Managing and learning with multiple models: Objectives and optimization algorithms

The quality of environmental decisions should be gauged according to managers' objectives. Management objectives generally seek to maximize quantifiable measures of system benefit, for instance population growth rate. Reaching these goals often requires a certain degree of learning about the system. Learning can occur by using management action in combination with a monitoring system. Furthermore, actions can be chosen strategically to obtain specific kinds of information. Formal decision making tools can choose actions to favor such learning in two ways: implicitly via the optimization algorithm that is used when there is a management objective (for instance, when using adaptive management), or explicitly by quantifying knowledge and using it as the fundamental project objective, an approach new to conservation.This paper outlines three conservation project objectives - a pure management objective, a pure learning objective, and an objective that is a weighted mixture of these two. We use eight optimization algorithms to choose actions that meet project objectives and illustrate them in a simulated conservation project. The algorithms provide a taxonomy of decision making tools in conservation management when there is uncertainty surrounding competing models of system function. The algorithms build upon each other such that their differences are highlighted and practitioners may see where their decision making tools can be improved. © 2010 Elsevier Ltd.

Multi-objective mission flight planning in civil unmanned aerial systems

Unmanned Aerial Vehicles (UAVs) are emerging as an ideal platform for a wide range of civil applications such as disaster monitoring, atmospheric observation and outback delivery. However, the operation of UAVs is currently restricted to specially segregated regions of airspace outside of the National Airspace System (NAS). Mission Flight Planning (MFP) is an integral part of UAV operation that addresses some of the requirements (such as safety and the rules of the air) of integrating UAVs in the NAS. Automated MFP is a key enabler for a number of UAV operating scenarios as it aids in increasing the level of onboard autonomy. For example, onboard MFP is required to ensure continued conformance with the NAS integration requirements when there is an outage in the communications link. MFP is a motion planning task concerned with finding a path between a designated start waypoint and goal waypoint. This path is described with a sequence of 4 Dimensional (4D) waypoints (three spatial and one time dimension) or equivalently with a sequence of trajectory segments (or tracks). It is necessary to consider the time dimension as the UAV operates in a dynamic environment. Existing methods for generic motion planning, UAV motion planning and general vehicle motion planning cannot adequately address the requirements of MFP. The flight plan needs to optimise for multiple decision objectives including mission safety objectives, the rules of the air and mission efficiency objectives. Online (in-flight) replanning capability is needed as the UAV operates in a large, dynamic and uncertain outdoor environment. This thesis derives a multi-objective 4D search algorithm entitled Multi- Step A* (MSA*) based on the seminal A* search algorithm. MSA* is proven to find the optimal (least cost) path given a variable successor operator (which enables arbitrary track angle and track velocity resolution). Furthermore, it is shown to be of comparable complexity to multi-objective, vector neighbourhood based A* (Vector A*, an extension of A*). A variable successor operator enables the imposition of a multi-resolution lattice structure on the search space (which results in fewer search nodes). Unlike cell decomposition based methods, soundness is guaranteed with multi-resolution MSA*. MSA* is demonstrated through Monte Carlo simulations to be computationally efficient. It is shown that multi-resolution, lattice based MSA* finds paths of equivalent cost (less than 0.5% difference) to Vector A* (the benchmark) in a third of the computation time (on average). This is the first contribution of the research. The second contribution is the discovery of the additive consistency property for planning with multiple decision objectives. Additive consistency ensures that the planner is not biased (which results in a suboptimal path) by ensuring that the cost of traversing a track using one step equals that of traversing the same track using multiple steps. MSA* mitigates uncertainty through online replanning, Multi-Criteria Decision Making (MCDM) and tolerance. Each trajectory segment is modeled with a cell sequence that completely encloses the trajectory segment. The tolerance, measured as the minimum distance between the track and cell boundaries, is the third major contribution. Even though MSA* is demonstrated for UAV MFP, it is extensible to other 4D vehicle motion planning applications. Finally, the research proposes a self-scheduling replanning architecture for MFP. This architecture replicates the decision strategies of human experts to meet the time constraints of online replanning. Based on a feedback loop, the proposed architecture switches between fast, near-optimal planning and optimal planning to minimise the need for hold manoeuvres. The derived MFP framework is original and shown, through extensive verification and validation, to satisfy the requirements of UAV MFP. As MFP is an enabling factor for operation of UAVs in the NAS, the presented work is both original and significant.

