Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Deep-Fat Frying using Canola Oil, Soya Oil and Vegetable Oil on the Proximate, Vitamins and Mineral Contents of Unripe Plantain (Musa x paradisiaca)

Musa x paradisiaca (plantain) isamong the major tropical staple foods. This study was designed to determine the proximate, mineral and fat soluble vitamins constituents of unripe plantain fried with three different oils (canola oil, soya oil and vegetable oil) and unripe plantain oven dried sample was used as the control. The vitamins were determined using High Performance Liquid Chromatography (HPLC). The result of the proximate analysis showed that the dried samples had higher levels of protein (25.2%), carbohydrate (50.1%), moisture content (13.3%), ash content (2.02%), crude fibre (7.73%) but low level of crude fat in comparison with the fried samples. Plantain fried with vegetable oil had the lowest levels of protein (20.84%), carbohydrate (26.93%), moisture content (13.1%), ash content (1.72%) and crude fibre (6.57%) but the highest level of crude fat (30.84%). Plantain fried with vegetable oil had the highest level of calcium (211.80±0.1 mg gG1) while the dried plantain samples had high level of sodium (233.80±0.07 mg gG1). Lead (Pb) was absent in the dried plantain samples. Dried plantain samples gave a higher value of vitamins A (0.08±0.01 mg gG1), D (0.02±0.01 mg gG1), E (0.01±0.00 mg gG1), K (0.05±0.00 mg gG1) compared to plantain sample fried with canola oil which gave low level of vitamin D (0.03±0.00 mg gG1), E (0.25±0.00 mg gG1) and K (0.03±0.00 mg gG1). However, there was a very high level in vitamin A (0.72±0.02 mg gG1). The result showed that drying in oven retains the nutritional contents of plantain compared to deep-fat frying. Canola oil is recommended for frying although soya oil is also good.
Characterization of Excess Risk for Locally Strongly Convex Population Risk

We establish upper bounds for the expected excess risk of models trained by proper iterative algorithms which approximate the local minima. Unlike the results built upon the strong globally strongly convexity or global growth conditions e.g., PL-inequality, we only require the population risk to be \emph{locally} strongly convex around its local minima. Concretely, our bound under convex problems is of order $\tilde{\cO}(1/n)$. For non-convex problems with $d$ model parameters such that $d/n$ is smaller than a threshold independent of $n$, the order of $\tilde{\cO}(1/n)$ can be maintained if the empirical risk has no spurious local minima with high probability. Moreover, the bound for non-convex problem becomes $\tilde{\cO}(1/\sqrt{n})$ without such assumption. Our results are derived via algorithmic stability and characterization of the empirical risk's landscape. Compared with the existing algorithmic stability based results, our bounds are dimensional insensitive and without restrictions on the algorithm's implementation, learning rate, and the number of iterations. Our bounds underscore that with locally strongly convex population risk, the models trained by any proper iterative algorithm can generalize well, even for non-convex problems, and $d$ is large.
REPAINT: Knowledge Transfer in Deep Reinforcement Learning

Accelerating learning processes for complex tasks by leveraging previously learned tasks has been one of the most challenging problems in reinforcement learning, especially when the similarity between source and target tasks is low. This work proposes REPresentation And INstance Transfer (REPAINT) algorithm for knowledge transfer in deep reinforcement learning. REPAINT not only transfers the representation of a pre-trained teacher policy in the on-policy learning, but also uses an advantage-based experience selection approach to transfer useful samples collected following the teacher policy in the off-policy learning. Our experimental results on several benchmark tasks show that REPAINT signiﬁcantly reduces the total training time in generic cases of task similarity. In particular, when the source tasks are dissimilar to, or sub-tasks of, the target tasks, REPAINT outperforms other baselines in both training-time reduction and asymptotic performance of return scores.
Income Inequality and the Depth of Economic Downturns

Abstract Using an international panel data set, we analyse the implications of rising income inequality for aggregate consumption. We document that greater concentration of (after-tax) income in the top decile is associated with a significantly larger and more persistent contraction in consumption in the aftermath of economic downturns. These findings are consistent with lower propensities to consume among wealthier households and imply that disparities in income flows at turning points of the business cycle can significantly influence macroeconomic outcomes.
Stock Price Crash Risk and the adoption of Poison Pills: Evidence from Brazil

