Reservoir Computing with Delayed Input for Fast and Easy Optimisation

Reservoir computing is a machine learning method that solves tasks using the response of a dynamical system to a certain input. As the training scheme only involves optimising the weights of the responses of the dynamical system, this method is particularly suited for hardware implementation. Furthermore, the inherent memory of dynamical systems which are suitable for use as reservoirs mean that this method has the potential to perform well on time series prediction tasks, as well as other tasks with time dependence. However, reservoir computing still requires extensive task-dependent parameter optimisation in order to achieve good performance. We demonstrate that by including a time-delayed version of the input for various time series prediction tasks, good performance can be achieved with an unoptimised reservoir. Furthermore, we show that by including the appropriate time-delayed input, one unaltered reservoir can perform well on six different time series prediction tasks at a very low computational expense. Our approach is of particular relevance to hardware implemented reservoirs, as one does not necessarily have access to pertinent optimisation parameters in physical systems but the inclusion of an additional input is generally possible.     

On the spectrum for Km+2 \ Km designs

The spectrum problem for the decomposition of K_n into copies of the graph K_m+2 \ K_m is solved for n ≡ 0 or 1 (mod 2m + 1). © 1997 John Wiley & Sons, Inc.     

Site-specific DNA functionalization through the tetrazene-forming reaction in ionic liquids

Development of multiple chemical tools for deoxyribonucleic acid (DNA) labeling has facilitated wide use of their functionalized conjugates, but significant practical and methodological challenges remain to achievement of site-specific chemical modification of the biomacromolecule. As covalent labeling processes are more challenging in aqueous solution, use of nonaqueous, biomolecule-compatible solvents such as an ionic liquid consisting of a salt with organic molecule architecture, could be remarkably helpful in this connection. Herein, we demonstrate site-specific chemical modification of unprotected DNAs through a tetrazene-forming amine–azide coupling reaction using an ionic liquid. This ionic liquid-enhanced reaction process has good functional group tolerance and precise chemoselectivity, and enables incorporation of various useful functionalities such as biotin, cholesterol, and fluorophores. A site-specifically labeled oligonucleotide, or aptamer interacting with a growth factor receptor (Her2) was successfully used in the fluorescence imaging of breast cancer cell lines. The non-traditional medium-promoted labeling strategy described here provides an alternative design paradigm for future development of chemical tools for applications involving DNA functionalization.

Site-specific chemical modification of unprotected DNAs through a phosphine-mediated amine–azide coupling reaction in ionic liquid.     

Maximum packings of uniform group divisible triple systems

Necessary and sufficient conditions are given for the existence of a maximum packing of Kⁿ_m with triangles with all possible minimal leaves. This is of course equivalent to a maximum packing of a group divisible triple system with n groups of size m. © 1996 John Wiley & Sons, Inc.     

Global Optimization For Molecular Clusters Using A New Smoothing Approach

Strategies involving smoothing of the objective function have been used to help solve difficult global optimization problems arising in molecular chemistry. This paper proposes a new smoothing approach and examines some basic issues in smoothing for molecular configuration problems. We first propose a new, simple algebraic way of smoothing the Lennard-Jones energy function, which is an important component of the energy in many molecular models. This simple smoothing technique is shown to have close similarities to previously-proposed, spatial averaging smoothing techniques. We also present some experimental studies of the behavior of local and global minimizers under smoothing of the potential energy in Lennard-Jones problems. An examination of minimizer trajectories from these smoothed problems shows significant limitations in the use of smoothing to directly solve global optimization problems.