Central Asia Forecasting 2021: Results from an Expert Survey

The 'Central Asia Forecasting' study, jointly implemented by the Friedrich Ebert Foundation (FES), the OSCE Academy in Bishkek, and the SPCE Hub, aims to help strengthen EU-Central Asia relations. The study results are intended to stimulate the debate on the region, foster understanding of the common challenges and opportunities, and encourage data-driven policymaking. It is a pilot project that will be followed by an annual or biennial study to analyse regional trends over time. The audience that we aim to address with this report comprises the broader public in Europe and Central Asia, civil society representatives, regional experts, researchers and especially EU foreign-policy makers. For this study, a human-judgement forecasting method was employed in the form of an opinion survey among experts and the informed public on developments in the region in the next three years. In total, 144 respondents took our 20-minute survey. About half of the respondents are Central Asian citizens and half are from outside the region. The majority are affiliated with academic institutions and think tanks. This report launch will present the analysis of the survey responses regarding domestic politics and regional affairs, global challenges affecting the region, and EU-Central Asian relations

Towards hardware acceleration of neuroevolution for multimedia processing applications on mobile devices

This paper addresses the problem of accelerating large artificial neural networks (ANN), whose topology and weights can evolve via the use of a genetic algorithm. The proposed digital hardware architecture is capable of processing any evolved network topology, whilst at the same time providing a good trade off between throughput, area and power consumption. The latter is vital for a longer battery life on mobile devices. The architecture uses multiple parallel arithmetic units in each processing element (PE). Memory partitioning and data caching are used to minimise the effects of PE pipeline stalling. A first order minimax polynomial approximation scheme, tuned via a genetic algorithm, is used for the activation function generator. Efficient arithmetic circuitry, which leverages modified Booth recoding, column compressors and carry save adders, is adopted throughout the design

Analysing co-evolution among artificial 3D creatures

This paper is concerned with the analysis of coevolutionary dynamics among 3D artificial creatures, similar to those introduced by Sims (1). Coevolution is subject to complex dynamics which are notoriously difficult to analyse. We introduce an improved analysis method based on Master Tournament matrices [2], which we argue is both less costly to compute and more informative than the original method. Based on visible features of the resulting graphs, we can identify particular trends and incidents in the dynamics of coevolution and look for their causes. Finally, considering that coevolutionary progress is not necessarily identical to global overall progress, we extend this analysis by cross-validating individuals from different evolutionary runs, which we argue is more appropriate than single-record analysis method for evaluating the global performance of individuals

Indirectly Encoding Neural Plasticity as a Pattern of Local Rules

Biological brains can adapt and learn from past experience. In neuroevolution, i.e. evolving artificial neural networks (ANNs), one way that agents controlled by ANNs can evolve the ability to adapt is by encoding local learning rules. However, a significant problem with most such approaches is that local learning rules for every connection in the network must be discovered separately. This paper aims to show that learning rules can be effectively indirectly encoded by extending the Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) method. Adaptive HyperNEAT is introduced to allow not only patterns of weights across the connectivity of an ANN to be generated by a function of its geometry, but also patterns of arbitrary learning rules. Several such adaptive models with different levels of generality are explored and compared. The long-term promise of the new approach is to evolve large-scale adaptive ANNs, which is a major goal for neuroevolution. © 2010 Springer-Verlag

Adaptation of Robot Behaviour through Online Evolution and Neuromodulated Learning

Abstract. We propose and evaluate a novel approach to the online syn-thesis of neural controllers for autonomous robots. We combine online evolution of weights and network topology with neuromodulated learn-ing. We demonstrate our method through a series of simulation-based ex-periments in which an e-puck-like robot must perform a dynamic concur-rent foraging task. In this task, scattered food items periodically change their nutritive value or become poisonous. Our results show that when neuromodulated learning is employed, neural controllers are synthesised faster than by evolution alone. We demonstrate that the online evolu-tionary process is capable of generating controllers well adapted to the periodic task changes. An analysis of the evolved networks shows that they are characterised by specialised modulatory neurons that exclusively regulate the output neurons

Optimization of Network Topology in Computer-Aided Detection Schemes Using Phased Searching with NEAT in a Time-Scaled Framework

In the field of computer-aided mammographic mass detection, many different features and classifiers have been tested. Frequently, the relevant features and optimal topology for the artificial neural network (ANN)-based approaches at the classification stage are unknown, and thus determined by trial-and-error experiments. In this study, we analyzed a classifier that evolves ANNs using genetic algorithms (GAs), which combines feature selection with the learning task. The classifier named “Phased Searching with NEAT in a Time-Scaled Framework” was analyzed using a dataset with 800 malig-nant and 800 normal tissue regions in a 10-fold cross-validation framework. The classification performance measured by the area under a receiver operating characteristic (ROC) curve was 0.856 ± 0.029. The result was also compared with four other well-established classifiers that include fixed-topology ANNs, support vector machines (SVMs), linear discriminant analysis (LDA), and bagged decision trees. The results show that Phased Searching outperformed the LDA and bagged decision tree classifiers, and was only significantly outperformed by SVM. Furthermore, the Phased Searching method required fewer features and discarded superfluous structure or topology, thus incurring a lower feature computational and training and validation time requirement. Analyses performed on the network complexities evolved by Phased Searching indicate that it can evolve optimal network topologies based on its complexi-fication and simplification parameter selection process. From the results, the study also concluded that the three classifiers – SVM, fixed-topology ANN, and Phased Searching with NeuroEvolution of Augmenting Topologies (NEAT) in a Time-Scaled Framework – are performing comparably well in our mammographic mass detection scheme.Ye

