The zero temperature coefficient in junctionless nanowire transistors

This Letter presents an analysis of the zero temperature coefficient (ZTC) bias in junctionless nanowire transistors (JNTs). Unlike in previous works, which had shown that JNT did not present a ZTC point, this work shows that ZTC may occur in JNTs depending mainly on the series resistance of the devices and its dependence on the temperature. Experimental results of drain current, threshold voltage, and series resistance are presented for both long and short channel n and p-type devices. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4744965]

Supersymmetric Isospectral Formalism for the Calculation of Near-Zero Energy States: Application to the Very Weakly Bound He-4 Trimer Excited State

We propose a novel mathematical approach for the calculation of near-zero energy states by solving potentials which are isospectral with the original one. For any potential, families of strictly isospectral potentials (with very different shape) having desirable and adjustable features are generated by supersymmetric isospectral formalism. The near-zero energy Efimov state in the original potential is effectively trapped in the deep well of the isospectral family and facilitates more accurate calculation of the Efimov state. Application to the first excited state in He-4 trimer is presented.

Turbulence driven particle transport in Texas Helimak

We analyze the turbulence driven particle transport in Texas Helimak [K. W. Gentle and H. He, Plasma Sci. Technol. 10, 284 (2008)], a toroidal plasma device with a one-dimensional equilibrium with magnetic curvature and shear. Alterations on the radial electric field, through an external voltage bias, change the spectral plasma characteristics inducing a dominant frequency for negative bias values and a broad band frequency spectrum for positive bias values. When applying a negative bias, the transport is high where the waves propagate with phase velocities near the plasma flow velocity, an indication that the transport is strongly affected by a wave particle resonant interaction. On the other hand, for positive bias values, the plasma has a reversed shear flow, and we observe that the transport is almost zero in the shearless radial region, an evidence of a transport barrier in this region. (c) 2012 American Institute of Physics. [doi:10.1063/1.3676607]

Understanding bias in geographic range size estimates

Aim Estimates of geographic range size derived from natural history museum specimens are probably biased for many species. We aim to determine how bias in these estimates relates to range size. Location We conducted computer simulations based on herbarium specimen records from localities ranging from the southern United States to northern Argentina. Methods We used theory on the sampling distribution of the mean and variance to develop working hypotheses about how range size, defined as area of occupancy (AOO), was related to the inter-specific distribution of: (1) mean collection effort per area across the range of a species (MC); (2) variance in collection effort per area across the range of a species (VC); and (3) proportional bias in AOO estimates (PBias: the difference between the expected value of the estimate of AOO and true AOO, divided by true AOO). We tested predictions from these hypotheses using computer simulations based on a dataset of more than 29,000 herbarium specimen records documenting occurrences of 377 plant species in the tribe Bignonieae (Bignoniaceae). Results The working hypotheses predicted that the mean of the inter-specific distribution of MC, VC and PBias were independent of AOO, but that the respective variance and skewness decreased with increasing AOO. Computer simulations supported all but one prediction: the variance of the inter-specific distribution of VC did not decrease with increasing AOO. Main conclusions Our results suggest that, despite an invariant mean, the dispersion and symmetry of the inter-specific distribution of PBias decreases as AOO increases. As AOO increased, range size was less severely underestimated for a large proportion of simulated species. However, as AOO increased, range size estimates having extremely low bias were less common.

On Wald Residuals in Generalized Linear Models

A rigorous asymptotic theory for Wald residuals in generalized linear models is not yet available. The authors provide matrix formulae of order O(n(-1)), where n is the sample size, for the first two moments of these residuals. The formulae can be applied to many regression models widely used in practice. The authors suggest adjusted Wald residuals to these models with approximately zero mean and unit variance. The expressions were used to analyze a real dataset. Some simulation results indicate that the adjusted Wald residuals are better approximated by the standard normal distribution than the Wald residuals.

TYPE-ZERO SADDLE-NODE BIFURCATIONS AND STABILITY REGION ESTIMATION OF NONLINEAR AUTONOMOUS DYNAMICAL SYSTEMS

A complete characterization of the stability boundary of a class of nonlinear dynamical systems that admit energy functions is developed in this paper. This characterization generalizes the existing results by allowing the type-zero saddle-node nonhyperbolic equilibrium points on the stability boundary. Conceptual algorithms to obtain optimal estimates of the stability region (basin of attraction) in the form of level sets of a given family of energy functions are derived. The behavior of the stability region and the corresponding estimates are investigated for parameter variation in the neighborhood of a type-zero saddle-node bifurcation value.

