1,651 research outputs found
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 and neutrinos at the precision level of current
standard solar model predictions. In this work we perform
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 . 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
Asian Students’ Cultural Orientation and Computer Self-Efficacy Significantly Related to Online Inquiry-Based Learning Outcomes on the Go-Lab Platform
Learning and teaching Mendelian genetics are central topics in school science. This study explored factors associated with the learning outcomes of Taiwanese junior high school students in an online inquiry learning environment. Research within face-to-face classroom settings had revealed that Asian students are more likely to be tutor-oriented and collectivistic learners. However, results of how these orientations affect learning in online environments are needed. In this analysis, seventh-grade students from Taiwan (N = 290) completed a genetics lesson using an Inquiry Learning Space (ILS) on the Go-Lab platform. Students were randomly assigned conditions in which support was provided either by general text or by an expert person in the form of a cartoon figure. In addition, students completed questionnaires assessing their cultural orientations, as well as their computer self-efficacy. Results revealed that the presence of a virtual expert did not influence students’ learning outcomes. However, the extent to which students identified as collectivistic and their level of computer self-efficacy were positively associated with the learning outcomes. Students’ computer self-efficacy was positively related to their behavioral intentions as well. These results illustrate the importance of Asian students’ disciplined personality and computer self-efficacy for online inquiry-based learning.</p
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