(v, k, λ) Configurations and self-dual codes

The incidence matrix of a (v, k, λ) configuration is used to construct a (2v, v) and a (2v + 2, v + 1) self-dual code. If the incidence matrix is a circulant, the codes obtained are quasi-cyclic and extended quasi-cyclic, respectively. The weight distributions of some codes of this type are obtained.

Self-consistent theory of localization in weakly disordered systems

An analytic treatment of localization in a weakly disordered system is presented for the case where the real lattice is approximated by a Cayley tree. Contrary to a recent assertion we find that the mobility edge moves inwards into the band as disorder increases from zero.

Optical Barker sequences and self-orthogonal convolutional codes

The relation between optical Barker codes and self-orthogonal convolutional codes is pointed out. It is then used to update the results in earlier publication.

Self-image (autoidolon) techniques for the realisation of optical computing type operations

Abstract is not available.

A Proof of Convergence of the B-RED and P-RED Algorithms for Random Early Detection

In [8], we recently presented two computationally efficient algorithms named B-RED and P-RED for random early detection. In this letter, we present the mathematical proof of convergence of these algorithms under general conditions to local minima.

Self-Assembly of a Pd-6-Molecular Double-Square and a Cu-3-Trigonalbipyramidal Cage via a New Tripodal Flexible Ligand

A new tripodal flexible ligand (L) containing pyrazolyl functionality has been prepared and successfully used to obtain a pd(6) (1) molecular double-square and a cu(3) trigonalbipyramidal cage (2), where complex 1 represents the first example of a double-square obtained using a flexible tripodal ligand.

Natural actor-critic algorithms

We present four new reinforcement learning algorithms based on actor-critic, natural-gradient and functi approximation ideas,and we provide their convergence proofs. Actor-critic reinforcement learning methods are online approximations to policy iteration in which the value-function parameters are estimated using temporal difference learning and the policy parameters are updated by stochastic gradient descent. Methods based on policy gradients in this way are of special interest because of their compatibility with function-approximation methods, which are needed to handle large or infinite state spaces. The use of temporal difference learning in this way is of special interest because in many applications it dramatically reduces the variance of the gradient estimates. The use of the natural gradient is of interest because it can produce better conditioned parameterizations and has been shown to further reduce variance in some cases. Our results extend prior two-timescale convergence results for actor-critic methods by Konda and Tsitsiklis by using temporal difference learning in the actor and by incorporating natural gradients. Our results extend prior empirical studies of natural actor-critic methods by Peters, Vijayakumar and Schaal by providing the first convergence proofs and the first fully incremental algorithms.

Spontaneous and reversible self-assembly of a polypeptide fragment of insulin-like growth factor binding protein-2 into fluorescent nanotubular structures

In this communication, we report the spontaneous and reversible in vitro self-assembly of a polypeptide fragment derived from the C-terminal domain of Insulin-like Growth Factor Binding Protein (IGFBP-2) into soluble nanotubular structures several micrometres long via a mechanism involving inter-molecular disulfide bonds and exhibiting enhanced fluorescence.