Optimal Dynamic Allocation of Conservation Funding Among Priority Regions

The optimal allocation of conservation resources between biodiverse conservation regions has generally been calculated using stochastic dynamic programming, or using myopic heuristics. These solutions are hard to interpret and may not be optimal. To overcome these two limitations, this paper approaches the optimal conservation resource allocation problem using optimal control theory. A solution using Pontryagin’s maximum principle provides novel insight into the general properties of efficient conservation resource allocation strategies, and allows more extensive testing of the performance of myopic heuristics. We confirmed that a proposed heuristic (minimize short-term loss) yields near-optimal results in complex allocation situations, and found that a qualitative allocation feature observed in previous analyses (bang-bang allocation) is a general property of the optimal allocation strategy.     

Truthful online double auction based dynamic resource provisioning for multi-objective trade-offs in IaaS clouds

Auction designs have recently been adopted for static and dynamic resource provisioning in IaaS clouds, such as Microsoft Azure and Amazon EC2. However, the existing mechanisms are mostly restricted to simple auctions, single-objective, offline setting, one-sided interactions either among cloud users or cloud service providers (CSPs), and possible misreports of cloud user’s private information. This paper proposes a more realistic scenario of online auctioning for IaaS clouds, with the unique characteristics of elasticity for time-varying arrival of cloud user requests under the time-based server maintenance in cloud data centers. We propose an online truthful double auction technique for balancing the multi-objective trade-offs between energy, revenue, and performance in IaaS clouds, consisting of a weighted bipartite matching based winning-bid determination algorithm for resource allocation and a Vickrey–Clarke–Groves (VCG) driven algorithm for payment calculation of winning bids. Through rigorous theoretical analysis and extensive trace-driven simulation studies exploiting Google cluster workload traces, we demonstrate that our mechanism significantly improves the performance while promising truthfulness, heterogeneity, economic efficiency, individual rationality, and has a polynomial-time computational complexity.

     

An optimized human resource management model for cloud-edge computing in the internet of things

The use of cloud-edge technology creates significant potential for cost reduction, efficiency and resource management. These features have encouraged users and organizations to use intelligence federated cloud-edge paradigm in Internet of Things (IoT). Human Resource Management (HRM) is one of the important challenges in federated cloud-edge computing. Since hardware and software resources in the edge environment are allocated for responding human requests, selecting optimal resources based on Quality of Service (QoS) factors is a critical and important issue in the IoT environments. The HRM can be considered as an NP-problem in a way that with proper selection, allocation and monitoring resource, system efficiency increases and response time decreases. In this study, an optimization model is presented for the HRM problem using Whale Optimization Algorithm (WOA) in cloud-edge computing. Experimental results show that the proposed model was able to improve minimum response time, cost of allocation and increasing number of allocated human resources in two different scenarios compared to the other meta-heuristic algorithms.

     

Efficient Nash Equilibrium Resource Allocation Based on Game Theory Mechanism in Cloud Computing by Using Auction

One of the most complex issues in the cloud computing environment is the problem of resource allocation so that, on one hand, the cloud provider expects the most profitability and, on the other hand, users also expect to have the best resources at their disposal considering the budget constraints and time. In most previous work conducted, heuristic and evolutionary approaches have been used to solve this problem. Nevertheless, since the nature of this environment is based on economic methods, using such methods can decrease response time and reducing the complexity of the problem. In this paper, an auction-based method is proposed which determines the auction winner by applying game theory mechanism and holding a repetitive game with incomplete information in a non-cooperative environment. In this method, users calculate suitable price bid with their objective function during several round and repetitions and send it to the auctioneer; and the auctioneer chooses the winning player based the suggested utility function. In the proposed method, the end point of the game is the Nash equilibrium point where players are no longer inclined to alter their bid for that resource and the final bid also satisfies the auctioneer’s utility function. To prove the response space convexity, the Lagrange method is used and the proposed model is simulated in the cloudsim and the results are compared with previous work. At the end, it is concluded that this method converges to a response in a shorter time, provides the lowest service level agreement violations and the most utility to the provider.     

