Miracle-dz/UniFAR-FTUs huggingface dataset:
TPM Vol

The Courage Measure (CM) was created to measure the individual difference of courage, but recent research has suggested that the CM may instead gauge persistence despite fear (PDF). If true, then prior research using the CM would need to be reevaluated. The current article performs two studies. The first study confirms the factor structure of the reduced CM. The second study calculates the heterotrait-monotrait (HTMT) ratio of correlations of the CM with the PDF dimension of the Multidimensional Persistence Scale (MPS). The results exceed typical cutoffs for HTMT ratios, and the average item intercorrelations of the CM were less than the item cross-correlations of the CM with the PDF dimension. These two results support that the CM and PDF dimension measure the same construct. Future research should integrate prior findings using the CM with PDF, which provides a new and substantial theoretical basis to understand the recently developed construct.
A RE ALL OUTLIERS ALIKE ? O N U NDERSTANDING THE D IVERSITY OF O UTLIERS FOR D ETECTING OOD S

Deep neural networks (DNNs) are known to produce incorrect predictions with very high conﬁdence on out-of-distribution (OOD) inputs. This limitation is one of the key challenges in the adoption of deep learning models in high-assurance systems such as autonomous driving, air trafﬁc management, and medical diagnosis. This challenge has received signiﬁcant attention recently
The Long-Run Effects of Consequential School Accountability

IZA DP No. 14503 JUNE 2021 The Long-Run Effects of Consequential School Accountability* The rise of high-stakes accountability programs was one of the most noticeable changes in the U.S. education system during the 1990s and early 2000s. We measure the impact of these programs on students’ long-run outcomes. We find that exposure to accountability modestly but detectably increased educational attainment – roughly . 02 years per year of exposure. Effects on income were positive, but again modest and insignificant in most specifications. Lastly, if accountability had substantial effects on human capital, treated individuals would be expected to sort into occupations requiring greater use of tested (math and literacy) skills, potentially at the expense of non-tested skills. Instead, we find that accountability had virtually no effect on occupational requirements. Our results suggest that accountability was likely net beneficial for students’ long-run outcomes, but not transformative. JEL Classification: I28, J24, H0
Goodwill from the appreciative inquiry ( AI ) perspective : innovation transforming intangible capital

We present a theoretical essay on the relationship between accounting goodwill and the premises of appreciative inquiry (AI). We also adopt a new approach to goodwill, characterizing it as a state of potential wealth that will be converted into real wealth in the future. This essay introduces analyses and inquiries that will help deepen reflections on this very controversial topic in accounting literature. We ask if the interactions recommended in what some authors call the “positive revolution” might be responsible for generating superior earnings in organizations. Considering people’s best experiences, can the environment provided by AI be directly related to the goodwill we postulate? The lack of consensus on the nature of goodwill and the recent debates that have returned to the agenda of standard-setters create the opportunity for new critical reflections and epistemological debate on the topic.
Selectively Editable Language Models

Pretrained language models abound in NLP applications, but they are largely static in time and retain stale representations of the world around them. We seek to selectively edit knowledge learned by a language model without affecting its outputs on unrelated samples. Specifically, we explore approaches that alter model understanding of named entities through novel training techniques. We build on methods first developed in general Model-Agnostic Meta-Learning (MAML) frameworks, which allow us to train model parameters on base language model objectives as well as a secondary "adaptability" task. Our experiments demonstrate that this technique improves knowledge editing with less performance degradation on unrelated samples than standard fine-tuning approaches
Environment, Social, and Governance Performance and Enterprise Innovation Capacity: A Study of the Top 100 Global Hi-Tech firms

Sustainable finance has gained much traction in recent years and considerable literature has emerged around the influence of an enterprise’s Environment, Social, and Governance (ESG) performance on financial performance. However, the present study contributes to this “doing well by doing good” debate by exploring the impact of ESG performance on a firm’s innovation capacity through the lens of resource-based-view and stakeholders theory. We employ a sample of the top 100 hi-tech global companies for a period of 2010-2017. Panel based regression and System Generalized Method of Moment (SGMM) techniques were used to address the issue of heterogeneity and endogeneity. The study findings assert that corporate ESG undertakings has a positively affect the innovation capacity of the global hi-tech firms. Thus, at a policy level, regulators shall design an ESG framework that is more conducive to the innovation endeavors of technology firms. Our results are robust to alternate econometric specifications.
Why East Asian students perform better in mathematics than their peers: An investigation using a machine learning approach

