3,110 research outputs found
Convenient Labelling Technique for Mass Spectrometry - Acid Catalyzed Deuterium and Oxygen-18 Exchange via Gas-liquid Chromatography
Mass spectrometry labelling technique - acid catalyzed deuterium and oxygen 18 exchange by gas-liquid chromatograph
The Impact of the Internet Tax Freedom Act on State Revenues
The Internet Tax Freedom Act, passed by the U.S. Congress, is both a result of and a potentially important influence on the growth of electronic commerce. This paper examines the impact of the moratorium imposed by the legislation prohibiting the collection of taxes on Internet commerce. Its significance with respect to business-to-business and business-to-consumer commerce is documented by analysis of current sales activities. The paper also explores the potential impact on a variety of sales categories if the moratorium is extended beyond the current three-year period of effect. The discussion illustrates how continuing the force of the legislation will have an increasingly significant effect on both states, which depend on sales tax revenue, and participants in Internet commerce
Emergence of long-range order in BaTiO3 from local symmetry-breaking distortions
By using a symmetry motivated basis to evaluate local distortions against
pair distribution function data (PDF), we show without prior bias, that the
off-centre Ti displacements in the archetypal ferroelectric BaTiO3 are zone
centred and rhombohedral-like in nature across its known ferroelectric and
paraelectric phases. With our newly-gained insight we construct a simple Monte
Carlo (MC) model which captures our main experimental findings and demonstrate
how the rich crystallographic phase diagram of BaTiO3 emerges from correlations
of local symmetry-breaking distortions alone. Our results strongly support the
order-disorder picture for these phase transitions, but can also be reconciled
with the soft-mode theory of BaTiO3 that is supported by some spectroscopic
techniques.Comment: 5 pages, 3 figure
Overstating the evidence - double counting in meta-analysis and related problems
Background: The problem of missing studies in meta-analysis has received much attention. Less attention has been paid to the more serious problem of double counting of evidence.
Methods: Various problems in overstating the precision of results from meta-analyses are described and illustrated with examples, including papers from leading medical journals. These problems include, but are not limited to, simple double-counting of the same studies, double counting of some aspects of the studies, inappropriate imputation of results, and assigning spurious precision to individual studies.
Results: Some suggestions are made as to how the quality and reliability of meta-analysis can be improved. It is proposed that the key to quality in meta-analysis lies in the results being transparent and checkable.
Conclusions: Existing quality check lists for meta-analysis do little to encourage an appropriate attitude to combining evidence and to statistical analysis. Journals and other relevant organisations should encourage authors to make data available and make methods explicit. They should also act promptly to withdraw meta-analyses when mistakes are found
INFORMATION SYSTEMS SUPPORT FOR ASSESSMENT OF MANAGEMENT PERFORMANCE: AN EXPERIMENTAL EVALUATION
This paper reports the results of a study to determine how individual managers assemble information from automated systems when the task is evaluating organization performance. An experiment was conducted in which managers were given varying forms of information over a period of time and required to accumulate the information they would need for a later evaluation decision. As the results show, there are differences in the way individuals select and assemble reported information system design practices
Harold Jeffreys's Theory of Probability Revisited
Published exactly seventy years ago, Jeffreys's Theory of Probability (1939)
has had a unique impact on the Bayesian community and is now considered to be
one of the main classics in Bayesian Statistics as well as the initiator of the
objective Bayes school. In particular, its advances on the derivation of
noninformative priors as well as on the scaling of Bayes factors have had a
lasting impact on the field. However, the book reflects the characteristics of
the time, especially in terms of mathematical rigor. In this paper we point out
the fundamental aspects of this reference work, especially the thorough
coverage of testing problems and the construction of both estimation and
testing noninformative priors based on functional divergences. Our major aim
here is to help modern readers in navigating in this difficult text and in
concentrating on passages that are still relevant today.Comment: This paper commented in: [arXiv:1001.2967], [arXiv:1001.2968],
[arXiv:1001.2970], [arXiv:1001.2975], [arXiv:1001.2985], [arXiv:1001.3073].
Rejoinder in [arXiv:0909.1008]. Published in at
http://dx.doi.org/10.1214/09-STS284 the Statistical Science
(http://www.imstat.org/sts/) by the Institute of Mathematical Statistics
(http://www.imstat.org
Spiking neurons with short-term synaptic plasticity form superior generative networks
Spiking networks that perform probabilistic inference have been proposed both
as models of cortical computation and as candidates for solving problems in
machine learning. However, the evidence for spike-based computation being in
any way superior to non-spiking alternatives remains scarce. We propose that
short-term plasticity can provide spiking networks with distinct computational
advantages compared to their classical counterparts. In this work, we use
networks of leaky integrate-and-fire neurons that are trained to perform both
discriminative and generative tasks in their forward and backward information
processing paths, respectively. During training, the energy landscape
associated with their dynamics becomes highly diverse, with deep attractor
basins separated by high barriers. Classical algorithms solve this problem by
employing various tempering techniques, which are both computationally
demanding and require global state updates. We demonstrate how similar results
can be achieved in spiking networks endowed with local short-term synaptic
plasticity. Additionally, we discuss how these networks can even outperform
tempering-based approaches when the training data is imbalanced. We thereby
show how biologically inspired, local, spike-triggered synaptic dynamics based
simply on a limited pool of synaptic resources can allow spiking networks to
outperform their non-spiking relatives.Comment: corrected typo in abstrac
Natural-gradient learning for spiking neurons.
In many normative theories of synaptic plasticity, weight updates implicitly depend on the chosen parametrization of the weights. This problem relates, for example, to neuronal morphology: synapses which are functionally equivalent in terms of their impact on somatic firing can differ substantially in spine size due to their different positions along the dendritic tree. Classical theories based on Euclidean-gradient descent can easily lead to inconsistencies due to such parametrization dependence. The issues are solved in the framework of Riemannian geometry, in which we propose that plasticity instead follows natural-gradient descent. Under this hypothesis, we derive a synaptic learning rule for spiking neurons that couples functional efficiency with the explanation of several well-documented biological phenomena such as dendritic democracy, multiplicative scaling, and heterosynaptic plasticity. We therefore suggest that in its search for functional synaptic plasticity, evolution might have come up with its own version of natural-gradient descent
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