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This paper presents an improved distributed ant colony optimisation algorithm for routing and spectrum assignment (RSA) on optical burst switched (OBS), flexible spectrum networks, with the spectrum continuity constraint imposed. The improved algorithm incorporates a distributed method for monitoring the congestion along the links and using this information to determine what route-spectrum combination should be selected to minimise the burst loss probability (BLP). Evaluations were performed on an optical burst switching simulator to determine the effect on BLP when a dynamic route congestion measure is considered as opposed to the use of the static route length measure. The evaluations, considering the effects of optical impairments, were made on different network topologies, with multiple spectrum widths provisioned on the network, and with different loads. The incorporation of congestion measures into the algorithm showed improvements in BLP over previous work in the range of 2–32% depending on the evaluation conditions.

In evolutionary robotics, robot controllers are often evolved in simulation, as using the physical robot for fitness evaluation can take a prohibitively long time. Simulators provide a quick way to evaluate controller fitness. A simulator is tasked with providing appropriate sensor information to the controller. If the robot has an on-board camera, an entire virtual visual environment is needed to simulate the camera’s signal. In the past, these visual environments have been constructed by hand, requiring the use of hand-crafted models, textures and lighting, which is a tedious and time-consuming process. This paper proposes a deep neural network-based architecture for simulating visual environments. The neural networks are trained exclusively from images captured from the robot, creating a 3-dimensional visual environment without using hand-crafted models, textures or lighting. It does not rely on any external domain specific datasets, as all training data is captured in the physical environment. Robot controllers were evolved in simulation to discern between objects with different colours and shapes, and they successfully completed the same task in the real world.

SUMMARYThis paper investigates artificial neural network (ANN)-based simulators as an alternative to physics-based approaches for evolving controllers in simulation for a complex snake-like robot. Prior research has been limited to robots or controllers that are relatively simple. Benchmarks are performed in order to identify effective simulator topologies. Additionally, various controller evolution strategies are proposed, investigated and compared. Using ANN-based simulators for controller fitness estimation during controller evolution is demonstrated to be a viable approach for the high-dimensional problem specified in this work.

It has been nearly 100 years since citrus growers in two distinct regions in the northern provinces of South Africa noticed unusual symptoms in their citrus trees, causing significant crop losses. They had no idea that these symptoms would later become part of an almost global pandemic of a disease called greening or huanglongbing (HLB). The rapid spread of the disease indicated that it might be caused by a transmissible pathogen, but it took50 years to identify the causative agent as '

This research proposed augmenting Evolutionary Robotics controllers with their own robotic simulator. The motivation behind this idea is that the controller could use the simulator to approximate the effects of its actions. This would allow the controller to maintain an approximation of the robot’s location and orientation. It was hypothesised that this information could enhance a controller’s navigation ability. Both regular and augmented controllers were tested and compared on a task that required them to employ adaptive navigation strategies to efficiently solve the task. Augmented controllers were shown to perform better and possess superior behaviours in comparison to regular controllers.

Psychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. This has motivated the genesis of two closely intertwined fields: (i) Translational Neuromodeling (TN), which develops “computational assays” for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output). While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use. In this paper, we present the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry. Collectively, the tools in TAPAS presently cover several important aspects of the desired end-to-end pipeline, including: (i) tailored experimental designs and optimization of measurement strategy prior to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition. Here, we review the different tools within TAPAS and illustrate how these may help provide a deeper understanding of neural and cognitive mechanisms of disease, with the ultimate goal of establishing automatized pipelines for predictions about individual patients. We hope that the openly available tools in TAPAS will contribute to the further development of TN/CP and facilitate the translation of advances in computational neuroscience into clinically relevant computational assays., Frontiers in Psychiatry, 12, ISSN:1664-0640

Taking inspiration from the navigation ability of humans, this study investigated a method of providing robotic controllers with a basic sense of position. It incorporated robotic simulators into robotic controllers to provide them with a mechanism to approximate the effects their actions had on the robot. Controllers with and without internal simulators were tested and compared. The proposed controller architecture was shown to outperform the regular controller architecture. However, the longer an internal simulator was executed, the more inaccurate it became. Thus, the performance of controllers with internal simulators reduced over time unless their internal simulator was periodically corrected.

