Miracle-dz/UniFAR-FTUs huggingface dataset:
Lack of Temporal Impairment in Patients With Mild Cognitive Impairment

In the present study, we investigate possible temporal impairment in patients with mild cognitive impairment (MCI) and the amount of temporal distortions caused by the presentation of emotional facial expressions (anger, shame, and neutral) in MCI patients and controls. Twelve older adults with MCI and 14 healthy older adults were enrolled in the present study. All participants underwent a complete neuropsychological evaluation. We used three timing tasks to tap temporal abilities, namely time bisection (standard intervals lasting 400 and 1600 ms), finger-tapping (free and 1 s), and simple reaction-time tasks. The stimuli used in the time bisection task were facial emotional stimuli expressing anger or shame to investigate a possible contribution of emotional information as previously observed in healthy adults. MCI patients showed temporal abilities comparable to controls. We observed an effect of facial emotional stimuli on time perception when data were analyzed in terms of proportion of long responses, and this result was mainly driven by the temporal overestimation when a facial expression of anger was presented in controls. Results seem to suggest that the severity of the cognitive dysfunction accounts more for subjective temporal impairment than a compromised internal clock.
A Risk-Oriented Model for Factor Timing Decisions

Alpha factors are built to perform well over time, on average. There are instances when they do not, and knowing these instances ex ante can be a significant source of added value for investors. The authors argue that factor failure is a function of its broad risk, and propose appropriate variables to measure it. They adopt a nonparametric model that predicts instances of likely factor failure, based on these variables, demonstrating that an implementable dynamic strategy based on our analysis generates a reward-to-risk ratio approximately four times that of a static approach, and about one and a half times that of an alternative dynamic approach based on momentum.
Prediction of burden descent speed in blast furnace based on extreme learning machine

The burden charging is the most important operation of the upper operations in blast furnace. The position and descent speed of burden layer can reflect the situation of blast furnace and can guide the operators in the next burden charging. In this paper, the descent speed prediction model of burden layer is established by extreme learning machine algorithm. The model can make single-step and multi-step predictions to the burden descent speed using real radar data and status information in blast furnace. In the simulation part, we collected the real production data in iron-making process and obtained the satisfied and accurate simulation results by employing the proposed scheme.
An Empirical Study of Investor Attitudinal Factors Influencing Herd Behavior:
Evidence from Pakistan Stock Exchange

Behavioral Finance is an evolving field that studies how psychological factors affect decision making under uncertainty. Herding behavior is one of the psychological factors that instigate investor to mimic the actions of other investors in the market rather than using his personal assessments. This study seeks to find the influence of certain attitudinal factors namely, decision conformity, hasty decision, mood, decision accuracy, and overconfidence, on the individual investor tendency to embrace herd behavior. Primary data for the study are collected using structured questionnaires from a sample of 194 investors who are trading at Islamabad and Lahore branches of Pakistan Stock Exchange. Multiple linear regression analysis is used to test the hypotheses of this study. Findings of this study provide evidence that attitudinal factors have a significant influence on investor’s tendency to take on herd behavior. It is concluded from the results of multiple linear regression that decision conformity, mood, and decision accuracy have a significant impact on individual investor tendency to adopt herd behavior. However, investor hasty decision and overconfidence is insignificant predictors of herd behavior. Keywords: Decision conformity, Hasty decision, Mood, Decision accuracy, Overconfidence, Herd behavior
Residential Consumption of Gas and Electricity in the US: The Role of Prices and Income

We study residential demand for electricity and gas, working with nationwide household-level data that cover recent years, namely 1997-2007. Our dataset is a mixed panel/multi-year cross-sections of dwellings/households in the 50 largest metropolitan areas in the United States as of 2008. To our knowledge, this is the most comprehensive set of data for examining household residential energy usage at the national level, containing the broadest geographical coverage, and with the longest longitudinal component (up to 6 observations per dwelling). We estimate static and dynamic models of electricity and gas demand. We find strong household response to energy prices, both in the short and long term. From the static models, we get estimates of the own price elasticity of electricity demand in the -0.860 to -0.667 range, while the own price elasticity of gas demand is -0.693 to -0.566. These results are robust to a variety of checks. Contrary to earlier literature (Metcalf and Hassett, 1999; Reiss and White, 2005), we find no evidence of significantly different elasticities across households with electric and gas heat. The price elasticity of electricity demand declines with income, but the magnitude of this effect is small. These results are in sharp contrast to much of the literature on residential energy consumption in the United States, and with the figures used in current government agency practice. Our results suggest that there might be greater potential for policies which affect energy price than may have been previously appreciated.
Accelerating SPARQL Queries and Analytics on RDF Data

