Miracle-dz/UniFAR-FTUs huggingface dataset:
DisTRibuTionAl imPACT of monETARy PoliCy: EviDEnCE

Inequality is a long-standing issue that has attracted significant attention of both policymakers and academics across disciplines. There exists a wealth of literature that explores the drivers and implications of inequality. In general, these studies associate inequality with structural factors such as 1) the emergence of skill-biased technologies (Bound and Johnson, 1992); 2) increased global trade (Feenstra and Hanson, 1996); and, 3) change in labor market institutions (Card, 2001).
Discovering Probabilistic Structures of Care

Medical protocols and guidelines can be looked upon as concurrent programs, where the patients dynamically change over time. Methods based on veriﬁcation and model-checking developed in the past have been shown to oﬀer insight into their correctness by adopting a logical point of view. However, there is uncertainty involved both in the management of the disease and the way the disease will develop, and, therefore, a probabilistic view on medical protocols seems more appropriate. On the other hand, representations using Bayesian networks usually involve a single patient group and do not capture the dynamic nature of care. In this paper, we propose a new method inspired by automata learning to represent and identify patient groups for obtaining insight into the care that patients have received. We evaluate this approach using data obtained from general practitioners and identify signiﬁcant diﬀerences in patients who were diagnosed with a transient ischemic attack (TIA). Finally, we discuss the implications of such a computational method for the analysis of medical protocols.
A General Framework for Energy-Efficient Cloud Computing Mechanisms

We present a general model for the operation of a cloud computing server comprised of one or more speed-scalable processors. Typically, tasks are submitted to such a cloud computing server in an online fashion, and the server operator has to schedule the tasks and decides on payments without knowledge about the tasks arriving in the future. Although very natural, this cloud computing problem on speed-scalable processors has not been studied from a mechanism design perspective in the online setting. We provide a mechanism for this setting, both for a single and multiprocessor environment, that has several desirable properties: (1) the induced game admits a subgame perfect equilibrium in pure strategies and therefore a pure Nash equilibrium, (2) the Price of Anarchy is constant, (3) the mechanism is budget balanced, i.e., the sum of the payments of the agents is equal to the total energy costs, (4) the communication complexity is low, (5) the mechanism is computationally tractable for both the service operator and the agents, and (6) the agents’ payment is also intuitive and easy to communicate to them. We also provide a second mechanism with a better Price of Anarchy, which in turn is more involved to implement. We are able to extend our mechanisms and results to the Bayesian setting, where the type of each agent is drawn independently from some underlying distribution and agents are minimizing their expected costs. In this setting we also show the same approximation factor of our mechanism as in the basic online setting in both the single and the multiprocessor environment.
The effect of Music Attention Control Training (MACT) for pre-adolescents with Autism Spectrum Disorder

By Vienna Sa University of the Pacific 2019 The purposes of this study are to investigate the effect of the Music Attention Control Training (MACT) on three types of attention (sustained, selective, switching) in pre-adolescents (10-14 years old) with Autism Spectrum Disorder (ASD) and to identify the impact of the level of severity (mild, moderate, severe) on changes in attention scores. This modified replication study included 23 participants randomly assigned to treatment and control group stratified based on severity of ASD. Significant results via two-tailed paired-sample t-test (p< .10) indicated significant positive trends with the treatment group for the 3 out of 4 subtests of selective attention: Hector Cancellation, Hector-B Cancellation, and Hecuba Visual Search; 1 out of 4 subtests of sustained attention: Sustained Attention Response Test (SART); and the single subtest of switching attention: Red & Blues, Bags & Shoes (RBBS). Results call for modifications to further support the role of MACT on attention skills with pre-adolescents with ASD. Implications for future research and contributions to clinical practices in music therapy
Exploring High-Performance Regions with Model Induced Metropolis-Hastings

