It is assumed that the initial and final poses of a heavy payload are specified. Based on these known postures of the payload, two proper configurations for robotic system are defined. Then, between these two initial and final poses, appropriate trajectories for multiple robotic arms relative to the moving base are planned without considering the postural stability of the system. Next, motion of the moving base is planned so that the stability of the overall system is guaranteed while its predetermined initial and final positions and velocities are fulfilled. To this end, the problem of stable planning is solved as an optimization problem. The obtained results reveal the merits of the proposed approach.     

Distributed unequal clustering algorithm in large-scale wireless sensor networks using fuzzy logic

Clustering is a promising and popular approach to organize sensor nodes into a hierarchical structure, reduce transmitting data to the base station by aggregation methods, and prolong the network lifetime. However, a heavy traffic load may cause the sudden death of nodes due to energy resource depletion in some network regions, i.e., hot spots that lead to network service disruption. This problem is very critical, especially for data-gathering scenarios in which Cluster Heads (CHs) are responsible for collecting and forwarding sensed data to the base station. To avoid hot spot problem, the network workload must be uniformly distributed among nodes. This is achieved by rotating the CH role among all network nodes and tuning cluster size according to CH conditions. In this paper, a clustering algorithm is proposed that selects nodes with the highest remaining energy in each region as candidate CHs, among which the best nodes shall be picked as the final CHs. In addition, to mitigate the hot spot problem, this clustering algorithm employs fuzzy logic to adjust the cluster radius of CH nodes; this is based on some local information, including distance to the base station and local density. Simulation results demonstrate that, by mitigating the hot spot problem, the proposed approach achieves an improvement in terms of both network lifetime and energy conservation.     

Fast Arc-Annotated Subsequence Matching in Linear Space

An arc-annotated string is a string of characters, called bases, augmented with a set of pairs, called arcs, each connecting two bases. Given arc-annotated strings P and Q the arc-preserving subsequence problem is to determine if P can be obtained from Q by deleting bases from Q. Whenever a base is deleted any arc with an endpoint in that base is also deleted. Arc-annotated strings where the arcs are “nested” are a natural model of RNA molecules that captures both the primary and secondary structure of these. The arc-preserving subsequence problem for nested arc-annotated strings is basic primitive for investigating the function of RNA molecules. Gramm et al. (ACM Trans. Algorithms 2(1): 44–65, 2006) gave an algorithm for this problem using O(nm) time and space, where m and n are the lengths of P and Q, respectively. In this paper we present a new algorithm using O(nm) time and O(n+m) space, thereby matching the previous time bound while significantly reducing the space from a quadratic term to linear. This is essential to process large RNA molecules where the space is likely to be a bottleneck. To obtain our result we introduce several novel ideas which may be of independent interest for related problems on arc-annotated strings.     

Energy landscape analysis for regulatory RNA finding using scalable distributed cyberinfrastructure

We investigate the folding energy landscape for a given RNA sequence through Boltzmann ensemble (BE) sampling of RNA secondary structures. The ensemble of sampled structures is used to derive distributions of energies and base‐pair distances between two configurations. We identify structural features that can be utilized for RNA gene finding. Characterization of the EL through BE sampling of secondary structures is computationally demanding and has multiple heterogeneous stages. We develop the Distributed Adaptive Runtime Environment to effectively address the computational requirements. Distributed Adaptive Runtime Environment is built upon an extensible and interoperable pilot‐job and supports the concurrent execution of a broad range of task sizes across a range of infrastructure. It is used to investigate two RNA systems of different sizes, S‐adenosyl methionine (SAM) binding RNA sequences known as SAM‐I riboswitches, and the S gene of the bovine corona virus RNA genome. We demonstrate how the implementation lowers the total time to solution for increases in RNA length, the number of sequences investigated, and the number of sampled structures. The distributions of energies and base‐pair distances reveal variations in folding dynamics and pathways among the SAM riboswitch sequences. Our results for BCoV RNA genome sequences also indicate sensitivity of folding to coding‐neutral variations in sequence. We search for a characteristic motif from within the SAM‐I consensus structure – a four‐way junction, among BE sampled structures for all 2910 SAM‐I sequences identified from Rfam (the curated ncRNA family database). We find that BE sampling provides insight into the variations in conformational distribution among sequences of the same ncRNA family. Therefore, BE sampling of secondary structures is a viable pre‐processing or post‐processing tool to complement comparative sequence analysis. The understanding gained shows how appropriately designed cyberinfrastructure can provide new insight into RNA folding and structure formation. Copyright © 2011 John Wiley & Sons, Ltd.     

Goal seeking in the problem of folding RNA tertiary structures

It is demonstrated that the problem of ribonucleic acid (RNA) molecule folding can be represented as the optimal decision making problem of the game theory. The difference is that it is impossible to simulate (calculate) all the possible conformations (states) of the RNA chain since the problem is NP complete. Therefore, it is necessary to perform goal selection: which states should and which should not be calculated. It is proposed to use the “X-tuning” method for this purpose; this method, based on the closest to final state actions (moves, rotations), essentially reduces the search. In spite of the fact that this method does not guarantee obtaining a global minimum, it provides a group of acceptable solutions, which is often sufficient in practice. It is shown that X-tuning can be applied in games with an opponent and in the problem of RNA folding (a game in nature).     

