Miracle-dz/UniFAR-FTUs huggingface dataset:
1 Gender , Beliefs , and Coordination with Externalities *

Groups such as committees or boards make many important decisions within organizations. Many of these decisions affect external parties. This paper uses an experimental approach to study how the gender composition of three-person groups affects choices and beliefs in a Coordination game with selfish and prosocial equilibria. We find that the social preferences of group members are a key determinant of the group’s coordination choice. Controlling for social preferences of the group, groups with more women are more likely to make choices that are kinder to external parties. Both men and women believe that women will make kinder choices more frequently. Men are expected to be 9 to 18 percentage points less kind than women overall, and in uniform gender groups. These results have implications for public policies intended to increase gender diversity and women’s representation on decision-making committees in the corporate sector, in politics, and in academia. JEL Classification: C72, C92, D91
Asymptotics of the Bootstrap via Stability with Applications to Inference with Model Selection

One of the most commonly used methods for forming conﬁdence intervals is the empirical bootstrap, which is especially expedient when the limiting distribution of the estimator is unknown. However, despite its ubiquitous role in machine learning, its theoretical properties are still not well understood. Recent developments in probability have provided new tools to study the bootstrap method. However, they have been applied only to speciﬁc applications and contexts, and it is unclear whether these techniques are applicable to the understanding of the consistency of the bootstrap in machine learning pipelines. In this paper, we derive general stability conditions under which the empirical bootstrap estimator is consistent and quantify the speed of convergence. Moreover, we propose alternative ways to use the bootstrap method to build conﬁdence intervals with coverage guarantees. Finally, we illustrate the generality and tightness of our results by examples of interest for machine learning including for two-sample kernel tests after kernel selection and the empirical risk of stacked estimators.
Controls of the lithospheric thermal field of an ocean-continent subduction zone: the southern Central Andes

In an ocean-continent subduction zone, the assessment of the lithospheric thermal state is essential to determine the controls of the deformation within the upper plate and the dip angle of the subducting lithosphere. In this study, we evaluate the degree of influence of both the configuration of the upper plate and variations of the subduction angle on the lithospheric thermal field of the southern Central Andes (29°–39°S). Here, the subduction angle increases from subhorizontal (5°) north of 33°S, to steep (~30°) in the south. We derived the 3D temperature and heat flow distribution of the lithosphere in the southern Central Andes considering conversion of S wave tomography to temperatures together with steady-state conductive modeling. We found that the orogen is overall warmer than the forearc and the foreland, and that the lithosphere of the northern part of the foreland appears colder than its southern counterpart. Sedimentary blanketing and the thickness of the radiogenic crust exert the main control on the shallow thermal field (< 50 km depth). Specific conditions are present where the oceanic slab is relatively shallow (< 85 km depth) and the radiogenic crust is thin, This configuration results in relatively colder temperatures compared to regions where the radiogenic crust is thick and the slab is steep. At depths >50 km, the temperatures of the overriding plate are mainly controlled by the mantle heat input and the subduction angle. The thermal field of the upper plate likely preserves the flat subduction angle and influences the spatial distribution of shortening.
THE AMERICAN SOCIETY OF MECHANICAL ENGINEERS

This paper presents the first attempt to stabilize rotating stall in a single-stage transonic axial flow compressor with inlet distortion using active feedback control. The experiments were conducted at the NASA Lewis Research Center on a single-stage transonic core compressor inlet stage. An array of 12 jet injectors located upstream of the compressor was used for forced response testing and feedback stabilization. Results for a circumferential total pressure distortion of about one dynamic head and a 120° extent (DC(60)=0.61) are reported in this paper. Part I (Spakovszky et al. (1998)) reports results for radial distortion. Control laws were designed using empirical transfer function estimates determined from forced response results. Distortion introduces coupling between the harmonics of circumferential pressure perturbations, requiring multivariable identification and control design techniques. The compressor response displayed a strong first spatial harmonic, dominated by the well known incompressible MooreGreitzer mode. Steady axisymmetric injection of 4% of the compressor mass flow resulted in a 6.2% reduction of stalling mass flow. Constant gain feedback, using unsteady asymmetric injection, yielded a further range extension of 9%. A more sophisticated robust H controller allowed a reduction in stalling mass flow of 10.2% relative to steady injection, yielding a total reduction in stalling mass flow of 16.4%.
Adversarial Teacher-Student Representation Learning for Domain Generalization