The Free Energy Principle (FEP) and Integrated Information Theory (IIT) are two ambitious theoretical approaches. The first aims to make a formal framework for describing self-organizing and life-like systems in general, and the second attempts a mathematical theory of conscious experience based on the intrinsic properties of a system. They are each concerned with complementary aspects of the properties of systems, one with life and behavior, the other with meaning and experience, so combining them has potential for scientific value. In this paper, we take a first step towards such a synthesis by expanding on the results of the evolutionary simulation study by Albantakis et al. (2014), which show a relationship between IIT-measures and fitness in differing complexities of tasks. We relate some basic FEP-related information theoretic measures to this result, finding that the surprisal of simulated agents’ observations is inversely related to the general increase in fitness and integration over evolutionary time. Moreover, surprisal fluctuates together with IIT-based consciousness measures in within-trial time. This suggests that the consciousness measures used in IIT indirectly depend on the relation between the agent and the external world, and that it should therefore be possible to relate them to the theoretical concepts used in the FEP. Lastly, we suggest a future approach for investigating this relationship empirically.

The Evolutionary Robotics (ER) process has been applied extensively to developing control programs to achieve locomotion in legged robots, as an automated alternative to the arduous task of manually creating control programs for such robots. The evolution of such controllers is typically performed in simulation by making use of a physics engine-based robotic simulator. Making use of such physics-based simulators does, however, have certain challenges associated with it, such as these simulators’ computational inefficiency, potential issues with lack of accuracy and the human effort required to construct such simulators. The current study therefore proposed and investigated an alternative method of simulation for a hexapod (six-legged) robot in the ER process, and directly compared this newly-proposed simulation method to traditional physics-based simulation. This alternative robotic simulator was built based solely on experimental data acquired directly from observing the behaviour of the robot. This data was used to construct a simulator for the robot based on Artificial Neural Networks (ANNs). To compare this novel simulation method to traditional physics simulation, the ANN-based simulators were used to evolve simple open-loop locomotion controllers for the robot in simulation. The real-world performance of these controllers was compared to that of controllers evolved in a more traditional physics-based simulator. The obtained results indicated that the use of ANN-based simulators produced controllers which could successfully perform the required locomotion task on the real-world robot. In addition, the controllers evolved using the ANN-based simulators allowed the real-world robot to move further than those evolved in the physics-based simulator and the ANN-based simulators were vastly more computationally efficient than the physics-based simulator. This study thus decisively indicated that ANN-based simulators offer a superior alternative to widely-used physics simulators in ER for the locomotion task considered.

This work describes a distributed algorithm inspired by ant colony optimisation for a solution to the problem of dynamic routing and wavelength assignment with wavelength continuity constraint in optical burst switched networks. The evaluation was conducted in a wavelength division multiplexed network environment with limited number of wavelength channels and in a flexible spectrum network environment undergoing transmission impairments. The simulations in the flexible spectrum network environment aim to replicate the effects of both linear and nonlinear physical layer impairments. Under these effects, an optical burst control packet could be lost during traversal of the network with no measure in place to notify the network of the loss or to free up optical resource reservations. The optical burst switching acknowledgement protocol has been modified in this work to account for a burst control packet failure by implementing a traversal acknowledgement to cater for its loss. The performance of the distributed ant-based algorithm has been extensively evaluated on several network topologies and compared with that obtained by shortest path routing and ant colony routing and wavelength assignment. The results show that the distributed ant-based algorithm significantly improves the burst transmission success probability in the wavelength division multiplexed network environment and provides a good solution in the flexible spectrum network environment undergoing transmission impairments.

Neural networks are computational structures which can be trained to perform tasks based on training examples or patterns. Recurrent neural networks are a type of network designed to process time-series data. Dropout is a neural network regularization technique. The literature advises that Dropout should not be directly applied to recurrent neural networks as its effects are too dramatic when applied recurrently. This direct approach is described as naive. Instead, there are two specialised recurrent neural network Dropout algorithms proposed by different authors. However, these specialised Dropout algorithms have not been tested against one another and the naive algorithm under identical experimental conditions. This paper compares all of these algorithms and finds that the naive approach performed as well as or better than the specialised Dropout algorithms.