Neuroevolutionary reinforcement learning for generalized control of simulated helicopters

This article presents an extended case study in the application of neuroevolution to generalized simulated helicopter hovering, an important challenge problem for reinforcement learning. While neuroevolution is well suited to coping with the domain’s complex transition dynamics and high-dimensional state and action spaces, the need to explore efficiently and learn on-line poses unusual challenges. We propose and evaluate several methods for three increasingly challenging variations of the task, including the method that won first place in the 2008 Reinforcement Learning Competition. The results demonstrate that (1) neuroevolution can be effective for complex on-line reinforcement learning tasks such as generalized helicopter hovering, (2) neuroevolution excels at finding effective helicopter hovering policies but not at learning helicopter models, (3) due to the difficulty of learning reliable models, model-based approaches to helicopter hovering are feasible only when domain expertise is available to aid the design of a suitable model representation and (4) recent advances in efficient resampling can enable neuroevolution to tackle more aggressively generalized reinforcement learning tasks

Barium in twilight zone suspended matter as a potential proxy for particulate organic carbon remineralization : results for the North Pacific

Author Posting. © Elsevier B.V., 2008. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Deep Sea Research Part II: Topical Studies in Oceanography 55 (2008): 1673-1683, doi:10.1016/j.dsr2.2008.04.020.This study focuses on the fate of exported organic carbon in the twilight zone at two contrasting environments in the North Pacific: the oligotrophic ALOHA site (22°45' N 158°W; Hawaii; studied during June–July 2004) and the mesotrophic Subarctic Pacific K2 site (47°N, 161°W; studied during July-August 2005). Earlier work has shown that non-lithogenic, excess particulate Ba (Baxs) in the mesopelagic water column is a potential proxy of organic carbon remineralization. In general Baxs contents were significantly larger at K2 than at ALOHA. At ALOHA the Baxs profiles from repeated sampling (5 casts) showed remarkable consistency over a period of three weeks, suggesting that the system was close to being at steady state. In contrast, more variability was observed at K2 (6 casts sampled) reflecting the more dynamic physical and biological conditions prevailing in this environment. While for both sites Baxs concentrations increased with depth, at K2 a clear maximum was present between the base of the mixed layer at around 50m and 500m, reflecting production and release of Baxs. Larger mesopelagic Baxs contents and larger bacterial production in the twilight zone at the K2 site indicate that more material was exported from the upper mixed layer for bacterial degradation deeper, compared to the ALOHA site. Furthermore, application of a published transfer function (Dehairs et al., 1997) relating oxygen consumption to the observed Baxs data indicated that the latter were in good agreement with bacterial respiration, calculated from bacterial production. These results corroborate earlier findings highlighting the potential of Baxs as a proxy for organic carbon remineralization. The range of POC remineralization rates calculated from twilight zone excess particulate Ba contents did also compare well with the depth dependent POC flux decrease as recorded by neutrally buoyant sediment traps, except in 1 case (out of 4). This discrepancy could indicate that differences in sinking velocities cause an 3 uncoupling of the processes occurring in the fine suspended particle pool from those affecting the larger particle pool which sustains the vertical flux, thus rendering comparison between both approaches risky.This research was supported by Federal Science Policy Office, Brussels through contracts EV/03/7A, SD/CA/03A, the Research Foundation Flanders through grant G.0021.04 and Vrije Universiteit Brussel via grant GOA 22, as well as the US National Science Foundation programs in Chemical and Biological Oceanography

Particle fluxes associated with mesoscale eddies in the Sargasso Sea

Author Posting. © Elsevier B.V., 2008. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Deep Sea Research Part II: Topical Studies in Oceanography 55 (2008): 1426-1444, doi:10.1016/j.dsr2.2008.02.007.We examined the impact of a cyclonic eddy and mode-water eddy on particle flux in the Sargasso Sea. The primary method used to quantify flux was based upon measurements of the natural radionuclide, 234Th, and these flux estimates were compared to results from sediment traps in both eddies, and a 210Po/210Pb flux method in the mode-water eddy. Particulate organic carbon (POC) fluxes at 150m ranged from 1 to 4 mmol C m-2 d-1 and were comparable between methods, especially considering differences in integration times scales of each approach. Our main conclusion is that relative to summer mean conditions at the Bermuda Atlantic Time-series Study (BATS) site, eddy-driven changes in biogeochemistry did not enhance local POC fluxes during this later, more mature stage of the eddy life cycle (>6 months old). The absence of an enhancement in POC flux puts a constraint on the timing of higher POC flux events, which are thought to have caused the local O2 minima below each eddy, and must have taken place >2 months prior to our arrival. The mode-water eddy did enhance preferentially diatom biomass in its center where we estimated a factor of 3 times higher biogenic Si flux than the BATS summer average. An unexpected finding in the highly depth resolved 234Th data sets are narrow layers of particle export and remineralization within the eddy. In particular, a strong excess 234Th signal is seen below the deep chlorophyll maxima which we attribute to remineralization of 234Th bearing particles. At this depth below the euphotic zone, de novo particle production in the euphotic zone has stopped, yet particle remineralization continues via consumption of labile sinking material by bacteria and/or zooplankton. These data suggest that further study of processes in ocean layers is warranted not only within, but below the euphotic zone.The EDDIES project was funded by the National Science Foundation Chemical, Biological, and Physical Oceanography Programs. Additional support for HPLC pigment analysis (Dr. Charles Trees, CHORS) was provided by NASA