Low-energy electronic recoil in xenon detectors by solar neutrinos

Low-energy electronic recoil caused by solar neutrinos in multi-ton xenon detectors is an important subject not only because it is a source of the irreducible background for direct searches of weakly-interacting massive particles (WIMPs), but also because it provides a viable way to measure the solar $pp$ and $^{7}\textrm{Be}$ neutrinos at the precision level of current standard solar model predictions. In this work we perform $\textit{ab initio}$ many-body calculations for the structure, photoionization, and neutrino-ionization of xenon. It is found that the atomic binding effect yields a sizable suppression to the neutrino-electron scattering cross section at low recoil energies. Compared with the previous calculation based on the free electron picture, our calculated event rate of electronic recoil in the same detector configuration is reduced by about $25\%$. We present in this paper the electronic recoil rate spectrum in the energy window of 100 eV - 30 keV with the standard per ton per year normalization for xenon detectors, and discuss its implication for low energy solar neutrino detection (as the signal) and WIMP search (as a source of background).Comment: 12 pages, 3 figure

A Case Study for Exploring Dental Patients’ Preferred Roles in Taiwan

The purpose of this study was to explore the dental patients’ preferred roles in Taiwan. A convenience sample of 66 patients, 26 recruited from one dental clinic, and 40 from one medical center, were interviewed and their preferences for participation in treatment decision making were established using a measurement tool designed to elicit decision-making preferences. Patients’ preferences for participation in treatment decision making were established using Control Preference Scale (CPS) tool. In addition, Unfolding theory provided a means of analyzing the data so that the degree of control preferred by each patient could be established. This study found that nearly 70% clinic patients perceived passive role in treatment decision making whereas 50% patients in medical centre. Further, the collaborative role was most commonly preferred, but an active role was more commonly perceived in clinics than in medical centre. Finally, the implications of the results for patient participation are discussed

D4AM: A General Denoising Framework for Downstream Acoustic Models

The performance of acoustic models degrades notably in noisy environments. Speech enhancement (SE) can be used as a front-end strategy to aid automatic speech recognition (ASR) systems. However, existing training objectives of SE methods are not fully effective at integrating speech-text and noisy-clean paired data for training toward unseen ASR systems. In this study, we propose a general denoising framework, D4AM, for various downstream acoustic models. Our framework fine-tunes the SE model with the backward gradient according to a specific acoustic model and the corresponding classification objective. In addition, our method aims to consider the regression objective as an auxiliary loss to make the SE model generalize to other unseen acoustic models. To jointly train an SE unit with regression and classification objectives, D4AM uses an adjustment scheme to directly estimate suitable weighting coefficients rather than undergoing a grid search process with additional training costs. The adjustment scheme consists of two parts: gradient calibration and regression objective weighting. The experimental results show that D4AM can consistently and effectively provide improvements to various unseen acoustic models and outperforms other combination setups. Specifically, when evaluated on the Google ASR API with real noisy data completely unseen during SE training, D4AM achieves a relative WER reduction of 24.65% compared with the direct feeding of noisy input. To our knowledge, this is the first work that deploys an effective combination scheme of regression (denoising) and classification (ASR) objectives to derive a general pre-processor applicable to various unseen ASR systems. Our code is available at https://github.com/ChangLee0903/D4AM

FFTPL: An Analytic Placement Algorithm Using Fast Fourier Transform for Density Equalization

We propose a flat nonlinear placement algorithm FFTPL using fast Fourier transform for density equalization. The placement instance is modeled as an electrostatic system with the analogy of density cost to the potential energy. A well-defined Poisson's equation is proposed for gradient and cost computation. Our placer outperforms state-of-the-art placers with better solution quality and efficiency