三角网格表面任意两点间并行近似测地线算法

为降低求解三角网格表面任意两点间近似测地线长度和路径问题的时间开销,提出一种基于局部细分法的并行近似测地线算法。采用类矩阵乘最短路径并行算法求解点对间初始最短路径,并用源分割法映射子网格数据;所有处理器并行执行,对其所拥有点对之间的初始最短路径周围三角面片上的边进行细分操作;最后基于局部细化后的细分图并行,求得所有点对间的近似测地线长度和路径。实验结果表明,该并行近似测地线算法能够有效降低求解该类问题的计算时间,计算效率大大提高。     

A regularized pan-sharpening approach based on self-similarity and Gabor prior

In this article, we propose a new regularization-based approach for pan-sharpening based on the concepts of self-similarity and Gabor prior. The given low spatial resolution (LR) and high spectral resolution multi-spectral (MS) image is modelled as degraded and noisy version of the unknown high spatial resolution (HR) version. Since this problem is ill-posed, we use regularization to obtain the final solution. In the proposed method, we first obtain an initial HR approximation of the unknown pan-sharpened image using self-similarity and sparse representation (SR) theory. Using self-similarity, we obtain the HR patches from the given LR observation by searching for matching patches in its coarser resolution, thereby obtaining LR–HR pairs. An SR framework is used to obtain the patch pairs for which no matches are available for the patches in LR observation. The entire set of matched HR patches constitutes initial HR approximation (initial estimate) to the final pan-sharpened image which is used to estimate the degradation matrix as used in our model. A regularization framework is then used to obtain the final solution in which we propose to use a new prior which we refer as Gabor prior that extracts the bandpass details from the registered panchromatic (Pan) image. In addition, we also include Markov random field (MRF) smoothness prior that preserves the smoothness in the final pan-sharpened image. MRF parameter is derived using the initial estimate image. The final cost function consists of data fitting term and two prior terms corresponding to Gabor and MRF. Since the derived cost function is convex, simple gradient-based method is used to obtain the final solution. The efficacy of the proposed method is evaluated by conducting the experiments on degraded as well as on un-degraded datasets of three different satellites, i.e., Ikonos-2, Quickbird, and Worldview-2. The results are compared on the basis of traditional measures as well as recently proposed quality with no reference (QNR) measure, which does not require the reference image.     

Quantum computing algorithm for electromagnetic field simulation

Quantum computing offers new concepts for the simulation of complex physical systems. A quantum computing algorithm for electromagnetic field simulation is presented here. The electromagnetic field simulation is performed on the basis of the Transmission Line Matrix (TLM) method. The Hilbert space formulation of TLM allows us to obtain a time evolution operator for the TLM method, which can then be interpreted as the time evolution operator of a quantum system, thus yielding a quantum computing algorithm. Further, the quantum simulation is done within the framework of the quantum circuit model of computation. Our aim has been to address the design problem in electromagnetics—given an initial condition and a final field distribution, find the structures which satisfy these. Quantum computing offers us the possibility to solve this problem from first principles. Using quantum parallelism we simulate a large number of electromagnetic structures in parallel in time and then try to filter out the ones which have the required field distribution.     

A barrier composite energy function approach for robot manipulators under alignment condition with position constraints

In this work, we propose an iterative learning control scheme with a novel barrier composite energy function approach to deal with position constrained robotic manipulators with uncertainties under alignment condition. The classical assumption of initial resetting condition is removed. Through rigorous analysis, we show that uniform convergence is guaranteed for joint position and velocity tracking error. By introducing a novel tan‐type barrier Lyapunov function into barrier composite energy function and keeping it bounded in closed‐loop analysis, the constraint on joint position vector will not be violated. A simulation study has further demonstrated the efficacy of the proposed scheme. Copyright © 2013 John Wiley & Sons, Ltd.     

Optimal output-transitions for linear systems

This article addresses the optimal (minimum-input-energy) output-transition problem for linear systems. The goal is to transfer the output from an initial value (for all time t?t_i) to a final output value (for all time t?t_f). Previous methods solve this output-transition problem by transforming it into a state-transition problem; the initial and final states (x(t_i),x(t_f), respectively) are chosen and a minimum-energy state-to-state transition problem is solved. However, the choice of the initial and final states can be ad hoc and the resulting output-transition cost (input energy) may not be minimal. The contribution of this article is the solution of the optimal output-transition problem. An example system with elastic dynamics is studied to illustrate the proposed method. Simulation results are presented that show substantial reduction of transition costs with the use of the proposed method when compared to the use of minimum-energy state-to-state transitions.     