Evolutionary Robotics (ER) is a field of study that has shown much promise in automating the development of robotic controllers and morphologies. The use of simulators as an alternative to real-world robots is often employed to reduce the time required to develop effective controllers in the ER process. However, the development of adequate simulators is often time-consuming and complex. Simulators are usually constructed from physics models and/or are based on empirically collected data. The vast majority of simulation approaches are based on physics models which can become complex and require specialised knowledge. Alternative simulation approaches that simplify and automate the modelling of real-world phenomena can provide certain advantages over traditional approaches. An alternative simulation approach, such as Artificial Neural Networks (ANNs) that model the real-world phenomena based on empirical data are relatively simple to construct and requires little specialised knowledge. ANN-based simulators are traditionally constructed before the ER process can begin and require the sampling of real-world experimental data. Disadvantages to the traditional approach to ANN-based simulator construction are that the simulator must be created before the ER process can be initiated and a large amount of behavioural data must be collected in order to accurately predict future behaviours. Previous research has successfully demonstrated that ANN-based simulators and controllers can be developed concurrently during the ER process for a simple differentially-steered wheeled robot, while the current research has demonstrated the concurrent approach using a complex snake-like robot. The viability of the concurrent approach was demonstrated on a real-world snake-like robot by performing trajectory planning tasks. Influential factors related to the success of the concurrent approach were also studied by investigating the effects various parameter settings had on success.

Robotic simulators are often used to speed up the Evolutionary Robotics (ER) process. Most simulation approaches are based on physics modelling. However, physics-based simulators can become complex to develop and require prior knowledge of the robotic system. Robotics simulators can be constructed using Machine Learning techniques, such as Artificial Neural Networks (ANNs). ANN-based simulator development usually requires a lengthy behavioural data collection period before the simulator can be trained and used to evaluate controllers during the ER process. The Bootstrapped Neuro-Simulation (BNS) approach can be used to simultaneously collect behavioural data, train an ANN-based simulator and evolve controllers for a particular robotic problem. This paper investigates proposed improvements to the BNS approach and demonstrates the viability of the approach by optimising gait controllers for a Hexapod and Snake robot platform.

The ability of an Ant Colony Optimization (ACO) algorithm to adapt on a dynamical network is considered. A previous ACO implementation which was tested on a static Optical Burst Switched (OBS) network with impairments has been altered to be simulated on a dynamic network where network links are brought online or offline. The factors affecting the adaptability of an ACO algorithm is studied and a solution to mitigate some of these factors is proposed. This paper shows that the chosen Pheromone Function is the greatest factor affecting an ACO's adaptability during a change and that other factors such as topology and magnitude of change has little to no affect on its adaptability. In an attempt to improve the ACO's adaptability during a change in its network, a sliding window Pheromone Function is proposed and tested yielding positive results.

In the last decade, hardware advancements have allowed for neural networks to become much larger in size. Dropout is a popular deep learning technique which has shown to improve the performance of large neural networks. Recurrent neural networks are powerful networks specialised at solving problems which use time series data. Three different approaches to incorporating Dropout with recurrent neural networks have been suggested. However, these approaches have not been evaluated under identical experimental conditions. This article investigates the performance of these Dropout approaches using a 2D physics simulation benchmark. After applying statistical tests it was found that using Dropout did improve network performance on the benchmark. However, contrary to the literature, the Dropout approach which was expected to perform poorly, performed well, and the approach which was expected to perform well, performed poorly.

The Evolutionary Robotics process requires the evaluation of large numbers of robot controllers in order to determine their relative fitnesses. The evaluation of many controllers is typically performed in simulation instead of real-world hardware in order to speed up the evolutionary process and avoid damage to robot hardware. Physics-based simulators are traditionally used in robotics for evaluating controllers. Effective traditional simulators may require a high level of accuracy and their creation requires specialised knowledge of the dynamics of the robotic system. Alternatively Artificial Neural Network based simulators are relatively simple to construct, are highly accurate, efficient and assume little specialised knowledge of the dynamics involved. This paper proposes a novel controller evolution method that integrates uncertainty information from an ensemble of Artificial Neural Network based simulators. By estimating the accuracy of simulator predictions during the controller evolution process, robot behaviours that are not accurately simulated are likely to be avoided. A performance comparison of a single versus ensembles configuration of Artificial Neural Network based simulators is investigated. The ensemble approach developed in this work has so far outperformed all prior controller evolution methods investigated for the presented problem.

