34,833 research outputs found
User Attraction via Wireless Charging in Cellular Networks
A strong motivation of charging depleted battery can be an enabler for
network capacity increase. In this light we propose a spatial attraction
cellular network (SAN) consisting of macro cells overlaid with small cell base
stations that wirelessly charge user batteries. Such a network makes battery
depleting users move toward the vicinity of small cell base stations. With a
fine adjustment of charging power, this user spatial attraction (SA) improves
in spectral efficiency as well as load balancing. We jointly optimize both
enhancements thanks to SA, and derive the corresponding optimal charging power
in a closed form by using a stochastic geometric approach.Comment: to be presented in IEEE International Symposium on Modeling and
Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt) Workshop on
Green Networks (GREENNET) 2016, Arizona, USA (8 pages, 4 figures
Look at the First Sentence: Position Bias in Question Answering
Many extractive question answering models are trained to predict start and
end positions of answers. The choice of predicting answers as positions is
mainly due to its simplicity and effectiveness. In this study, we hypothesize
that when the distribution of the answer positions is highly skewed in the
training set (e.g., answers lie only in the k-th sentence of each passage), QA
models predicting answers as positions can learn spurious positional cues and
fail to give answers in different positions. We first illustrate this position
bias in popular extractive QA models such as BiDAF and BERT and thoroughly
examine how position bias propagates through each layer of BERT. To safely
deliver position information without position bias, we train models with
various de-biasing methods including entropy regularization and bias
ensembling. Among them, we found that using the prior distribution of answer
positions as a bias model is very effective at reducing position bias,
recovering the performance of BERT from 37.48% to 81.64% when trained on a
biased SQuAD dataset.Comment: 13 pages, EMNLP 202
A simple modification of the maximal mixing scenario for three light neutrinos
We suggest a simple modification of the maximal mixing scenario (with
permutation symmetry) for three light neutrinos. Our neutrino mass matrix has
smaller permutation symmetry (), and
is consistent with all neutrino experiments except the Cl experiment.
The resulting mass eigenvalues for three neutrinos are for . Then these light neutrinos can account for
of the dark matter for . Our
model predicts the oscillation probability
in the range sensitive to the future experiments such as CHORUS and NOMAD.Comment: The title has been changed, to appear in Z. Phys.
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