Using a machine learning approach, we attempt to identify the school-, student-, and country-related factors that predict East Asian students’ higher PISA mathematics scores compared to their international peers. We identify student- and school-related factors, such as metacognition–assess credibility, mathematics learning time, early childhood education and care, grade repetition, school type and size, class size, and student behavior hindering learning, as important predictors of the higher average mathematics scores of East Asian students. Moreover, country-level factors, such as the proportion of youth not in education, training, or employment and the number of R&D researchers, are also found to have high predicting power. The results also highlight the nonlinear and complex relationships between educational inputs and outcomes.
Information Design Using Copulas

We propose an equivalent formulation of a two player information design problem in terms of choosing a copula, as opposed to choosing a joint distribution of a signal and the state variable. We then propose a copula based signal ordering which is both necessary and sufficient for the more informative signal to generate higher dispersion of the distribution of posteriors. Applications involving Bayesian persuasion with a continuous state variable, a buyer's incentives to acquire information and a seller's incentives to provide information in a bilateral trade setting, and the role of insider information in European and American call option valuations are presented.
Strengthening IPR Protection and Innovation

Though much of the theoretical economics literature have assumed a positive relationship between intellectual property rights (IPR) protection and innovation, there has been no empirical evidence that clearly supports such a relationship. In particular, this relationship may also depend on the measures of innovation. Exploiting the establishment of Chinese specialized IP courts across regions over time as a shock to the IPR protection, we empirically investigate how IPR protection impacts innovation. Measuring innovation by both input and output, we find that establishing an IP court reduces the number of patents filed by public firms located in the IP court's jurisdictional area by about 10%, while increasing those firms' R&D spending (scaled by firm assets) by 6%. These seemingly contradictory findings suggest that the relationship between strengthening IPR protection and firm innovation is a subtle one: it increases firms' incentives to innovate on one hand, while reducing their incentives to rely on the patent system to protect their innovation on the other hand. The finding that stronger IPR protection leads to a lower number of patents also points to a litigation cost channel in which firms reduce their patent filings concerning of the litigation risks brought by those patents. Overall, our findings suggest that IPR protection changes firms' innovation incentives and their interaction with the patent system.
Limiting the Leader: Fairness Concerns in Team Production with Leader-Determined Monitoring

We use a laboratory experiment to investigate the extent to which leaders---faced with opportunistic incentives---employ monitoring to improve team production. Participants are assigned to teams, with one person appointed as the leader. The leader has the power to commit to a monitoring option, which replaces the default equal sharing rule with one that distributes team revenue in proportion to individual investments. Additionally, the leader can announce a claim to a portion of the team revenue, which is paid before shares are distributed. Theoretically, there are multiple equilibria involving monitoring and full investment, characterized by the largest claim the non-leaders are willing to cede to the leader. We appeal to fairness concerns and repeated interaction in order to provide sharper predictions that pivot around a `fair claim'. In the experiments, leaders are mostly unsuccessful at increasing team production as they claim too much or forgo the monitoring option too often, especially when it is costly to monitor. When there is no cost, nearly half of the leaders successfully increase team production towards full investment, by relying on constant monitoring and resisting the temptation to issue unfair claims. These results highlight the potential for opportunistic incentives to undermine efficiency-enhancing leadership, even when the leader can commit to her decisions.
What Microeconomic Fundamentals Drive Global Oil Prices During 1986-2020?

The global financial crisis of 2007–2009 caused major economic disturbances in the oil market. In this paper, we consider five variables that describe the microeconomics of the supply of and demand for oil, and evaluate their importance before, during and after the global financial crisis. We consider five dissimilar regimes during the period of January 1986 to the end of 2020: two regimes prior to the global financial crisis, the regime during the crisis, and two regimes after the crisis. The main hypothesis tested is that oil fundamentals of supply and demand remained important, even though the five regimes were dissimilar. We built five boosted and over-fitted neural networks to capture the exact relationships between spot oil prices and oil data related to these prices. This analysis shows that, while the inputs into an accurate neural network can remain the same, the impact of each variable can change considerably during different regimes.
Advertising as Information for Ranking E-Commerce Search Listings

Search engines and e-commerce platforms have substantial difficulty exposing new products to their users on account of an information problem: new products typically do not have enough sales or other user-engagement that enables platforms to reliably assess product quality. This paper evaluates the role of advertising in providing information to the platform regarding new product quality so as to solve this "cold-start" problem and engineer higher quality organic listings. If sellers' advertising decisions predict the quality of new products, incorporating the advertising propensity of sellers into the search ranking algorithm can help obtain higher quality listings. Using a large-scale experiment implemented at JD.com---a large e-commerce platform in China---we show that using ad propensity information for ranking new products benefits both the platform and consumers, in the short run. Our findings showcase a new channel by which advertising can potentially improve outcomes for consumers and platforms in e-commerce, through its ability to reveal information that can be used by platforms to improve search ranking algorithms.
Accelerators as a Tool for Encouraging Female Entrepreneurship

