Frost heave in freezing soils: a quasi-static model for ice lens growth

Frost heave can have a very destructive impact on infrastructure in permafrost regions. The complexity of nanoscale ice-mineral interactions and their relation to the macroscale frost heave phenomenon make ice lens growth modeling an interesting but challenging task. Taking into account the limiting assumption of the constant segregation temperature in the segregation potential model, we propose here a new quasi-static model for ice lens growth under a time varying temperature based on the water activity criterion. In this model, the conventional pressure potential gradient in Darcy's law is replaced by a water activity based chemical potential gradient for the calculation of water flow velocity, which provides a better prediction of ice lens growth and is useful to describe the ice nucleation and the state of water at a specific temperature. Moreover, based on the analysis of the new developed model, a mathematical description of the segregation potential is provided here. The modified Kozeny-Carman equation is applied to determine the water permeability of a given soil. In our new model, the effects of the equivalent water pressure are taken into account to modify the freezing characteristic function. Hence, the temperature- and equivalent water pressure- dependent hydraulic permeability in the frozen fringe is mathematically determined and improved. By coupling the quasi-static model with the modified hydraulic permeability function, the numerical calculation of ice lens growth is validated based on the experimental data of the temperature of the ice lens measured in the laboratory. The prediction of ice lens growth using the proposed method contributes and facilitates the simplified calculation of frost heave in the field and/or laboratory scenarios at a quasi-static state, and thus enables a better understanding of phase change and fluid flow in partially frozen granular media (soils)

Pre-nuclear level of I-129 in Chinese loess-paleosol sections: A search for the natural I-129 level for dating in terrestrial environments

Due to its long half-life (15.7 Myr), radioactive I-129 has great potential for dating geologic materials as old as 100 Myr. Thus, knowing the natural level of I-129 is crucial to dating applications. The initial ratio of I-129/I-127 in the ocean has been quantified by a number of researchers who have reached a consensus value. However, the applicability of I-129 dating in the terrestrial environment remains problematic because the lack of an initial I-129/I-127 value. In this work, samples of loess-paleosol sections from the Chinese Loess Plateau (CLP) were analyzed for I-129/I-127, aiming to provide an Initial I-129/I-127 ratio that can be adopted for dating purposes in terrestrial environments. A value of (2.0 +/- 1.0) x 10(-11) for the I-129/I-127 ratio was found in two investigated loess-paleosol sections from Xifeng and Luochuan, China. This ratio is one order of magnitude higher than the initial value reported for the marine environment. Alteration of the natural initial I-129 In the investigated samples due to the downward migration of anthropogenic I-129 and by excess fissiogenic I-129 from uranium was not supported. Consequently, the I-129/I-127 ratio measured is considered to be a pristine value, and the difference from that In the marine systems is attributed to an Isotopic dilution effect. (C) 2018 Elsevier Ltd. All rights reserved

What is a good question? Task-oriented asking with fact-level masking

Asking questions is an important element of real-life collaboration on reasoning tasks like question answering. For example, a legal assistant chatbot may be unable to make accurate recommendations without specific information on the user's circumstances. However, large language models are usually deployed to solve reasoning tasks directly without asking follow-up questions to the user or third parties. We term this problem task-oriented asking (TOA). Zero-shot chat models can perform TOA, but their training is primarily based on next-token prediction rather than whether questions contribute to successful collaboration. To enable the training and evaluation of TOA models, we present a definition and framework for natural language task-oriented asking, the problem of generating questions that result in answers useful for a reasoning task. We also present fact-level masking (FLM), a procedure for converting natural language datasets into self-supervised TOA datasets by omitting particular critical facts. Finally, we generate a TOA dataset from the HotpotQA dataset using FLM and evaluate several zero-shot language models on it. Our experiments show that current zero-shot models struggle to ask questions that retrieve useful information, as compared to human annotators. These results demonstrate an opportunity to use FLM datasets and the TOA framework to train and evaluate better TOA models

Mining Firm-level Uncertainty in Stock Market: A Text Mining Approach

The traditional finance paradigm seeks to understand uncertainty and their impact on stock market. However, most previous studies try to quantify uncertainty at macro-level such as the EPU index. There are few studies tapping into firm-level uncertainty. In this paper, we address this empirical anomaly by integrating text mining tools to measure the firm-level uncertainty score from news content. We focus on companies listed in S&P 1500. We crawled a total of 2,196,975 news articles from LexisNexis database from April 2007 to July 2017. We extracted uncertainty related information as features by using named entity extraction, LM dictionary, and other linguistic features. We employed nonlinear machine learning models to investigate the impact on stocks future returns by uncertainty-related features. To address the theoretical problem, we use traditional asset pricing techniques to test the relationship among information derived uncertainty and the financial market performance

