Miracle-dz/UniFAR-FTUs huggingface dataset:
NATIONAL IDENTITY, IMMIGRATION AND SUBJECTIVE WELL-BEING

This paper studies the relationship between inflows of migrants into local areas and the selfreported well-being of natives. We document a negative association between inflows of migrants and the mental health of natives, but we uncover no significant relationship for life satisfaction. We find these estimated population level impacts masks considerable heterogeneity according to patterns of attachment to national identity. For people with an ethnic form (here proxied by Englishness), we find the disutility associated with immigration can be substantive, whereas for those with a civic form (Britishness) the estimated impact on mental health is not statistically significant (or substantive) and for life satisfaction positive. Our proposed explanation draws on identity economics which suggests that people assign themselves into social categories (e.g. English or British) with each category having different norms relating to how people should look and act. We put forward subjective well-being and in particular differences in national identity across individuals as an important factor underpinning the sharp variation in public attitudes towards immigration. More broadly, our analysis suggests that focusing on objective indicators, as is the norm (e.g. wages, unemployment), may give a limited understanding of the consequences of immigration for the welfare of natives.
Coinsurance Effects and Their Dependence on Financial Constraints_Jan11_2021

Coinsurance has differentiated effects that likely interact with financial constraints. We measure coinsurance of multi-segment firms using Granger causality between CDS spread-implied default risks of single-segment firms. Higher coinsurance results on average in lower cost of debt, higher leverage, higher cost of equity and unchanged WACC. Coinsurance lowers the cost of debt for financially unconstrained firms and increases leverage for constrained firms. Coinsurance lowers the cost of equity and the WACC only for intermediately constrained firms. Our findings indicate a wealth transfer from shareholders to debt holders and shed new light on the interplay of default risk and financial constraints. This version: January 11, 2021 JEL classification: G32, G33, L25
Effects of Elk and Bison Herbivory on Narrowleaf Cottonwood

Abstract. Ungulate browsing influences the structure and composition of woody plant communities, including species composition and biomass production as well as age distribution, recruitment, and mortality. We evaluated effects of elk and bison herbivory on narrowleaf cottonwood (Populus angustifolia) communities in a semiarid ecosystem in southern Colorado. Cottonwoods in this ecosystem have been aged at ≥300 years old and are among the oldest cottonwood trees in North America. We compared browsing intensity and structural and productivity responses of cottonwood to ungulate herbivory. We compared responses in sites with elk and bison, sites with elk but no bison, and sites where both ungulates were excluded. We found that the majority of browsing on cottonwood occurred during summer in this high desert ecosystem. Areas with both elk and bison had higher browse utilization than areas with only elk, but diet data indicated that elk consumed a much greater proportion of cottonwood than bison. Overall, browse utilization observed in this study was low to moderate compared to other studies, and our results may not be representative of sites experiencing intense year-round herbivory. Removal of all ungulate herbivory led to taller and denser cottonwood suckers; however, other environmental factors, in addition to herbivory, still strongly limit cottonwood growth and recruitment in this ecosystem.
Estimating Abundance of Desert Mule Deer Using Fecal DNA–Based Capture-Recapture

