Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating the safety of wastewater reclamation and reuse using enteric virus monitoring data

The reliability of wastewater reclamation and reuse to meet a given annual risk of infection, considering the variability of enteric virus concentrations, has been investigated. Two concepts related to the reliability and safety of wastewater reclamation and reuse are presented. The first is reliability, defined as the probability that the risk of infection from enteric viruses in reclaimed wastewater does not exceed an acceptable risk. The second is based on the expectation of the acceptable annual risk in which the exposure to enteric viruses may be estimated stochastically by numerical simulation. To assess the potential risks associated with the use of reclaimed wastewater in various reuse applications, four exposure scenarios were tested: golf course irrigation, food crop irrigation, recreational impoundments, and groundwater recharge. Past monitoring data on enteric virus concentrations in unchlorinated secondary effluents in California were used. Because enteric virus concentrations in unchlorinated secondary effluents were found to vary over a wide range, characterizing their variability was found to be extremely important. The reliability criterion of meeting the less than 10−4 annual risk of infection (less than or equal to one infection per 10 000 population per year) at least 95% of the time was used to assess the safety of using reclaimed wastewater in the four different exposure scenarios. The methodologies used in this study should be refined, based on a larger enteric virus database developed using standardized field and laboratory protocols.
Does Tax Policy Work When Consumers Have Imperfect Price Information? Theory and Evidence

We investigate how the pass-through rate of commodity taxes depends on competition in a setting where consumers have imperfect information about prices. We use a theoretical search model that has two key predictions: First, the larger the number of price sensitive consumers, the higher the pass-through rate. Second, there is a hump-shaped relationship between the average pass-through experienced by consumers and the number of sellers. We test our theoretical predictions by studying pass-through in the context of a tax decrease and increase in the German retail fuel market. We estimate pass-through of these tax changes to diesel and gasoline prices using a unique dataset containing the universe of price changes at fuel stations in Germany and France and a synthetic difference-in-differences strategy. Our empirical results are in line with our theoretical predictions. Finally, we show that our theoretical framework can encompass and reconcile a large number of empirical observations in previous studies.
Audit Partner Style and Financial Statement Comparability: New Evidence from the U.S. Market

We investigate the effect of audit partner style on financial statement comparability in a U.S. setting using newly-available data from 2016 to 2020. We document an audit partner style effect on comparability incremental to audit firm and audit office effects. Our results are consistent across several comparability measures (accruals similarity, audit fees, and audit report lag), and our findings are economically significant. For example, controlling for the effect of the same Big 4 audit firm and office, the audit partner effect leads to a 15.94% decrease in the mean difference in discretionary accruals compared to a 6.5% (0.07%) decrease for firm pairs audited by the same Big 4 audit office (Big 4 audit firm). Our results are robust to controlling for product similarity, auditor switching, propensity score matching, placebo tests, and self-selection tests. Our findings support the PCAOB’s assertion that internal firm monitoring mechanisms are not enough to fully overcome individual partner’s agency-related incentives and that audit partners are able to exert their own personal style on the financial statements of the firms they audit. <br>
Real Interest Rate Behaviour and Monetary Policy in Myanmar:Based on Financial Libralisation Hypothesis

McKinnon’s complementarity hypothesis articulates that money and physical capital complements each other instead of substituting under a repressed financial sector. In spite of various economic and financial reforms in Myanmar, the financial sector of Myanmar is lagging behind international standards. Thus, this study is designed to test McKinnon’s complementarity hypothesis in case of Myanmar. Time series studies has been analyzed for said purpose. This study not only determines unit root problem but also tests unit root problem in presence of structural break. Long run relationship is testified through bounds test in presence of structural break. Long run and short run estimates are determined in order to check McKinnon’s complementarity hypothesis in static and dynamic setting as bounds testing approach distinguish feature is to provide static long run estimates along with dynamic short run estimates. Moreover, this study applies innovative accounting approach to determine strength of casual relationship between variables of the study. Results of the study confirms McKinnon’s complementarity hypothesis in long run and no evidence is found in case of short run. Thus, it can be deduced that investment is restricted due to availability of finance rather than cost of capital in Myanmar.
Employee Satisfaction and Firm Innovation Performance

For firms to be able to compete in the complex and dynamic nature of today's economy, they need to continually innovate. Thereupon, it is important to understand the drivers of innovation at firms. In this paper, we study the effect of employee satisfaction on firms' innovation performance. We examine a social media platform, where employees voluntarily and anonymously share their outlook and opinions on various dimensions of their workplace environment. Using a sample of 905,930 employee reviews and 1,620,229 granted patents of 698 firms, we find that employee satisfaction with their workplace environment can significantly impact firms' innovation performance. More specifically, we find that while employee satisfaction with career opportunities enhances firms' innovation performance, their satisfaction with work/life balance negatively affects firms' innovation performance. Further, we find that employee satisfaction with their compensation and benefits can significantly reduce the value of firms' innovation in terms of both scientific and economic values, but it does not significantly affect the quantity of innovation outcomes. We also conduct analysis on sub-sample of firms based on their characteristics (i.e., revenue, geographical location, and industry), and discuss the implications of our findings for research and practice.
The implications of rating systems on workforce performance