This paper presents a novel algorithmic technique to quantitatively rank the relative importance and power of countries throughout history. The algorithm is based on PageRank, the kernel of Google's webpage ranking algorithm. Changes are suggested to enable PageRank to rank countries based on historic signed political and trade treaties. Relative changes in the ranking of countries can be observed by executing the algorithm on a sliding window over time. The algorithm is demonstrated on a case study period. The results show that the proposed approach does indeed rank countries sensibly in the period under consideration.

Inverse kinematics refers to determining the forces that must be applied to a particular system to result in a desired configuration of the system. In robotics, inverse kinematics means calculating the robot actuator movements necessary to make a robot perform a specific task. Calculating the inverse kinematics using traditional methods is a complex and time consuming task. This paper reports on a novel approach to predicting the inverse kinematics of a mobile robot using Neural Networks (NNs). The main advantage of using artificial intelligence to determine inverse kinematics is that minimal human input and intervention is required. This research makes use of Feedforward NNs to predict the motor velocities and the time that they must be maintained to make the robot reach a specified destination. Inertia and friction are automatically incorporated into the NN predictions. Experimental evidence is presented which shows that the proposed approach can successfully produce commands which allow the robot to traverse a given path.

Evolutionary Robotics (ER) strives for the automatic creation of robotic controllers and morphologies. The ER process is normally performed in simulation in order to reduce the time required and robot wear. Simulator development is a time consuming process which requires expert knowledge and must traditionally be completed before the ER process can commence. Traditional simulators have limited accuracy, can be computationally expensive and typically do not account for minor operational differences between physical robots.This research proposes the automatic creation of simulators concurrently with the normal ER process. The simulator is derived from an Artificial Neural Network (ANN) to remove the need for formulating an analytical model for the robot. The ANN simulator is improved concurrently with the ER process through real-world controller evaluations which continuously generate behavioural data. Simultaneously, the ER process is informed by the improving simulator to evolve better controllers which are periodically evaluated in the real-world. Hence, the concurrent processes provide further targeted behavioural data for simulator improvement.The concurrent and real-time creation of both controllers and ANN-based simulators is successfully demonstrated for a differentially-steered mobile robot. Various parameter settings in the proposed algorithm are investigated to determine factors pertinent to the success of the proposed approach. Controllers are developed for trajectory planning using Evolutionary Robotics and a differentially-steered mobile robot.A novel approach is proposed for the concurrent development of controllers and a simulator.Robot behaviours are simulated using Artificial Neural Networks.An extensive parameter comparison study of the proposed approach is conducted.

This Working Paper analyses the economic importance of the Belgian ports based largely on the annual accounts data for the year 2016. As the years prior to 2016 have been described in earlier papers in the same series, we mainly focus on the figures for 2016 and developments between 2015 and 2016. On the back of strong growth, direct value added in the Belgian ports remained more or less stable in 2016 at around € 18 000 million (current prices) or roughly 4.3% of Belgium’s GDP. Direct value added declined in the Flemish seaports, mainly in the port of Antwerp. Ghent and Zeebrugge could only partly compensate for the fall in Antwerp’s value added, while Ostend showed a small decline itself. The inland ports as a whole grew over the period 2015-2016; the port of Brussels registered a decline and the Liège port complex an increase. Indirect value added is around 82% of the direct figure. After declining from 2012, direct employment in the Belgian ports was more or less stable in 2016 at around 115 000 FTE or approximately 2.8% of Belgium’s total domestic employment. Direct employment in the Flemish seaports increased, mainly in the ports of Zeebrugge, Ghent and Antwerp. Ostend showed a decline in employment. The inland ports recorded lower employment; the port of Brussels registered a decline, as did the Liège port complex. Indirect employment is around 1.2 times the direct figure. Delving deeper into the data and trying to explain the above trends in terms of the structural composition of the Belgian ports shows that all ports are concentrated on a few sectors, and within those sectors often on just a handful of companies. Based on the figures of the traffic, the Flemish ports can be considered as real bridgeheads for trade with the UK. Developments regarding the modalities and consequences of the Brexit therefor should be followed with the greatest attention. Given the existing import and export volumes in terms of tonnage, it seems it will mostly be a challenge in Zeebrugge and to some extent for Antwerp.

