Risk-sensitive optimal feedback control accounts for sensorimotor behavior under uncertainty.

Many aspects of human motor behavior can be understood using optimality principles such as optimal feedback control. However, these proposed optimal control models are risk-neutral; that is, they are indifferent to the variability of the movement cost. Here, we propose the use of a risk-sensitive optimal controller that incorporates movement cost variance either as an added cost (risk-averse controller) or as an added value (risk-seeking controller) to model human motor behavior in the face of uncertainty. We use a sensorimotor task to test the hypothesis that subjects are risk-sensitive. Subjects controlled a virtual ball undergoing Brownian motion towards a target. Subjects were required to minimize an explicit cost, in points, that was a combination of the final positional error of the ball and the integrated control cost. By testing subjects on different levels of Brownian motion noise and relative weighting of the position and control cost, we could distinguish between risk-sensitive and risk-neutral control. We show that subjects change their movement strategy pessimistically in the face of increased uncertainty in accord with the predictions of a risk-averse optimal controller. Our results suggest that risk-sensitivity is a fundamental attribute that needs to be incorporated into optimal feedback control models.

Risk-sensitive optimal feedback control accounts for sensorimotor behavior under uncertainty

Many aspects of human motor behavior can be understood using optimality principles such as optimal feedback control. However, these proposed optimal control models are risk-neutral; that is, they are indifferent to the variability of the movement cost. Here, we propose the use of a risk-sensitive optimal controller that incorporates movement cost variance either as an added cost (risk-averse controller) or as an added value (risk-seeking controller) to model human motor behavior in the face of uncertainty. We use a sensorimotor task to test the hypothesis that subjects are risk-sensitive. Subjects controlled a virtual ball undergoing Brownian motion towards a target. Subjects were required to minimize an explicit cost, in points, that was a combination of the final positional error of the ball and the integrated control cost. By testing subjects on different levels of Brownian motion noise and relative weighting of the position and control cost, we could distinguish between risk-sensitive and risk-neutral control. We show that subjects change their movement strategy pessimistically in the face of increased uncertainty in accord with the predictions of a risk-averse optimal controller. Our results suggest that risk-sensitivity is a fundamental attribute that needs to be incorporated into optimal feedback control models. © 2010 Nagengast et al.

An Infinite Latent Attribute Model for Network Data

Latent variable models for network data extract a summary of the relational structure underlying an observed network. The simplest possible models subdivide nodes of the network into clusters; the probability of a link between any two nodes then depends only on their cluster assignment. Currently available models can be classified by whether clusters are disjoint or are allowed to overlap. These models can explain a "flat" clustering structure. Hierarchical Bayesian models provide a natural approach to capture more complex dependencies. We propose a model in which objects are characterised by a latent feature vector. Each feature is itself partitioned into disjoint groups (subclusters), corresponding to a second layer of hierarchy. In experimental comparisons, the model achieves significantly improved predictive performance on social and biological link prediction tasks. The results indicate that models with a single layer hierarchy over-simplify real networks.

Augmented attribute representations

We propose a new learning method to infer a mid-level feature representation that combines the advantage of semantic attribute representations with the higher expressive power of non-semantic features. The idea lies in augmenting an existing attribute-based representation with additional dimensions for which an autoencoder model is coupled with a large-margin principle. This construction allows a smooth transition between the zero-shot regime with no training example, the unsupervised regime with training examples but without class labels, and the supervised regime with training examples and with class labels. The resulting optimization problem can be solved efficiently, because several of the necessity steps have closed-form solutions. Through extensive experiments we show that the augmented representation achieves better results in terms of object categorization accuracy than the semantic representation alone. © 2012 Springer-Verlag.

Scanning gate microscopy of current-annealed single layer graphene

We have used scanning gate microscopy to explore the local conductivity of a current-annealed graphene flake. A map of the local neutrality point (NP) after annealing at low current density exhibits micron-sized inhomogeneities. Broadening of the local e-h transition is also correlated with the inhomogeneity of the NP. Annealing at higher current density reduces the NP inhomogeneity, but we still observe some asymmetry in the e-h conduction. We attribute this to a hole-doped domain close to one of the metal contacts combined with underlying striations in the local NP. © 2010 American Institute of Physics.

Influence of ion energy and substrate temperature on the optical and electronic properties of tetrahedral amorphous carbon (ta-C) films

The properties of amorphous carbon (a-C) deposited using a filtered cathodic vacuum arc as a function of the ion energy and substrate temperature are reported. The sp3 fraction was found to strongly depend on the ion energy, giving a highly sp3 bonded a-C denoted as tetrahedral amorphous carbon (ta-C) at ion energies around 100 eV. The optical band gap was found to follow similar trends to other diamondlike carbon films, varying almost linearly with sp2 fraction. The dependence of the electronic properties are discussed in terms of models of the electronic structure of a-C. The structure of ta-C was also strongly dependent on the deposition temperature, changing sharply to sp2 above a transition temperature, T1, of ≈200°C. Furthermore, T1 was found to decrease with increasing ion energy. Most film properties, such as compressive stress and plasmon energy, were correlated to the sp3 fraction. However, the optical and electrical properties were found to undergo a more gradual transition with the deposition temperature which we attribute to the medium range order of sp2 sites. We attribute the variation in film properties with the deposition temperature to diffusion of interstitials to the surface above T1 due to thermal activation, leading to the relaxation of density in context of a growth model. © 1997 American Institute of Physics.

Directional algebraic reconstruction technique for electrical impedance tomography

We assume that the resistance matrix can be found in electrical impedance tomography from the assumption of linear dependence between the voltages and the currents and with the help of the resistance matrix and the transfer impedance between the electrodes, a directional algebraic reconstruction technique is proposed. The goal is to reconstruct the resistivity distribution by weighting the matrices that are obtained by calculating the orthogonal distance of the underlying mesh elements from the neighbouring port resistivity lines. These weighting matrices, which only depend on the topology of the underlying mesh, can be calculated offline and result in a computationally efficient online procedure with a reasonable image reconstruction performance. Simulation results are provided to validate this approach.

Options for achieving a 50% cut in industrial carbon emissions by 2050.

Carbon emissions from industry are dominated by production of goods in steel, cement plastic, paper, and aluminum. Demand for these materials is anticipated to double at least by 2050, by which time global carbon emissions must be reduced by at least 50%. To evaluate the challenge of meeting this target the global flows of these materials and their associated emissions are projected to 2050 under five technical scenarios. A reference scenario includes all existing and emerging efficiency measures but cannot provide sufficient reduction. The application of carbon sequestration to primary production proves to be sufficient only for cement The emissions target can always be met by reducing demand, for instance through product life extension, material substitution, or "light-weighting". Reusing components shows significant potential particularly within construction. Radical process innovation may also be possible. The results show that the first two strategies, based on increasing primary production, cannot achieve the required emissions reductions, so should be balanced by the vigorous pursuit of material efficiency to allow provision of increased material services with reduced primary production.