Abstract Enhancing workforce performance is the key to success for professional firms. Firms often evaluate workers based on their performance compared with their peers or against an objective standard. Which of these rating systems leads to higher workforce performance? To answer this question, we construct game-theoretic models of two performance rating systems: (i) a Relative rating system where workers compete with each other for a constrained number of high ratings, and (ii) an Absolute rating system where workers are awarded high ratings by performing at or above a standard threshold. We derive the workers’ equilibrium performance as a function of their ability and the characteristics of the rating pool. From a firm’s perspective, we find that an Absolute rating system can lead to higher performance than a Relative rating system when the rating pool size is small or the workers’ cost of effort relative to their efficiency rate is low, and the reverse holds true otherwise. When considering the workers’ perspective, we find that higher ability workers prefer an Absolute system due to its predictable nature, while lower ability workers prefer a Relative system as it provides them an opportunity to outperform other workers.
Macronutrients accumulation in useable parts of lettuce as affected by nickel and selenium concentrations in nutrient solution.

The accumulation of macronutrients in leaves of let tuce plants cultivated in the Hoagland nutrient sol ution under various concentrations of nickel (0, 50 or 10 0 µM) and selenium (0, 5 or 20 µM) was investigated. The Ca/P, Ca/Mg, K/Mg, K/Ca, K/Ca+Mg ratios have also been calculated. The obtained results suggest that interact ions between analysed elements depend on Se and Ni proportion in the nutrient solution. When applied separately, bot h nickel and selenium significantly decreased macronutrients accumulation in lettuce leaves. Introduction of 5 µM Se into the medium containing nickel (50 or 100 µM) resulted in significant increase of macronutrients accumulation as compared to plants treated with nickel without seleniu m addition. The presence of 20 µM Se in nutrient solution containing 50 µM Ni reduced accumulation of all analysed macronutrients in leaves, except from increase in phosph orus accumulation. Selenium at 20 µM concentration introduced into the medium containing 100 µM Ni induced the increase of sulphur accumulation and it did not cause significant changes in the accumulation of other macronutr ients. However, the presence of 5 µM Se increased nickel content in leaves of plants treated with 50 µM Ni as compar ed to those without selenium addition. Selenium at concentrations of 5 and 20 µM at the presence of 100 µM Ni d ecreased nickel content in lettuce leaves and promot ed productivity. It should be concluded that selenium at 5 µM concentration had positive effects on yields of let tuce cultivated on nickel-contaminated nutrient solution, whi ch was probably associated with increased intake and accumulation of macronutrients by plants.
Influence of dispersion medium structure on the physicochemical properties of aging colloidal suspensions investigated using the synthetic clay Laponite

Hypothesis: Aging in colloidal suspensions manifests as a reduction in kinetic freedom of the colloids. In aqueous suspensions of charged colloids, the role of inter-particle electrostatics interactions on the aging dynamics is well debated. Despite water being the dispersion medium, the influence of water structure on the physicochemical properties of aging colloids has never been considered before. Laponite, a model hectorite clay, could be used to evaluate the relative contributions of medium structure and electrostatics in determining the physicochemical properties of aging colloidal suspensions. Experiments: The structure of the dispersion medium is modified either by incorporating uncharged/charged kosmotropic (structure-inducing) or chaotropic (structure-disrupting) molecules or by changing suspension temperature. A new protocol, wherein the medium is heated before adding clay particles, is also introduced to evaluate the effects of hydrogen bond disruptions on suspension aging. Dynamic light scattering, rheological measurements and particle-scale imaging are employed to evaluate the physicochemical properties of the suspensions. Findings: A strong influence of medium structure is evident when inter-particle electrostatic interactions are weak. Enhancement and disruption of hydrogen bonds in the medium are, respectively, strongly correlated with acceleration and delay of suspension aging dynamics. The physicochemical properties of charged clay colloidal suspensions are therefore controlled by altering hydrogen bonding in the dispersion medium.
Wave Transport and Localization in Prime Number Landscapes