Maximizing phylogenetic diversity in biodiversity conservation: Greedy solutions to the Noah's Ark problem

The Noah's Ark Problem (NAP) is a comprehensive cost-effectiveness methodology for biodiversity conservation that was introduced by Weitzman (1998) and utilizes the phylogenetic tree containing the taxa of interest to assess biodiversity. Given a set of taxa, each of which has a particular survival probability that can be increased at some cost, the NAP seeks to allocate limited funds to conserving these taxa so that the future expected biodiversity is maximized. Finding optimal solutions using this framework is a computationally difficult problem to which a simple and efficient "greedy" algorithm has been proposed in the literature and applied to conservation problems. We show that, although algorithms of this type cannot produce optimal solutions for the general NAP, there are two restricted scenarios of the NAP for which a greedy algorithm is guaranteed to produce optimal solutions. The first scenario requires the taxa to have equal conservation cost; the second scenario requires an ultrametric tree. The NAP assumes a linear relationship between the funding allocated to conservation of a taxon and the increased survival probability of that taxon. This relationship is briefly investigated and one variation is suggested that can also be solved using a greedy algorithm.     

A flexible annealing chaotic neural network to maximum clique problem

Based on the analysis and comparison of several annealing strategies, we present a flexible annealing chaotic neural network which has flexible controlling ability and quick convergence rate to optimization problem. The proposed network has rich and adjustable chaotic dynamics at the beginning, and then can converge quickly to stable states. We test the network on the maximum clique problem by some graphs of the DIMACS clique instances, p-random and k random graphs. The simulations show that the flexible annealing chaotic neural network can get satisfactory solutions at very little time and few steps. The comparison between our proposed network and other chaotic neural networks denotes that the proposed network has superior executive efficiency and better ability to get optimal or near-optimal solution.     

Optimal vaccine allocation for COVID-19 in the Netherlands: A data-driven prioritization

For the control of COVID-19, vaccination programmes provide a long-term solution. The amount of available vaccines is often limited, and thus it is crucial to determine the allocation strategy. While mathematical modelling approaches have been used to find an optimal distribution of vaccines, there is an excessively large number of possible allocation schemes to be simulated. Here, we propose an algorithm to find a near-optimal allocation scheme given an intervention objective such as minimization of new infections, hospitalizations, or deaths, where multiple vaccines are available. The proposed principle for allocating vaccines is to target subgroups with the largest reduction in the outcome of interest. We use an approximation method to reconstruct the age-specific transmission intensity (the next generation matrix), and express the expected impact of vaccinating each subgroup in terms of the observed incidence of infection and force of infection. The proposed approach is firstly evaluated with a simulated epidemic and then applied to the epidemiological data on COVID-19 in the Netherlands. Our results reveal how the optimal allocation depends on the objective of infection control. In the case of COVID-19, if we wish to minimize deaths, the optimal allocation strategy is not efficient for minimizing other outcomes, such as infections. In simulated epidemics, an allocation strategy optimized for an outcome outperforms other strategies such as the allocation from young to old, from old to young, and at random. Our simulations clarify that the current policy in the Netherlands (i.e., allocation from old to young) was concordant with the allocation scheme that minimizes deaths. The proposed method provides an optimal allocation scheme, given routine surveillance data that reflect ongoing transmissions. This approach to allocation is useful for providing plausible simulation scenarios for complex models, which give a more robust basis to determine intervention strategies.     

Finding Near-Optimal Groups of Epidemic Spreaders in a Complex Network

In this paper, we present algorithms to find near-optimal sets of epidemic spreaders in complex networks. We extend the notion of local-centrality, a centrality measure previously shown to correspond with a node''s ability to spread an epidemic, to sets of nodes by introducing combinatorial local centrality. Though we prove that finding a set of nodes that maximizes this new measure is NP-hard, good approximations are available. We show that a strictly greedy approach obtains the best approximation ratio unless P = NP and then formulate a modified version of this approach that leverages qualities of the network to achieve a faster runtime while maintaining this theoretical guarantee. We perform an experimental evaluation on samples from several different network structures which demonstrate that our algorithm maximizes combinatorial local centrality and consistently chooses the most effective set of nodes to spread infection under the SIR model, relative to selecting the top nodes using many common centrality measures. We also demonstrate that the optimized algorithm we develop scales effectively.     