Domain generalization (DG) aims to transfer the learning task from a single or multiple source domains to unseen target domains. To extract and leverage the information which exhibits sufﬁcient generalization ability, we propose a simple yet effective approach of Adversarial Teacher-Student Representation Learning, with the goal of deriving the domain generalizable representations via generating and exploring out-of-source data distributions. Our proposed framework advances Teacher-Student learning in an adversarial learning manner, which alternates be-tween knowledge-distillation based representation learning and novel-domain data augmentation. The former progressively updates the teacher network for deriving domain-generalizable representations, while the latter synthesizes data out-of-source yet plausible distributions. Extensive image classiﬁcation experiments on benchmark datasets in multiple and single source DG settings conﬁrm that, our model exhibits sufﬁcient generalization ability and performs favorably against state-of-the-art DG methods.
Economic Studies on Wildlife Management and Conservation

The present research consists of four articles addressing wildlife management and conservation using different methodological approaches of applied economics. The thesis encompasses various economic analyses on three carnivore species mainly: the wolf (Canis lupus), the brown bear (Ursus arctos), and the lynx (Lynx lynx). Other wildlife species are also present to a relatively lesser extent: the moose (Alces alces), the roe deer (Capreolus capreolus), the wild boar (Sus scrofa), and the wolverine (Gulo gulo). The first three articles comprise applied analyses in Sweden, and the last article covers North America as well as Eurasia. The first article uses revealed preference methods to address the impact of large carnivores and licensed carnivore hunting on hunting rental prices in Sweden. The results suggest that regulated carnivore hunting exerts a statistically significant and positive effect on hunting lease prices whilst carnivore presence influences lease prices negatively. The analysis is performed using least absolute deviation estimations to minimize the effect of outliers. The second article expands the analysis of the first paper by implementing an unconditional quantile regression analysis. This methodological approach allows to study the effect of large carnivores over different segments of the hunting rental price distribution. The outcome confirms that carnivores reduce lease prices in the quantiles near the median, yet no significant impact is found for the lower quantiles. The third article introduces a spatial dimension in the analysis. It formulates a dynamic bioeconomic model to estimate the effects of carnivores and hunting pressure on game harvests in Sweden. A linearized version of the bioeconomic model is then estimated using dynamic spatial econometrics. The model accounts for spatial and temporal dimensions in order to explore spatial effects in game harvests and estimate the value of the impact of large carnivores on the harvest of ungulate game. The results elicit dynamic spatial spillovers in roe deer and wild boar harvests. Lynx presence and human hunting pressure reduce roe deer and wild boar harvests, respectively. The wolf and the brown bear decrease moose harvests, Economic Studies on Wildlife Management and Conservation however moose does not exhibit spatial effects, seemingly due to Swedish hunting regulations for this particular species. The last article explores the implications of immediate emotions on group outcomes for conserving two carnivore species, the wolf and the wolverine. By conducting an online public goods experiment, the study examines the degree of cooperation across group participants after inducing positive and negative emotions with audio-visual stimuli. The results indicate that positive emotions seemingly enhance cooperative behavior for wolf conservation yet no corresponding evidence is found for the wolverine. Furthermore, for a given induced emotion, monetary contributions do not differ significantly across the two animal species.
Fast Bayesian Inference for Gaussian Cox Processes via Path Integral Formulation

Gaussian Cox processes are widely-used point process models that use a Gaussian process to describe the Bayesian a priori uncertainty present in latent intensity functions. In this paper, we propose a novel Bayesian inference scheme for Gaussian Cox processes by exploiting a conceptually-intuitive path integral formulation. The proposed scheme does not rely on domain discretization, scales linearly with the number of observed events, has a lower complexity than the state-of-theart variational Bayesian schemes with respect to the number of inducing points, and is applicable to a wide range of Gaussian Cox processes with various types of link functions. Our scheme is especially beneficial under the multi-dimensional input setting, where the number of inducing points tends to be large. We evaluate our scheme on synthetic and real-world data, and show that it achieves comparable predictive accuracy while being tens of times faster than reference methods.
Comparative study of the effect of several trains on the rotation motion of ballast stones

The ballast pick-up (or ballast train-induced-wind erosion (BTE)) phenomenon is a limiting factor for the maximum allowed operational train speed. The determination of the conditions for the initiation of the motion of the ballast stones due to the wind gust created by high-speed trains is critical to predict the start of ballast pick-up because, once the motion is initiated, a saltation-like chain reaction can take place. The aim of this paper is to present a model to evaluate the effect of a random aerodynamic impulse on stone motion initiation, and an experimental study performed to check the capability of the proposed model to classify trains by their effect on the ballast due to the flow generated by the trains. A measurement study has been performed at kp 69 + 500 on the Madrid – Barcelona High Speed Line. The obtained results show the feasibility of the proposed method, and contribute to a technique for BTE characterization, which can be relevant for the development of train interoperability standards.
University of Southern Denmark Internet-based treatment of anxiety and depression in patients with ischaemic heart disease attending cardiac rehabilitation