In this paper, we present a newly developed simulator based on the Omnet++ framework, for simulating Dense Wavelength Division Multiplexed, optical burst switched networks in the presence of optical impairments. The simulator is validated against a reduced link load model for optical burst switched networks, and simulations are performed in order to determine the effects of flexi-grid and impairments modelling on simulation results. We find that flexi-grid substantially reduces the burst loss probability on the simulated network, and that impairments modelling has a noticeable effect on simulation results, particularly in flexi-grid scenarios.

Bootstrapped Neuro-Simulation (BNS) is a method of concurrent simulator and robot controller evolution. The algorithm requires little domain knowledge and no pre-investigation data gathering. Additionally, it bridges the reality gap effectively, rapidly evolves functional controllers, and recovers from damage automatically. In this paper, the first evidence of the ability of BNS to evolve closed-loop controllers is shown; in this case to solve a light-following problem. The algorithm is then evaluated for its damage recovery ability for these closed-loop controllers and shown to be very effective, with only minor adaptations.

Free-living nematodes are present in all marine and estuarine environments around South Africa. They are most common in sediments, comprising up to 92 % of the living carbon community. Nematodes are grouped with other marine and estuarine organisms that are less than 1 mm in size but greater than 50 μm and are collectively referred to as meiofauna. A total of 338 marine and estuarine nematode species have been recorded from South Africa of which 30 are endemic. A number of factors affect meiofaunal community structure including particle size of the sediment, organic matter content, salinity, temperature, redox potential, oxygen concentration and, on exposed sandy beaches, desiccation and wave action. The proportion of nematodes within the meiofauna component increased from 20 % on the east coast to 60 % on the south coast in the Port Elizabeth area on sandy shores. A similar study at Robben Island, off the west coast of South Africa, showed nematodes to represent more than 86 % of the meiofauna. Most of the local research on nematodes has been conducted in estuaries that are permanently open. As on sea shores, nematodes dominated the meiofauna in these estuaries, and their abundance was affected by particle size, oxygen and desiccation. Research at Saldanha Bay in South Africa is also referred to and shows that estuarine nematodes can be used as biomonitors. High concentrations of heavy metals below the rafts existed, and nematode fauna was dominated by a Sabatieria sp. Away from the raft, Microlaimus sp. was the dominant genus, and the substrate had significantly lower concentrations of heavy metals.

The plant-parasitic nematodes that parasitise citrus in South Africa are discussed, with the citrus nematode (Tylenchulus semipenetrans) being the most important. Citrus is grown in more than 125 countries with South Africa ranking 12th in terms of production. The cultivars grown in southern Africa include sweet orange, grapefruit, lemon, lime and mandarin, as well as some hybrids. Whereas more than 30 years ago, 90 % of citrus trees in South Africa were budded on rough lemon rootstocks, a vigorous grower but highly susceptible to citrus nematode; now only a very small proportion of new plantings use this rootstock. Also, in the past, nursery trees were often situated between old nematode-infected trees within orchards, which resulted in infected nursery stock being planted into new orchards. The citrus nematode is not indigenous to South Africa, but it has, nevertheless, spread to all citrus-growing areas in the country. The South African Citrus Improvement Scheme was initiated in 1973 to establish a more productive citrus industry by supplying the best available planting material, free from harmful pests and diseases. All budwood is rendered free from graft transmissible diseases through shoot-tip grafting, and irrigation water is kept free of nematodes and other pathogens. Nurseries participating in the scheme changed to growing trees in containers raised above the ground and filled with a nematode-free potting medium. The main effect of the citrus nematode on citrus production is reduced fruit size, resulting in lower prices, especially in the export market. Control using three consecutive applications of the organophosphate nematicide cadusafos can have a significant effect in reducing citrus nematode numbers to undetectable levels. Such treatment should, however, be applied at the commencement of the rainy season to ensure that the nematicide is distributed throughout the root area. Furthermore, the timing between the three applications should be scheduled so that it will not allow second-stage juveniles to hatch and develop into egg-laying adult females before the follow-up application is applied.