Abstract. Wildlife conservationists and managers often need to estimate abundance and demographic parameters to monitor the status of populations, and to ensure that these populations are meeting management goals. DNA capture-recapture surveys have become increasingly common in situations where physical surveys are consistently difficult or counts are small or variable. Desert mule deer (Odocoileus hemionus eremicus) inhabit rugged terrain, occur at low population densities, and demonstrate cryptic behaviors, making monitoring and assessing population status difficult. We assessed the effectiveness of a fecal DNA–based capture-recapture method for estimating abundance and survival of desert mule deer. We designed and implemented a fecal DNA sampling design focused at water sources during the dry season (June–July) of 2015–2017 in the Little Chuckwalla Mountains (study area ∼968 km2) located in the Sonoran Desert of southeastern California. We conducted 6, 5, and 4 surveys in 2015, 2016, and 2017, respectively. We used the capture-recapture data in the POPAN open-population model to estimate abundance and the Cormack–Jolly–Seber model to estimate annual survival. We detected 234, 193, and 211 unique individuals in 2015, 2016, and 2017, respectively. Over the 3-year study, population abundance estimates ranged from 386 in 2015 (95% CI, 264–509) to 301 in 2017 (95% CI, 260–342). Precision (i.e., coefficient of variation [CV]) of male and female abundance estimates ranged from 7% to 16%, and CV for males and females combined ranged from 5% to 15%. Annual apparent survival for females and males was 0.91 (95% CI, 0.59–0.98) and 0.71 (95% CI, 0.51–0.85), respectively. We concluded that fecal DNA–based capture-recapture is an effective method for estimating abundance and survival of desert mule deer. This approach could be expanded to encompass additional areas adjacent to our study site, with the potential to monitor the larger metapopulation of desert mule deer in the Sonoran Desert of California. More generally, DNA mark-recapture may be widely applicable to other species that congregate around water sources (or any required resource) during part of the year. Results from this study will be useful for developing population monitoring plans and for studies evaluating the effects of human development or environmental factors on desert mule deer and other desert ungulates.
Rocky Mountain Bristlecone Pine (Pinus aristata) is a Confirmed Host to Mountain Pine Beetle (Dendroctonus ponderosae)

Abstract. Mountain pine beetle (MPB) (Dendroctonus ponderosae Hopkins) is a native bark beetle that reproduces in pine (Pinus) species across western North America. High population levels can result in widespread host tree mortality. Over the past 2 decades, MPB has been responsible for pine mortality across millions of forested hectares in the western United States. Although a majority of the pine species found in the western United States are considered hosts to MPB, the host status of Rocky Mountain (RM) bristlecone pine (Pinus aristata) is unclear. We surveyed stands across the range of RM bristlecone pine in Colorado, USA, and quantified MPB-caused mortality within the past 10 years in stands where RM bristlecone and at least one other pine species co-occurred. We also evaluated in the field whether successful MPB brood production occurred in RM bristlecone pine. Our results confirm that RM bristlecone pine is susceptible to MPB attack and suitable for MPB reproduction. In mixed-species stands, pine species availability influenced MPB attack occurrence. The proportion of trees experiencing fatal beetle attack within a particular Pinus species was best predicted by the basal area proportion of that species in the stand prior to the most recent 10 years of mortality. These results indicate that RM bristlecone pine is vulnerable to ongoing climate change–induced contact with MPB.
Evaluation of Structural Characteristics of Huadian Oil Shale Kerogen Using Direct Techniques (Solid-State 13C NMR, XPS, FT-IR, and XRD)

A sample of kerogen isolated from Huadian oil shale was studied using a combination of solid-state 13C NMR, X-ray photoelectron spectroscopy (XPS), Fourier transform infrared (FT-IR), and X-ray diffraction (XRD) techniques to evaluate its structural characteristics. 13C NMR results indicate that the carbon skeletal structure of this kerogen is mainly composed of a fairly high fraction of aliphatic carbon (86.1%), with a very low aromaticity (fa) of 9.7%; methylene (CH2) carbons dominate in all types of aliphatic carbons and the majority of them exist as many long straight chains but not saturated alicyclics. The average methylene carbon chain length (Cn) is between 12 and 24. There are only one fused aromatic ring (e.g., naphthalene) or two single aromatic rings (one benzene ring and one penta-heterocycle) per 100 carbon atoms. However, aromatic rings in this kerogen have a very high value of substitutive degree (δ = 0.42–0.75). Furthermore, XRD analysis suggests that most methylene straight chains and ar.
Australian industrial relations in 2020: COVID-19, crisis and opportunity

