Selectively grouping neurons in recurrent networks of lateral inhibition

Winner-take-all networks have been proposed to underlie many of the brain's fundamental computational abilities. However, not much is known about how to extend the grouping of potential winners in these networks beyond single neuron or uniformly arranged groups of neurons. We show that competition between arbitrary groups of neurons can be realized by organizing lateral inhibition in linear threshold networks. Given a collection of potentially overlapping groups (with the exception of some degenerate cases), the lateral inhibition results in network dynamics such that any permitted set of neurons that can be coactivated by some input at a stable steady state is contained in one of the groups. The information about the input is preserved in this operation. The activity level of a neuron in a permitted set corresponds to its stimulus strength, amplified by some constant. Sets of neurons that are not part of a group cannot be coactivated by any input at a stable steady state. We analyze the storage capacity of such a network for random groups--the number of random groups the network can store as permitted sets without creating too many spurious ones. In this framework, we calculate the optimal sparsity of the groups (maximizing group entropy). We find that for dense inputs, the optimal sparsity is unphysiologically small. However, when the inputs and the groups are equally sparse, we derive a more plausible optimal sparsity. We believe our results are the first steps toward attractor theories in hybrid analog-digital networks.     

A Constraint-Based Approach to Fast and Exact Structure Prediction in Three-Dimensional Protein Models

Simplified protein models are used for investigating general properties of proteins and principles of protein folding. Furthermore, they are suited for hierarchical approaches to protein structure prediction. A well known protein model is the HP-model of Lau and Dill [Lau, K. F., & Dill, K. A. (1989)]. A lattice statistical mechanics model of the conformational and sequence spaces of proteins. Macromolecules, 22, 3986–3997) which models the important aspect of hydrophobicity. One can define the HP-model for various lattices, among them two-dimensional and three-dimensional ones. Here, we investigate the three-dimensional case. The main motivation for studying simplified protein models is to be able to predict model structures much more quickly and more accurately than is possible for real proteins. However, up to now there was a dilemma: the algorithmically tractable, simple protein models can not model real protein structures with good quality and introduce strong artifacts. We present a constraint-based method that largely improves this situation. It outperforms all existing approaches for lattice protein folding in HP-models. This approach is the first one that can be applied to two three-dimensional lattices, namely the cubic lattice and the face-centered-cubic (FCC) lattice. Moreover, it is the only exact method for the FCC lattice. The ability to use the FCC lattice is a significant improvement over the cubic lattice. The key to our approach is the ability to compute maximally compact sets of points (used as hydrophobic cores), which we accomplish for the first time for the FCC lattice.     

Comparison of analysis methods for package-induced stresses on moulded Hall sensors

Due to the piezoresistive and the piezo-Hall effect in semiconductor materials, Hall sensors show a strong temperature dependency and also a drift when subjected to temperature cycles Manic et al. (2000). Four factors mainly influence the mechanical stress in the sensitive layer. These are the geometry of the device, the differences of the coefficients of thermal expansion of the package materials, the temperature-dependent material properties and the time-dependent, viscous material properties. The objective of this investigation was to determine the mechanical stress in a moulded Hall sensor during the packaging process by finite-element simulation in comparison to experimental methods. It is shown that after each process-step the mechanical stress in the sensitive layer changes over time depending on the absolute value and the rate of the temperature change. Measurements of the inverse bending radius of glued and moulded chips show good agreement to the simulations.     

Photostability of liquid crystals and alignment layers

Abstract— Photostability of liquid‐crystal (LC) materials and surface alignment layers was evaluated using a UV lamp and a blue laser beam. Both organic polyimide (PI) and inorganic silicon‐dioxide (SiO₂) alignment layers were studied under nitrogen environment. Two commercial TFT‐grade LC mixtures (low‐birefringence MLC‐9200‐000 and high‐birefringence TL‐216) were used for comparisons. Results indicate that SiO₂ alignment layers are much more robust than PI layers, and low birefringence LCs are more stable than the high‐birefringence ones. At the He‐Cd laser wavelength (λ = 442 nm), both LC mixtures and SiO₂ alignment layers are hardly damaged. To lengthen the lifetime of an LCD projector, inorganic SiO₂ alignment layers, high‐optical‐density UV filter, long cutoff‐wavelength blue filter, and short‐conjugation (low birefringence) LC materials should be considered.     

On Karnaugh maps and magic squares

In 1953, Karnaugh maps were developed at the Harvard Computation Laboratory for the minimization of Boolean algebraic expressions (as an approach to logic circuit synthesis) [5].     

Behavioral Refinement of Graph Transformation-Based Models

Model-driven software engineering requires the refinement of abstract models into more concrete, platform-specific ones. To create and verify such refinements, behavioral models capturing recon- figuration or communication scenarios are presented as instances of a dynamic meta-model, i.e., a typed graph transformation system specifying the concepts and basic operations scenarios may be composed of. Possible refinement relations between models can now be described based on the corresponding meta-models.In contrast to previous approaches, refinement relations on graph transformation systems are not defined as fixed syntactic mappings between abstract transformation rules and, e.g., concrete rule expressions, but allow for a more loose, semantically defined relation between the transformation systems, resulting in a more flexible notion of refinement.     

Visualization of Crushing Evolution in Granular Materials under Compression Using DEM

Granular materials forming part of natural slopes, embankments, subgrades of foundations, and pavement structures are subjected to both static and dynamic loads during their engineering lives. As a result of these loads, particle crushing may occur. The present study demonstrates that the discrete-element method (DEM) can be used to visualize the evolution of this breakage process. In particular, the evolution of crushing in a simulated granular material subjected to uniaxial compression is presented. Even though DEM does not normally consider particle breakage, it is possible to simulate crushing by replacing one particle that has failed in tension with a combination of many particles of different sizes. The results from the simulation indicate that crushing does not develop uniformly throughout the sample, but rather concentrates in certain regions. These observations agree with experimental results of uniaxial tests conducted on sand. Other results from the simulation satisfactorily agree with experimental results previously reported by other researchers. In this way, by using a simplified failure criterion, DEM can be used to visualize and understand the evolution of granular crushing. This is something that is very difficult to do with laboratory tests alone.     

A formal relationship between program slicing and partial evaluation

A formal relationship between program slicing and partial evaluation is established. It is proved that for terminating programs, a residual program produced by partial evaluation is semantically equivalent to a conditioned slice. Received May 2002 Revised Octber 2004 and July 2005 Accepted August 2005 by D. J. Cooke     

A Gesture Based Interface for Human-Robot Interaction

Service robotics is currently a highly active research area in robotics, with enormous societal potential. Since service robots directly interact with people, finding natural and easy-to-use user interfaces is of fundamental importance. While past work has predominately focussed on issues such as navigation and manipulation, relatively few robotic systems are equipped with flexible user interfaces that permit controlling the robot by natural means. This paper describes a gesture interface for the control of a mobile robot equipped with a manipulator. The interface uses a camera to track a person and recognize gestures involving arm motion. A fast, adaptive tracking algorithm enables the robot to track and follow a person reliably through office environments with changing lighting conditions. Two alternative methods for gesture recognition are compared: a template based approach and a neural network approach. Both are combined with the Viterbi algorithm for the recognition of gestures defined through arm motion (in addition to static arm poses). Results are reported in the context of an interactive clean-up task, where a person guides the robot to specific locations that need to be cleaned and instructs the robot to pick up trash.