Accelerating SPARQL Queries and Analytics on RDF Data Razen Mohammad Al-Harbi The complexity of SPARQL queries and RDF applications poses great challenges on distributed RDF management systems. SPARQL workloads are dynamic and consist of queries with variable complexities. Hence, systems that use static partitioning su↵er from communication overhead for workloads that generate excessive communication. Concurrently, RDF applications are becoming more sophisticated, mandating analytical operations that extend beyond SPARQL queries. Being primarily designed and optimized to execute SPARQL queries, which lack procedural capabilities, existing systems are not suitable for rich RDF analytics. This dissertation tackles the problem of accelerating SPARQL queries and RDF analytics on distributed shared-nothing RDF systems. First, a distributed RDF engine, coined AdPart, is introduced. AdPart uses lightweight hash partitioning for sharding triples using their subject values; rendering its startup overhead very low. The locality-aware query optimizer of AdPart takes full advantage of the partitioning to (i) support the fully parallel processing of join patterns on subjects and (ii) minimize data communication for general queries by applying hash distribution of intermediate results instead of broadcasting, wherever possible. By exploiting hashbased locality, AdPart achieves better or comparable performance to systems that employ sophisticated partitioning schemes. To cope with workloads dynamism, AdPart is extended to dynamically adapt to workload changes. AdPart monitors the data access patterns and dynamically redistributes and replicates the instances of the most frequent patterns among workers. 5 Consequently, the communication cost for future queries is drastically reduced or even eliminated. Experiments with synthetic and real data verify that AdPart starts faster than all existing systems and gracefully adapts to the query load. Finally, to support and accelerate rich RDF analytical tasks, a vertex-centric RDF analytics framework is proposed. The framework, named SPARTex, bridges the gap between RDF and graph processing. To do so, SPARTex: (i) implements a generic SPARQL operator as a vertex-centric program. The operator is coupled with an optimizer that generates e cient execution plans. (ii) It allows SPARQL to invoke vertex-centric programs as stored procedures. Finally, (iii) it provides a unified inmemory data store that allows the persistence of intermediate results. Consequently, SPARTex can e ciently support RDF analytical tasks consisting of complex pipeline of operators.
Significant feedbacks of wetland methane release on climate change and the causes of their uncertainty

Emissions from wetlands are the single largest source of the atmospheric greenhouse gas (GHG) methane (CH4). This may increase in a warming climate, leading to a positive feedback on climate change. For the first time, we extend interactive wetland CH4 emissions schemes to include the recently quantified, significant process of CH4 transfer through tropical trees. We constrain the parameterisations using a multi-site flux study, and biogeochemical and inversion models. This provides an estimate and uncertainty range in contemporary, large-scale wetland emissions and their response to temperature. To assess the potential for future wetland CH4 emissions to feedback on climate, the schemes are forced with simulated climate change using a ‘pattern-scaling’ system, which links altered atmospheric radiative forcing to meteorology changes. We perform multiple simulations emulating 34 Earth System Models over different anthropogenic GHG emissions scenarios (RCPs). We provide a detailed assessment of the causes of uncertainty in predicting wetland CH4–climate feedback. Despite the constraints applied, uncertainty from wetland CH4 emission modelling is greater that from projected climate spread (under a given RCP). Limited knowledge of contemporary global wetland emissions restricts model calibration, producing the largest individual cause of wetland parameterisation uncertainty. Wetland feedback causes an additional temperature increase between 0.6% and 5.5% over the 21st century, with a feedback on climate ranging from 0.01 to 0.11 Wm−2 K−1. Wetland CH4 emissions amplify atmospheric CH4 increases by up to a further possible 25.4% in one simulation, and reduce remaining allowed anthropogenic emissions to maintain the RCP2.6 temperature threshold by 8.0% on average.
Image face calibration positioning based on 3000FPS algorithm

In the face recognition tasks, face pose can have influence on the results of recognition. To improve the accuracy of recognition, we introduced 3000FPS algorithm for face image calibration positioning. Random forests is used to establish each key point, and the output of every tree in random forests, binary feature, is exploited to compose local binary feature, which is used to train a softmax classifier. The experiment show that the recognition accuracy of the algorithm can reach 93.33% on the Helen face dataset, and 94.14% on the LFPW. Meanwhile, multi-face can be aligned and located even though the input image is partially obscured.
Community Cultural Wealth Pedagogies: Cultivating Autoethnographic Counternarratives and Migration Capital