This introduction to the Journal of Industrial Relations’ 2020 Annual Review of Industrial Relations provides an overview of the six Annual Review articles, an international review and two practitioner reviews. The COVID-19 pandemic presented a crisis for the labour market, intensifying serious existing issues such as stagnant wage growth, the gender pay gap and employer non-compliance with minimum wage laws. The pandemic also presented an opportunity for the Australian government to direct its economic stimulus measures in a targeted manner that addressed these existing problems concurrently with the immediate pandemic-related issues. However, 2020 will be marked by this missed opportunity.
Sociopolitical Development and Healthcare Career Interest, Self-Efficacy, and Outcome Expectations Among Rural Youth

The purpose of this preliminary study was to examine the indirect effects of social cognitive career variables (healthcare career [HC] self-efficacy and outcome expectations) in the relationship between youth sociopolitical development (SPD) and HC interests. Bootstrapping analysis was conducted using samples of eighth grade students (N = 139) attending schools in rural areas in communities that consist predominantly of Immigrants of Color. Preliminary findings for the total sample suggested an indirect path between SPD and HC interests through HC self-efficacy, whereas the indirect path through HC outcome expectations was not significant. The model for immigrant Youth of Color participants suggested a direct relationship between SPD and HC interest, whereas an indirect relationship between SPD and HC interests was found for White participants. Based on the results of this preliminary analysis, implications for career programming and research pertaining to rural immigrant communities are discussed.
Antecedents and Appraisals of Triumph across Four Countries

This study examined cross-cultural similarities and differences in antecedents and appraisals of triumph. Participants in the U.S., Serbia, Russia, and Japan provided open-ended descriptions of previous antecedent events that elicited experiences of triumph, and completed a standard appraisal questionnaire about those events. Events that elicited pride were also included for comparison. The open-ended responses were coded using a framework that delineated theoretical characteristics of triumph based on previous research. Findings indicated cross-cultural similarities in the antecedents and appraisals of triumph-eliciting events. Cultural variations were also found, especially between Japan and the other cultural groups and with regard to self-evaluations, which suggested the role of culture in triggering and appraising emotion-eliciting events. These findings extended empirical evidence about these important components of triumph, further contributing to its possibility as a discrete emotion.
Extensive pleiotropism and allelic heterogeneity mediate metabolic effects of IRX3 and IRX5

Obesity genes working together The biological causes of obesity are not well understood. Sobreira et al. examined the chromatin interactions between key genes in a locus known for its associations with obesity in human patients. In addition to directly interrogating the connections between these genes and examining the mechanisms that regulate their activity, the authors used mouse models to study the target genes' effects on both adipose tissue and brain cells that play a role in regulating dietary preferences. Science, abf1008, this issue p. 1085 Enhancer interactions regulate the activity of obesity-associated genes IRX3 and IRX5 in both adipose and brain tissue. Whereas coding variants often have pleiotropic effects across multiple tissues, noncoding variants are thought to mediate their phenotypic effects by specific tissue and temporal regulation of gene expression. Here, we investigated the genetic and functional architecture of a genomic region within the FTO gene that is strongly associated with obesity risk. We show that multiple variants on a common haplotype modify the regulatory properties of several enhancers targeting IRX3 and IRX5 from megabase distances. We demonstrate that these enhancers affect gene expression in multiple tissues, including adipose and brain, and impart regulatory effects during a restricted temporal window. Our data indicate that the genetic architecture of disease-associated loci may involve extensive pleiotropy, allelic heterogeneity, shared allelic effects across tissues, and temporally restricted effects.
Are Proinflammatory Cytokines Relevant for the Diagnosis of Prostate Cancer?