Good results for early treatment of clinically isolated syndrome prior to multiple sclerosis with interferon beta-1b and glatiramer

Background: The first sign of developing multiple sclerosis is a clinically isolated syndrome that resembles a multiple sclerosis relapse. Objective/methods: The objective was to review the clinical trials of two medicines in clinically isolated syndromes (interferon β and glatiramer acetate) to determine whether they prevent progression to definite multiple sclerosis. Results: In the BENEFIT trial, after 2 years, 45% of subjects in the placebo group developed clinically definite multiple sclerosis, and the rate was lower in the interferon β-1b group. Then all subjects were offered interferon β-1b, and the original interferon β-1b group became the early treatment group, and the placebo group became the delayed treatment group. After 5 years, the number of subjects with clinical definite multiple sclerosis remained lower in the early treatment than late treatment group. In the PreCISe trial, after 2 years, the time for 25% of the subjects to convert to definite multiple sclerosis was prolonged in the glatiramer group. Conclusions: Interferon β-1b and glatiramer acetate slow the progression of clinically isolated syndromes to definite multiple sclerosis. However, it is not known whether this early treatment slows the progression to the physical disabilities experienced in multiple sclerosis.

Oral fingolimod for relapsing-remitting multiple sclerosis

Background: Most people with multiple sclerosis have the relapsing-remitting type. Objective/methods: The objective was to evaluate two clinical trials of fingolimod in relapsing multiple sclerosis. Results: FREEDOMS (FTY720 Research Evaluation Effects of Daily Oral therapy in Multiple Sclerosis), a Phase III placebo-controlled trial, showed that fingolimod (0.5 or 1.25 mg) reduced the relapse rate and disability in multiple sclerosis, compared to placebo. Fingolimod (0.5 or 1.25 mg) has been compared to interferon β-1a in a Phase III clinical trial (TRANSFORMS; Trial Assessing Injectable Interferon versus FTY720 Oral in Relapsing-Remitting Multiple Sclerosis) and shown to be more efficacious than interferon β-1a in reducing relapse rates. However, fingolimod did increase the risk of infections and skin cancers. Conclusions: Only the lower dose of fingolimod (0.5 mg), which possibly has less toxicity, should be considered for prevention of relapses in relapsing-remitting multiple sclerosis.

Performance optimization for parallel processing on a multiple-CPU server

Symmetric multi-processor (SMP) systems, or multiple-CPU servers, are suitable for implementing parallel algorithms because they employ dedicated communication devices to enhance the inter-processor communication bandwidth, so that a better performance can be obtained. However, the cost for a multiple-CPU server is high and therefore, the server is usually shared among many users. The work-load due to other users will certainly affect the performance of the parallel programs so it is desirable to derive a method to optimize parallel programs under different loading conditions. In this paper, we present a simple method, which can be applied in SPMD type parallel programs, to improve the speedup by controlling the number of threads within the programs.

