A boundary integral equation method for a class of crack problems in anisotropic elasticity

A boundary integral procedure for the solution of an important class of crack problems in anisotropic elasticity is outlined. A specific numerical example is considered in order to assess the effectiveness of the procedure.     

Transposition based contextual insertion on linear DNA strands with deletion precedence

Insertion and deletion operations are two most important operations in molecular computation, and recently these two operations gained interest in the context of molecular computation. In the proposed system, single contextual insertion and usual (with two contexts) contextual deletions are used. In addition, it is assumed that the deletion operation has higher precedence than insertions, if both are possible at a moment. Given a word t, called a context, and the single contextual insertion is performed as follows. For each occurrence of the context t in the given string y, the word t is replaced as txt. In practice, this can be achieved by a replicative transposition process. Similarly, given a pair of words (u, v), called a context, the (u, v)-contextual deletion removes the word in between u and v. Finally, the power of this system and some closure properties are studied.     

生物多样性影响人类健康：机制假说及 关键问题

生物多样性与人类健康休戚与共，在中国大力开展生物多样性保护和健康中国建设的背景下，实现二者的协同具有重要意义，但中国的相关研究和实践仍处于起步阶段，有必要回顾国际经验、探索前进方向。梳理了生物多样性与人类健康关系的认知发展历程，辨析了“同一健康”“生态健康”“星球健康”3种主流整体健康思想的异同，总结了当前有关生物多样性影响人类健康的医学和心理学机制、假说与理论模型。在此基础上，从阐释现象、理论机制、调节优化3个层面提出了生物多样性影响人类健康领域的15组关键研究问题，并提出了加强生物多样性促进人类健康能力的绿色空间规划设计策略。系统地展现了生物多样性影响人类健康领域的认知历程、作用机制、关键问题及实现路径，以期为后续研究和实践提供借鉴。     

Privacy-preserving computation of participatory noise maps in the cloud

This paper presents a privacy-preserving system for participatory sensing, which relies on cryptographic techniques and distributed computations in the cloud. Each individual user is represented by a personal software agent, deployed in the cloud, where it collaborates on distributed computations without loss of privacy, including with respect to the cloud service providers. We present a generic system architecture involving a cryptographic protocol based on a homomorphic encryption scheme for aggregating sensing data into maps, and demonstrate security in the Honest-But-Curious model both for the users and the cloud service providers. We validate our system in the context of NoiseTube, a participatory sensing framework for noise pollution, presenting experiments with real and artificially generated data sets, and a demo on a heterogeneous set of commercial cloud providers. To the best of our knowledge our system is the first operational privacy-preserving system for participatory sensing. While our validation pertains to the noise domain, the approach used is applicable in any crowd-sourcing application relying on location-based contributions of citizens where maps are produced by aggregating data – also beyond the domain of environmental monitoring.     

一种量子衍生神经网络模型算法及应用

为提高神经网络的逼近和预测能力，提出一种各维输入为离散序列的量子衍生神经网络模型及算法。该模型为三层结构，隐层为量子衍生神经元，输出层为普通神经元。量子衍生神经元由量子旋转门和多位受控旋转门组成，利用多位受控旋转门中目标量子位的输出向输入端的反馈，实现对输入序列的整体记忆，利用受控旋转门输出中多位量子比特的纠缠获得量子衍生神经元的输出。基于量子计算理论设计了该模型的学习算法。该模型可从宽度和深度两方面获取输入序列的特征。仿真结果表明，当输入节点数和序列长度满足一定关系时，该模型明显优于普通神经网络。     

Hybrid multiobjective artificial bee colony for multiple sequence alignment

In the bioinformatics community, it is really important to find an accurate and simultaneous alignment among diverse biological sequences which are assumed to have an evolutionary relationship. From the alignment, the sequences homology is inferred and the shared evolutionary origins among the sequences are extracted by using phylogenetic analysis. This problem is known as the multiple sequence alignment (MSA) problem. In the literature, several approaches have been proposed to solve the MSA problem, such as progressive alignments methods, consistency-based algorithms, or genetic algorithms (GAs). In this work, we propose a Hybrid Multiobjective Evolutionary Algorithm based on the behaviour of honey bees for solving the MSA problem, the hybrid multiobjective artificial bee colony (HMOABC) algorithm. HMOABC considers two objective functions with the aim of preserving the quality and consistency of the alignment: the weighted sum-of-pairs function with affine gap penalties (WSP) and the number of totally conserved (TC) columns score. In order to assess the accuracy of HMOABC, we have used the BAliBASE benchmark (version 3.0), which according to the developers presents more challenging test cases representing the real problems encountered when aligning large sets of complex sequences. Our multiobjective approach has been compared with 13 well-known methods in bioinformatics field and with other 6 evolutionary algorithms published in the literature.     

Fast Two-Output Secure Computation with Optimal Error Probability

Cut-and-choose paradigm makes Yao's protocol for two-party computation secure in malicious model with an error probability.In CRYPTO 2013,based on multi-phase cut-and-choose,Lindell reduced this probability to the optimal value.However,this work can only compute single-output functions with optimal error probability.We transform multi-phase cut-and-choose for singleoutput case into one that can deal with two-output functions,meanwhile maintaining the optimal error probability.Based on this new paradigm,we propose an efficient two-output secure computation protocol.Besides,by utilizing the specific property of the output garbled keys,we solve the authenticity issue of the generator's output with only symmetric cryptographic operations linear in the output length of the generator,which is the most efficient method so far in standard model without Random oracle (Ro).     

Computation Controllable Mode Decision and Motion Estimation for Scalable Video Coding

This paper proposes an efficient computation‐aware mode decision and search point (SP) allocation algorithm for spatial and quality scalabilities in Scalable Video Coding. In our proposal, a linear model is derived to allocate the computation for macroblocks in enhancement layers by using the rate distortion costs of the base layer. In addition, an adaptive SP decision algorithm is proposed to decide the number of SPs for motion estimation under the constraint of the allocated computation. Experiment results demonstrate that the proposed algorithm allocates the computation resource efficiently and outperforms other works in rate distortion performance under the same computational availability constraint.     

Drinking water distribution systems contamination management to reduce public health impacts and system service interruptions

Decisions on protecting public health against drinking water systems contamination threats should be made with careful consideration of credibility of threat observations and adverse impacts of response on system serviceability. Decision support models are developed in this study to prepare water utility operators for making these critical decisions during the intense course of an emergency. A pressure-dependent demand model is developed to simulate the system hydraulics and contaminant propagation under pressure-deficit conditions that emerge after the response actions are executed. Contrary to conventional demand-driven models, this hydraulic analysis approach prevents potential occurrence of negative pressures during the simulation and may identify better response protocols through exploring a larger search space. Response mechanisms of contaminant containment and discharge are optimized using evolutionary algorithms to achieve public health protection with minimum service interruption. Sensitivity analyses are conducted to assess optimal response performance for varying response delay, number of hydrants, and intrusion characteristics. Different methods for quantifying impacts on public health and system serviceability are explored and the sensitivity of the optimal response plan to these different formulations is investigated. The simulation-optimization schemes are demonstrated and discussed using a virtual water distribution system.