Citation for pulished version (APA): Helmark, L. , Ahm, R. , Andersen, C. M. , Skovbakke, S. J., Kok, R. N. , Wiil, U. K. , Schmidt, T. , Hjelmborg, J. V. B. , Frostholm, L. , Frydendal, D. H. , Hansen, T. B. , Zwisler, A. D. O. , & Pedersen, S. S. (2021). Internet-based treatment of anxiety and depression in patients with ischaemic heart disease attending cardiac rehabilitation: a feasibility study (eMindYourHeart). European Heart Journal Digital Health, 2(2), 323–335. https://doi.org/10.1093/ehjdh/ztab037
Short Communication Cyclooxygenase-2 and Inducible Nitric Oxide Synthase Gene Polymorphisms and Risk of Reflux Esophagitis, Barrett’s Esophagus, and Esophageal Adenocarcinoma

The incidence of esophageal adenocarcinoma has increased in recent years, and Barrett’s esophagus is a recognized risk factor. Gastroesophageal reflux of acid and/or bile is linked to these conditions and to reflux esophagitis. Inflammatory disorders can lead to carcinogenesis through activation of ‘‘prosurvival genes,’’ including cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase (iNOS). Increased expression of these enzymes has been found in esophageal adenocarcinoma, Barrett’s esophagus, and reflux esophagitis. Polymorphic variants in COX-2 and iNOS genes may be modifiers of risk of these conditions. In a population-based case-control study, we examined associations of the COX-2 8473 T>C and iNOS Ser Leu (C>T) polymorphisms with risk of esophageal adenocarcinoma, Barrett’s esophagus, and reflux esophagitis. Genomic DNA was extracted from blood samples collected from cases of esophageal adenocarcinoma (n = 210), Barrett’s esophagus (n = 212), and reflux esophagitis (n = 230) and normal population controls frequency matched for age and sex (n = 248). Polymorphisms were genotyped using TaqMan allelic discrimination assays. Odds ratios and 95% confidence intervals were obtained from logistic regression models adjusted for potential confounding factors. The presence of at least one COX-2 8473 C allele was associated with a significantly increased risk of esophageal adenocarcinoma (adjusted odds ratio, 1.58; 95% confidence interval, 1.04-2.40). There was no significant association between this polymorphism and risk of Barrett’s esophagus or reflux esophagitis or between the iNOS Ser Leu polymorphism and risk of these esophageal conditions. Our study suggests that the COX-2 8473 C allele is a potential genetic marker for susceptibility to esophageal adenocarcinoma. (Cancer Epidemiol Biomarkers Prev 2008;17(3):727–31)
Towards Better Visual Explanations for Deep Image Classiﬁers

Understanding and explaining the decisions of neural networks is of great importance, for safe deployment as well as for legal reasons. In this paper, we consider visual explanations for deep image classiﬁers that are both informative and un-derstandable by humans. Motivated by the recent FullGrad method, we ﬁnd that bringing information from multiple layers is very effective in producing explanations. Based on this observation, we propose a new method, DeepMaps, that combines information from hidden activities. We show that our method outranks alternative explanations with respect to metrics established in the literature, which are based on pixel perturbations. While these evaluations are based on changes in the class scores, we propose to directly consider the change in the network’s decisions. Noting that perturbation-based metrics can fail to distinguish random explanations from sensible ones, we propose to measure the quality of a given explanation by comparing it to explanations for randomly selected other images. We demonstrate through experiments that DeepMaps outperforms existing methods according to the resulting evaluation metrics as well.
THE EFFECT OF PLACE

This study aims to identify residents’ attitudes regarding place image, place identity and residents welcoming tourists, and determine the effect of these attitudes on attitudes to positive impacts of tourism. Result of the analyses reveal that place identity has a significant effect on attitudes to positive impacts, on residents welcoming tourists and on place image. Also, it was concluded that residents welcoming tourists has a significant effect on attitudes to positive impacts, and place image has a significant effect on residents welcoming tourists.
Assessing the Adoption Behavior of E-Learning in a Developing Country in South East Asia: Predicting an Alternative Path to Behavioral Intention to Use

