Intervalley-Scattering Induced Electron-Phonon Energy Relaxation in Many-Valley Semiconductors at Low Temperatures

We report on the effect of elastic intervalley scattering on the energy transport between electrons and phonons in many-valley semiconductors. We derive a general expression for the electron-phonon energy flow rate at the limit where elastic intervalley scattering dominates over diffusion. Electron heating experiments on heavily doped n-type Si samples with electron concentration in the range $3.5-16.0\times 10^{25}$ m$^{-3}$ are performed at sub-1 K temperatures. We find a good agreement between the theory and the experiment.Comment: v2: Notations changed: $\Delta_i$ --> $\delta v_i$, $\tau_{eff}$ removed. Eq. (1) changed, Eq. (2) added and complete derivation of Eq. (3) included. Some further discussion about single vs. many valley added [3rd paragraph after Eq. (7)]. End notes removed and new reference added [Kragler and Thomas]. Typos in references correcte

Competing with stationary prediction strategies

In this paper we introduce the class of stationary prediction strategies and construct a prediction algorithm that asymptotically performs as well as the best continuous stationary strategy. We make mild compactness assumptions but no stochastic assumptions about the environment. In particular, no assumption of stationarity is made about the environment, and the stationarity of the considered strategies only means that they do not depend explicitly on time; we argue that it is natural to consider only stationary strategies even for highly non-stationary environments.Comment: 20 page

Adaptive Mixture Methods Based on Bregman Divergences

We investigate adaptive mixture methods that linearly combine outputs of $m$ constituent filters running in parallel to model a desired signal. We use "Bregman divergences" and obtain certain multiplicative updates to train the linear combination weights under an affine constraint or without any constraints. We use unnormalized relative entropy and relative entropy to define two different Bregman divergences that produce an unnormalized exponentiated gradient update and a normalized exponentiated gradient update on the mixture weights, respectively. We then carry out the mean and the mean-square transient analysis of these adaptive algorithms when they are used to combine outputs of $m$ constituent filters. We illustrate the accuracy of our results and demonstrate the effectiveness of these updates for sparse mixture systems.Comment: Submitted to Digital Signal Processing, Elsevier; IEEE.or

Inhibition in multiclass classification

The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems. These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches

Inhibition in multiclass classification

The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems. These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches

Genetic determinants of glucose-6-phosphate dehydrogenase activity in Kenya

Background: The relationship between glucose-6-phosphate dehydrogenase (G6PD) deficiency and clinical phenomena such as primaquine-sensitivity and protection from severe malaria remains poorly defined, with past association studies yielding inconsistent and conflicting results. One possibility is that examination of a single genetic variant might underestimate the presence of true effects in the presence of unrecognized functional allelic diversity. Methods: We systematically examined this possibility in Kenya, conducting a fine-mapping association study of erythrocyte G6PD activity in 1828 Kenyan children across 30 polymorphisms at or around the G6PD locus. Results: We demonstrate a strong functional role for c.202G>A (rs1050828), which accounts for the majority of variance in enzyme activity observed (P=1.5 × 10-200, additive model). Additionally, we identify other common variants that exert smaller, intercorrelated effects independent of c.202G>A, and haplotype analyses suggest that each variant tags one of two haplotype motifs that are opposite in sequence identity and effect direction. We posit that these effects are of biological and possible clinical significance, specifically noting that c.376A>G (rs1050829) augments 202AG heterozygote risk for deficiency trait by two-fold (OR = 2.11 [1.12 - 3.84], P=0.014). Conclusions: Our results suggest that c.202G>A is responsible for the majority of the observed prevalence of G6PD deficiency trait in Kenya, but also identify a novel role for c.376A>G as a genetic modifier which marks a common haplotype that augments the risk conferred to 202AG heterozygotes, suggesting that variation at both loci merits consideration in genetic association studies probing G6PD deficiency-associated clinical phenotypes. </p

Competitive portfolio selection using stochastic predictions

We study a portfolio selection problem where a player attempts to maximise a utility function that represents the growth rate of wealth. We show that, given some stochastic predictions of the asset prices in the next time step, a sublinear expected regret is attainable against an optimal greedy algorithm, subject to tradeoff against the \accuracy" of such predictions that learn (or improve) over time. We also study the effects of introducing transaction costs into the model

Higher education and unemployment in Europe : an analysis of the academic subject and national effects

This paper examines the impact of an academic degree and field of study on short and long-term unemployment across Europe (EU15). Labour Force Survey (LFS) data on over half a million individuals are utilised for that purpose. The harmonized LFS classification of level of education and field of study overcomes past problems of comparability across Europe. The study analyses (i) the effect of an academic degree at a European level, (ii) the specific effect of 14 academic subjects and (iii) country specific effects. The results indicate that an academic degree is more effective on reducing the likelihood of short-term than long-term unemployment. This general pattern even though it is observed for most of the academic subjects its levels show significant variation across disciplines and countries