Choosing to make an effort: the role of striatum in signaling physical effort of a chosen action.

The possibility that we will have to invest effort influences our future choice behavior. Indeed deciding whether an action is actually worth taking is a key element in the expression of human apathy or inertia. There is a well developed literature on brain activity related to the anticipation of effort, but how effort affects actual choice is less well understood. Furthermore, prior work is largely restricted to mental as opposed to physical effort or has confounded temporal with effortful costs. Here we investigated choice behavior and brain activity, using functional magnetic resonance imaging, in a study where healthy participants are required to make decisions between effortful gripping, where the factors of force (high and low) and reward (high and low) were varied, and a choice of merely holding a grip device for minimal monetary reward. Behaviorally, we show that force level influences the likelihood of choosing an effortful grip. We observed greater activity in the putamen when participants opt to grip an option with low effort compared with when they opt to grip an option with high effort. The results suggest that, over and above a nonspecific role in movement anticipation and salience, the putamen plays a crucial role in computations for choice that involves effort costs.

Analysis of high pressure premixed flames using Equivalent Reactor Networks for predicting NOx emissions

This paper describes a computational study of lean premixed high pressure methane-air flames, using Computational Fluid Dynamics (CFD) together with a reactor network approach. A detailed chemical reaction mechanism is employed to predict pollutant concentrations, placing emphasis on nitrogen oxide emissions. The reacting flow field is divided into separate zones in which homogeneity of the physical and chemical conditions prevails. The defined zones are interconnected forming an Equivalent Reactor Network (ERN). Three flames are examined for which experimental data is available. Flame A is characterised by an equivalence ratio of 0.43 while Flames B and C are richer with equivalence ratios of 0.5 and 0.56 respectively. Computations are performed for a range of operating conditions, quantifying the effect in the emitted NOx levels. Model predictions are compared against the available experimental data. Sensitivity analysis is performed to investigate the effect of the network size, in order to define the optimum number of reactors for accurate predictions of the species mass fractions. © 2012 Elsevier Ltd. All rights reserved.

Fluid-structure interaction simulations of a tension-cone inflatable aerodynamic decelerator

A series of fluid-structure interaction simulations of an aerodynamic tension-cone supersonic decelerator prototype intended for large mass payload deployment in planetary explorations are discussed. The fluid-structure interaction computations combine large deformation analysis of thin shells with large-eddy simulation of compressible turbulent flows using a loosely coupled approach to enable quantification of the dynamics of the vehicle. The simulation results are compared with experiments carried out at the NASA Glenn Research Center. Reasonably good agreement between the simulations and the experiment is observed throughout a deflation cycle. The simulations help to illuminate the details of the dynamic progressive buckling of the tension-cone decelerator that ultimately results in the collapse of the structure as the inflation pressure is decreased. Furthermore, the tension-cone decelerator exhibits a transient oscillatory behavior under impulsive loading that ultimately dies out. The frequency of these oscillations was determined to be related to the acoustic time scale in the compressed subsonic region between the bow shock and the structure. As shown, when the natural frequency of the structure and the frequency of the compressed subsonic region approximately match, the decelerator exhibits relatively large nonaxisymetric oscillations. The observed response appears to be a fluid-structure interaction resonance resulting from an acoustic chamber (pistonlike) mode exciting the structure. Copyright © 2013 by Christopher Porter, R. Mark Rennie, Eric J. Jumper.

Encoding of mixtures in a simple olfactory system.

Natural odors are usually mixtures; yet, humans and animals can experience them as unitary percepts. Olfaction also enables stimulus categorization and generalization. We studied how these computations are performed with the responses of 168 locust antennal lobe projection neurons (PNs) to varying mixtures of two monomolecular odors, and of 174 PNs and 209 mushroom body Kenyon cells (KCs) to mixtures of up to eight monomolecular odors. Single-PN responses showed strong hypoadditivity and population trajectories clustered by odor concentration and mixture similarity. KC responses were much sparser on average than those of PNs and often signaled the presence of single components in mixtures. Linear classifiers could read out the responses of both populations in single time bins to perform odor identification, categorization, and generalization. Our results suggest that odor representations in the mushroom body may result from competing optimization constraints to facilitate memorization (sparseness) while enabling identification, classification, and generalization.

Encoding of mixtures in a simple olfactory system

Natural odors are usually mixtures; yet, humans and animals can experience them as unitary percepts. Olfaction also enables stimulus categorization and generalization. We studied how these computations are performed with the responses of 168 locust antennal lobe projection neurons (PNs) to varying mixtures of two monomolecular odors, and of 174 PNs and 209 mushroom body Kenyon cells (KCs) to mixtures of up to eight monomolecular odors. Single-PN responses showed strong hypoadditivity and population trajectories clustered by odor concentration and mixture similarity. KC responses were much sparser on average than those of PNs and often signaled the presence of single components in mixtures. Linear classifiers could read out the responses of both populations in single time bins to perform odor identification, categorization, and generalization. Our results suggest that odor representations in the mushroom body may result from competing optimization constraints to facilitate memorization (sparseness) while enabling identification, classification, and generalization