Pedagogies employing critical traditions have increasingly been used to ameliorate achievement disparities and centralize issues of power in the education of Students of Color. In this study, I trace a teacher’s journey—new to critical pedagogies—as she learned about community cultural wealth and incorporated family histories as counterstorytelling curricula with her sixth-grade class of immigrant students in California’s Central Valley. I examine the pedagogical implementation with examples of students’ meaning making. The teacher and students demonstrated what I am advancing as migration capital—or knowledges, sensibilities, and skills cultivated through the array of migration/immigration experiences to the United States or its borderlands. This study highlights the potential of community cultural wealth pedagogies and as pedagogical tools to counter deficit narratives with Latina/o immigrant youth.
The perceptions of stakeholders in small-scale sporting events

Abstract Small-scale events represent an important asset for the development of tourist destinations. As local stakeholders play a major role in conceptualizing and developing small-scale events, this study sheds light upon stakeholders’ perceptions toward such events. The current study was carried out through qualitative interviews based on the case of the FIS (International Ski Federation) World Junior Alpine Ski Championships 2019. The findings of this study reveal that for small-scale event success, the involvement of local stakeholders in the decision-making process is key. Furthermore, stakeholder involvement makes the decision-making processes when staging an event more effective across all organizational phases. The findings invite event managers and policy makers to involve local stakeholders in the organization processes of small-scale events. The research on small-scale events is enriched by the evidence that partial involvement of the stakeholders in the organizational processes reaches a limit in what Freeman (2010) describes as “stakeholder management capacity”.
Towards a Hedonic Pricing Method for the Bucharest Private Housing Market

This paper aims at exploring the drivers of dwellings’sale prices in Bucharest, Romania, over the period 2014-2018. Several housing structural attributes are covered, such as the number of rooms, useful and constructed surface, type of comfort, floor, the number of bathrooms, balconies and parking, the seniority from construction, as well as from renovation, structure type, height regime, and the duration until completion of the real estate transaction. By estimating a standard hedonic price model via OLS regression for a sample of 765 transactions, we notice that all the selected variables, except the floor level and seniority from construction, positively influence the property prices. However, in case of useful and constructed surface, nonlinear relationships with property prices were acknowledged. Robustness checks in form of quantile regressions reinforce the empirical findings.
Multi-scale search for black-box optimization : theory & algorithms

This thesis focuses on a special class of Mathematical Programming (MP) algorithms for continuous black-box optimization. Black-box optimization has been a recurrent subject of interest for decades, as many real-world applications can be modeled as black-box optimization problems. In particular, this research work studies algorithms that partition the problem’s decision space over multiple scales in search for the optimal solution and investigates three central topics. • Plenty of such algorithms have been proposed and analyzed independently. Furthermore, the theoretical analysis has been dominantly concerned with the asymptotic (limit) behavior of the algorithms and their convergence to optimal points, whereas finite-time behavior is more crucial in practice. • Due to their systematic sampling of the decision space, the aforementioned algorithms are inherently exploratory. This prevents from using them effectively on computationally expensive optimization problems. • The bulk of these algorithms has been tailored towards single-objective optimization, whilst in practice, most real-world problems involve the optimization of multiple objectives. The present thesis refers to these algorithms as Multi-scale Search Optimization (MSO) algorithms and addresses the above issues as follows. First, a theoretical methodology is presented to consolidate the analysis of MSO algorithms and study their finite-time convergence based on three basic assumptions: i). local Hölder continuity of the objective function f ; ii). partitions boundedness; and iii). partitions sphericity. Moreover, the worst-case finite-time performance and convergence rate of several leading MSO algorithms, viz. Lipschitzian optimization methods, Multilevel Coordinate Search (MCS), DIviding RECTangles (DIRECT), and optimistic optimization methods have been shown.
Pollution atmosphérique et climat

Changement climatique et qualite de l'air sont intimement lies : a travers les politiques de gestion mises en oeuvre pour attenuer ces menaces environnementales majeures mais aussi a travers les processus geophysiques qui les gouvernent. Afin de pouvoir etudier l'evolution de la pollution atmospherique en Europe en prenant en compte l'influence conjointe du changement climatique et du transport a longue distance, nous avons concu, developpe et mis en oeuvre un systeme complet de modelisation regionale du climat et de la qualite de l'air. En utilisant des scenarios prospectifs de derniere generation relatifs au changement climatique (emissions de gaz a effet de serre) mais aussi pour les polluants a courte duree de vie, nous avons pu proposer une quantification de l'evolution future de la qualite de l'air en Europe. D'apres le scenario le plus volontariste, la pollution liee a l'ozone sera reduite de maniere substantielle mais les efforts positifs induits par les politiques de gestion de la qualite de l'air seront contrebalances par le changement climatique et le transport a longue distance pour le scenario statu-quo. En ce qui concerne les particules, les reductions d'emissions futures reduiront de maniere efficace les niveaux d'exposition.
Toward Discovering a National Identity for Millennials: Examining Their Personal Value Orientations for Regional, Institutional, and Demographic Similarities or Variations