Hypothesis test on a mixture forward-incubation-time epidemic model with application to COVID-19 outbreak

The distribution of the incubation period of the novel coronavirus disease that emerged in 2019 (COVID-19) has crucial clinical implications for understanding this disease and devising effective disease-control measures. Qin et al. (2020) designed a cross-sectional and forward follow-up study to collect the duration times between a specific observation time and the onset of COVID-19 symptoms for a number of individuals. They further proposed a mixture forward-incubation-time epidemic model, which is a mixture of an incubation-period distribution and a forward time distribution, to model the collected duration times and to estimate the incubation-period distribution of COVID-19. In this paper, we provide sufficient conditions for the identifiability of the unknown parameters in the mixture forward-incubation-time epidemic model when the incubation period follows a two-parameter distribution. Under the same setup, we propose a likelihood ratio test (LRT) for testing the null hypothesis that the mixture forward-incubation-time epidemic model is a homogeneous exponential distribution. The testing problem is non-regular because a nuisance parameter is present only under the alternative. We establish the limiting distribution of the LRT and identify an explicit representation for it. The limiting distribution of the LRT under a sequence of local alternatives is also obtained. Our simulation results indicate that the LRT has desirable type I errors and powers, and we analyze a COVID-19 outbreak dataset from China to illustrate the usefulness of the LRT.Comment: 34 pages, 2 figures, 2 table

Dual-attention Focused Module for Weakly Supervised Object Localization

The research on recognizing the most discriminative regions provides referential information for weakly supervised object localization with only image-level annotations. However, the most discriminative regions usually conceal the other parts of the object, thereby impeding entire object recognition and localization. To tackle this problem, the Dual-attention Focused Module (DFM) is proposed to enhance object localization performance. Specifically, we present a dual attention module for information fusion, consisting of a position branch and a channel one. In each branch, the input feature map is deduced into an enhancement map and a mask map, thereby highlighting the most discriminative parts or hiding them. For the position mask map, we introduce a focused matrix to enhance it, which utilizes the principle that the pixels of an object are continuous. Between these two branches, the enhancement map is integrated with the mask map, aiming at partially compensating the lost information and diversifies the features. With the dual-attention module and focused matrix, the entire object region could be precisely recognized with implicit information. We demonstrate outperforming results of DFM in experiments. In particular, DFM achieves state-of-the-art performance in localization accuracy in ILSVRC 2016 and CUB-200-2011.Comment: 8 pages, 6 figures and 4 table

Large inter-city inequality in consumption-based CO₂ emissions for China's pearl river basin cities

Cities are leading carbon mitigation but are heterogeneous in their mitigation policies due to different socioeconomic backgrounds. Given that cities are increasingly inextricably linked, formulating mitigation policies of different cities cannot be easily achieved without comprehensive carbon inventories, who taking the inter-city supply chains into account. The Pearl River Basin is one of the important economic zones in China, with huge disparity in its cities, but very limited information is available on their consumption-based CO2 emissions. To fill this gap, we compiled a consumption-based inventory of 47 cities in the Basin for 2012. We found that the total consumption-based emissions of 47 cities was 933.8 Mt, accounting for 13.1% of China's emissions. There were huge differences in the consumption-based emissions, ranging from 3.6 Mt (Heyuan City) to 153.1 Mt (Shenzhen City). The consumption-based emissions were highly concentrated in the largest seven cities, which accounted for 52.8% of the total emissions of the Basin. The consumption-based emissions per capita also varied greatly, from 1.2 to 14.5 tons per capita. Large scale infrastructure was the biggest driving force for most cities, resulting in 42.1% to 75.6% of the emissions. At sector-level, construction, heavy industry and services were leading in emissions, contributing more than 80% of emissions. The major inter-city carbon transfers occurred within upstream cities in the developing regions and downstream cities in the Pearl River Delta respectively, instead of the transfers between upstream and downstream cities. The findings highlight that the regional mitigation strategies could mainly focus on cities in intra-province boundary, rather than inter-province boundary, and also the city-level mitigation strategies should pay attention to the key emission sectors and drivers in respect of the heterogeneity of cities