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 $S_3$ permutation symmetry) for three light neutrinos. Our neutrino mass matrix has smaller permutation symmetry $S_{2}$ ($\nu_{\mu} \leftrightarrow \nu_{e}$), and is consistent with all neutrino experiments except the $^{37}$Cl experiment. The resulting mass eigenvalues for three neutrinos are $m_{1} \approx (2.55-1.27) \times 10^{-3} eV, m_{2,3} \approx (0.71-1.43) eV$ for $\Delta m_{LSND}^{2} = 0.5 - 2.0 eV^2$. Then these light neutrinos can account for $\sim (2.4-4.8)$ of the dark matter for $h = 0.8 (0.5)$. Our model predicts the $\nu_{\mu} \rightarrow \nu_{\tau}$ 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.