Global optimization methods tend to focus on exploitation of known optima, often getting stuck in local optima. For problems with costly evaluations, it is more effective to initially identify the high-performance regions. This thesis proposes an algorithm designed to provide good coverage of the highperformance regions of an objective function using few objective function evaluations. The algorithm performs consecutive Metropolis-Hastings random walks on an RBFN meta-model of the objective function. After each walk, it adds the endpoint to the training set, then retrains the RBFN. Experiments show that the algorithm explores good solutions in significantly fewer objective function evaluations than state of the art algorithms, such Niching ES. The efficiency of the algorithm can be significantly increased by raising the acceptance function to some power. The map of the high-performance regions obtained can be used to initialize a more greedy optimization method. Moreover, the MIMH algorithm can be readily used to sample efficiently from a distribution the shape of which is determined by a costly evaluation function.
Doing Good For Borrowing? Evidence from a Quasi Natural Experiment on Bank Deregulation

We study the relation between corporate social responsibility and external financial market development by exploiting the Interstate Banking and Branching Efficiency Act (IBBEA) in the U.S. as the quasi-natural experiment. The deregulation leads to a significant and persistent decrease in firm CSR, suggesting firms show “doing good” for the access to finance in the uncompetitive credit market. Firms susceptible of capture by banks prior to IBBEA show more pronounced decline in CSR. We rule out the alternative hypothesis of banking relationship change from lending to transactional basis caused by the deregulation. The evidence suggests that firms’ involuntary CSR activities in uncompetitive financial markets are socially inefficient. WP No 20-011
Influence of sampling date and substrate on nitrogen mineralization: comparison of laboratory-incubation and buried-bag methods for two Massachusetts forest soils

Nitrogen (N) mineralization potential and net N mineralization insitu were measured monthly over 7 months for the forest floor horizons (Oi, Oe, Oa) and mineral soil (0–15 cm) of a pine stand and the mineral soil (0–15 cm) of a maple stand in Massachusetts, United States. In all cases, N mineralization potential per unit organic matter (anaerobic laboratory incubation) varied significantly by sampling month but was unrelated to the seasonal pattern for net N mineralization (buried-bag method). The organic horizons in the pine stand exhibited the most variable N mineralization potential, with the Oe horizon having more than a fourfold seasonal range. For the pine stand the Oe horizon also had the highest N mineralization potential (per unit organic matter) and the highest net N mineralization insitu (per unit area). In general, temporal and depth-wise variability should be considered when sites are assessed with respect to the pool of mineralizable N.
Inducing Cooperation through Reward Reshaping based on Peer Evaluations in Deep Multi-Agent Reinforcement Learning

We propose a deep reinforcement learning algorithm for semi-cooperative multi-agent tasks, where agents are equipped with their separate reward functions, yet with some willingness to cooperate. It is intuitive that defining and directly maximizing a global reward function leads to cooperation because there is no concept of selfishness among agents. However, it may not be the best way of inducing such cooperation due to problems that arise from training multiple agents with a single reward ( e.g. , credit assignment). In addition, agents may intentionally be given separate reward functions to induce task prioritization whereas a global reward function may be difficult to define without diluting the effect of different tasks and causing their reward factors to be disregarded. Our algorithm, called Peer Evaluation-based Dual DQN (PED-DQN), proposes to give peer evaluation signals to observed agents, which quantify how they strategically value a certain transition. This exchange of peer evaluation among agents over time turns out to render agents to gradually reshape their reward functions so that their action choices from the myopic best response tend to result in a more cooperative joint action.
Migrating passerines can lose more body mass reversibly than previously thought