This study was driven by an initial finding that female founders’ participation rates in Israeli accelerators are significantly higher (15.3%) than their participation rate in the Israeli startup sector (7.4%). Linking accelerators’ design to the known barriers for female entrepreneurship, we examined how accelerators may enhance female entrepreneurship by addressing their specific needs. Based on a dataset (N = 779) of structured interviews with startup founders who participated in accelerator programs in Israel during 2011–2019, we present evidence that female founders seek and gain more entrepreneurial training during their participation in accelerators than do male founders; place more emphasis on and succeed more in strengthening their networks and entrepreneurial self-confidence; and increase their entrepreneurial self-efficacy more than men. Female founders also seek to increase their legitimacy more than do men, but did not report making more progress in this aspect. Finally, both the goal of and progress in obtaining access to capital received lower ratings from female founders than from male founders. We discuss the implications of our findings for the use of accelerators and other support programs as means of increasing women’s participation rates in innovative entrepreneurship.
Trust in DeFi: An Empirical Study of the Decentralized Exchange

We provide empirical evidence that the decentralized cryptocurrency exchange can help reach the decentralized consensus of cryptocurrency value. We focus on Binance (the largest centralized cryptocurrency exchange) and Uniswap (the largest decentralized cryptocurrency exchange), and show that investors on Binance and Uniswap trade in response to prices on the two exchanges. More importantly, we find that the size of Uniswap user base exerts an asymmetric impact on investors’ trading. When its size increases, Uniswap user base exerts a larger impact on investors’ trading on Binance towards the Uniswap price, compared to the opposite direction. With a quasi-exogenous shock on Uniswap user base, we establish the causal impact of Uniswap user base on trading. Our results suggest that Uniswap as a decentralized exchange can reflect the consensus of cryptocurrency value that investors believe in because of blockchain’s transparency and trustworthiness. Our findings imply that the decentralized infrastructure built on blockchain and smart contracts can provide an alternative solution to cases where a consensus underwritten by a credible central party is not feasible or too costly to obtain.
Arbitrage in Cost-Based Redispatch: Evidence from Germany

The European Union's push towards market-based procurement of redispatch services has sparked fears of so called Inc-Dec-Gaming, i.e. the incentive to engage in arbitrage between the national wholesale market and the local redispatch market. The latter would increase both likelihood and severity of grid congestions. Such incentives might already be present in Germany where participation is mandatory and reimbursement based on costs if the marginal cost estimates are not accurate. This paper develops a method to identify such behavior. We test for the presence of Inc-Dec-Gaming at the plant level using a random forest prediction model and time series regressions based on a sample of German power plants. We do find evidence of arbitrage among a small cluster of German power plants.
Effect of cephalandra indica against advanced glycation end products, sorbitol accumulation and aldose reductase activity in homoeopathic formulation

Background: Extreme generation of free radicals leads to oxidative stress which has been apprehensive in several disease processes such as diabetic complications and vascular and neurodegenerative diseases. Objective: The present study was designed to evaluate the potential of homoeopathic preparations of Cephalandra indica L. against oxidative stress. Materials and Methods: Potencies of Cephalandra indica (mother tincture, 6C and 30C) were procured from Dr. Willmar Schwabe India Pvt. Ltd. The antioxidant activity of Cephalandra indica was evaluated by employing various in vitro antioxidant methods. Results: The total phenol content was found to be 1905, 849 and 495 mg/g gallic acid equivalents in mother tincture, 6C and 30C of Cephalandra indica and total antioxidant capacity was found to be 2710, 759 and 510 μM/g ascorbic acid equivalents, respectively. Mother tincture, 6C and 30C of Cephalandra indica was found to have strong reducing power, 2,2-diphenyl-1-picrylhydrazyl radical, hydrogen peroxide, nitric oxide and superoxide radical scavenging activity. Percentage inhibition of AGEs formation by mother tincture, 6C and 30C of Cephalandra indica (10–50 μl) was found to be 30.34%–91.77%, 29.98%–65.71% and 33.05%–57.75%, respectively. Mother tincture, 6C and 30C of Cephalandra indica showed inhibitory effect against sorbitol accumulation with IC50value of 26.12 μl, 203.10 μl and 897.3 μl, respectively, whereas, in aldose reductase inhibition assay, the IC50value was 32.54 μl, 175.02 μl and 834.34 μl, respectively. Conclusion: The results revealed that homoeopathic preparations of Cephalandra indica exhibit protective effect against oxidative stress.
Less atmospheric radiative heating by dust due to the synergy of coarser size and aspherical shape