PurposeThe purpose of this paper is to examine the effect of stock price crash risk on the adoption of poison pills.Design/methodology/approachThe authors estimate logit and probit regressions. Their sample includes 185 Brazilian public firms for the period 2010–2018. Following previous studies, the authors use the negative skewness of firm-specific weekly returns and the down-to-up volatility of firm-specific weekly returns as measures of firm's stock price crash risk. As proxies of poison pills, the authors employ the “conventional” poison pills in their baseline models and the “eternity” poison pills, which prevent the removal of poison pills from bylaws, in additional models.FindingsThe authors find that stock price crash risk measures are not associated with poison pill adoption. However, although stock price crash risk does not lead to poison pill adoption as a complementary corporate governance mechanism that protects firms against hostile takeover attempts, further results show that managers do not draw on stock price crash risk as a pretext to entrench themselves. Additional analyses also highlight that CEO power seems to play a role in moderating the relationship between stock price crash risk and eternity poison pill adoption.Originality/valueThe authors contribute to the literature on stock price crash risk, which calls for research in international contexts to better understand the effect of stock price crash risk on country-specific idiosyncratic features. The authors discuss a controversial anti-takeover mechanism that has been debated by Brazilian policymakers.
Management Earnings Guidance as a Commitment Device

This paper examines whether issuing management earnings guidance motivates a firm to raise its level of performance. The failure of management to attain a forecast may reflect poorly on its industry understanding, knowledge of the firm, and management capability. Accordingly, we hypothesize and find that management, having issued guidance, are motivated to hone the firm’s production function to raise firm performance. We find that firms alter their operating activities to increase performance rather than manipulating their accruals. The enhancement in firm performance from using managerial earnings guidance as a commitment device is accomplished by reductions in operating leverage and is strongest among firms issuing moderately aggressive forecasts. Inconsistent with concerns that forecasting causes myopia, performance improvements persist for several years.
Economic Evaluation of Asset Pricing Models Under Predictability

This paper performs an out-of-sample comparison of linear factor asset pricing models from an economic perspective under predictability. I assess the economic value added of several factor models when a Bayesian investor is faced with a portfolio allocation problem whereby each model imposes cross-sectional restrictions on the parameters of a predictive stock return regression. The empirical framework explicitly accounts for investor skepticism about the model, i.e., mispricing uncertainty. Using several US portfolios as test assets, I find that the q5 model of Hou et al. (2020), as well as the behavioral factor models of Stambaugh and Yuan (2017) and Daniel et al. (2020) outperform competing models across investment horizons. At the longest evaluated horizon (one year), a benchmark portfolio built using historical data produces larger portfolio gains than all the factor models, but in the short run (at the one-month horizon), their performance is comparable.
A Socio-Legal Analysis of WhatsApp Privacy Policy 2021 in India: A Contemporary Study

A vast majority of social networking sites seem to have inculcated prejudice against users’ privacy, whether it’s in the form of unreasonable default settings, insufficient privacy controls, data retention, third party abuse, or a unilateral right to amend privacy policies. Their attempt is probably to acquire a large and irrevocable right of ownership over a user's information and equivalent for vested company interests like advertising. The chance of data aggregation further poses many real threats to the privacy of users. In contrast, corporations like Facebook and WhatsApp are increasingly seen turning a blind eye to such issues or shrugging them off as unreasonable and unrealistic. But this is pretty standard practice as far as most companies go. Almost all companies update their privacy policies at some point or the other, and to continue using that, users have to accept these. WhatsApp has updated its privacy policy, and it will cause more concern because it brings deeper integration with Facebook, its parent company, and its different treatment between Indian users and their users in the European Union (EU). This paper analyses that the WhatsApp new privacy policy, 2021 qualify for being a standard form of contract because the user has no ability, chance, and/or expertise to negotiate a lot of favorable terms with the corporation and is consequently placed in a 'take it or leave it' approach in India.
ICO versus Credit versus Venture Capital Financing under Stochastic Demand: A comment on 'Entrepreneurial Incentives and the Role of Initial Coin Offerings' by Rodney Garratt and Maarten van Oordt

The article 'Entrepreneurial Incentives and the Role of Initial Coin Offerings' by Garratt and van Oordt revisits a classical principal-agent problem in corporate finance: How do financing choices of firms and the according firm ownership structure impact effort choices of the management and therefore firm value? Garratt and Oordt extend this classic discussion by analyzing initial coin offerings (ICOs) as a startup financing instrument in contrast to classic financing instruments such as debt, (entrepreneurial) equity and venture capital. <br><br>Their paper shows, depending on the firm's characteristics, ICO financing can align the entrepreneur's incentives with those of his investors better or worse, in comparison to venture capital and debt financing. Therefore, the novel ICO financing instrument is a valuable alternative to credit and venture capital also from a social perspective. <br><br>I find the efficiency comparison of financing choices for entrepreneurial incentives to be a very interesting and important contribution. In this discussion, I shed light on this topic from a different angle. Garratt and van Oordt base their analysis on the simplifying assumption that demand for the platform product realizes once and for all in t=1. This allows Garratt and van Oordt to abstract from token-exchange rate risk. Here, instead, I will allow platform demand to evolve stochastically across time. Moreover, unlike in Garratt and van Oordt, I analyze the general case where not necessarily all tokens are sold in the market at every point in time. Instead, there can exist token speculation where investors hold on to tokens for a certain period of time, effectively reducing the token supply, or additional tokens can be mined by the entrepreneur, which increases the token supply, even though the latter activity might constitute a breach of contract. <br><br>By allowing the token supply and product demand to fluctuate across time, the token exchange rate becomes stochastic, causing exchange rate risk to the ICO investors, the entrepreneur, and platform retailers.
Price Rigidities and Credit Risk