Multi-objective four-dimensional vehicle motion planning in large dynamic environments

This paper presents Multi-Step A* (MSA*), a search algorithm based on A* for multi-objective 4D vehicle motion planning (three spatial and one time dimension). The research is principally motivated by the need for offline and online motion planning for autonomous Unmanned Aerial Vehicles (UAVs). For UAVs operating in large, dynamic and uncertain 4D environments, the motion plan consists of a sequence of connected linear tracks (or trajectory segments). The track angle and velocity are important parameters that are often restricted by assumptions and grid geometry in conventional motion planners. Many existing planners also fail to incorporate multiple decision criteria and constraints such as wind, fuel, dynamic obstacles and the rules of the air. It is shown that MSA* finds a cost optimal solution using variable length, angle and velocity trajectory segments. These segments are approximated with a grid based cell sequence that provides an inherent tolerance to uncertainty. Computational efficiency is achieved by using variable successor operators to create a multi-resolution, memory efficient lattice sampling structure. Simulation studies on the UAV flight planning problem show that MSA* meets the time constraints of online replanning and finds paths of equivalent cost but in a quarter of the time (on average) of vector neighbourhood based A*.

Modeling circulation of train-set with multiple routing

Planning on utilization of train-set is one of the key tasks of transport organization for passenger dedicated railway in China. It also has strong relationships with timetable scheduling and operation plans at a station. To execute such a task in a railway hub pooling multiple railway lines, the characteristics of multiple routing for train-set is discussed in term of semicircle of train-sets' turnover. In programming the described problem, the minimum dwell time is selected as the objectives with special derive constraints of the train-set's dispatch, the connecting conditions, the principle of uniqueness for train-sets, and the first plus for connection in the same direction based on time tolerance σ. A compact connection algorithm based on time tolerance is then designed. The feasibility of the model and the algorithm is proved by the case study. The result indicates that the circulation model and algorithm about multiple routing can deal with the connections between the train-sets of multiple directions, and reduce the train's pulling in or leaving impact on the station's throat.

Multiple types of child maltreatment and adolescent mental health in Viet Nam

Objective To examine the prevalence of multiple types of maltreatment (MTM), potentially confounding factors and associations with depression, anxiety and self-esteem among adolescents in Viet Nam. Methods In 2006 we conducted a cross-sectional survey of 2591 students (aged 12–18 years; 52.1% female) from randomly-selected classes in eight secondary schools in urban (Hanoi) and rural (Hai Duong) areas of northern Viet Nam (response rate, 94.7%). Sequential multiple regression analyses were performed to estimate the relative influence of individual, family and social characteristics and of eight types of maltreatment, including physical, emotional and sexual abuse and physical or emotional neglect, on adolescent mental health. Findings Females reported more neglect and emotional abuse, whereas males reported more physical abuse, but no statistically significant difference was found between genders in the prevalence of sexual abuse. Adolescents were classified as having nil (32.6%), one (25.9%), two (20.7%), three (14.5%) or all four (6.3%) maltreatment types. Linear bivariate associations between MTM and depression, anxiety and low self-esteem were observed. After controlling for demographic and family factors, MTM showed significant independent effects. The proportions of the variance explained by the models ranged from 21% to 28%. Conclusion The combined influence of adverse individual and family background factors and of child maltreatment upon mental health in adolescents in Viet Nam is consistent with research in non-Asian countries. Emotional abuse was strongly associated with each health indicator. In Asian communities where child abuse is often construed as severe physical violence, it is important to emphasize the equally pernicious effects of emotional maltreatment.

Multiple analysis of three common genetic alterations associated with thrombophilia

We have previously reported the use of a novel mini-sequencing protocol for detection of the factor V Leiden variant, the first nucleotide change (FNC) technology. This technology is based on a single nucleotide extension of a primer, which is hybridized immediately adjacent to the site of mutation. The extended nucleotide that carries a reporter molecule (fluorescein) has the power to discriminate the genotype at the site of mutation. More recently, the prothrombin 20210 and thermolabile methylene tetrahydrofolate reductase (MTHFR) 677 variants have been identified as possible risk factors associated with thrombophilia. This study describes the use of the FNC technology in a combined assay to detect factor V, prothrombin and MTHFR variants in a population of Australian blood donors, and describes the objective numerical methodology used to determine genotype cut-off values for each genetic variation. Using FNC to test 500 normal blood donors, the incidence of Factor V Leiden was 3.6% (all heterozygous), that of prothrombin 20210 was 2.8% (all heterozygous) and that of MTHFR was 10% (homozygous). The combined FNC technology offers a simple, rapid, automatable DNA-based test for the detection of these three important mutations that are associated with familial thrombophilia. (C) 2000 Lippincott Williams and Wilkins.