Abstract. Mineral dust aerosols cool and warm the atmosphere by scattering and absorbing solar (shortwave: SW) and thermal (longwave: LW) radiation. However, significant uncertainties remain in dust radiative effects, largely due to differences in the dust size distribution and spectral optical properties simulated in Earth system models. Dust models typically underestimate the coarse dust load (more than 2.5 µm in diameter) and assume a spherical shape, which leads to an overestimate of the fine dust load (less than 2.5 µm) after the dust emissions in the models are scaled to match observed dust aerosol optical depth at 550 nm (DAOD550). Here, we improve the simulated dust properties with data sets that leverage measurements of size-resolved dust concentration, asphericity factor, and refractive index in a coupled global chemical transport model with a radiative transfer module. After the adjustment of size-resolved dust concentration and spectral optical properties, the global and annual average of DAOD550 from the simulation increases from 0.023 to 0.029 and falls within the range of a semi-observationally based estimate (0.030 ± 0.005). The reduction of fine dust load after the adjustment leads to a reduction of the SW cooling at the top of the atmosphere (TOA). To improve agreement against a semi-observationally based estimate of the radiative effect efficiency at TOA, we find that a less absorptive SW dust refractive index is required for coarser aspherical dust. Thus, only a minor difference is estimated for the net global dust radiative effect at TOA (−0.08 vs. −0.00 W m−2 on a global scale). Conversely, our sensitivity simulations reveal that the surface warming is substantially enhanced near the strong dust source regions (less cooling to −0.23 from −0.60 W m−2 on a global scale). Thus, less atmospheric radiative heating is estimated near the major source regions (less heating to 0.15 from 0.59 W m−2 on a global scale), because of enhanced LW warming at the surface by the synergy of coarser size and aspherical shape.
IMAGE DENOISING FOR SPECKLE NOISE REDUCTION IN ULTRASOUND IMAGES USING DWT TECHNIQUE

Volume 2, Issue 6, June 2013 Page 384 ABSTRACT This paper presents image denoising and Speckle noise reduction model using different wavelets and combination of wiener filter along with deconvolution filters. Wavelets are the latest research area in the field of image processing and enhancement. The results show a comparison of Haar, Symlets and Coiflets Wavelets for Image denoising for biomedical images. Wavelet analysis represents the next logical step: a windowing technique with variable-sized regions. Wavelet analysis allows the use of long time intervals where we want more precise low-frequency information, and shorter regions where we want high-frequency information. The main objective of Image denoising techniques is necessary to remove such noises while retaining as much as possible the important signal features. Introductory section offer brief idea about different available denoising schemes. Ultrasonic imaging is a widely used medical imaging procedure because it is economical, comparatively safe, transferable, and adaptable. Though, one of its main shortcomings is the poor quality of images, which are affected by speckle noise. The existence of speckle is unattractive since it disgrace image quality and it affects the tasks of individual interpretation and diagnosis. Results are both Qualitative and Quantitative analyses by obtaining the denoised version of the input image by DWT along with wiener filter Technique and comparing it with the input image used. Quantitative analysis would be performed by checking attained Mean Square Error estimation and PSNR of the denoised image. Also, elapsed time for these processing techniques has also been presented. Another important parameter of PSF (Point Spread Function) of restored image is added to check the level of distortion in output image.
Tackling SQuAD 2.0 Using Character Embeddings, Coattention and QANet

Question Answering (QA) systems allow users to retrieve information using natural language queries [1]. In this project, we are training and testing QA models on SQuAD 2.0, a large dataset containing human-labelled question-answer pairings [2], with the goal of evaluating in-domain performance. Using a Bidirectional Attention Flow (BiDAF) model with word embeddings as a baseline, we identified, implemented and evaluated techniques to improve accuracy on the SQuAD task. Our initial experiments, which added character embeddings and a coattention layer to the baseline model, yielded mixed results. Therefore, we started over with a new model using Transformer-style encoder layers, based on the QANet [3]. This model posed many challenges, particularly in adapting to the unanswerable component of the SQuAD 2.0 dataset, and thus did not come close to achieving the performance of BiDAF-based models.
Policy Gradients Incorporating the Future

Reasoning about the future -- understanding how decisions in the present time affect outcomes in the future -- is one of the central challenges for reinforcement learning (RL), especially in highly-stochastic or partially observable environments. While predicting the future directly is hard, in this work we introduce a method that allows an agent to"look into the future"without explicitly predicting it. Namely, we propose to allow an agent, during its training on past experience, to observe what \emph{actually} happened in the future at that time, while enforcing an information bottleneck to avoid the agent overly relying on this privileged information. This gives our agent the opportunity to utilize rich and useful information about the future trajectory dynamics in addition to the present. Our method, Policy Gradients Incorporating the Future (PGIF), is easy to implement and versatile, being applicable to virtually any policy gradient algorithm. We apply our proposed method to a number of off-the-shelf RL algorithms and show that PGIF is able to achieve higher reward faster in a variety of online and offline RL domains, as well as sparse-reward and partially observable environments.