Miracle-dz/UniFAR-FTUs huggingface dataset:
Running head: MOOD AND INFLUENZA VACCINATION IN OLDER ADULTS 1 Mood and Influenza Vaccination in Older Adults: A Randomised Controlled Trial

Objective Positive mood on the day of vaccination has been associated with subsequent antibody responses to the influenza vaccine in older adults. The primary aim of this trial was to examine whether a brief intervention was able to enhance positive mood at the time of vaccination in a clinical context. Secondary aims included exploratory analyses of the effects of the intervention on non-specific and influenza-specific immunity. Methods One hundred and three older adults (65-85 years) participated in a two-arm, parallel, single-blind, randomised controlled trial. Participants viewed either a 15-minute video package designed to induce positive mood or a matched neutral control video, immediately prior to receiving a standard dose quadrivalent influenza vaccination. State affect and secretory IgA levels were assessed immediately prior to, and following, the interventions. Antigen-specific IgG responses to the vaccination were assessed at 4 and 16-weeks postvaccination. Results The positive mood intervention resulted in significant improvements in state positive affect, compared with the neutral control. Secretory IgA levels significantly increased across both groups. Antigen-specific IgG responses to influenza vaccination were not statistically significantly different between groups, although point-estimates of effect size favoured participants who viewed the positive mood intervention for most strains at both 4 and 16weeks post-vaccination. Conclusions A 15-minute intervention can improve positive mood in older adults prior to vaccination. Future trials should examine whether enhancing mood at the time of vaccination could enhance the effectiveness of influenza vaccination on patients and benefit health services.
Policy Optimization by Local Improvement through Search

Imitation learning has emerged as a powerful strategy for learning initial policies that can be refined with reinforcement learning techniques. Most strategies in imitation learning, however, rely on per-step supervision either from expert demonstrations, referred to as behavioral cloning (Pomerleau, 1989; 1991) or from interactive expert policy queries such as DAgger (Ross et al., 2011). These strategies differ on the state distribution at which the expert actions are collected – the former using the state distribution of the expert, the latter using the state distribution of the policy being trained. However, the learning signal in both cases arises from the expert actions. On the other end of the spectrum, approaches rooted in Policy Iteration, such as Dual Policy Iteration (Sun et al., 2018b) do not choose next step actions based on an expert, but instead use planning or search over the policy to choose an action distribution to train towards. However, this can be computationally expensive, and can also end up training the policy on a state distribution that is far from the current policy’s induced distribution. In this paper, we propose an algorithm that finds a middle ground by using Monte Carlo Tree Search (MCTS) (Kocsis & Szepesvári, 2006) to perform local trajectory improvement over rollouts from the policy. We provide theoretical justification for both the proposed local trajectory search algorithm and for our use of MCTS as a local policy improvement operator. We also show empirically that our method (Policy Optimization by Local Improvement through Search or POLISh) is much faster than methods that plan globally, speeding up training by a factor of up to 14 in wall clock time. Furthermore, the resulting policy outperforms strong baselines in both reinforcement learning and imitation learning.
A Pilot Study of a Digital Skill Tree in Gameful Education

Gameful digital applications have been adopted in higher education to help increase student engagement and improve learning. However, many studies have only evaluated educational applications that combine some common game design elements—such as points, leaderboards, or levels. Consequently, we still lack studies exploring different ways of designing gameful learning experiences. Therefore, we introduce the design and implementation of a digital system employing a skill tree to mediate instructor feedback and assignment grading in a university course, Additionally, we present the results of a pilot evaluation with 16 students in which we summarized the positive and negative aspects of the experience to derive lessons learned for the use of digital skill trees in similar contexts. Finally, we suggest topics for further investigation.
SUPPLEMENT TO “PRECAUTIONARY SAVINGS, ILLIQUID ASSETS, AND THE AGGREGATE CONSEQUENCES OF SHOCKS TO HOUSEHOLD INCOME

