A Comparison of Air Leakage Prediction Techniques for Auxiliary Ventilation Ducting Systems

This paper briefly reviews prediction techniques for determination of leakage and friction along auxiliary ventilation ducting systems. In order to compare various prediction techniques that have been developed over the past, a macroscopic investigation of air leakage and friction resistance of auxiliary ventilation ducting systems has been undertaken. Measurements were conducted on 450 and 915 mm diameter fabric ducting over 100 m duct length to determine frictional resistances and the extent of leakage. Due to the high degree of accuracy required and the large volume of data that needed to be collected, electronic pressure transducers were used with computer for data recording. Conceptual models that describe the leakage characteristics of auxiliary ventilation ducting systems were developed based on this information. It was found that these models provided good correlation with most of the existing prediction techniques. The experimental methodology relying on computer data acquisition has allowed the accuracy of measured values to be treated with a high degree of confidence. The reliability of the developed models allows prediction of leakage, frictional impedance and airflow with enhanced confidence

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

Family firm and analyst forecasts in an emerging economy

Purpose: The purpose of this paper is to examine how family firms affect analyst forecast dispersion, accuracy and optimism and how earnings smoothness as the moderating factor, affects these relationships in an emerging market context. Design/methodology/approach: This paper uses the population sample of firms listed on the Taiwan Stock Exchange from 2009 to 2010 as the research sample, which includes 963 firm-year observations. Findings: The findings show that analysts following family firms are more likely to have more dispersed, less accurate and more optimism biased forecasts than those following nonfamily firms. Earning smoothness is mainly used by nonfamily firms as a signalling strategy to improve analyst forecast quality. In contrast, earnings smoothness is mainly used by families as a garbling strategy, stimulating forecast optimism. Only earnings smoothness in family firms with a high level of family ownership concentration is likely to be signalling-oriented to improve analyst forecast accuracy and mitigate analyst optimism biases. Originality/value: Emerging markets are not only featured by prevailing principal-principal conflicts but also have multiple levels of agency conflicts among large shareholders, minority shareholders and professionally hired managers. This research reveals the multiple governance roles of family owners in affecting analyst forecast quality, including their entrenchment role in extracting private benefits of control through opaque environments and market discipline distortion role in aligning interests between managers and families without prioritising meeting or beating analyst forecasts, both at the cost of minority shareholders. This research further disentangles the intertwined signaling oriented and garbiling oriented incentives associated with earnings smoothness under family governance

Self-ICL: Zero-Shot In-Context Learning with Self-Generated Demonstrations

Large language models (LMs) have exhibited superior in-context learning (ICL) ability to adopt to target tasks by prompting with a few input-output demonstrations. Towards better ICL, different methods are proposed to select representative demonstrations from existing training corpora. However, such a setting is not aligned with real-world practices, as end-users usually query LMs without accesses to demonstration pools. Inspired by evidence suggesting LMs' zero-shot capabilities are underrated, and the role of demonstrations are primarily for exposing models' intrinsic functionalities, we introduce Self-ICL, a simple framework for zero-shot ICL. Given a test input, Self-ICL first prompts the model to generate pseudo-inputs. Next, the model predicts pseudo-labels for the pseudo-inputs via zero-shot prompting. Finally, we construct pseudo-demonstrations from pseudo-input-label pairs, and perform ICL for the test input. Evaluation on BIG-Bench Hard shows Self-ICL steadily surpasses zero-shot and zero-shot chain-of-thought baselines on head-to-head and all-task average performance. Our findings suggest the possibility to bootstrap LMs' intrinsic capabilities towards better zero-shot performance.Comment: Work in progres

Large Language Models Perform Diagnostic Reasoning

We explore the extension of chain-of-thought (CoT) prompting to medical reasoning for the task of automatic diagnosis. Motivated by doctors' underlying reasoning process, we present Diagnostic-Reasoning CoT (DR-CoT). Empirical results demonstrate that by simply prompting large language models trained only on general text corpus with two DR-CoT exemplars, the diagnostic accuracy improves by 15% comparing to standard prompting. Moreover, the gap reaches a pronounced 18% in out-domain settings. Our findings suggest expert-knowledge reasoning in large language models can be elicited through proper promptings.Comment: Accepted as a Tiny Paper at ICLR 2023 (10 pages, 5 figures

Atomic ionization by sterile-to-active neutrino conversion and constraints on dark matter sterile neutrinos with germanium detectors

The transition magnetic moment of a sterile-to-active neutrino conversion gives rise to not only radiative decay of a sterile neutrino, but also its non-standard interaction (NSI) with matter. For sterile neutrinos of keV-mass as dark matter candidates, their decay signals are actively searched for in cosmic X-ray spectra. In this work, we consider the NSI that leads to atomic ionization, which can be detected by direct dark matter experiments. It is found that this inelastic scattering process for a nonrelativistic sterile neutrino has a pronounced enhancement in the differential cross section at energy transfer about half of its mass, manifesting experimentally as peaks in the measurable energy spectra. The enhancement effects gradually smear out as the sterile neutrino becomes relativistic. Using data taken with germanium detectors that have fine energy resolution in keV and sub-keV regimes, constraints on sterile neutrino mass and its transition magnetic moment are derived and compared with those from astrophysical observations