Knowing the amount of body mass that migrants can lose is essential for the estimation of potential flight ranges, assessing the importance of stopover sites or modelling migration strategies. ‘Lean’ body mass, (the mass without fat stores) is often used to describe the limit of body‐mass loss but birds also use protein as fuel for flight. The mean body mass of 621 passerines mist‐netted in Mauritania between 2001 and 2004 with a fat score of 0 (no visible subcutaneous fat stores) and a muscle score of 0 (emaciated flight muscle) was much lower compared to the lean body mass values found in the literature. Recaptures showed that these birds were able to refuel again. The mean body mass of 122 Garden Warbler Sylvia borin with both a fat score and a muscle score of 0 was 12.8 g. The mean potential flight range of four Garden Warblers captured in the desert was 35% higher when 12.8 g was used for the estimation compared to the 15.0 g often used in previous studies. This has implications for future studies of the migration strategies of passerines, and suggests that, in the past, the fuel load of migrating passerines has been underestimated.
Generating Factual Documents by Synthesizing Knowledge Sources

From youth, humans can read and process large amounts of information to write 1 articles, book reports, and conduct deep conversation. Existing large-scale language 2 models are yet incapable of such meaningful generation. We propose a knowledge-3 grounded document writing task for pre-training an encoder-decoder language 4 model to enable such knowledge synthesis. We will pre-train a model on networks 5 of knowledge-grounded documents from encyclopedias and news, leveraging high-6 quality source citations common in these ﬁelds. We present the datasets that we 7 have collected thus far, methods for large-context knowledge grounded synthesis, 8 and preliminary results indicating the applicability of our framework. 9
Flake It 'Till You Make It: Using Automated Repair to Induce and Fix Latent Test Flakiness

Since flaky tests pass or fail nondeterministically, without any code changes, they are an unreliable indicator of program quality. Developers may quarantine or delete flaky tests because it is often too time consuming to repair them. Yet, since decommissioning too many tests may ultimately degrade a test suite's effectiveness, developers may eventually want to fix them, a process that is challenging because the nondeterminism may have been introduced previously. We contend that the best time to discover and repair a flaky test is when a developer first creates and best understands it. We refer to tests that are not currently flaky, but that could become so, as having latent flakiness. We further argue that efforts to expose and repair latent flakiness are valuable in ensuring the future-reliability of the test suite, and that the testing cost is greater if latent flakiness is left to manifest itself later. Using concrete examples from a real-world program, this paper posits that automated program repair techniques will prove useful for surfacing latent flakiness.
The Impact of Household Overindebtedness on Physical and Mental Health

We analyse data collected from a survey of Northern Irish households experiencing varying degrees of financial hardship and examine whether debt affects physical health, mental health and health-related behaviours. Our results indicate that neither the size of the debt, the type of debt nor the number of different lenders used affect health. Instead, we find that the subjective experience of feeling financially stressed has a robust relationship with most aspects of health including ability to self-care, problems performing usual activities, pain problems and psychological health. Additionally, we find that the adverse effect of financial stress on health is mediated through worse diets, lower levels of physical activity and increased consumption of cigarettes and drugs. This research is timely as cuts to social care spending in the UK government are already exacerbating indebtedness in low-income households and putting strain on the healthcare system. WP No 15-007
Skill and Efficacy Development in a Human Services Grant Writing Course

In the field of Human Services, taking grant writing courses and developing related research and critical thinking skills are essential to successful service-related careers. This study examines how specific instructional strategies utilized in a Human Services grant writing course serve as meaningful and effective learning experiences that enhance core competencies as well as intrapersonal development. Four semester-long grant writing class sections during one academic year participated in the process. One hundred and eighty-nine, upper-division human services students participated in the study. Specific grant writing competencies increased and student intrapersonal growth was reported utilizing both quantitative and qualitative analyses.
Universiteit Leiden Opleiding Informatica

In the Netherlands, women between the ages of 50 and 75 can be voluntarily screened for breast-cancer. These scans are currently manually examined by radiologists across the country. In this thesis, we explore the possibilities of using well-known neural networks for automatic classification. Using the mammograms and results from the Dutch national breast cancer screening we try to train classifiers for the problem of whole-mammogram breast cancer detection. Contemporary research uses accuracy and receiver operating characteristic area under curve (ROC-AUC) as a performance measure. However, accuracy is not a useful measure for skewed data sets and AUC is a flawed measure for various reasons. In this paper we discuss a metric based on the sensitivity/specificity trade-off and evaluate our classifiers using this metric. Using the well-known image classification networks AlexNet and ResNet, we create and evaluate a reusable image processing pipeline using our performance metric.
Cross-lingual Training for Retrieval-based Dialogue Systems