Transverse sphericity of primary charged particles in minimum bias proton-proton collisions at root s=0.9, 2.76 and 7 TeV

Measurements of the sphericity of primary charged particles in minimum bias proton-proton collisions at root s = 0.9, 2.76 and 7 TeV with the ALICE detector at the LHC are presented. The observable is measured in the plane perpendicular to the beam direction using primary charged tracks with p(T) > 0.5 GeV/c in vertical bar eta vertical bar < 0.8. The mean sphericity as a function of the charged particle multiplicity at mid-rapidity (N-ch) is reported for events with different p(T) scales ("soft" and "hard") defined by the transverse momentum of the leading particle. In addition, the mean charged particle transverse momentum versus multiplicity is presented for the different event classes, and the sphericity distributions in bins of multiplicity are presented. The data are compared with calculations of standard Monte Carlo event generators. The transverse sphericity is found to grow with multiplicity at all collision energies, with a steeper rise at low N-ch, whereas the event generators show an opposite tendency. The combined study of the sphericity and the mean p(T) with multiplicity indicates that most of the tested event generators produce events with higher multiplicity by generating more back-to-back jets resulting in decreased sphericity (and isotropy). The PYTHIA6 generator with tune PERUGIA-2011 exhibits a noticeable improvement in describing the data, compared to the other tested generators.

A general class of zero-or-one inflated beta regression models

This paper proposes a general class of regression models for continuous proportions when the data contain zeros or ones. The proposed class of models assumes that the response variable has a mixed continuous-discrete distribution with probability mass at zero or one. The beta distribution is used to describe the continuous component of the model, since its density has a wide range of different shapes depending on the values of the two parameters that index the distribution. We use a suitable parameterization of the beta law in terms of its mean and a precision parameter. The parameters of the mixture distribution are modeled as functions of regression parameters. We provide inference, diagnostic, and model selection tools for this class of models. A practical application that employs real data is presented. (C) 2011 Elsevier B.V. All rights reserved.

Decreasing the number of false positives in sequence classification

Abstract Background A large number of probabilistic models used in sequence analysis assign non-zero probability values to most input sequences. To decide when a given probability is sufficient the most common way is bayesian binary classification, where the probability of the model characterizing the sequence family of interest is compared to that of an alternative probability model. We can use as alternative model a null model. This is the scoring technique used by sequence analysis tools such as HMMER, SAM and INFERNAL. The most prevalent null models are position-independent residue distributions that include: the uniform distribution, genomic distribution, family-specific distribution and the target sequence distribution. This paper presents a study to evaluate the impact of the choice of a null model in the final result of classifications. In particular, we are interested in minimizing the number of false predictions in a classification. This is a crucial issue to reduce costs of biological validation. Results For all the tests, the target null model presented the lowest number of false positives, when using random sequences as a test. The study was performed in DNA sequences using GC content as the measure of content bias, but the results should be valid also for protein sequences. To broaden the application of the results, the study was performed using randomly generated sequences. Previous studies were performed on aminoacid sequences, using only one probabilistic model (HMM) and on a specific benchmark, and lack more general conclusions about the performance of null models. Finally, a benchmark test with P. falciparum confirmed these results. Conclusions Of the evaluated models the best suited for classification are the uniform model and the target model. However, the use of the uniform model presents a GC bias that can cause more false positives for candidate sequences with extreme compositional bias, a characteristic not described in previous studies. In these cases the target model is more dependable for biological validation due to its higher specificity.

Bias and learning in temporal binding: intervals between actions and outcomes are compressed by prior bias

It has consistently been shown that agents judge the intervals between their actions and outcomes as compressed in time, an effect named intentional binding. In the present work, we investigated whether this effect is result of prior bias volunteers have about the timing of the consequences of their actions, or if it is due to learning that occurs during the experimental session. Volunteers made temporal estimates of the interval between their action and target onset (Action conditions), or between two events (No-Action conditions). Our results show that temporal estimates become shorter throughout each experimental block in both conditions. Moreover, we found that observers judged intervals between action and outcomes as shorter even in very early trials of each block. To quantify the decrease of temporal judgments in experimental blocks, exponential functions were fitted to participants’ temporal judgments. The fitted parameters suggest that observers had different prior biases as to intervals between events in which action was involved. These findings suggest that prior bias might play a more important role in this effect than calibration-type learning processes.