QoS-aware web service composition using genetic algorithms

Web service technology is increasingly being used to build various e-Applications, in domains such as e-Business and e-Science. Characteristic benefits of web service technology are its inter-operability, decoupling and just-in-time integration. Using web service technology, an e-Application can be implemented by web service composition — by composing existing individual web services in accordance with the business process of the application. This means the application is provided to customers in the form of a value-added composite web service. An important and challenging issue of web service composition, is how to meet Quality-of-Service (QoS) requirements. This includes customer focused elements such as response time, price, throughput and reliability as well as how to best provide QoS results for the composites. This in turn best fulfils customers’ expectations and achieves their satisfaction. Fulfilling these QoS requirements or addressing the QoS-aware web service composition problem is the focus of this project. From a computational point of view, QoS-aware web service composition can be transformed into diverse optimisation problems. These problems are characterised as complex, large-scale, highly constrained and multi-objective problems. We therefore use genetic algorithms (GAs) to address QoS-based service composition problems. More precisely, this study addresses three important subproblems of QoS-aware web service composition; QoS-based web service selection for a composite web service accommodating constraints on inter-service dependence and conflict, QoS-based resource allocation and scheduling for multiple composite services on hybrid clouds, and performance-driven composite service partitioning for decentralised execution. Based on operations research theory, we model the three problems as a constrained optimisation problem, a resource allocation and scheduling problem, and a graph partitioning problem, respectively. Then, we present novel GAs to address these problems. We also conduct experiments to evaluate the performance of the new GAs. Finally, verification experiments are performed to show the correctness of the GAs. The major outcomes from the first problem are three novel GAs: a penaltybased GA, a min-conflict hill-climbing repairing GA, and a hybrid GA. These GAs adopt different constraint handling strategies to handle constraints on interservice dependence and conflict. This is an important factor that has been largely ignored by existing algorithms that might lead to the generation of infeasible composite services. Experimental results demonstrate the effectiveness of our GAs for handling the QoS-based web service selection problem with constraints on inter-service dependence and conflict, as well as their better scalability than the existing integer programming-based method for large scale web service selection problems. The major outcomes from the second problem has resulted in two GAs; a random-key GA and a cooperative coevolutionary GA (CCGA). Experiments demonstrate the good scalability of the two algorithms. In particular, the CCGA scales well as the number of composite services involved in a problem increases, while no other algorithms demonstrate this ability. The findings from the third problem result in a novel GA for composite service partitioning for decentralised execution. Compared with existing heuristic algorithms, the new GA is more suitable for a large-scale composite web service program partitioning problems. In addition, the GA outperforms existing heuristic algorithms, generating a better deployment topology for a composite web service for decentralised execution. These effective and scalable GAs can be integrated into QoS-based management tools to facilitate the delivery of feasible, reliable and high quality composite web services.