In recent years, cross-lingual approaches have been successfully applied to a variety of tasks and have shown potential when available data is scarce. That potential can be eﬀectively leveraged when building a retrieval-based dialogue system. In this paper we investigated diﬀerent methods of cross-lingual training of retrieval-based dialogue systems. We compare several cross-lingual approaches, including adversarial pretraining on resource-rich language dataset and further ﬁne-tuning of pretrained models with low-resource language dialogue data. This adversarial architecture extends Dual Encoder network with language discriminator. Other approaches are based on diﬀerent training strategies, such as mixing data in diﬀerent languages, adversarial learning and pretraining on big data. Experiments show that adversarial learning performs competitively, which is also true for the data mixing strategy.
EFFECTS OF INCIDENT RADIATION AND NITROGEN AVAILABILITY ON THE QUALITY PARAMETERS OF TRITICALE GRAINS IN ARGENTINA

The effects of radiation conditions and nitrogen (N) availability on triticale were investigated. The relationship between shading (S) and N supply and their effect on the industrial use of triticale flour were studied. Incident radiation/shade conditions affected all the parameters measured. Nitrogen supply influenced yield, protein content, sodium dodecyl sulphate-sedimentation index (SDS-SI), alkaline water retention capacity (AWRC), cookie factor and diameter of four cookies (D4C). The proportion of proteins of high molecular weight was increased with nitrogen supply and pre-anthesis shade, while post-anthesis shade increased proteins of lower molecular weight. The interaction between variety and N supply was significant for yield, SDS-SI and AWRC. Only yield and specific weight were affected by N × S interaction. Significant effects of the interaction of variety, N supply and shade conditions were observed for specific weight.
Cladocera, Ostracoda and Copepoda assemblages in different side-arms of the Danube in Gemenc floodplain (Danube-Dráva National Park, Hungary)

The Gemenc floodplain (river km 1497-1467) is one of the last remaining extensive inundated floodplain of the Danube. Due to the river regulation works in the 19 century this area have changed, the floodplain remained more or less isolated from the main stream, the length of the side arms decreased and a drying-out process was observed during the 20 century. The most important human impacts, which changed the hydrological condition of the floodplain, were formerly the building-up of the drainage canals (fok-system) and later the river regulations for flood control and navigation (GUTI 2001).
On The Gamma Gumbel Distribution∗†

In this paper, we study some mathematical properties of the gamma Gumbel distribution. We provide explicit expressions for the moments, quantile function, Rényi entropy, and order statistics. We also discuss the estimation of the model parameters using the maximum likelihood technique, method of moments and Bayesian method. We provide an application to a real data set which illustrates the usefulness of the model.
Utilizing Metadata to Select a Recommendation Algorithm for a User or an Item

In general, recommender systems solve the problem of information overload by helping users of services to ﬁnd items in which they are interested. There are plenty of algorithms and models that can be used to build such a subset of items, and their performance may vary not only for diﬀerent datasets but for separate parts of a single one. This issue leads to the ”algorithm selection problem”. Many state-of-art solutions to this problem are based on meta-learning. It associates the task features with the performance of the base-level algorithms and models. This paper presents the method of recommendation algorithm selection for particular users (or items) that uses binary representations of both explicit metadata of them and computable statistical meta-features. There are two diﬀerent techniques within the proposed method, which are based on classiﬁcation or clustering of such binary data, respectively. The meta-learning process is almost automated. The ﬁndings of the experiments prove that the usage of the method for recommendation algorithm selection is reasonable and eﬀective. In most cases, a recommender system that uses the metamodel shows lower rating prediction errors compared to any other one utilizing a single model or algorithm for all the users (or items), while in a small number of tests their performance is just the same. The detailed analysis of the evaluation results allows for aﬃrm-ing that the described metamodels can be used in real-world systems to improve the experience of particular users.
The Mediating Role of Media Internalization in Body Scrutinization and Body Image Dissatisfaction in Thai Homosexual Male Youth