This paper is an annual publication issued by the Microeconomic Analysis service of the National Bank of Belgium. The Flemish maritime ports (Antwerp, Ghent, Ostend, Zeebrugge), the Autonomous Port of Liège and the port of Brussels play a major role in their respective regional economies and in the Belgian economy, not only in terms of industrial activity but also as intermodal centers facilitating the commodity flow. This update paper1 provides an extensive overview of the economic importance and development of the Flemish maritime ports, the Liège port complex and the port of Brussels for the period 2010 - 2015, with an emphasis on 2015. Focusing on the three major variables of value added, employment and investment, the report also provides some information based on the social balance sheet and an overview of the financial situation in these ports as a whole. These observations are linked to a more general context, along with a few cargo statistics. Annual accounts data from the Central Balance Sheet Office were used for the calculation of direct effects, the study of financial ratios and the analysis of the social balance sheet. The indirect effects of the activities concerned were estimated in terms of value added and employment, on the basis of data from the National Accounts Institute. As a result of the underlying calculation method the changes of indirect employment and indirect value added can differ from one another. The developments concerning economic activity in the six ports in 2014 - 2015 are summarized in the table on the next page. In 2015 the growth of maritime traffic in the Flemish maritime ports was due to developments in the port of Antwerp and the port of Ghent. Direct value added increased in all Flemish maritime ports in 2015. However, direct employment is continuing to decline. Investment was down everywhere except in the port of Zeebrugge. Cargo traffic in the Liège port complex declined in 2015, whereas it slightly slowed down in the port of Brussels. At the same time, direct value added in Liège shrank while it rose sharply in the port of Brussels. By contrast, direct employment was down in both ports. This report provides a comprehensive account of these issues, giving details for each economic sector, although the comments are confined to the main changes that occurred in 2015.

Ant Colony Optimisation (ACO) has been extensively applied to the network routing problem. Simulated ants are used to explore the network while recording information regarding their success by means of pheromones that are deposited on the route. A balance must be found between exploration of new routes and exploitation of established routes. Modern Monte Carlo game play algorithms, like Upper Confidence Bound applied to Trees (UCT), also have to decide which game branches to explore and which solutions should be exploited. The Upper Confidence Bound 1 (UCB1) formula is used to choose move branches, thus creating a balance between exploration and exploitation. This paper investigates the use of the UCB1 formula in an ACO algorithm to determine which routes should be selected. UCB1 was incorporated into an ACO algorithm that allocates a path (from source to destination) and an appropriate wavelength to packets to be routed in a network, which employs Optical Burst Switching (OBS). The new algorithm was evaluated against an existing ant-based algorithm on three network topologies in order to determine its effectiveness. Results obtained indicated that the proposed algorithm outperformed the existing algorithm in most scenarios.

This paper proposes two adaptations to DynDE, a differential evolution-based algorithm for solving dynamic optimization problems. The first adapted algorithm, Competitive Population Evaluation (CPE), is a multi-population DE algorithm aimed at locating optima faster in the dynamic environment. This adaptation is based on allowing populations to compete for function evaluations based on their performance. The second adapted algorithm, Reinitialization Midpoint Check (RMC), is aimed at improving the technique used by DynDE to maintain populations on different peaks in the search space. A combination of the CPE and RMC adaptations is investigated. The new adaptations are empirically compared to DynDE using various problem sets. The empirical results show that the adaptations constitute an improvement over DynDE and compares favorably to other approaches in the literature. The general applicability of the adaptations is illustrated by incorporating the combination of CPE and RMC into another Differential Evolution-based algorithm, jDE , which is shown to yield improved results.

This paper investigates optimization in dynamic environments where the numbers of optima are unknown or fluctuating. The authors present a novel algorithm, Dynamic Population Differential Evolution (DynPopDE), which is specifically designed for these problems. DynPopDE is a Differential Evolution based multi-population algorithm that dynamically spawns and removes populations as required. The new algorithm is evaluated on an extension of the Moving Peaks Benchmark. Comparisons with other state-of-the-art algorithms indicate that DynPopDE is an effective approach to use when the number of optima in a dynamic problem space is unknown or changing over time.

The effects of stress are frequently studied, yet its proximal causes remain unclear. Here we demonstrate that subjective estimates of uncertainty predict the dynamics of subjective and physiological stress responses. Subjects learned a probabilistic mapping between visual stimuli and electric shocks. Salivary cortisol confirmed that our stressor elicited changes in endocrine activity. Using a hierarchical Bayesian learning model, we quantified the relationship between the different forms of subjective task uncertainty and acute stress responses. Subjective stress, pupil diameter and skin conductance all tracked the evolution of irreducible uncertainty. We observed a coupling between emotional and somatic state, with subjective and physiological tuning to uncertainty tightly correlated. Furthermore, the uncertainty tuning of subjective and physiological stress predicted individual task performance, consistent with an adaptive role for stress in learning under uncertain threat. Our finding that stress responses are tuned to environmental uncertainty provides new insight into their generation and likely adaptive function., Acute stress has broad physiological and behavioural consequences, yet the precise factors that generate stress responses are not known. Here, de Berker and colleagues demonstrate that acute stress responses dynamically track environmental uncertainty and predict ability to learn under uncertain threat.