Interactive processes link the multiple symptoms of fatigue in sport competition

Muscle physiologists often describe fatigue simply as a decline of muscle force and infer this causes an athlete to slow down. In contrast, exercise scientists describe fatigue during sport competition more holistically as an exercise-induced impairment of performance. The aim of this review is to reconcile the different views by evaluating the many performance symptoms/measures and mechanisms of fatigue. We describe how fatigue is assessed with muscle, exercise or competition performance measures. Muscle performance (single muscle test measures) declines due to peripheral fatigue (reduced muscle cell force) and/or central fatigue (reduced motor drive from the CNS). Peak muscle force seldom falls by >30% during sport but is often exacerbated during electrical stimulation and laboratory exercise tasks. Exercise performance (whole-body exercise test measures) reveals impaired physical/technical abilities and subjective fatigue sensations. Exercise intensity is initially sustained by recruitment of new motor units and help from synergistic muscles before it declines. Technique/motor skill execution deviates as exercise proceeds to maintain outcomes before they deteriorate, e.g. reduced accuracy or velocity. The sensation of fatigue incorporates an elevated rating of perceived exertion (RPE) during submaximal tasks, due to a combination of peripheral and higher CNS inputs. Competition performance (sport symptoms) is affected more by decision-making and psychological aspects, since there are opponents and a greater importance on the result. Laboratory based decision making is generally faster or unimpaired. Motivation, self-efficacy and anxiety can change during exercise to modify RPE and, hence, alter physical performance. Symptoms of fatigue during racing, team-game or racquet sports are largely anecdotal, but sometimes assessed with time-motion analysis. Fatigue during brief all-out racing is described biomechanically as a decline of peak velocity, along with altered kinematic components. Longer sport events involve pacing strategies, central and peripheral fatigue contributions and elevated RPE. During match play, the work rate can decline late in a match (or tournament) and/or transiently after intense exercise bursts. Repeated sprint ability, agility and leg strength become slightly impaired. Technique outcomes, such as velocity and accuracy for throwing, passing, hitting and kicking, can deteriorate. Physical and subjective changes are both less severe in real rather than simulated sport activities. Little objective evidence exists to support exercise-induced mental lapses during sport. A model depicting mind-body interactions during sport competition shows that the RPE centre-motor cortex-working muscle sequence drives overall performance levels and, hence, fatigue symptoms. The sporting outputs from this sequence can be modulated by interactions with muscle afferent and circulatory feedback, psychological and decision-making inputs. Importantly, compensatory processes exist at many levels to protect against performance decrements. Small changes of putative fatigue factors can also be protective. We show that individual fatigue factors including diminished carbohydrate availability, elevated serotonin, hypoxia, acidosis, hyperkalaemia, hyperthermia, dehydration and reactive oxygen species, each contribute to several fatigue symptoms. Thus, multiple symptoms of fatigue can occur simultaneously and the underlying mechanisms overlap and interact. Based on this understanding, we reinforce the proposal that fatigue is best described globally as an exercise-induced decline of performance as this is inclusive of all viewpoints.

Chronological age and age-related cognitive deficits are associated with an increase in multiple types of driving errors in late life

Objective: Older driver research has mostly focused on identifying that small proportion of older drivers who are unsafe. Little is known about how normal cognitive changes in aging affect driving in the wider population of adults who drive regularly. We evaluated the association of cognitive function and age, with driving errors. Method: A sample of 266 drivers aged 70 to 88 years were assessed on abilities that decline in normal aging (visual attention, processing speed, inhibition, reaction time, task switching) and the UFOV® which is a validated screening instrument for older drivers. Participants completed an on-road driving test. Generalized linear models were used to estimate the associations of cognitive factor with specific driving errors and number of errors in self-directed and instructor navigated conditions. Results: All errors types increased with chronological age. Reaction time was not associated with driving errors in multivariate analyses. A cognitive factor measuring Speeded Selective Attention and Switching was uniquely associated with the most errors types. The UFOV predicted blindspot errors and errors on dual carriageways. After adjusting for age, education and gender the cognitive factors explained 7% of variance in the total number of errors in the instructor navigated condition and 4% of variance in the self-navigated condition. Conclusion: We conclude that among older drivers errors increase with age and are associated with speeded selective attention particularly when that requires attending to the stimuli in the periphery of the visual field, task switching, errors inhibiting responses and visual discrimination. These abilities should be the target of cognitive training.