Optimal adaptive designs for binary response trials

Summary. We derive the optimal allocation between two treatments in a clinical trial based on the following optimality criterion: for fixed variance of the test statistic, what allocation minimizes the expected number of treatment failures? A sequential design is described that leads asymptotically to the optimal allocation and is compared with the randomized play‐the‐winner rule, sequential Neyman allocation, and equal allocation at similar power levels. We find that the sequential procedure generally results in fewer treatment failures than the other procedures, particularly when the success probabilities of treatments are smaller.     

Efficient selection of tagging single-nucleotide polymorphisms in multiple populations

Common genetic polymorphism may explain a portion of the heritable risk for common diseases, so considerable effort has been devoted to finding and typing common single-nucleotide polymorphisms (SNPs) in the human genome. Many SNPs show correlated genotypes, or linkage disequilibrium (LD), suggesting that only a subset of all SNPs (known as tagging SNPs, or tagSNPs) need to be genotyped for disease association studies. Based on the genetic differences that exist among human populations, most tagSNP sets are defined in a single population and applied only in populations that are closely related. To improve the efficiency of multi-population analyses, we have developed an algorithm called MultiPop-TagSelect that finds a near-minimal union of population-specific tagSNP sets across an arbitrary number of populations. We present this approach as an extension of LD-select, a tagSNP selection method that uses a greedy algorithm to group SNPs into bins based on their pairwise association patterns, although the MultiPop-TagSelect algorithm could be used with any SNP tagging approach that allows choices between nearly equivalent SNPs. We evaluate the algorithm by considering tagSNP selection in candidate-gene resequencing data and lower density whole-chromosome data. Our analysis reveals that an exhaustive search is often intractable, while the developed algorithm can quickly and reliably find near-optimal solutions even for difficult tagSNP selection problems. Using populations of African, Asian, and European ancestry, we also show that an optimal multi-population set of tagSNPs can be substantially smaller (up to 44%) than a typical set obtained through independent or sequential selection.Electronic Supplementary Material Supplementary material is available for this article at and is accessible for authorized users.     

Greedy Selection of Species for Ancestral State Reconstruction on Phylogenies: Elimination Is Better than Insertion

Accurate reconstruction of ancestral character states on a phylogeny is crucial in many genomics studies. We study how to select species to achieve the best reconstruction of ancestral character states on a phylogeny. We first show that the marginal maximum likelihood has the monotonicity property that more taxa give better reconstruction, but the Fitch method does not have it even on an ultrametric phylogeny. We further validate a greedy approach for species selection using simulation. The validation tests indicate that backward greedy selection outperforms forward greedy selection. In addition, by applying our selection strategy, we obtain a set of the ten most informative species for the reconstruction of the genomic sequence of the so-called boreoeutherian ancestor of placental mammals. This study has broad relevance in comparative genomics and paleogenomics since limited research resources do not allow researchers to sequence the large number of descendant species required to reconstruct an ancestral sequence.     

From perceptual supplementation to the accessibility of digital spaces: The case of free exploration of city maps for blind persons

Accessibility of urban and digital spaces is still a challenge for the blind or visually impaired people. Information and communication technologies open new opportunities to design innovative solutions for the sake of accessibility for all. For this concern, the development of perceptual supplementation systems is a promising solution. In the framework of the Tactile interactions for orientation, information and presence (ITOIP) project, we aim at developing the use of the Tactos technology which enables blind users to access digital shapes through the sense of touch. Within this project, we designed new applications for Tactos with the partnership of a blind and visually impaired persons local association. Our research is grounded on sensory motor theories of perception. We illustrate our approach with the case of Tactos map: an application that enables street maps free exploration with no need of sight. During the design of Tactos map, we were confronted with the problem of enabling users to access content beyond the screen viewport. This problem and the proposed solution highlights the dialog between theoretical and design perspectives at the heart of our approach. The performances and remarks from the first Tactos map users let us envision several issues to address in our future work.     