We develop a capital structure model in which firms feature differential flexibility in adjusting output prices to shocks. Inflexible-price firms have lower profits and higher cash-flow volatility, leading in equilibrium to lower financial leverage, shorter debt duration, higher cost of debt, more stringent debt covenants, and higher precautionary cash holdings. Moreover, a cash-flow volatility shock increases the cost of debt more for inflexible-price firms. We confirm these predictions empirically and exploit the 2008 Lehman Brothers bankruptcy to show that inflexible-price firms with higher pre-shock rollover risk exposure experience a significantly larger increase in credit spreads following the event than other firms.
Women Shelters: The Last Resort Option to Escape From Violence

This study aims to analyze the effect of shelters on domestic violence against women and their mental health status by using the variation from the EU-supported project of women shelters to fight domestic violence started in 2012 and implemented in 26 pilot provinces in Turkey. The study uses 2008-2014 data from national research on domestic violence against women. The estimation results show that the project reduced the probability of physical domestic violence against women whose education level is less than college by 2-2.5 percentage points in the treated region. The reduction in the intensity of physical domestic violence is more substantial, around 30 percentage points. Further estimations are conducted to find out the heterogeneous effect on subgroups, the results of which show that the mothers with two and three children benefit most from the project. The effects are even higher for non-working mothers. In addition to the success in physical domestic violence, there are significant reductions in sexual and psychological violence against mothers with 2 or 3 children and non-working mothers. As a result of the reductions in domestic violence, the mental health status of women increased by 2.5 percentage points in the region. The improvements in mental health are greater for mothers and non-working mothers, against whom the reductions in domestic violence are also high. The results show that the shelters did not trigger a male backlash effect in a country where patriarchal attitudes and norms are strong. <br>
Real Effects of Corporate Taxation: A Review

In this study, I review the empirical literature on the real effects of corporate taxation. I define real effects broadly as firms' investment responses, corporate risk taking, capital structure choices, and aggregate outcomes such as GDP growth. I base my analysis on 79 empirical studies on the investment effects of corporate taxation and contrast these results to theoretical predictions. Consistent with theory, there seems to be a consensus that higher corporate tax rates reduce corporate investment, foreign direct investment (FDI), aggregate growth, and innovation. Similarly, many papers examine bonus depreciation, which consistently increases investment. At the same time, there is little evidence on the employment effects of corporate taxes and on the role of several tax base elements in shaping investments. Importantly, the role of tax avoidance (opportunities) in the tax effect on investment has received very little attention from the empirical literature over the past two decades. I also derive several other potential avenues for future research.
Understanding the Angular Momentum Evolution of T Tauri and Herbig Ae/Be Stars

We investigate a sample of six Herbig Ae/Be stars belonging to the Orion OB1 association, as well as 73 low-mass objects, members of the σ Orionis cluster, in order to explore the angular momentum evolution at early stages of evolution, and its possible connection with main-sequence Ap/Bp magnetic stars. Using FIES and HECTOCHELLE spectra, we obtain projected rotational velocities through two independent methods. Individual masses, radii, and ages are computed using evolutionary models, distance, and cluster extinction. Under the assumption that similar physical processes operate in both T Tauri and Herbig Ae/Be stars, we construct snapshots of the protostar’s rotation against mass during the first 10 Myr with the aid of a rotational model that includes a variable disk lifetime, changes in the stellar moment of inertia, a dipolar magnetic field with variable strength, and angular momentum loss through stellar winds powered by accretion. We use these snapshots, as well as the rotational data, to infer a plausible scenario for the angular momentum evolution. We find that magnetic field strengths of a few kilo-Gauss at 3 Myr are required to match the rotational velocities of both groups of stars. Models with masses between 2 M ⊙ and 3 M ⊙ display larger angular momentum values by a factor of ∼3, in comparison to stars of similar spectral types on the main sequence. Even though some quantitative estimates on this dramatic decrease with age for Ap/Bp magnetic main-sequence stars are presented, the results obtained for the angular momentum evolution do not explain their low rotation rates.
Monopole-antimonopole pair production in primordial magnetic fields

