Does the level of graphical detail of a virtual handball thrower influence a goal-keeper's motor response?

The authors investigated how different levels of detail (LODs) of a virtual throwing action can influence a handball goalkeeper's motor response. Goalkeepers attempted to stop a virtual ball emanating from five different graphical LODs of the same virtual throwing action. The five levels of detail were: a textured reference level (L0), a non-textured level (L1), a wire-frame level (L2), a point-light-display (PLD) representation (L3) and a PLD level with reduced ball size (L4). For each motor response made by the goalkeeper we measured and analyzed the time to respond (TTR), the percentage of successful motor responses, the distance between the ball and the closest limb (when the stopping motion was incorrect) and the kinematics of the motion. Results showed that TTR, percentage of successful motor responses and distance with the closest limb were not significantly different for any of the five different graphical LODs. However the kinematics of the motion revealed that the trajectory of the stopping limb was significantly different when comparing the L1 and L3 levels, and when comparing the L1 and L4 levels. These differences in the control of the goalkeeper's actions suggests that the different level of information available in the PLD representations ( L3 and L4) are causing the goalkeeper to adopt different motor strategies to control the approach of their limb to stop the ball.

Revealing differences in gene network inference algorithms on the network level by ensemble methods

Motivation: The inference of regulatory networks from large-scale expression data holds great promise because of the potentially causal interpretation of these networks. However, due to the difficulty to establish reliable methods based on observational data there is so far only incomplete knowledge about possibilities and limitations of such inference methods in this context.

Results: In this article, we conduct a statistical analysis investigating differences and similarities of four network inference algorithms, ARACNE, CLR, MRNET and RN, with respect to local network-based measures. We employ ensemble methods allowing to assess the inferability down to the level of individual edges. Our analysis reveals the bias of these inference methods with respect to the inference of various network components and, hence, provides guidance in the interpretation of inferred regulatory networks from expression data. Further, as application we predict the total number of regulatory interactions in human B cells and hypothesize about the role of Myc and its targets regarding molecular information processing.

Technological regimes, Schumpeterian patterns of innovation and firm-level productivity growth

The article investigates the relationships between technological regimes and firm-level productivity performance, and it explores how such a relationship differs in different Schumpeterian patterns of innovation. The analysis makes use of a rich dataset containing data on innovation and other economic characteristics of a large representative sample of Norwegian firms in manufacturing and service industries for the period 1998–2004. First, we decompose TFP growth into technical progress and efficiency changes by means of data envelopment analysis. We then estimate an empirical model that relates these two productivity components to the characteristics of technological regimes and a set of other firm-specific factors. The results indicate that: (i) TFP growth has mainly been achieved through technical progress, while technical efficiency has on average decreased; (ii) the characteristics of technological regimes are important determinants of firm-level productivity growth, but their impacts on technical progress are different from the effects on efficiency change; (iii) the estimated model works differently in the two Schumpeterian regimes. Technical progress has been more dynamic in Schumpeter Mark II industries, while efficiency change has been more important in Schumpeter Mark I markets.

The level sets of the resolvent norm and convexity properties of Banach spaces

We construct a bounded linear operator on a separable, reflexive and strictly convex Banach space whose resolvent norm is constant in a neighbourhood of zero.