Abstract shapes of RNA

The function of a non-protein-coding RNA is often determined by its structure. Since experimental determination of RNA structure is time-consuming and expensive, its computational prediction is of great interest, and efficient solutions based on thermodynamic parameters are known. Frequently, however, the predicted minimum free energy structures are not the native ones, leading to the necessity of generating suboptimal solutions. While this can be accomplished by a number of programs, the user is often confronted with large outputs of similar structures, although he or she is interested in structures with more fundamental differences, or, in other words, with different abstract shapes. Here, we formalize the concept of abstract shapes and introduce their efficient computation. Each shape of an RNA molecule comprises a class of similar structures and has a representative structure of minimal free energy within the class. Shape analysis is implemented in the program RNAshapes. We applied RNAshapes to the prediction of optimal and suboptimal abstract shapes of several RNAs. For a given energy range, the number of shapes is considerably smaller than the number of structures, and in all cases, the native structures were among the top shape representatives. This demonstrates that the researcher can quickly focus on the structures of interest, without processing up to thousands of near-optimal solutions. We complement this study with a large-scale analysis of the growth behaviour of structure and shape spaces. RNAshapes is available for download and as an online version on the Bielefeld Bioinformatics Server.     

A greedier approach for finding tag SNPs

MOTIVATION: Recent studies have shown that a small subset of Single Nucleotide Polymorphisms (SNPs) (called tag SNPs) is sufficient to capture the haplotype patterns in a high linkage disequilibrium region. To find the minimum set of tag SNPs, exact algorithms for finding the optimal solution could take exponential time. On the other hand, approximation algorithms are more efficient but may fail to find the optimal solution. RESULTS: We propose a hybrid method that combines the ideas of the branch-and-bound method and the greedy algorithm. This method explores larger solution space to obtain a better solution than a traditional greedy algorithm. It also allows the user to adjust the efficiency of the program and quality of solutions. This algorithm has been implemented and tested on a variety of simulated and biological data. The experimental results indicate that our program can find better solutions than previous methods. This approach is quite general since it can be used to adapt other greedy algorithms to solve their corresponding problems. AVAILABILITY: The program is available upon request.     

Question Popularity Analysis and Prediction in Community Question Answering Services

With the blooming of online social media applications, Community Question Answering (CQA) services have become one of the most important online resources for information and knowledge seekers. A large number of high quality question and answer pairs have been accumulated, which allow users to not only share their knowledge with others, but also interact with each other. Accordingly, volumes of efforts have been taken to explore the questions and answers retrieval in CQA services so as to help users to finding the similar questions or the right answers. However, to our knowledge, less attention has been paid so far to question popularity in CQA. Question popularity can reflect the attention and interest of users. Hence, predicting question popularity can better capture the users’ interest so as to improve the users’ experience. Meanwhile, it can also promote the development of the community. In this paper, we investigate the problem of predicting question popularity in CQA. We first explore the factors that have impact on question popularity by employing statistical analysis. We then propose a supervised machine learning approach to model these factors for question popularity prediction. The experimental results show that our proposed approach can effectively distinguish the popular questions from unpopular ones in the Yahoo! Answers question and answer repository.     

A life history model of somatic damage associated with resource acquisition: damage protection or prevention?

A resource acquisition-allocation model is developed to examine the trade-off between reproduction and somatic protection. Unlike previous studies, resource intake is not assumed to be constrained: instead, resource intake is free to vary, with increased intake being associated with an increased risk of somatic damage. This gives rise to an optimal resource intake as well as an optimal allocation strategy. This paper studies the relative importance of acquisition and allocation strategies in regulating acquisition-related mortality. Under the optimal allocation strategy mortality rate increases with age, in accordance with the disposable soma theory of aging. Contrary to the usual interpretation of the disposable soma theory, this increase in mortality can arise from an increase in the resource acquisition effort rather than a decrease in the resources allocated to protection. At early ages resource acquisition is found to be the primary path for regulating life history costs, whilst allocating resources to protection becomes more important later in life. Models for targeted and non-targeted damage repair are considered and the robustness of our results to the structure and parameterization of the model is discussed. The results from our models are discussed in light of published data. Resource acquisition is shown to be a potentially important mechanism for controlling somatic damage which deserves further study.     

