SQG-Differential Evolution for difficult optimization problems under a tight function evaluation budget

In the context of industrial engineering, it is important to integrate efficient computational optimization methods in the product development process. Some of the most challenging simulation-based engineering design optimization problems are characterized by: a large number of design variables, the absence of analytical gradients, highly non-linear objectives and a limited function evaluation budget. Although a huge variety of different optimization algorithms is available, the development and selection of efficient algorithms for problems with these industrial relevant characteristics, remains a challenge. In this communication, a hybrid variant of Differential Evolution (DE) is introduced which combines aspects of Stochastic Quasi-Gradient (SQG) methods within the framework of DE, in order to improve optimization efficiency on problems with the previously mentioned characteristics. The performance of the resulting derivative-free algorithm is compared with other state-of-the-art DE variants on 25 commonly used benchmark functions, under tight function evaluation budget constraints of 1000 evaluations. The experimental results indicate that the new algorithm performs excellent on the 'difficult' (high dimensional, multi-modal, inseparable) test functions. The operations used in the proposed mutation scheme, are computationally inexpensive, and can be easily implemented in existing differential evolution variants or other population-based optimization algorithms by a few lines of program code as an non-invasive optional setting. Besides the applicability of the presented algorithm by itself, the described concepts can serve as a useful and interesting addition to the algorithmic operators in the frameworks of heuristics and evolutionary optimization and computing

Building nonparametric nn-body force fields using Gaussian process regression

Constructing a classical potential suited to simulate a given atomic system is a remarkably difficult task. This chapter presents a framework under which this problem can be tackled, based on the Bayesian construction of nonparametric force fields of a given order using Gaussian process (GP) priors. The formalism of GP regression is first reviewed, particularly in relation to its application in learning local atomic energies and forces. For accurate regression it is fundamental to incorporate prior knowledge into the GP kernel function. To this end, this chapter details how properties of smoothness, invariance and interaction order of a force field can be encoded into corresponding kernel properties. A range of kernels is then proposed, possessing all the required properties and an adjustable parameter $n$ governing the interaction order modelled. The order $n$ best suited to describe a given system can be found automatically within the Bayesian framework by maximisation of the marginal likelihood. The procedure is first tested on a toy model of known interaction and later applied to two real materials described at the DFT level of accuracy. The models automatically selected for the two materials were found to be in agreement with physical intuition. More in general, it was found that lower order (simpler) models should be chosen when the data are not sufficient to resolve more complex interactions. Low $n$ GPs can be further sped up by orders of magnitude by constructing the corresponding tabulated force field, here named "MFF".Comment: 31 pages, 11 figures, book chapte

Aging Affects the Mental Rotation of Left and Right Hands

BACKGROUND:Normal aging significantly influences motor and cognitive performance. Little is known about age-related changes in action simulation. Here, we investigated the influence of aging on implicit motor imagery. METHODOLOGY/PRINCIPAL FINDINGS:Twenty young (mean age: 23.9+/-2.8 years) and nineteen elderly (mean age: 78.3+/-4.5 years) subjects, all right-handed, were required to determine the laterality of hands presented in various positions. To do so, they mentally rotated their hands to match them with the hand-stimuli. We showed that: (1) elderly subjects were affected in their ability to implicitly simulate movements of the upper limbs, especially those requiring the largest amplitude of displacement and/or with strong biomechanical constraints; (2) this decline was greater for movements of the non-dominant arm than of the dominant arm. CONCLUSIONS/SIGNIFICANCE:These results extend recent findings showing age-related alterations of the explicit side of motor imagery. They suggest that a general decline in action simulation occurs with normal aging, in particular for the non-dominant side of the body

Task-Dependent Interaction between Parietal and Contralateral Primary Motor Cortex during Explicit versus Implicit Motor Imagery

Both mental rotation (MR) and motor imagery (MI) involve an internalization of movement within motor and parietal cortex. Transcranial magnetic stimulation (TMS) techniques allow for a task-dependent investigation of the interhemispheric interaction between these areas. We used image-guided dual-coil TMS to investigate interactions between right inferior parietal lobe (rIPL) and left primary motor cortex (M1) in 11 healthy participants. They performed MI (right index-thumb pinching in time with a 1 Hz metronome) or hand MR tasks, while motor evoked potentials (MEPs) were recorded from right first dorsal interosseous. At rest, rIPL conditioning 6 ms prior to M1 stimulation facilitated MEPs in all participants, whereas this facilitation was abolished during MR. While rIPL conditioning 12 ms prior to M1 stimulation had no effect on MEPs at rest, it suppressed corticomotor excitability during MI. These results support the idea that rIPL forms part of a distinct inhibitory network that may prevent unwanted movement during imagery tasks

Learning the Optimal Control of Coordinated Eye and Head Movements

Various optimality principles have been proposed to explain the characteristics of coordinated eye and head movements during visual orienting behavior. At the same time, researchers have suggested several neural models to underly the generation of saccades, but these do not include online learning as a mechanism of optimization. Here, we suggest an open-loop neural controller with a local adaptation mechanism that minimizes a proposed cost function. Simulations show that the characteristics of coordinated eye and head movements generated by this model match the experimental data in many aspects, including the relationship between amplitude, duration and peak velocity in head-restrained and the relative contribution of eye and head to the total gaze shift in head-free conditions. Our model is a first step towards bringing together an optimality principle and an incremental local learning mechanism into a unified control scheme for coordinated eye and head movements

Surprised at All the Entropy: Hippocampal, Caudate and Midbrain Contributions to Learning from Prediction Errors