Christian Bayer: christian.bayer@uni-bonn.de Ralph Luetticke: r.luetticke@ucl.ac. uk Lien Pham-Dao: lien.pham-dao@bundesbank.de Volker Tjaden: volker.tjaden@mailbox.org The paper was circulated before as “Household Income Risk, Nominal Frictions, and Incomplete Markets. ” We would like to thank the Editor and four anonymous referees, Thomas Hintermaier, Andreas Kleiner, Keith Kuester, Alexander Kriwoluzky, and seminar participants in Bonn, Birmingham, Hamburg, Madrid, Mannheim, Konstanz, the EEA-ESEM 2013 in Gothenburg, the SED Meetings 2013 in Seoul, the SPP Meeting in Mannheim, the Padova Macro Workshop, the 18th Workshop on Dynamic Macro in Vigo, the NASM 2013 in Los Angeles, the VfS 2013 Meetings in Düsseldorf, the 2014 Konstanz Seminar on Monetary Theory and Monetary Policy, the ASSET 2014 in Aix-en-Provence, and SITE 2015 for helpful comments and suggestions. Special thanks go to Moritz Kuhn, Ulrike Steins, and Ariel Mecikovsky for helping us merge SCF and SIPP waves, respectively. The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FTP/2007-2013)/ERC Grant agreement 282740. The views expressed in this paper do not reflect the Bundesbank’s views or the views of the Eurosystem as a whole. The replication files for this paper are available at http://www.erc-hump-unibonn.net/code-and-data.
Modeling Severe Thunderstorm Risk in the United States

The annual aggregate losses from severe thunderstorms have accounted for approximately half of all U.S. catastrophic insured losses since 1990, according to Insurance Services Office’s Property Claim Services (PCS). In 2006, 2007, and 2008, nearly 90%, 55%, and 40%, respectively, of total catastrophic insured losses in the U.S. were directly related to severe thunderstorms. AIR developed the first probabilistic severe thunderstorm model for the insurance industry to manage severe thunderstorm risk proactively. The model simulates severe thunderstorm events representing large atmospheric systems which can spawn hundreds of tornadoes, hailstorms, and straight-line windstorms in a span of one to several days. The model is parameterized using historical data available in a severe thunderstorm database maintained by NOAA’s Storm Prediction Center (SPC). Underreporting and population biases present in the historical data are overcome using a combined approach of smoothing and data augmentation. The engineering component of the model relates event’s intensity to physical damage in terms of damage functions, which provide an estimate of mean damage by wind speeds or hail impact energy. The financial module translates damage into financial loss. Ground-up losses are calculated by applying the appropriate damage function to the replacement value of the insured property. Insured losses are determined by applying the policy conditions to the ground-up loss estimates. The hazards component of the model has been validated against NOAA data and other published information. The modeled losses are in good agreement with historical losses reported by PCS. The AIR model accounts for the policy conditions specific to the United States and has been used widely in the insurance and reinsurance industries to meet the wide spectrum of risk management requirements. INTRODUCTION According to the National Weather Service (NWS) definition, a thunderstorm is called “severe” if it produces a tornado, winds of at least 58 mph (50 knots), and/or hail measuring a minimum of 0.75 inches in diameter. Tornadoes, spawned from severe thunderstorms, are some of the most destructive forces in nature. The United States has the most tornado development of any country in the world [1]. They cause an average of 70 deaths, 1,500 injuries, and an estimated USD 1.1 billion in property damages per year in the United States. Hailstorms typically receive less attention than very damaging tornadoes [2]. However, on an annual basis, hailstorms cause more than USD 1 billion in property damage in the United States. The level of damage from hailstorms depends on a number of factors including hailstone size, the rate at which the hailstones fall, the storm duration, the size of the storm area, and the wind speeds that accompany the hailstorm. Significant property damage usually occurs when hailstones are golf-ball size and larger. High winds can increase the kinetic energy of hailstones and blow them at angles significantly off the vertical, thus increasing the likelihood of broken windows and cladding damage. Larger
Provenance-guided synthesis of Datalog programs