Traditional simulators can be complex, time-consuming and require specialized knowledge to develop while still being unable to adequately model reality. Artificial Neural Networks (ANNs) can be trained to simulate real-world robots and therefore serve as an alternative to traditional approaches of robot simulation during the Evolutionary Robotics (ER) process. ANN-based simulators require little specialized knowledge and can automatically incorporate many real-world peculiarities. This paper reports a simulator that consisted of ANNs which were trained to predict changes in the position of a real-world snakelike robot. Navigational behaviours were evolved in simulation and subsequently verified on the real-world robot. This paper demonstrated that ANNs are a viable alternative to traditional simulators for evolving controllers for snake-like robots.

Evolutionary Robotics (ER) makes use of evolutionary algorithms to evolve controllers and morphologies of robots. Despite successful demonstrations in laboratory experiments, ER has not been widely adopted by industry as means of robot design. A possible reason for this is that current ER approaches ignore issues that are important when designing robots for practical use. For example, the availability and cost of components used for robot construction should be considered. A robot designed by the ER process may require specialised custom components to be built to support the physical functioning of the design, if the components selected by an ER process are not widely available. Alternatively, the ER designed robot may be too expensive to be constructed. This paper demonstrates that standard off-the-shelf components can be used by the ER process to design a robot. A robot arm is used as a sample problem, which is successfully optimised to use components from a fixed list while minimising cost.

Several algorithms aimed at dynamic optimisation problems have been developed. This paper reports on the incorporation of a self-adaptive Brownian radius into competitive differential evolution (CDE). Four variations of a novel technique to achieving the self-adaptation is suggested and motivated. An experimental investigation over a large number of benchmark instances is used to determine the most effective of the four variations. The new algorithm is compared to its base algorithm on an extensive set of benchmark problems and its performance analysed. Finally, the new algorithm is compared to other algorithms by means of reported results found in the literature. The results indicate that CDE is improved the the incorporation of the self-adaptive Brownian radius and that the new algorithm compares well with other algorithms.

Phytophthora citrophthora from citrus in eastern Corsica and Spain consists of distinct clonal lineages. In South Africa the extent of genetic variation among citrus-associated P. citrophthora isolates is unknown. This was investigated with isolates from South Africa (n =60), Spain (n =10) and six isolates representing three P. citrophthora groups CTR1, CTR2 and CTR3 previously identified with isozyme polymorphisms (Mchau and Coffey 1994). South African and Spanish isolates belonged to two lineages (G1, G2) based on an internal transcribed spacer (ITS) phylogeny, random amplified microsatellites (RAMS) and random amplified polymorphic DNA (RAPD) profiling. Although combined RAMS and RAPD data identified 14 genotypes, unweighted pair group method with arithmetic mean (UPGMA) analyses grouped the isolates into two clusters corresponding to lineages G1 and G2. Lineage G1 predominated among isolates from South Africa (92%) and Spain (100%). Phylogenetic analyses of the β-tubulin, cytochrome c oxidase subunit I (COX1) and ITS regions did not support the hypothesis that the two lineages represent distinct phylogenetic species but suggested that isozyme group CTR2 and possibly CTR3 are species distinct from P. citrophthora sensu stricto. Mating-type analyses, using tester strains from groups CTR2 and CTR3 revealed that most G1 lineage isolates (n =57) were sterile but that some were of the A1 mating type (n =8) whereas all G2 lineage isolates were A2 (n =5). The mating-type designation was confirmed with P. capsici tester strains. However, when A1 (G1 lineage) and A2 (G2 lineage including CTR1 reference isolates) mating-type isolates were paired in all possible combinations, no oogonia or antheridia were produced. This suggests that only tester strains P. capsici, CTR2 and CTR3 were able to produce sexual structures and that lineages G1 and G2 are sterile and reproductively isolated, which is supported by molecular data.