Influential concepts in neuroscientific research cast the brain a predictive machine that revises its predictions when they are violated by sensory input. This relates to the predictive coding account of perception, but also to learning. Learning from prediction errors has been suggested for take place in the hippocampal memory system as well as in the basal ganglia. The present fMRI study used an action-observation paradigm to investigate the contributions of the hippocampus, caudate nucleus and midbrain dopaminergic system to different types of learning: learning in the absence of prediction errors, learning from prediction errors, and responding to the accumulation of prediction errors in unpredictable stimulus configurations. We conducted analyses of the regions of interests' BOLD response towards these different types of learning, implementing a bootstrapping procedure to correct for false positives. We found both, caudate nucleus and the hippocampus to be activated by perceptual prediction errors. The hippocampal responses seemed to relate to the associative mismatch between a stored representation and current sensory input. Moreover, its response was significantly influenced by the average information, or Shannon entropy of the stimulus material. In accordance with earlier results, the habenula was activated by perceptual prediction errors. Lastly, we found that the substantia nigra was activated by the novelty of sensory input. In sum, we established that the midbrain dopaminergic system, the hippocampus, and the caudate nucleus were to different degrees significantly involved in the three different types of learning: acquisition of new information, learning from prediction errors and responding to unpredictable stimulus developments. We relate learning from perceptual prediction errors to the concept of predictive coding and related information theoretic accounts

The Impact of Augmented Information on Visuo-Motor Adaptation in Younger and Older Adults

BACKGROUND: Adjustment to a visuo-motor rotation is known to be affected by ageing. According to previous studies, the age-related differences primarily pertain to the use of strategic corrections and the generation of explicit knowledge on which strategic corrections are based, whereas the acquisition of an (implicit) internal model of the novel visuo-motor transformation is unaffected. The present study aimed to assess the impact of augmented information on the age-related variation of visuo-motor adjustments. METHODOLOGY/PRINCIPAL FINDINGS: Participants performed aiming movements controlling a cursor on a computer screen. Visual feedback of direction of cursor motion was rotated 75 degrees relative to the direction of hand motion. Participants had to adjust to this rotation in the presence and absence of an additional hand-movement target that explicitly depicted the input-output relations of the visuo-motor transformation. An extensive set of tests was employed in order to disentangle the contributions of different processes to visuo-motor adjustment. Results show that the augmented information failed to affect the age-related variations of explicit knowledge, adaptive shifts, and aftereffects in a substantial way, whereas it clearly affected initial direction errors during practice and proprioceptive realignment. CONCLUSIONS: Contrary to expectations, older participants apparently made no use of the augmented information, whereas younger participants used the additional movement target to reduce initial direction errors early during practice. However, after a first block of trials errors increased, indicating a neglect of the augmented information, and only slowly declined thereafter. A hypothetical dual-task account of these findings is discussed. The use of the augmented information also led to a selective impairment of proprioceptive realignment in the younger group. The mere finding of proprioceptive realignment in adaptation to a visuo-motor rotation in a computer-controlled setup is noteworthy since visual and proprioceptive information pertain to different objects

Computational Models of Timing Mechanisms in the Cerebellar Granular Layer

A long-standing question in neuroscience is how the brain controls movement that requires precisely timed muscle activations. Studies using Pavlovian delay eyeblink conditioning provide good insight into this question. In delay eyeblink conditioning, which is believed to involve the cerebellum, a subject learns an interstimulus interval (ISI) between the onsets of a conditioned stimulus (CS) such as a tone and an unconditioned stimulus such as an airpuff to the eye. After a conditioning phase, the subject’s eyes automatically close or blink when the ISI time has passed after CS onset. This timing information is thought to be represented in some way in the cerebellum. Several computational models of the cerebellum have been proposed to explain the mechanisms of time representation, and they commonly point to the granular layer network. This article will review these computational models and discuss the possible computational power of the cerebellum

The Cysteine Rich Necrotrophic Effector SnTox1 Produced by Stagonospora nodorum Triggers Susceptibility of Wheat Lines Harboring Snn1

The wheat pathogen Stagonospora nodorum produces multiple necrotrophic effectors (also called host-selective toxins) that promote disease by interacting with corresponding host sensitivity gene products. SnTox1 was the first necrotrophic effector identified in S. nodorum, and was shown to induce necrosis on wheat lines carrying Snn1. Here, we report the molecular cloning and validation of SnTox1 as well as the preliminary characterization of the mechanism underlying the SnTox1-Snn1 interaction which leads to susceptibility. SnTox1 was identified using bioinformatics tools and verified by heterologous expression in Pichia pastoris. SnTox1 encodes a 117 amino acid protein with the first 17 amino acids predicted as a signal peptide, and strikingly, the mature protein contains 16 cysteine residues, a common feature for some avirulence effectors. The transformation of SnTox1 into an avirulent S. nodorum isolate was sufficient to make the strain pathogenic. Additionally, the deletion of SnTox1 in virulent isolates rendered the SnTox1 mutated strains avirulent on the Snn1 differential wheat line. SnTox1 was present in 85% of a global collection of S. nodorum isolates. We identified a total of 11 protein isoforms and found evidence for strong diversifying selection operating on SnTox1. The SnTox1-Snn1 interaction results in an oxidative burst, DNA laddering, and pathogenesis related (PR) gene expression, all hallmarks of a defense response. In the absence of light, the development of SnTox1-induced necrosis and disease symptoms were completely blocked. By comparing the infection processes of a GFP-tagged avirulent isolate and the same isolate transformed with SnTox1, we conclude that SnTox1 may play a critical role during fungal penetration. This research further demonstrates that necrotrophic fungal pathogens utilize small effector proteins to exploit plant resistance pathways for their colonization, which provides important insights into the molecular basis of the wheat-S. nodorum interaction, an emerging model for necrotrophic pathosystems