In this paper, we study the wave transport and localization properties of novel aperiodic structures that manifest the intrinsic complexity of prime number distributions in imaginary quadratic fields. In particular, we address structure-property relationships and wave scattering through the prime elements of the nine imaginary quadratic fields (i.e., of their associated rings of integers) with class number one, which are unique factorization domains (UFDs). Our theoretical analysis combines the rigorous Green’s matrix solution of the multiple scattering problem with the interdisciplinary methods of spatial statistics and graph theory analysis of point patterns to unveil the relevant structural properties that produce wave localization effects. The onset of a Delocalization-Localization Transition (DLT) is demonstrated by a comprehensive study of the spectral properties of the Green’s matrix and the Thouless number as a function of their optical density. Furthermore, we employ Multifractal Detrended Fluctuation Analysis (MDFA) to establish the multifractal scaling of the local density of states in these complex structures and we discover a direct connection between localization, multifractality, and graph connectivity properties. Finally, we use a semi-classical approach to demonstrate and characterize the strong coupling regime of quantum emitters embedded in these novel aperiodic environments. Our study provides access to engineering design rules for the fabrication of novel and more efficient classical and quantum sources as well as photonic devices with enhanced light-matter interaction based on the intrinsic structural complexity of prime numbers in algebraic fields.
Diffusion of Cosmic Rays in MHD Turbulence with Magnetic Mirrors

As the fundamental physical process with many astrophysical implications, the diffusion of cosmic rays (CRs) is determined by their interaction with magnetohydrodynamic (MHD) turbulence. We consider the magnetic mirroring effect arising from MHD turbulence on the diffusion of CRs. Due to the intrinsic superdiffusion of turbulent magnetic fields, CRs with large pitch angles that undergo mirror reflection, i.e., bouncing CRs, are not trapped between magnetic mirrors, but move diffusively along the turbulent magnetic field, leading to a new type of parallel diffusion, i.e., mirror diffusion. This mirror diffusion is in general slower than the diffusion of nonbouncing CRs with small pitch angles that undergo gyroresonant scattering. The critical pitch angle at the balance between magnetic mirroring and pitch-angle scattering is important for determining the diffusion coefficients of both bouncing and nonbouncing CRs and their scalings with the CR energy. We find nonuniversal energy scalings of diffusion coefficients, depending on the properties of MHD turbulence.
A decade of joint MOJAVE-Fermi AGN monitoring: localisation of the gamma-ray emission region

Within the MOJAVE VLBA programme (Monitoring of Jets in AGN with VLBA Experiments), we have accumulated observational data at 15 GHz for hundreds of jets in gamma-ray bright active galactic nuclei since the beginning of the Fermi scientiﬁc observations in 2008 August. We investigated a time delay between the ﬂux density of AGN parsec-scale radio emission at 15 GHz and 0.1–300 GeV Fermi LAT photon ﬂux, taken from constructed light curves using weekly and adaptive binning. The correlation analysis shows that radio is lagging gamma-ray radiation by up to 8 months in the observer’s frame, while in the source frame, the typical delay is about 2–3 months. If the jet radio emission, excluding the opaque core, is considered, signiﬁcant correlation is found at greater time lags. We supplement these results with VLBI kinematics and core shift data to conclude that the dominant high-energy production zone is typically located at a distance of several parsecs from the central nucleus. We also found that quasars have on average more signiﬁcant correlation peak, more distant gamma-ray emission region from the central engine and shorter variability time-scale compared to those of BL Lacertae objects.
Costs and Benefits of Wasserstein Fair Regression

Real-world applications of machine learning tools in high-stakes domains are often regulated to be fair, in the sense that the predicted target should satisfy some quantitative notion of parity with respect to a protected attribute. However, the exact tradeoff between fairness and accuracy with a real-valued target is not clear. In this paper, we characterize the inherent tradeoff between statistical parity and accuracy in the regression setting by providing a lower bound on the error of any fair regressor. Our lower bound is sharp, algorithm-independent, and admits a simple interpretation: when the moments of the target differ between groups, any fair algorithm has to make a large error on at least one of the groups. We further extend this result to give a lower bound on the joint error of any (approximately) fair algorithm, using the Wasserstein distance to measure the quality of the approximation. On the upside, we establish the first connection between individual fairness, accuracy parity, and the Wasserstein distance by showing that if a regressor is individually fair, it also approximately verifies the accuracy parity, where the gap is given by the Wasserstein distance between the two groups. Inspired by our theoretical results, we develop a practical algorithm for fair regression through the lens of representation learning, and conduct experiments on a real-world dataset to corroborate our findings.
What Context Features Can Transformer Language Models Use?