The debilitating impact of body image dissatisfaction has been well-documented, leading to attempts to propose ways to alleviate the dissatisfaction. These proposals are drawn primarily from findings from studies on female youth. The current study extends this work, and contributes to an emerging initiative to attempt to reduce body image dissatisfaction in homosexual male youth. Past empirical findings have identified these adolescents to be particularly prone to body image dissatisfaction. With the formal operational stage of cognitive development, adolescents are particularly self-conscious and scrutinize their body image more critically, leading to body image dissatisfaction. The aim of the current study was to identify a potential protective factor that might help reduce the impact of body scrutinization in Thai homosexual male adolescents. These participants were selected due to the relative lack of published research on body image in Asian homosexual males. Additionally, despite its orientation toward the collectivistic fulfillment of social roles and obligations, Thai culture is quite accepting of sexual minorities. Homosexual male body image ideals are portrayed in the media with relatively high frequency. For this reason, internalization of media influence was selected as a potential mediator of the relationship between body scrutinization and body image dissatisfaction. Data were collected from 114 Thai homosexual male youths who responded to online questionnaires measuring body scrutinization, media internalization, and body image dissatisfaction. As hypothesized, findings demonstrated that body scrutinization significantly predicted both body image dissatisfaction and media internalization. Media internalization significantly but partially mediated the relationship between body scrutinization and body image dissatisfaction. Implications were discussed in terms of the preventative and therapeutic potentials for reducing media influence on body image dissatisfaction when the scrutinization was engaged.
Seasonal dynamics of autotrophic and heterotrophic plankton metabolism and PCO2 in a subarctic Greenland fjord

We measured net planktonic community production (NCP), community respiration (CR), and gross primary production (GPP) in September, February, and May in a subarctic Greenland fjord influenced by glacial meltwater and terrestrial runoff. Potential controls of pelagic carbon cycling, including the role of terrestrial carbon, were investigated by relating surface‐water partial pressure of CO2 (PCO2), NCP, GPP, and CR to physicochemical conditions, chlorophyll a (Chl a) concentration, phytoplankton production, inventories of particulate (POC) and dissolved organic carbon (DOC) and vertical flux of POC. The planktonic community was net heterotrophic in the photic zone in September (NCP 5 ‒21 ± 45 mmol O2 m‒2 d‒1) and February (NCP 5 ‒17 mmol O2 m‒2 d‒1) but net autotrophic during a developing spring bloom in May (NCP 5 129 ± 102 mmol O2 m‒2 d‒1). In September, higher temperatures, shorter day lengths, and lower Chl a concentrations compared with May caused increased rates of CR, lower GPP rates, and net heterotrophy in the photic zone. The GPP required to exceed CR and where NCP becomes positive was low (in May: 1.58 ± 0.48 µmol O2 L‒1 d‒1 and September: 3.06 ± 0.82 µmol O2 L‒1 d‒1) and in the range of open ocean values, indicating that allochtonous carbon did not stimulate CR. The PCO2 in the surface water was below atmospheric levels (September average 25.0 ± 0.71 Pa, February 35.4 ± 0.40 Pa, and May 19.8 ± 1.21 Pa), rendering the ecosystem a sink of atmospheric CO2. NCP was identified as an important driver of surface PCO2 , with high rates of autotrophy and vertical export of POC reducing surface PCO2 during summer. In winter, net heterotrophy added CO2 to the water column, but this postive effect on PCO2 was balanced by simultaneous cooling of the water column, which decreased PCO2 because of increased solubility of CO2. High autochthonous production implies a relatively limited influence of allochthonous carbon on pelagic carbon balance and CO2 dynamics in the fjord.