Background/Aim: Inflammation plays an important role in prostate cancer (PCa). We evaluated proinflammatory cytokines regarding differential diagnosis of PCa in men with PSA levels between 2-10 ng/ml. Patients and Methods: Serum samples of 79 men (PSA 2-10 ng/ml) were analyzed for 10 proinflammatory cytokines (IL-6, IL-8, TNF-α, IFN-γ, IL-10, IL-1β, IL-2, IL-4, IL-12p70, IL-13) and results were evaluated regarding presence of PCa and disease severity. Results: Significant differences between PCa patients and controls were found for IL-6 (p=0.002), IL-8 (p=0.030), and TNF-α (p=0.009), although they were not predictors of PCa in a logistic regression analysis. In addition, IL-6 and TNF-α levels were significantly higher in patients with high-risk PCa (p<0.05). No significant differences were observed regarding the other cytokines. Conclusion: In patients with PSA levels between 2-10 ng/ml, IL-6, IL-8, and TNF-α are associated with PCa, and IL-6 and TNF-α are associated with high-risk PCa.
A Workflow Analysis of Context-driven Conversational Recommendation

A number of recent works have made seminal contributions to the understanding of user intent and recommender interaction in conversational recommendation. However, to date, these studies have not focused explicitly on context-driven interaction that underlies the typical use of more pervasive Question Answering (QA) focused conversational assistants like Amazon Alexa, Apple Siri, and Google Assistant. In this paper, we aim to understand a general workflow of natural context-driven conversational recommendation that arises from a pairwise study of a human user interacting with a human simulating the role of a recommender. In our analysis of this intrinsically organic human-to-human conversation, we observe a clear structure of interaction workflow consisting of a preference elicitation and refinement stage, followed by inquiry and critiquing stages after the first recommendation. To better understand the nature of these stages and the conversational flow within them, we augment existing taxonomies of intent and action to label all interactions at each stage and analyze the workflow. From this analysis, we identify distinct conversational characteristics of each stage, e.g., (i) the preference elicitation stage consists of significant iteration to clarify, refine, and obtain a mutual understanding of preferences, (ii) the inquiry and critiquing stage consists of extensive informational queries to understand features of the recommended item and to (implicitly) specify critiques, and (iii) explanation appears to drive a substantial portion of the post-recommendation interaction, suggesting that beyond the purpose of justification, explanation serves a critical role to direct the evolving conversation itself. Altogether, we contribute a novel qualitative and quantitative analysis of workflow in conversational recommendation that further refines our existing understanding of this important frontier of conversational systems and suggests a number of critical avenues for further research to better automate natural recommendation conversations.
Graph Neural Networks for Friend Ranking in Large-scale Social Platforms

Graph Neural Networks (GNNs) have recently enabled substantial advances in graph learning. Despite their rich representational capacity, GNNs remain under-explored for large-scale social modeling applications. One such industrially ubiquitous application is friend suggestion: recommending users other candidate users to befriend, to improve user connectivity, retention and engagement. However, modeling such user-user interactions on large-scale social platforms poses unique challenges: such graphs often have heavy-tailed degree distributions, where a significant fraction of users are inactive and have limited structural and engagement information. Moreover, users interact with different functionalities, communicate with diverse groups, and have multifaceted interaction patterns. We study the application of GNNs for friend suggestion, providing the first investigation of GNN design for this task, to our knowledge. To leverage the rich knowledge of in-platform actions, we formulate friend suggestion as multi-faceted friend ranking with multi-modal user features and link communication features. We design a neural architecture GraFRank to learn expressive user representations from multiple feature modalities and user-user interactions. Specifically, GraFRank employs modality-specific neighbor aggregators and cross-modality attentions to learn multi-faceted user representations. We conduct experiments on two multi-million user datasets from Snapchat, a leading mobile social platform, where GraFRank outperforms several state-of-the-art approaches on candidate retrieval (by 30% MRR) and ranking (by 20% MRR) tasks. Moreover, our qualitative analysis indicates notable gains for critical populations of less-active and low-degree users.
Incremental Spatio-Temporal Graph Learning for Online Query-POI Matching