A penalty-based grouping genetic algorithm for multiple composite SaaS components clustering in Cloud

Software as a Service (SaaS) in Cloud is getting more and more significant among software users and providers recently. A SaaS that is delivered as composite application has many benefits including reduced delivery costs, flexible offers of the SaaS functions and decreased subscription cost for users. However, this approach has introduced a new problem in managing the resources allocated to the composite SaaS. The resource allocation that has been done at the initial stage may be overloaded or wasted due to the dynamic environment of a Cloud. A typical data center resource management usually triggers a placement reconfiguration for the SaaS in order to maintain its performance as well as to minimize the resource used. Existing approaches for this problem often ignore the underlying dependencies between SaaS components. In addition, the reconfiguration also has to comply with SaaS constraints in terms of its resource requirements, placement requirement as well as its SLA. To tackle the problem, this paper proposes a penalty-based Grouping Genetic Algorithm for multiple composite SaaS components clustering in Cloud. The main objective is to minimize the resource used by the SaaS by clustering its component without violating any constraint. Experimental results demonstrate the feasibility and the scalability of the proposed algorithm.

Pseudo-marginal algorithms with multiple CPUs

The emergence of pseudo-marginal algorithms has led to improved computational efficiency for dealing with complex Bayesian models with latent variables. Here an unbiased estimator of the likelihood replaces the true likelihood in order to produce a Bayesian algorithm that remains on the marginal space of the model parameter (with latent variables integrated out), with a target distribution that is still the correct posterior distribution. Very efficient proposal distributions can be developed on the marginal space relative to the joint space of model parameter and latent variables. Thus psuedo-marginal algorithms tend to have substantially better mixing properties. However, for pseudo-marginal approaches to perform well, the likelihood has to be estimated rather precisely. This can be difficult to achieve in complex applications. In this paper we propose to take advantage of multiple central processing units (CPUs), that are readily available on most standard desktop computers. Here the likelihood is estimated independently on the multiple CPUs, with the ultimate estimate of the likelihood being the average of the estimates obtained from the multiple CPUs. The estimate remains unbiased, but the variability is reduced. We compare and contrast two different technologies that allow the implementation of this idea, both of which require a negligible amount of extra programming effort. The superior performance of this idea over the standard approach is demonstrated on simulated data from a stochastic volatility model.

Genome-wide meta-analysis identifies novel multiple sclerosis susceptibility loci

Objective: To perform a 1-stage meta-analysis of genome-wide association studies (GWAS) of multiple sclerosis (MS) susceptibility and to explore functional consequences of new susceptibility loci. Methods: We synthesized 7 MS GWAS. Each data set was imputed using HapMap phase II, and a per single nucleotide polymorphism (SNP) meta-analysis was performed across the 7 data sets. We explored RNA expression data using a quantitative trait analysis in peripheral blood mononuclear cells (PBMCs) of 228 subjects with demyelinating disease. Results: We meta-analyzed 2,529,394 unique SNPs in 5,545 cases and 12,153 controls. We identified 3 novel susceptibility alleles: rs170934T at 3p24.1 (odds ratio [OR], 1.17; p ¼ 1.6 � 10�8) near EOMES, rs2150702G in the second intron of MLANA on chromosome 9p24.1 (OR, 1.16; p ¼ 3.3 � 10�8), and rs6718520A in an intergenic region on chromosome 2p21, with THADA as the nearest flanking gene (OR, 1.17; p ¼ 3.4 � 10�8). The 3 new loci do not have a strong cis effect on RNA expression in PBMCs. Ten other susceptibility loci had a suggestive p < 1 � 10�6, some of these loci have evidence of association in other inflammatory diseases (ie, IL12B, TAGAP, PLEK, and ZMIZ1). Interpretation: We have performed a meta-analysis of GWAS in MS that more than doubles the size of previous gene discovery efforts and highlights 3 novel MS susceptibility loci. These and additional loci with suggestive evidence of association are excellent candidates for further investigations to refine and validate their role in the genetic architecture of MS.