Millennials are a powerful workforce group and are quickly becoming established business leaders, consumers, and investors. Yet, millennials are often described as a uniformly homogeneous generation, despite mounting evidence of variances across their private and workplace behaviors, attitudes and preferences, and personal values. This article examines the personal value orientations of millennials in the Unites States, reporting consistencies, variations, and contrasts based on a large sample drawn from seven diverse universities. Results of this article suggest more similarities across a national population of millennials than differences, suggesting a national identity among American millennials. Practical implications of our findings and future research are discussed.
Are Trading Costs Invisible in Trading Decisions?

This article examines whether professional traders recognize transaction costs when making trade decisions. In both single and multi-period settings, professional traders are far more reluctant to realize a gross loss than an actual loss (net of transaction costs). The tendency to ignore transaction costs may lead to higher levels of overconfidence (or trading activity) because traders are more susceptible to misinterpreting negative performance outcomes than positive ones. Traders who experience higher and more frequent gross trading profits trade more actively, but they also end up losing the most money on a net basis.
Acolhimento à demanda espontânea

A atencao basica, enquanto um dos eixos estruturantes do SUS, vive um momento especial ao ser assumida como uma das prioridades do Ministerio da Saude e do governo federal. Entre os seus desafios atuais, destacam-se aqueles relativos ao acesso e acolhimento, a efetividade e resolutividade das suas praticas, ao recrutamento, provimento e fixacao de profissionais, a capacidade de gestao/coordenacao do cuidado e, de modo mais amplo, as suas bases de sustentacao e legitimidade social.
Systemic Risk Driven Portfolio Selection

How can we construct portfolios that perform well in the face of systemic events? The global financial crisis of 2007–2008 and the coronavirus disease 2019 pandemic have highlighted the importance of accounting for extreme form of risks. In “Systemic Risk-Driven Portfolio Selection,” Capponi and Rubtsov investigate the design of portfolios that trade off tail risk and expected growth of the investment. The authors show how two well-known risk measures, the value-at-risk and the conditional value-at-risk, can be used to construct portfolios that perform well in the face of systemic events. The paper uses U.S. stock data from the S&P500 Financials Index and Canadian stock data from the S&P/TSX Capped Financial Index, and it demonstrates that portfolios accounting for systemic risk attain higher risk-adjusted expected returns, compared with well-known benchmark portfolio criteria, during times of market downturn.
An Empirical Evaluation of the Link between Women Participation in Village Savings and Loans Association (VSLA) and Poverty Reduction in Ethiopia

: Women's participation is a fundamental prerequisite for gender equality and genuine democracy. It facilitates women's direct engagement in decision-making and is a means of ensuring better accountability to women. This paper assesses the impact of women participation in Village Savings and Loans Association (VSLA) on poverty reduction in Hawassa city, Ethiopia. Using a mixed research methodology, and applying propensity score matching, the study found that women participation in VSLA has a significant positive impact in increasing average monthly household income of participant woman. Besides, women participation in VSLA has a significant positive association with improvements in household diet, health, children’s education, and women’s involvement in household decisions. The study recommends a reinforcement of government and nongovernmental organizations in providing regular and need based capacity building trainings for VSLA participants followed by a concerted effort to link VSLA participants with formal microfinance institutions.
Violating the Law of One Price: Should We Make a Federal Case Out of it?

We use new disaggregated data on consumer prices to determine why there is variability in prices of similar goods across U.S. cities. We address questions similar to those that have arisen in the international context: is this variability purely a result of market segmentation or do sticky nominal prices play a role? We also examine how the degree of tradability of a good influences price variability. Surprisingly, we find that variability is larger for traded-goods. We attribute this finding to greater price stickiness for non-traded goods. Distance between cities accounts for a significant amount of the variation in prices between pairs of cities. But we also find that nominal price stickiness plays an even more significant role.
Integrate Metadata by Semantic Recommendation: A Psychology Inspired Method

Complicated data structure can handicap the integration of systems in enterprises and universities. It is important to find an efficient way to deal with the mass documents of these heterogeneous and distributed systems to be integrated. In this paper, a three layered data scheme is introduced for data recommendation in software engineering process inspired by spreading activation theory. Metadata can be more efficiently managed with this scheme in integration and analysis.