To some extent in Bangladesh, all efforts that were put into availing technology-based learning has not paid off well as most of the learners are reluctant to use the evolving technologies. The major impediments to the use of various e-learning platforms include a lack of self-confidence and inadequate technical skills, along with insufficient knowledge. Previous studies have mainly focused on the adoption behavior of information system and information technology among students, educators, and the profession, but little is known about how to enhance learners’ skills and capacity prior to the adoption of the emerging e-learning platforms among the HEIs in Bangladesh. This study aims to apply the Unified Theory of Acceptance and Use of Technology (UTAUT) to explore the protagonist factors that may expedite learners’ experience and the skills needed to embrace e-learning. The study analyzed data from 250 learners (students) collected from three purposively selected leading HEIs - the American International University-Bangladesh (AIUB), Daffodil International University (DIU) and the Northern University Bangladesh (NUB) in Bangladesh. The study used a paper-based self-administered questionnaire to conduct a survey comprised of statements (manifest variables), adopted from prior studies on the adoption behavior of Information Technology (IT) and Information System (IS). All responses were initially encoded into SPSS then processed through SMART PLS 2.0, a standard structural equation modeling simulation, for testing the study hypotheses. The findings revealed that the learners’ behavioral intention to embrace e-learning is highly infiltrated by the variables: effort expectancy (EE), social influence (SI), and performance expectancy (PE); likewise, variables such as i) Social influence (SI), ii) Effort Expectancy (EE), and iii) Facilitating Condition (FC) enhancing learners perceived behavioral control (PBC) of using e-learning platform prior to forming BIU. The study could contribute to the existing regional literature on e-learning acceptance; also, it could be quite useful for HEIs in Bangladesh for accelerating the e-learning penetration rate within their institution.
Spatio‐Temporal Clustering of Seismicity Enabled by Off‐Fault Plasticity

While significant progress has been made in understanding earthquake source processes in linear elastic domains, the effect of more realistic rheologies including plasticity is poorly understood. Here, we simulate the sequence of earthquake and aseismic slip of a 2D antiplane rate‐and‐state fault embedded in a full‐space elastic‐plastic bulk. We show that off‐fault plasticity may lead to partial ruptures as well as temporal clustering of seismic events. Furthermore, the interaction of fault slip and off‐fault plasticity results in pockets of slip deficit. While the energy dissipated through plastic deformation remains a small fraction of the total energy budget, its impact on the source characteristics is disproportionally large through the redistribution of stresses and viscous relaxation. Our results suggest a new mechanism of dynamic heterogeneity in earthquake physics that may have important implications on earthquake size distribution and energy budget.
Online Meta-Learning via Learning with Layer-Distributed Memory

We demonstrate that efﬁcient meta-learning can be achieved via end-to-end training of deep neural networks with memory distributed across layers. The persistent state of this memory assumes the entire burden of guiding task adaptation. Moreover, its distributed nature is instrumental in orchestrating adaptation. Ablation experiments demonstrate that providing relevant feedback to memory units distributed across the depth of the network enables them to guide adaptation throughout the entire network. Our results show that this is a successful strategy for simplifying meta-learning – often cast as a bi-level optimization problem – to standard end-to-end training, while outperforming gradient-based, prototype-based, and other memory-based meta-learning strategies. Additionally, our adaptation strategy naturally handles online learning scenarios with a signiﬁcant delay between observing a sample and its corresponding label – a setting in which other approaches struggle. Adaptation via distributed memory is effective across a wide range of learning tasks, ranging from classiﬁcation to online few-shot semantic segmentation.
Concrete integral abutment bridges with reinforced concrete piles

The use of reinforced concrete (RC) piles in integral abutment bridges (IABs) has not been widespread due to concerns over pile flexibility and the potential for concrete cracking. This is the reason why the use of steel piles is the preferred solution in the United States. However, in various countries where IABs are still seldom used, RC piles are more readily available and economical. Hence, an understanding of the behaviour of IABs with RC piles can lead to a wider implementation of integral solutions. This paper presents the results of a parametric study conducted to evaluate how both the design variables and the accuracy of the modelling approach influence the potential use of integral solutions with RC piles in prestressed concrete bridges up to 200 m long. Finite element modelling was used and four levels of approximation (LoA) were established for the analyses, ranging from simple linear‐elastic to more complex non‐linear models. The results show that existing concerns over concrete cracking control can be overcome if adequate options in design are used together with the adequate LoAs in structural analysis. Integral solutions with RC piles for bridges up to 200 m long can generally be adopted, although in comparisons with non‐integral designs a significant additional amount of prestressing steel is to be expected. The results also include a set of charts with practical estimates to help designers in their first approach to the preliminary design of an IAB.