We propose a new approach to synthesize Datalog programs from input-output specifications. Our approach leverages query provenance to scale the counterexample-guided inductive synthesis (CEGIS) procedure for program synthesis. In each iteration of the procedure, a SAT solver proposes a candidate Datalog program, and a Datalog solver evaluates the proposed program to determine whether it meets the desired specification. Failure to satisfy the specification results in additional constraints to the SAT solver. We propose efficient algorithms to learn these constraints based on “why” and “why not” provenance information obtained from the Datalog solver. We have implemented our approach in a tool called ProSynth and present experimental results that demonstrate significant improvements over the state-of-the-art, including in synthesizing invented predicates, reducing running times, and in decreasing variances in synthesis performance. On a suite of 40 synthesis tasks from three different domains, ProSynth is able to synthesize the desired program in 10 seconds on average per task—an order of magnitude faster than baseline approaches—and takes only under a second each for 28 of them.
Technological Innovations in the Insurance Industry

The main idea of this article is an overview and analysis of the technological innovations in the insurance industry. First of all, the authors have characterised the driving factors of innovative development in the insurance industry. Secondly, the innovation in the financial sector was described as an invention and as an adoption. The main part of the paper was based on an overview of the use of technological innovations (such as software, analytics, sensors, algorithmic) for the effective insurance value chain. At the end, the authors have made a SWOT-analysis of the technological innovations.
Effect of Marketing Strategy on Customer Loyalty in the Nigerian Oil and Gas Industry: The Mediating Role of Consumer Perceived Value

In competitive markets, customer loyalty is the result of successful marketing strategy that creates value for consumers. The purpose of this paper is to evaluate the effects of marketing strategy on customer loyalty; and the mediating role of consumer perceived value in the Nigerian oil and gas companies. To achieve these objectives, an empirical study was conducted via the administration of 550 self-administered copies of questionnaire to a randomly customers of ten (10) Oil and Gas Companies, maintained from a business-to-business national service provider. Using the framework from Lee and Green (2011), data collected was subjected to regression and correlation analysis. Findings based on the survey revealed that marketing strategy positively affected both performance measures (customer loyalty and consumer perceived value) in the Nigerian oil and gas companies. Specifically, apart from promotion(β=0.10), in descending order, the results suggest that, product (β=0.34), price (β=0.34), and place (β=0.34) are the key marketing strategies that influenced both customer loyalty and consumer perceived value in the Nigerian oil and gas industry. The findings also indicate partial mediation of consumer perceived value, between marketing strategies and customer loyalty. The model provides predictive implications on improved consumer perceived value and customer loyalty, given the activities of some factors manifesting marketing strategies. Hence, to improve both consumer’s perceived value and customer loyalty, oil and gas companies could control their marketing strategies.
V. CHERNOZHUKOV, I. FERNÁNDEZ-VAL, AND B. MELLY

This supplemental material contains four appendices. In Appendix SA, we show the validity of bootstrap confidence bands for general counterfactual functionals. In Appendix SB, we compare the performance of quantile and distribution regression as estimators of the conditional and counterfactual distribution functions in Monte Carlo simulations. We calibrate our data generating process to fit several characteristics of the Current Population Survey data used in Section 6 of the paper. We find that quantile regression works better than distribution regression when the distribution of the dependent variable is continuous, but it performs worse in the presence of a small and realistic amount of rounding in the data. Appendices SC and SD complement the empirical results presented in Section 6. Appendix SC contains descriptive statistics, additional results for men, and all the results for women. Appendix SD gives more details about the variance decomposition of the composition effect into between-group and within-group components.
Occupational Attainment and Earnings in Southeast Asia: The Role of Personality Traits∗