Query and Point-of-Interest (POI) matching, aiming at recommending the most relevant POIs from partial query keywords, has become one of the most essential functions in online navigation and ride-hailing applications. Existing methods for query-POI matching, such as Google Maps and Uber, have a natural focus on measuring the static semantic similarity between contextual information of queries and geographical information of POIs. However, it remains challenging for dynamic and personalized online query-POI matching because of the non-stationary and situational context-dependent query-POI relevance. Moreover, the large volume of online queries requires an adaptive and incremental model training strategy that is efficient and scalable in the online scenario. To this end, in this paper, we propose an Incremental Spatio-Temporal Graph Learning (IncreSTGL) framework for intelligent online query-POI matching. Specifically, we first model dynamic query-POI interactions as microscopic and macroscopic graphs. Then, we propose an incremental graph representation learning module to refine and update query-POI interaction graphs in an online incremental fashion, which includes: (i) a contextual graph attention operation quantifying query-POI correlation based on historical queries under dynamic situational context, (ii) a graph discrimination operation capturing the sequential query-POI relevance drift from a holistic view of personalized preference and social homophily, and (iii) a multi-level temporal attention operation summarizing the temporal variations of query-POI interaction graphs for subsequent query-POI matching. Finally, we introduce a lightweight semantic matching module for online query-POI similarity measurement. To demonstrate the effectiveness and efficiency of the proposed algorithm, we conduct extensive experiments on two real-world datasets collected from a leading online navigation and map service provider in China.
It’s Not Just the Site, It’s the Contents: Intra-domain Fingerprinting Social Media Websites Through CDN Bursts

The website fingerprinting (or inter-domain WSF), enhanced by various machine learning techniques, has shown its power to identify websites a user has visited. To our best knowledge, a finer-grained problem of web page fingerprinting (or intra-domain WPF) has not been systematically studied by our research community. The WPF attackers, such as government agencies enforcing Internet censorship, are keen to identify the particular web pages (e.g., a political dissident’s social media page) visited by the target user. In this work, we investigate the intra-domain WPF among social media websites, against the realistic on-path passive attack scenario. We reveal that delivering large-size data such as images and videos via Content Delivery Networks (CDNs), which is a common practice in social media websites, makes intra-domain WPF highly feasible. The network traffic generated during rendering a social media page exhibits temporal and volumetric patterns that are sufficiently recognizable by machine learning algorithms. We characterize such patterns as CDN bursts, and use features extracted from them to empower classification algorithms to achieve a high classification accuracy (96%) and a low false positive rate (0.02%).
REST: Reciprocal Framework for Spatiotemporal-coupled Predictions

In recent years, Graph Convolutional Networks (GCNs) have been applied to benefit spatiotemporal predictions. The current shell for spatiotemporal predictions often relies heavily on the quality of handcraft, fixed graphical structures, however, we argue that such a paradigm could be expensive and sub-optimal in many applications. To raise the bar, this paper proposes to jointly mine the spatial dependencies and model temporal patterns in a coupled framework, i.e., to make spatiotemporal-coupled predictions. We come up with a novel Reciprocal SpatioTemporal (REST) framework, which introduces Edge Inference Networks (EINs) to couple with GCNs. From the temporal side to the spatial side, EINs infer spatial dependencies among time series vertices and generate multi-modal directed weighted graphs to serve GCNs. And from the temporal side to the spatial side, GCNs utilize these spatial dependencies to make predictions and then introduce feedback to optimize EINs. The REST framework is incrementally trained for higher performance of spatiotemporal prediction, powered by the reciprocity between its comprised two components from such an iterative joint learning process. Additionally, to maximize the power of the REST framework, we design a phased heuristic approach, which effectively stabilizes training procedure and prevents early-stop. Extensive experiments on two real-world datasets have demonstrated that the proposed REST framework significantly outperforms baselines, and can learn meaningful spatial dependencies beyond predefined graphical structures.