Simulated annealing with different vessel assignment strategies for the continuous berth allocation problem

The berth allocation problem is an optimization problem concerning seaside operations at container terminals. This study investigates the dynamic and continuous berth allocation problem (BAP), whose objective is to minimize the total weighted service time and the deviation cost from vessels’ preferred position. The problem is formulated as a mixed integer programming model. Due to that the BAP is NP-hard, two efficient and effective simulated annealing (SA) algorithms are proposed to locate vessels along the quay. The first SA assigns vessels to available positions along the quay from the left to the right, while the second assigns vessels from both sides. Both small and large-scale instances in the literature are tested to evaluate the effectiveness of the proposed SA algorithms using the optimization software Gurobi and heuristic algorithms from the literature. The results indicate that the proposed SAs can provide optimal solutions in small-scale instances and updates the best solutions in large-scale instances. The improvement over other comparing heuristics is statistically significant.     

An Extended N-Player Network Game and Simulation of Four Investment Strategies on a Complex Innovation Network

As computer science and complex network theory develop, non-cooperative games and their formation and application on complex networks have been important research topics. In the inter-firm innovation network, it is a typical game behavior for firms to invest in their alliance partners. Accounting for the possibility that firms can be resource constrained, this paper analyzes a coordination game using the Nash bargaining solution as allocation rules between firms in an inter-firm innovation network. We build an extended inter-firm n-player game based on nonidealized conditions, describe four investment strategies and simulate the strategies on an inter-firm innovation network in order to compare their performance. By analyzing the results of our experiments, we find that our proposed greedy strategy is the best-performing in most situations. We hope this study provides a theoretical insight into how firms make investment decisions.     

Short-term fast forecasting based on family behavior pattern recognition for small-scale users load

Household electricity consumption has been rising gradually with the improvement of living standards. Making short-term load forecasting at the small-scale users plays an increasingly important role in the future power network planning and operation. To meet the efficiency of the dispatching system and the demand of human daily power consumption, an optimal forecasting model Attention-CNN-GRU of small-scale users load at various periods of the day based on family behavior pattern recognition is proposed in this study. The low-level data information (smart meter data) is used to build the high-level model (small-scale users load). Attention mechanism and convolutional neural networks (CNN) can further enhance the prediction accuracy of gated recurrent unit (GRU) and notably shorten its prediction time. The recognition of family behavior patterns can be achieved through the users’ smart meter data, and users are aggregated into K categories. The results of optimal K category prediction under the family behavior model are summarized as the final prediction outcome. This idea framework is tested on real users’ smart meter data, and its performance is comprehensively compared with different benchmarks. The results present strong compatibility in the small-scale users load forecasting model at various periods of the day and swift short-term prediction of users load compared to other prediction models. The time is shortened by 1/4 compared with the GRU/LSTM model. Furthermore, the accuracy is improved to 92.06% (MAPE is 7.94%).

     

A comparison of utility-oriented algorithms for scheduling parallel tasks in multi-cluster grid

Scheduling parallel tasks in multi-cluster grid can be seen as two interdependent problems: cluster allocation and scheduling parallel task on the allocated cluster. In this paper both rigid and moldable parallel tasks are considered. We propose a theoretical model of utility-oriented parallel task scheduling in multi-cluster grid with advance reservations. On the basis of the model we present an approximation algorithm, a repair strategy based genetic algorithm and greedy heuristics MaxMax, T-Sufferage and R-Sufferage to solve the two interdependent problems. We compare the performance of these algorithms in aspect of utility optimality and timing results. Simulation results show on average the (1+α)-approximation algorithm achieves the best trade-off between utility optimality and timing. Genetic algorithm could achieve better utility than greedy heuristics and approximate algorithm at expensive time cost. Greedy heuristics do not perform equally well when adapted to different utility functions while the approximation algorithm shows its intrinsic stable performance.