Rural labor markets in emerging countries offer limited occupation choices and earning opportunities to individuals, and, decisions are often made out of necessity rather than opportunities. Given these difficult circumstances, it is all the more important to understand the role of personality traits for occupational attainment and earnings. Using new micro level data from individuals living in rural areas of Thailand and Vietnam, this paper aims to (i) examine the role of personality traits in occupational attainment and earnings in a rural emerging market setting and (ii) compare the outcomes to existing results from industrialized countries. We find that conscientious individuals are more likely to engage in self-employment or professional jobs. In relation to earnings, less Neuroticism and low trust are rewarded across all occupations. Other non-cognitive skills such as locus of control, risk, and patience play an inferior role. Overall, our results show that while occupational attainment is determined by similar factors in both developing and developed countries, there are differential effects of personality profiles on earnings in the two settings.
Capital Flows , Speculation , and Capital Controls ∗

Addressing policy-makers’ concerns that the post-GFC international financial flows to emerging economies have been speculative, we build a model with information frictions and use it to analyze different forms of capital controls. Motivated by speculative profits the investors in our model purchase riskier portfolios, generate large and volatile capital flows. They willingly accept volatile consumption which is the source of substantial welfare losses. We find that A) welfare gains from imposing capital controls can be substantial, equivalent to about 80 times the cost of business cycles, B) controls that activate, hence distort decisions, only during large outflows or inflows are preferable to the often-used transaction tax that is always active, C) controls may unintentionally lead to increased volatility in the domestic financial markets. The calibrated model matches well Brazil’s volatile capital flows and exchange rate and their response to a transaction tax.
Comparative Political Studies

This article investigates a widespread yet understudied trend in EU politics: the shift of legislative decision making from public inclusive to informal secluded arenas and the subsequent adoption of legislation as “early agreements. ” Since its introduction in 1999, “fast-track legislation” has increased dramatically, accounting for 72% of codecision files in the Sixth European Parliament. Drawing from functionalist institutionalism, distributive bargaining theory, and sociological institutionalism, this article explains under what conditions informal decision making is likely to occur. The authors test their hypotheses on an original data set of all 797 codecision files negotiated between mid-1999 and mid-2009. Their analysis suggests that fast-track legislation is systematically related to the number of participants, legislative workload, and complexity. These findings back a functionalist argument, emphasizing the transaction costs of intraorganizational coordination and information gathering. However, redistributive and salient acts are regularly decided informally, and the Council presidency’s priorities have no significant effect on fast-track legislation. Hence, the authors cannot confirm explanations based on issue properties or actors’ privileged institutional positions. Article at University College London on August 1, 2014 cps.sagepub. com Downloaded from
Contextual Translation Embedding for Visual Relationship Detection and Scene Graph Generation

Relations amongst entities play a central role in image understanding. Due to the complexity of modeling (<italic>subject</italic>, <italic>predicate</italic>, <italic>object</italic>) relation triplets, it is crucial to develop a method that can not only recognize seen relations, but also generalize to unseen cases. Inspired by a previously proposed visual translation embedding model, or VTransE <xref ref-type="bibr" rid="ref1">[1]</xref> , we propose a context-augmented translation embedding model that can capture both common and rare relations. The previous VTransE model maps entities and predicates into a low-dimensional embedding vector space where the predicate is interpreted as a translation vector between the embedded features of the bounding box regions of the <italic>subject</italic> and the <italic>object</italic>. Our model additionally incorporates the contextual information captured by the bounding box of the <italic>union</italic> of the subject and the object, and learns the embeddings guided by the constraint <italic>predicate</italic> <inline-formula><tex-math notation="LaTeX">$\approx$</tex-math><alternatives><mml:math><mml:mo>≈</mml:mo></mml:math><inline-graphic xlink:href="hung-ieq1-2992222.gif"/></alternatives></inline-formula> <italic>union</italic> (<italic>subject</italic>, <italic>object</italic>) <inline-formula><tex-math notation="LaTeX">$-$</tex-math><alternatives><mml:math><mml:mo>-</mml:mo></mml:math><inline-graphic xlink:href="hung-ieq2-2992222.gif"/></alternatives></inline-formula> <italic>subject</italic> <inline-formula><tex-math notation="LaTeX">$-$</tex-math><alternatives><mml:math><mml:mo>-</mml:mo></mml:math><inline-graphic xlink:href="hung-ieq3-2992222.gif"/></alternatives></inline-formula> <italic>object</italic>. In a comprehensive evaluation on multiple challenging benchmarks, our approach outperforms previous translation-based models and comes close to or exceeds the state of the art across a range of settings, from small-scale to large-scale datasets, from common to previously unseen relations. It also achieves promising results for the recently introduced task of scene graph generation.
MCTModel: A Multi-clouds Trust Model Based on SLA in Cloud Computing