Healthy aging is associated with decline in basic motor functioning and higher motor control. Here, we investigated age-related differences in the brain-wide functional connectivity (FC) pattern of the subthalamic nucleus (STN), which plays an important role in motor response control. As earlier studies revealed functional coupling between STN and basal ganglia, which both are known to influence the conservativeness of motor responses on a superordinate level, we tested the hypothesis that STN FC with the striatum becomes dysbalanced with age. To this end, we performed a seed-based resting-state analysis of fMRI data from 361 healthy adults (mean age: 41.8, age range: 18–85) using bilateral STN as the seed region of interest. Age was included as a covariate to identify regions showing age-related changes of FC with the STN seed. The analysis revealed positive FC of the STN with several previously described subcortical and cortical regions like the anterior cingulate and sensorimotor cortex, as well as not-yet reported regions including central and posterior insula. With increasing age, we observed reduced positive FC with caudate nucleus, thalamus, and insula as well as increased positive FC with sensorimotor cortex and putamen. Furthermore, an age-related reduction of negative FC was found with precuneus and posterior cingulate cortex. We suggest that this reduced de-coupling of brain areas involved in self-relevant but motor-unrelated cognitive processing (i.e. precuneus and posterior cingulate cortex) from the STN motor network may represent a potential mechanism behind the age-dependent decline in motor performance. At the same time, older adults appear to compensate for this decline by releasing superordinate motor control areas, in particular caudate nucleus and insula, from STN interference while increasing STN-mediated response control over lower level motor areas like sensorimotor cortex and putamen.

The Upper Confidence for Trees (UCT) algorithm has been shown to perform well in complex games, but samples undesirable areas of the search space when building its game tree. This paper explores the design and parametric considerations for augmenting the UCT algorithm with an Artificial Neural Network (NN) to dynamically prune the game tree created, thus limiting the game tree created. The expansion phase of UCT is augmented with a trained NN to create a novel UCT-NN variant that includes prior knowledge and strategy. This paper considers the game of Go-Moku for investigating the design and parametric considerations of UCT-NN. The parameters considered are the exploration and exploitation balancing C parameter, the NN training and structural design parameters and the various pruning schemes which could be used in UCT-NN. Parameter tuning techniques are provided for managing the parametric concerns in the proposed algorithm. Results of parameter experiments indicate that a single value of C = 1.41 is suitable for the games studied. Suitable values were found for the structural and training parameters of NN, which were required to test various pruning schemes. Of the various pruning schemes considered, an exponentially decaying scheme is found to be superior in the UCT-NN algorithm where a large amount of moves are initially pruned, but fewer moves on deeper ply.

Longitudinally extensive transverse myelitis is characteristic but not pathognomonic for neuromyelitis optica spectrum disorders (NMOSDs) and may mimic local tumors. In this retrospective study based on a cohort of 175 NMOSD patients we identified seven patients who initially presented with a longitudinally extensive spinal cord lesion and underwent spinal cord biopsy due to magnetic resonance imaging (MRI)-suspected malignancies. Remarkably, routine neuropathology was inconclusive and did not guide the diagnostic process to anti-aquaporin-4 (AQP4)-seropositive NMOSD. Serious postoperative complications occurred in 5/7 patients and persisted during follow-up in 2/7 patients (29%). Considering these sequelae, AQP4-antibody testing should be mandatory in patients with inconclusive longitudinally extensive spinal cord lesions prior to biopsy. peerReviewed

In this chapter, we introduce the algorithm called: SADynPopDE, a self adaptive multi-population DE-based optimization algorithm, aimed at dynamic optimization problems in which the number of optima in the environment fluctuates over time. We compare the performance of SADynPopDE to those of two algorithms upon which it is based: DynDE and DynPopDE. DynDE extends DE for dynamic environments by utilizing multiple sub-populations which are encouraged to converge to distinct optima by means of exclusion. DynPopDE extends DynDE by: using competitive population evaluation to selectively evolve sub-populations, using a midpoint check during exclusion to determine whether sub-populations are indeed converging to the same optimum, dynamically spawning and removing sub populations, and using a penalty factor to aid the stagnation detection process. The use of self-adaptive control parameters into DynPopDE, allows a more effective algorithm, and to remove the need to fine-tune the DE crossover and scale factors.