Towards the evolutionary emergence of increasingly complex advantageous behaviours

The generation of complex entities with advantageous behaviours beyond our manual design capability requires long-term incremental evolution with continuing emergence. In this paper, we argue that artificial selection models, such as traditional genetic algorithms, are fundamentally inadequate for this goal. Existing natural selection systems are evaluated, revealing both significant achievements and pitfalls. Thus, some requirements for the perpetuation of evolutionary emergence are established. An (artificial) environment containing simple virtual autonomous organisms with neural controllers has been created to satisfy these requirements and to aid in the development of an accompanying theory of evolutionary emergence. Resulting behaviours are reported alongside their neural correlates. In a particular example, the collective behaviour of one species provides a selective force which is overcome by another species, demonstrating the incremental evolutionary emergence of advantageous behaviours via naturally-arising coevolution. While the results fall short of the ultimate goal, experience with the system has provided some useful lessons for the perpetuation of emergence towards increasingly complex advantageous behaviours

Customizing kernel functions for SVM-based hyperspectral image classification

Previous research applying kernel methods such as support vector machines (SVMs) to hyperspectral image classification has achieved performance competitive with the best available algorithms. However, few efforts have been made to extend SVMs to cover the specific requirements of hyperspectral image classification, for example, by building tailor-made kernels. Observation of real-life spectral imagery from the AVIRIS hyperspectral sensor shows that the useful information for classification is not equally distributed across bands, which provides potential to enhance the SVM's performance through exploring different kernel functions. Spectrally weighted kernels are, therefore, proposed, and a set of particular weights is chosen by either optimizing an estimate of generalization error or evaluating each band's utility level. To assess the effectiveness of the proposed method, experiments are carried out on the publicly available 92AV3C dataset collected from the 220-dimensional AVIRIS hyperspectral sensor. Results indicate that the method is generally effective in improving performance: spectral weighting based on learning weights by gradient descent is found to be slightly better than an alternative method based on estimating ";relevance"; between band information and ground trut

Pentamidine Is Not a Permeant but a Nanomolar Inhibitor of the Trypanosoma brucei Aquaglyceroporin-2

The chemotherapeutic arsenal against human African trypanosomiasis, sleeping sickness, is limited and can cause severe, often fatal, side effects. One of the classic and most widely used drugs is pentamidine, an aromatic diamidine compound introduced in the 1940s. Recently, a genome-wide loss-of-function screen and a subsequently generated trypanosome knockout strain revealed a specific aquaglyceroporin, TbAQP2, to be required for high-affinity uptake of pentamidine. Yet, the underlying mechanism remained unclear. Here, we show that TbAQP2 is not a direct transporter for the di-basic, positively charged pentamidine. Even though one of the two common cation filters of aquaglyceroporins, i.e. the aromatic/arginine selectivity filter, is unconventional in TbAQP2, positively charged compounds are still excluded from passing the channel. We found, instead, that the unique selectivity filter layout renders pentamidine a nanomolar inhibitor of TbAQP2 glycerol permeability. Full, non-covalent inhibition of an aqua(glycero)porin in the nanomolar range has not been achieved before. The remarkable affinity derives from an electrostatic interaction with Asp265 and shielding from water as shown by structure-function evaluation and point mutation of Asp265. Exchange of the preceding Leu264 to arginine abolished pentamidine-binding and parasites expressing this mutant were pentamidine-resistant. Our results indicate that TbAQP2 is a high-affinity receptor for pentamidine. Taken together with localization of TbAQP2 in the flagellar pocket of bloodstream trypanosomes, we propose that pentamidine uptake is by endocytosis

Evolutionary discriminative confidence estimation for spoken term detection

The final publication is available at Springer via http://dx.doi.org/10.1007/s11042-011-0913-zSpoken term detection (STD) is the task of searching for occurrences of spoken terms in audio archives. It relies on robust confidence estimation to make a hit/false alarm (FA) decision. In order to optimize the decision in terms of the STD evaluation metric, the confidence has to be discriminative. Multi-layer perceptrons (MLPs) and support vector machines (SVMs) exhibit good performance in producing discriminative confidence; however they are severely limited by the continuous objective functions, and are therefore less capable of dealing with complex decision tasks. This leads to a substantial performance reduction when measuring detection of out-of-vocabulary (OOV) terms, where the high diversity in term properties usually leads to a complicated decision boundary. In this paper we present a new discriminative confidence estimation approach based on evolutionary discriminant analysis (EDA). Unlike MLPs and SVMs, EDA uses the classification error as its objective function, resulting in a model optimized towards the evaluation metric. In addition, EDA combines heterogeneous projection functions and classification strategies in decision making, leading to a highly flexible classifier that is capable of dealing with complex decision tasks. Finally, the evolutionary strategy of EDA reduces the risk of local minima. We tested the EDA-based confidence with a state-of-the-art phoneme-based STD system on an English meeting domain corpus, which employs a phoneme speech recognition system to produce lattices within which the phoneme sequences corresponding to the enquiry terms are searched. The test corpora comprise 11 hours of speech data recorded with individual head-mounted microphones from 30 meetings carried out at several institutes including ICSI; NIST; ISL; LDC; the Virginia Polytechnic Institute and State University; and the University of Edinburgh. The experimental results demonstrate that EDA considerably outperforms MLPs and SVMs on both classification and confidence measurement in STD, and the advantage is found to be more significant on OOV terms than on in-vocabulary (INV) terms. In terms of classification performance, EDA achieved an equal error rate (EER) of 11% on OOV terms, compared to 34% and 31% with MLPs and SVMs respectively; for INV terms, an EER of 15% was obtained with EDA compared to 17% obtained with MLPs and SVMs. In terms of STD performance for OOV terms, EDA presented a significant relative improvement of 1.4% and 2.5% in terms of average term-weighted value (ATWV) over MLPs and SVMs respectively.This work was partially supported by the French Ministry of Industry (Innovative Web call) under contract 09.2.93.0966, ‘Collaborative Annotation for Video Accessibility’ (ACAV) and by ‘The Adaptable Ambient Living Assistant’ (ALIAS) project funded through the joint national Ambient Assisted Living (AAL) programme