Conditional bounds for small prime solutions of linear equations

Let a 1, a 2, a 3 be non-zero integers with gcd(a 1 a 2, a 3)=1 and let b be an arbitrary integer satisfying gcd (b, a i, a j) =1 for i≠j and b≡a 1+a 2+a 3 (mod 2). In a previous paper [3] which completely settled a problem of A. Baker, the 2nd and 3rd authors proved that if a 1, a 2, a 3 are not all of the same sign, then the equation a 1 p 1+a 2 p 2+a 3 p 3=b has a solution in primes p j satisfying {Mathematical expression} where A>0 is an absolute constant. In this paper, under the Generalized Riemann Hypothesis, the authors obtain a more precise bound for the solutions p j . In particular they obtain A0. An immediate consquence of the main result is that the Linnik's courtant is less than or equal to 2. © 1992 Springer-Verlag.postprin

Pairwise Confusion for Fine-Grained Visual Classification

Fine-Grained Visual Classification (FGVC) datasets contain small sample sizes, along with significant intra-class variation and inter-class similarity. While prior work has addressed intra-class variation using localization and segmentation techniques, inter-class similarity may also affect feature learning and reduce classification performance. In this work, we address this problem using a novel optimization procedure for the end-to-end neural network training on FGVC tasks. Our procedure, called Pairwise Confusion (PC) reduces overfitting by intentionally {introducing confusion} in the activations. With PC regularization, we obtain state-of-the-art performance on six of the most widely-used FGVC datasets and demonstrate improved localization ability. {PC} is easy to implement, does not need excessive hyperparameter tuning during training, and does not add significant overhead during test time.Comment: Camera-Ready version for ECCV 201

Systemic lupus erythematosus (SLE) is associated with telomerase activation

published_or_final_versio

An AUC-based Permutation Variable Importance Measure for Random Forests

The random forest (RF) method is a commonly used tool for classification with high dimensional data as well as for ranking candidate predictors based on the so-called random forest variable importance measures (VIMs). However the classification performance of RF is known to be suboptimal in case of strongly unbalanced data, i.e. data where response class sizes differ considerably. Suggestions were made to obtain better classification performance based either on sampling procedures or on cost sensitivity analyses. However to our knowledge the performance of the VIMs has not yet been examined in the case of unbalanced response classes. In this paper we explore the performance of the permutation VIM for unbalanced data settings and introduce an alternative permutation VIM based on the area under the curve (AUC) that is expected to be more robust towards class imbalance. We investigated the performance of the standard permutation VIM and of our novel AUC-based permutation VIM for different class imbalance levels using simulated data and real data. The results suggest that the standard permutation VIM loses its ability to discriminate between associated predictors and predictors not associated with the response for increasing class imbalance. It is outperformed by our new AUC-based permutation VIM for unbalanced data settings, while the performance of both VIMs is very similar in the case of balanced classes. The new AUC-based VIM is implemented in the R package party for the unbiased RF variant based on conditional inference trees. The codes implementing our study are available from the companion website: http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/070_drittmittel/janitza/index.html

Energy-Efficient Event Detection by Participatory Sensing Under Budget Constraints

Dynamic event detection by using participatory sensing paradigms has received growing interests recently, where detection tasks are assigned to smart-device users who can potentially collect needed sensory data from device-equipped sensors. Typical applications include, but are not limited to, noise and air pollution detections, people gathering, even disaster prediction. Given this problem, although many existing centralized solutions are effective and widely used, they usually cause heavy communication overhead. Thus, it is strongly desired to design distributed solutions to reduce energy consumption, while achieving a high level of detection accuracy with limited sensing task budget. In this paper, we first present two novel centralized detection algorithms as the performance benchmark, which make use of the Minimum Cut theory and support vector machine (SVM)-based pattern recognition techniques. Then, we introduce a novel distributed and energy-efficient event detection framework under task budget constraint, where we formulate an optimization problem and derive an optimal utility function. Finally, based on a real trace-driven data set in an urban area of Beijing, extensive simulation results demonstrate the effectiveness of our proposed algorithms

Sensitivity and Asymptotic Analysis of Inter-Cell Interference Against Pricing for Multi-Antenna Base Stations