Trust has become a critical issue in the development of cloud computing in recent years. In most of the existing models, only a single cloud provider (called single cloud) is selected to trade with the cloud consumers. In this paper, in order to select reliable cloud providers, we proposes a novel trust model based on service level agreement (SLA) using multiple clouds, named MCTModel. In the model, a cloud consumer can apply for the multiple service providers for their own services, and trust value is updated according to the cloud providers’ performance of SLA in the process of service usage for the different demands for the different attributes. The time decay model is applied to calculate the value of trust, making the trust value more accurate. Simulations show trust model of the multiple clouds can ensure good service to users compared the trust model of single cloud.
BERT for Named Entity Recognition in Contemporary and Historic German

We apply a pre-trained transformer based representational language model, i.e. BERT (Devlin et al., 2018), to named entity recognition (NER) in contemporary and historical German text and observe state of the art performance for both text categories. We further improve the recognition performance for historical German by un-supervised pre-training on a large corpus of historical German texts of the Berlin State Library and show that best performance for historical German is obtained by unsupervised pre-training on historical German plus supervised pre-training with contemporary NER ground-truth.
Observers in Curved-Spacetime Quantum Field Theory

Faculteit der Natuurwetenschappen, Wiskunde en Informatica Institute for Mathematics, Astrophysics and Particle Physics Master’s in Physics and Astronomy Observers in Curved-Spacetime Quantum Field Theory by Berend Visser The vacuum of a quantum field theory becomes ambiguous when a curved background is considered instead of a flat one. In order to lift this ambiguity we attempt to find distinguished states corresponding to an observer measuring properties of the quantum field. One approach we take is to demand that the vacuum should locally give a specific tensorial form to the stress-energy tensor. Because the calculation of the renormalized stress-energy is both ambiguous and very hard to perform we conclude that this criterion is unsuitable as a prescription for defining a vacuumstate. After this we try to define states using a local frequency-splitting prescription. We find that this definition is mathematically sound, but yields us a large class of vacua in stead of just one because the proper time of an observer can only be defined locally. We formulate some conjectures regarding the unitary equivalence of these states.
Representing human habits: towards a habit support agent

Human behaviour is constrained by obligations on the one hand, by the routines and habits that constitute our normal behaviour on the other. In this paper, we present the core knowledge structures of HabInt, a Socially Adaptive Electronic Partner that supports its user in trying to adopt, break or maintain habitual behaviours. We argue that HabInt’s role is best conceived of as that of an extended mind of the user. Hence, we pose as requirements that HabInt’s representation of the relevant aspects of the user and her world should ideally correspond to that of the user herself, and use the same vocabulary. Furthermore, the knowledge structures of HabInt should be flexible and explicitly represent both its user’s actual habitual behaviours and her desired habitual behaviours. This paper presents knowledge structures that satisfy the aforementioned requirements. We interleave their syntactic specification with a case study to show their intended usage as well as their expressive power.