Supervised Learning in Multilayer Spiking Neural Networks

The current article introduces a supervised learning algorithm for multilayer spiking neural networks. The algorithm presented here overcomes some limitations of existing learning algorithms as it can be applied to neurons firing multiple spikes and it can in principle be applied to any linearisable neuron model. The algorithm is applied successfully to various benchmarks, such as the XOR problem and the Iris data set, as well as complex classifications problems. The simulations also show the flexibility of this supervised learning algorithm which permits different encodings of the spike timing patterns, including precise spike trains encoding.Comment: 38 pages, 4 figure

Reactions of (-)-sparteine with alkali metal HMDS complexes : conventional meets the unconventional

Conventional (-)-sparteine adducts of lithium and sodium 1,1,1,3,3,3-hexamethyldisilazide (HMDS) were prepared and characterised, along with an unexpected and unconventional hydroxyl-incorporated sodium sodiate, [(-)-sparteine·Na(-HMDS)Na·(-)-sparteine]+[Na4(-HMDS)4(OH)]--the complex anion of which is the first inverse crown ether anion

Microscopic modeling of photoluminescence of strongly disordered semiconductors

A microscopic theory for the luminescence of ordered semiconductors is modified to describe photoluminescence of strongly disordered semiconductors. The approach includes both diagonal disorder and the many-body Coulomb interaction. As a case study, the light emission of a correlated plasma is investigated numerically for a one-dimensional two-band tight-binding model. The band structure of the underlying ordered system is assumed to correspond to either a direct or an indirect semiconductor. In particular, luminescence and absorption spectra are computed for various levels of disorder and sample temperature to determine thermodynamic relations, the Stokes shift, and the radiative lifetime distribution.Comment: 35 pages, 14 figure

The RNA workbench: best practices for RNA and high-throughput sequencing bioinformatics in Galaxy

RNA-based regulation has become a major research topic in molecular biology. The analysis of epigenetic and expression data is therefore incomplete if RNA-based regulation is not taken into account. Thus, it is increasingly important but not yet standard to combine RNA-centric data and analysis tools with other types of experimental data such as RNA-seq or ChIP-seq. Here, we present the RNA workbench, a comprehensive set of analysis tools and consolidated workflows that enable the researcher to combine these two worlds. Based on the Galaxy framework the workbench guarantees simple access, easy extension, flexible adaption to personal and security needs, and sophisticated analyses that are independent of command-line knowledge. Currently, it includes more than 50 bioinformatics tools that are dedicated to different research areas of RNA biology including RNA structure analysis, RNA alignment, RNA annotation, RNA-protein interaction, ribosome profiling, RNA-seq analysis and RNA target prediction. The workbench is developed and maintained by experts in RNA bioinformatics and the Galaxy framework. Together with the growing community evolving around this workbench, we are committed to keep the workbench up-to-date for future standards and needs, providing researchers with a reliable and robust framework for RNA data analysis. Availability: The RNA workbench is available at https://github.com/bgruening/galaxy-rna-workbench

Anatomy of BioJS, an open source community for the life sciences

BioJS is an open source software project that develops visualization tools for different types of biological data. Here we report on the factors that influenced the growth of the BioJS user and developer community, and outline our strategy for building on this growth. The lessons we have learned on BioJS may also be relevant to other open source software projects

Multi‐year carbon budget of a mature commercial short rotation coppice willow plantation

Energy derived from second generation perennial energy crops is projected to play an increasingly important role in the decarbonization of the energy sector. Such energy crops are expected to deliver net greenhouse gas emissions reductions through fossil fuel displacement and have potential for increasing soil carbon (C) storage. Despite this, few empirical studies have quantified the ecosystem‐level C balance of energy crops and the evidence base to inform energy policy remains limited. Here, the temporal dynamics and magnitude of net ecosystem carbon dioxide (CO2) exchange (NEE) were quantified at a mature short rotation coppice (SRC) willow plantation in Lincolnshire, United Kingdom, under commercial growing conditions. Eddy covariance flux observations of NEE were performed over a four‐year production cycle and combined with biomass yield data to estimate the net ecosystem carbon balance (NECB) of the SRC. The magnitude of annual NEE ranged from −147 ± 70 to −502 ± 84 g CO2‐C m−2 year−1 with the magnitude of annual CO2 capture increasing over the production cycle. Defoliation during an unexpected outbreak of willow leaf beetle impacted gross ecosystem production, ecosystem respiration, and net ecosystem exchange during the second growth season. The NECB was −87 ± 303 g CO2‐C m−2 for the complete production cycle after accounting for C export at harvest (1,183 g C m−2), and was approximately CO2‐C neutral (−21 g CO2‐C m−2 year−1) when annualized. The results of this study are consistent with studies of soil organic C which have shown limited changes following conversion to SRC willow. In the context of global decarbonization, the study indicates that the primary benefit of SRC willow production at the site is through displacement of fossil fuel emissions

Random-phase approximation and its applications in computational chemistry and materials science

The random-phase approximation (RPA) as an approach for computing the electronic correlation energy is reviewed. After a brief account of its basic concept and historical development, the paper is devoted to the theoretical formulations of RPA, and its applications to realistic systems. With several illustrating applications, we discuss the implications of RPA for computational chemistry and materials science. The computational cost of RPA is also addressed which is critical for its widespread use in future applications. In addition, current correction schemes going beyond RPA and directions of further development will be discussed.Comment: 25 pages, 11 figures, published online in J. Mater. Sci. (2012

Assessment of correlation energies based on the random-phase approximation

The random-phase approximation to the ground state correlation energy (RPA) in combination with exact exchange (EX) has brought Kohn-Sham (KS) density functional theory one step closer towards a universal, "general purpose first principles method". In an effort to systematically assess the influence of several correlation energy contributions beyond RPA, this work presents dissociation energies of small molecules and solids, activation energies for hydrogen transfer and non-hydrogen transfer reactions, as well as reaction energies for a number of common test sets. We benchmark EX+RPA and several flavors of energy functionals going beyond it: second-order screened exchange (SOSEX), single excitation (SE) corrections, renormalized single excitation (rSE) corrections, as well as their combinations. Both the single excitation correction as well as the SOSEX contribution to the correlation energy significantly improve upon the notorious tendency of EX+RPA to underbind. Surprisingly, activation energies obtained using EX+RPA based on a KS reference alone are remarkably accurate. RPA+SOSEX+rSE provides an equal level of accuracy for reaction as well as activation energies and overall gives the most balanced performance, which makes it applicable to a wide range of systems and chemical reactions.Comment: 14 pages, 5 figures, full articl