Multi-Target Tracking by Online Learning a CRF Model of Appearance and Motion Patterns

We introduce an online learning approach for multi-target tracking. Detection responses are gradually associated into tracklets in multiple levels to produce final tracks. Unlike most previous approaches which only focus on producing discriminative motion and appearance models for all targets, we further consider discriminative features for distinguishing difficult pairs of targets. The tracking problem is formulated using an online learned CRF model, and is transformed into an energy minimization problem. The energy functions include a set of unary functions that are based on motion and appearance models for discriminating all targets, as well as a set of pairwise functions that are based on models for differentiating corresponding pairs of tracklets. The online CRF approach is more powerful at distinguishing spatially close targets with similar appearances, as well as in tracking targets in presence of camera motions. An efficient algorithm is introduced for finding an association with low energy cost. We present results on four public data sets, and show significant improvements compared with several state-of-art methods.     

含假结RNA二级结构类的图语法

用最小自由能法预测RNA二级结构是NP困难问题,其根本原因是假结的存在。近几年的预测算法都针具有一定结构特征的假结寻找多项式时间算法进行预测。论文针对RNA二级结构图提出一种图语法,该语法由初始结构图集和重写规则集构成,用重写规则在初始结构图上的不断重写得到的结构图都是该语法的语言。分析了5个主流RNA二级结构预测算法的目标集,给出它们的图语法,使得目标集的结构特征一目了然,目标集间的真包含关系也通过图语法直观地体现出来。     

Eco-Physic: Eco-Physical design initiative for very large databases

In the Big Data Era, the management of energy consumption by servers and data centers has become a challenging issue for companies, institutions, and countries. In data-centric applications, Database Management Systems are one of the major energy consumers when executing complex queries involving very large databases. Several initiatives have been proposed to deal with this issue, covering both the hardware and software dimensions. They can be classified in two main approaches assuming that either (a) the database is already deployed on a given platform, or (b) it is not yet deployed. In this study, we focus on the first set of initiatives with a particular interest in physical design, where optimization structures (e.g., indexes, materialized views) are selected to satisfy a given set of non-functional requirements such as query performance for a given workload. In this paper, we first propose an initiative, called Eco-Physic, which integrates the energy dimension into the physical design when selecting materialized views, one of the redundant optimization structures. Secondly, we provide a multi-objective formalization of the materialized view selection problem, considering two non-functional requirements: query performance and energy consumption while executing a given workload. Thirdly, an evolutionary algorithm is developed to solve the problem. This algorithm differs from the existing ones by being interactive, so that database administrators can adjust some energy sensitive parameters at the final stage of the algorithm execution according to their specifications. Finally, intensive experiments are conducted using our mathematical cost model and a real device for energy measurements. Results underscore the value of our approach as an effective way to save energy while optimizing queries through materialized views structures.     

A particle swarm optimization approach with refinement procedure for nurse rostering problem

This study attempts to develop a model satisfying the rules of a typical hospital environment based both on published research data and on requirements of a local hospital under study. A mathematical formulation for the studied nurse rostering problem (NRP) is presented first. Due to the combinatorial nature of the NRP model, a particle swarm optimization (PSO) approach is proposed to solve this highly complicated NRP. The structure of the problem constraints is analyzed and used as base for generating workstretch patterns. These patterns serve as the base for generating fast initial solutions, and will later be improved upon by the proposed PSO algorithm. This study also proposes a simple yet effective procedure for attempting possible refinements on the solutions obtained by the PSO before reporting the final solutions. When fair shift assignment is considered as the decision objective, computational results show that the proposed PSO algorithm with refinement procedure is able to produce optimal solutions in all real test problems in a very efficient manner.     

Constructions in the category of fuzzy T-proximity spaces

This paper is devoted to study the initial fuzzy T-proximity structures of the fuzzy T-proximity spaces defined by Hashem and Morsi in 2002, where T stands for any continuous triangular norm. In this paper, we show that all initial and final lifts in the category T-FPS of these fuzzy T-proximity spaces exist and hence the initial and final fuzzy T-proximity structures exist. We introduce a characterization for the initial fuzzy T-proximity structures, so as a special initial fuzzy T-proximity spaces, the subspaces and product spaces of these fuzzy T-proximity spaces can also be characterized. We also show that the fuzzy topology associated to the initial fuzzy T-proximity structure of a family of fuzzy T-proximity spaces, coincides with the initial fuzzy topology of the family of fuzzy topologies associated to these fuzzy T-proximity spaces.     

基于多元概率推理模型的中文书写纹识别

针对中文短文本分类问题,从集成学习的角度提出一种基于多元概率推理模型的书写纹识别方法。将初始样本集划分为等粒度、可交叉的样本子集,构造具有差异性的子空间,在各子空间上采用基于概率推理模型的基分类器训练样本,通过概率求和法融合所有基分类器的输出得到训练样本的最终识别结果。实验结果表明,该方法对于网络书写纹具有较好的识别效果,查全率、查准率和Fl度量值分别高达81．6％、85．9％和83．69％。