We thoroughly investigate the downlink beamforming problem of a two-tier network in a reversed time-division duplex system, where the interference leakage from a tier-2 base station (BS) toward nearby uplink tier-1 BSs is controlled through pricing. We show that soft interference control through the pricing mechanism does not undermine the ability to regulate interference leakage while giving flexibility to sharing the spectrum. Then, we analyze and demonstrate how the interference leakage is related to the variations of both the interference prices and the power budget. Moreover, we derive a closed-form expression for the interference leakage in an asymptotic case, where both the charging BSs and the charged BS are equipped with a large number of antennas, which provides further insights into the lowest possible interference leakage that can be achieved by the pricing mechanism

Model study of tropospheric trace species distributions during PEM-West A

A three-dimensional mesoscale transport/photochemical model is used to study the transport and photochemical transformation of trace species over eastern Asia and western Pacific for the period from September 20 to October 6, 1991, of the Pacific Exploratory Mission-West A experiment. The influence of emissions from the continental boundary layer that was evident in the observed trace species distributions in the lower troposphere over the ocean is well simulated by the model. In the upper troposphere, species such as O3, NOy (total reactive nitrogen species), and SO2 which have a significant source in the stratosphere are also simulated well in the model, suggesting that the upper tropospheric abundances of these species are strongly influenced by stratospheric fluxes and upper tropospheric sources. In the case of SO2 the stratospheric flux is identified to be mostly from the Mount Pinatubo eruption. Concentrations in the upper troposphere for species such as CO and hydrocarbons, which are emitted in the continental boundary layer and have a sink in the troposphere, are significantly underestimated by the model. Two factors have been identified to contribute significantly to the underestimate: one is emissions upwind of the model domain (eastern Asia and western Pacific); the other is that vertical transport is underestimated in the model. Model results are also grouped by back trajectories to study the contrast between compositions of marine and continental air masses. The model-calculated altitude profiles of trace species in continental and marine air masses are found to be qualitatively consistent with observations. However, the difference in the median values of trace species between continental air and marine air is about twice as large for the observed values as for model results. This suggests that the model underestimates the outflow fluxes of trace species from the Asian continent and the Pacific rim countries to the ocean. Observed altitude profiles for species like CO and hydrocarbons show a negative gradient in continental air and a positive gradient in marine air. A mechanism which may be responsible for the altitude gradients is proposed

Temperature-compensated fibre optic strain gauge

Inventor name used in this publication: Hwa Ywa TamInventor name used in this publication: Siu Lau HoUS7796844; US7796844 B2; US7796844B2; US7,796,844; US 7,796,844 B2; 7796844; Appl. No. 12/177,830Inventor name used in this publication: Shun Yee LiuUSVersion of Recor

Evaluation of moderate alcohol use and cognitive function among men using a mendelian randomization design in the guangzhou biobank cohort study

Observational studies usually show that moderate alcohol use is associated with better cognitive function. Such studies are vulnerable to residual confounding arising from systematic differences between moderate alcohol users and others. A Mendelian randomization study carried out in a suitable population, such as southern Chinese men, in which alcohol use is low to moderate and is influenced by genotype, offers an alternative and superior approach for clarifying the causal effect of moderate alcohol use on cognitive function. The authors used aldehyde dehydrogenase 2 (ALDH2) genotype (AA, GA, or GG) as an instrumental variable in 2-stage least squares analysis to obtain unbiased estimates of the relation of alcohol consumption (measured in alcohol units (10 g ethanol) per day) with cognitive function, assessed from delayed 10-word recall score (n = 4,707) and Mini-Mental State Examination (MMSE) score (n = 2,284), among men from the Guangzhou Biobank Cohort Study (2003-2008). ALHD2 genotype was strongly associated with alcohol consumption, with an F statistic of 71.0 in 2-stage least squares analysis. Alcohol consumption was not associated with delayed 10-word recall score (-0.03 words per alcohol unit, 95% confidence interval:-0.18, 0.13) or MMSE score (0.06 points per alcohol unit, 95% confidence interval:-0.22, 0.34). Moderate alcohol use is unlikely to be cognitively protective. © The Author 2012. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved.postprin