We show that monopoles can be pair produced by cosmological magnetic fields in the early universe. The pair production gives rise to relic monopoles, and at the same time induces a self-screening of the magnetic fields. By studying these effects we derive limits on the monopole mass, and also on the initial amplitude of primordial magnetic fields. Monopoles of GUT scale mass can even be produced if primordial magnetic fields exist at sufficiently high redshifts.
MAVFI: An End-to-End Fault Analysis Framework with Anomaly Detection and Recovery for Micro Aerial Vehicles

Safety and resilience are critical for autonomous unmanned aerial vehicles (UAVs). We introduce MAVFI, the micro aerial vehicles (MAVs) resilience analysis methodology to assess the effect of silent data corruption (SDC) on UAVs' mission metrics, such as flight time and success rate, for accurately measuring system resilience. To enhance the safety and resilience of robot systems bound by size, weight, and power (SWaP), we offer two low-overhead anomaly-based SDC detection and recovery algorithms based on Gaussian statistical models and autoencoder neural networks. Our anomaly error protection techniques are validated in numerous simulated environments. We demonstrate that the autoencoder-based technique can recover up to all failure cases in our studied scenarios with a compu-tational overhead of no more than 0.0062%. Our application-aware resilience analysis framework, MAVFI, can be utilized to comprehensively test the resilience of other Robot Operating System (ROS)-based applications and is publicly available at https://github.com/harvard-edge/MAVBench/tree/mavfi.
Intelligent Reflecting Surface Assisted Free-Space Optical Communications

Free-space optical (FSO) systems are able to offer the high data-rate, secure, and cost-efficient communication links required for applications such as wireless front- and backhauling for 5G and 6G communication networks. Despite the substantial advancement of FSO systems over the past decades, the requirement of a line-of-sight connection between transmitter and receiver remains a key limiting factor for their deployment. In this article, we discuss the potential role of intelligent reflecting surfaces (IRSs) as a solution to relax this requirement. We present an overview of existing optical IRS technologies; compare optical IRSs with radio frequency IRSs and optical relays; and identify various open problems for future research on IRS-assisted FSO communications.
Drawing Multiple Augmentation Samples Per Image During Training Efficiently Decreases Test Error

In computer vision, it is standard practice to draw a single sample from the data augmentation procedure for each unique image in the mini-batch. However recent work has suggested drawing multiple samples can achieve higher test accuracies. In this work, we provide a detailed empirical evaluation of how the number of augmentation samples per unique image influences model performance on held out data when training deep ResNets. We demonstrate drawing multiple samples per image consistently enhances the test accuracy achieved for both small and large batch training. Crucially, this benefit arises even if different numbers of augmentations per image perform the same number of parameter updates and gradient evaluations (requiring the same total compute). Although prior work has found variance in the gradient estimate arising from subsampling the dataset has an implicit regularization benefit, our experiments suggest variance which arises from the data augmentation process harms generalization. We apply these insights to the highly performant NFNet-F5, achieving 86.8$\%$ top-1 w/o extra data on ImageNet.
Storms, Variability, and Multiple Equilibria on Hot Jupiters

Observations of hot-Jupiter atmospheres show large variations in the location of the “hot spot” and the amplitude of spectral features. Atmospheric flow simulations using the commonly employed forcing and initialization have generally produced a large, monolithic patch of nearly stationary hot area located eastward of the substellar point at high altitude. Here we perform high-resolution (T682) pseudospectral simulations that accurately capture small-scale eddies and waves inherent in hot-Jupiter atmospheres due to ageostrophy. The atmospheres contain a large number of intense storms over a wide range of scales, including the planetary scale. The latter sized storms dictate the large-scale spatial distribution and temporal variability of hot, as well as cold, regions over the planet. In addition, they exhibit quasi-periodic life cycles within multiple equilibrium states—all identifiable in the disk-integrated time series of the temperature flux.
Passivation of Sponge Iron and GAC in Fe0/GAC Mixed-Potential Corrosion Reactor

The Fe0/GAC mixed-potential corrosion reactor was used to treat the complicated, toxic, and refractory ABS resin wastewater. In the 100 days continuous run, the effect of the packing particles passivation on the treatment efficiency of the Fe0/GAC mixed-potential corrosion reactor was investigated seriously. The formation mechanism of the compounds in passive film was investigated first by SEM, EDS, and X-ray dot-mapping, which was the precondition for the control of the passivation of packing particles. The results show that the passive film consisted of five kinds of compounds such as Fe3(PO)2·8H2O, FePO4 ·3H2O, Fe2O3, Fe3O4, and FeS, which obstructed the formation of macroscopic galvanic cells between sponge iron (Fe0) and GAC and decreased the COD treatment efficiency of the Fe0/GAC mixed-potential corrosion reactor from 45 to 55% to 0%. The formation of passive film mainly resulted from the elements of S and P, which were from the SO42– and PO43– in ABS resin wastewater. Therefore, the inorganic ions.