Low-rank optimization with trace norm penalty

The paper addresses the problem of low-rank trace norm minimization. We propose an algorithm that alternates between fixed-rank optimization and rank-one updates. The fixed-rank optimization is characterized by an efficient factorization that makes the trace norm differentiable in the search space and the computation of duality gap numerically tractable. The search space is nonlinear but is equipped with a Riemannian structure that leads to efficient computations. We present a second-order trust-region algorithm with a guaranteed quadratic rate of convergence. Overall, the proposed optimization scheme converges superlinearly to the global solution while maintaining complexity that is linear in the number of rows and columns of the matrix. To compute a set of solutions efficiently for a grid of regularization parameters we propose a predictor-corrector approach that outperforms the naive warm-restart approach on the fixed-rank quotient manifold. The performance of the proposed algorithm is illustrated on problems of low-rank matrix completion and multivariate linear regression. © 2013 Society for Industrial and Applied Mathematics.

Joint experimental and numerical approach to three-dimensional shock control bump research

Previous studies of transonic shock control bumps have often been either numerical or experimental. Comparisons between the two have been hampered by the limitations of either approach. The present work aims to bridge the gap between computational fluid dynamics and experiment by planning a joint approach from the outset. This enables high-quality validation data to be produced and ensures that the conclusions of either aspect of the study are directly relevant to the application. Experiments conducted with bumps mounted on the floor of a blowdown tunnel were modified to include an additional postshock adverse pressure gradient through the use of a diffuser as well as introducing boundary-layer suction ahead of the test section to enable the in-flow boundary layer to be manipulated. This has the advantage of being an inexpensive and highly repeatable method. Computations were performed on a standard airfoil model, with the flight conditions as free parameters. The experimental and computational setups were then tuned to produce baseline conditions that agree well, enabling confidence that the experimental conclusions are relevant. The methods are then applied to two different shock control bumps: a smoothly contoured bump, representative of previous studies, and a novel extended geometry featuring a continuously widening tail, which spans the wind-tunnel width at the rear of the bump. Comparison between the computational and experimental results for the contour bump showed good agreement both with respect to the flow structures and quantitative analysis of the boundary-layer parameters. It was seen that combining the experimental and numerical data could provide valuable insight into the flow physics, which would not generally be possible for a one-sided approach. The experiments and computational fluid dynamics were also seen to agree well for the extended bump geometry, providing evidence that, even though thebumpinteracts directly with the wind-tunnel walls, it was still possible to observe the key flow physics. The joint approach is thus suitable even for wider bump geometries. Copyright © 2013 by S. P. Colliss, H. Babinsky, K. Nubler, and T. Lutz. Published by the American Institute of Aeronautics and Astronautics, Inc.

High dimensional imagery geometry and it's applications

Because of information digitalization and the correspondence of digits and the coordinates, Information Science and high-dimensional space have consanguineous relations. With the transforming from the information issues to the point analysis in high-dimensional space, we proposed a novel computational theory, named High dimensional imagery geometry (HDIG). Some computational algorithms of HDIG have been realized using software, and how to combine with groups of simple operators in some 2D planes to implement the geometrical computations in high-dimensional space is demonstrated in this paper. As the applications, two kinds of experiments of HDIG, which are blurred image restoration and pattern recognition ones, are given, and the results are satisfying.

Electron resonant tunneling through InAs/GaAs quantum dots embedded in a Schottky diode with an AlAs insertion layer

Molecular beam epitaxy was employed to manufacture self-assembled InAs/GaAs quantum dot Schottky resonant tunneling diodes. By virtue of a thin AlAs insertion barrier, the thermal current was effectively reduced and electron resonant tunneling through quantum dots under both forward and reverse biased conditions was observed at relatively high temperature of 77 K. The ground states of quantum dots were found to be at similar to 0.19 eV below the conduction band of GaAs matrix. The theoretical computations were in conformity with experimental data. (c) 2006 The Electrochemical Society.

Computational information geometry and its applications

Digitization is the main feature of modern Information Science. Conjoining the digits and the coordinates, the relation between Information Science and high-dimensional space is consanguineous, and the information issues are transformed to the geometry problems in some high-dimensional spaces. From this basic idea, we propose Computational Information Geometry (CIG) to make information analysis and processing. Two kinds of applications of CIG are given, which are blurred image restoration and pattern recognition. Experimental results are satisfying. And in this paper, how to combine with groups of simple operators in some 2D planes to implement the geometrical computations in high-dimensional space is also introduced. Lots of the algorithms have been realized using software.

on string languages generated by spiking neural p systems

We continue the study of spiking neural P systems by considering these computing devices as binary string generators: the set of spike trains of halting computations of a given system constitutes the language generated by that system. Although the "direct" generative capacity of spiking neural P systems is rather restricted (some very simple languages cannot be generated in this framework), regular languages are inverse-morphic images of languages of finite spiking neural P systems, and recursively enumerable languages are projections of inverse-morphic images of languages generated by spiking neural P systems.