Transformer-based language models benefit from conditioning on contexts of hundreds to thousands of previous tokens. What aspects of these contexts contribute to accurate model prediction? We describe a series of experiments that measure usable information by selectively ablating lexical and structural information in transformer language models trained on English Wikipedia. In both mid- and long-range contexts, we find that several extremely destructive context manipulations—including shuffling word order within sentences and deleting all words other than nouns—remove less than 15% of the usable information. Our results suggest that long contexts, but not their detailed syntactic and propositional content, are important for the low perplexity of current transformer language models.
Short proofs for long induced paths

We present a modification of the Depth first search algorithm, suited for finding long induced paths. We use it to give simple proofs of the following results. We show that the induced size-Ramsey number of paths satisfies $\hat{R}_{\mathrm{ind}}(P_n)\leq 5 \cdot 10^7n$ , thus giving an explicit constant in the linear bound, improving the previous bound with a large constant from a regularity lemma argument by Haxell, Kohayakawa and Łuczak. We also provide a bound for the k-colour version, showing that $\hat{R}_{\mathrm{ind}}^k(P_n)=O(k^3\log^4k)n$ . Finally, we present a new short proof of the fact that the binomial random graph in the supercritical regime, $G(n,\frac{1+\varepsilon}{n})$ , contains typically an induced path of length $\Theta(\varepsilon^2) n$ .
Random feature neural networks learn Black-Scholes type PDEs without curse of dimensionality

This article investigates the use of random feature neural networks for learning Kolmogorov partial (integro-)differential equations associated to Black-Scholes and more general exponential L\'evy models. Random feature neural networks are single-hidden-layer feedforward neural networks in which only the output weights are trainable. This makes training particularly simple, but (a priori) reduces expressivity. Interestingly, this is not the case for Black-Scholes type PDEs, as we show here. We derive bounds for the prediction error of random neural networks for learning sufficiently non-degenerate Black-Scholes type models. A full error analysis is provided and it is shown that the derived bounds do not suffer from the curse of dimensionality. We also investigate an application of these results to basket options and validate the bounds numerically. These results prove that neural networks are able to \textit{learn} solutions to Black-Scholes type PDEs without the curse of dimensionality. In addition, this provides an example of a relevant learning problem in which random feature neural networks are provably efficient.
Shape of atomic nuclei in heavy ion collisions

In the hydrodynamic model description of heavy ion collisions, the final-state anisotropic flow vn are linearly related to the strength of the multi-pole shape of the nucleon density distribution in the transverse plane εn, vn ∝ εn for n = 1,2,3,4. The εn are sensitive to the shape of the colliding ions, characterized by the quadrupole β2, octupole β3 and hexadecapole β4 deformations. This sensitivity is investigated analytically and also in a Monte Carlo Glauber model, and we observe a robust linear relation, ⟨εn⟩ = an + bnβ n, for events in fixed centrality. The ⟨ε1⟩ has a contribution from β3 and β4, and ⟨ε3⟩ from β4, but there are little cross contributions between β2 and ε3 and between β3 and ε2. Additionally, ⟨εn⟩ are insensitive to non-axial shape parameters such as the triaxiality. These are good news because we can use measurements of v2, v3 and v4 to constrain simultaneously the β2, β3, and β4 values. This is best done by comparing two colliding ions with similar mass numbers and therefore nearly identical an, to obtain simple equation that relates the βn of the two species. This opens up the possibility to map the shape of the atomic nuclei at a timescale (< 10−24s) much shorter than nuclear structure physics (< 10−21s), which ultimately may provide information complementary to those obtained in the nuclear structure experiments.
Self-supervised GANs with Label Augmentation

Recently, transformation-based self-supervised learning has been applied to generative adversarial networks (GANs) to mitigate catastrophic forgetting in the discriminator by introducing a stationary learning environment. However, the separate self-supervised tasks in existing self-supervised GANs cause a goal inconsistent with generative modeling due to the fact that their self-supervised classifiers are agnostic to the generator distribution. To address this problem, we propose a novel self-supervised GAN that unifies the GAN task with the self-supervised task by augmenting the GAN labels (real or fake) via self-supervision of data transformation. Specifically, the original discriminator and self-supervised classifier are unified into a label-augmented discriminator that predicts the augmented labels to be aware of both the generator distribution and the data distribution under every transformation, and then provide the discrepancy between them to optimize the generator. Theoretically, we prove that the optimal generator could converge to replicate the real data distribution. Empirically, we show that the proposed method significantly outperforms previous self-supervised and data augmentation GANs on both generative modeling and representation learning across benchmark datasets.
Optimal Accounting of Differential Privacy via Characteristic Function

Characterizing the privacy degradation over compositions, i.e., privacy accounting, is a fundamental topic in differential privacy (DP) with many applications to differentially private machine learning and federated learning. We propose a unification of recent advances (Renyi DP, privacy profiles, $f$-DP and the PLD formalism) via the \emph{characteristic function} ($\phi$-function) of a certain \emph{dominating} privacy loss random variable. We show that our approach allows \emph{natural} adaptive composition like Renyi DP, provides \emph{exactly tight} privacy accounting like PLD, and can be (often \emph{losslessly}) converted to privacy profile and $f$-DP, thus providing $(\epsilon,\delta)$-DP guarantees and interpretable tradeoff functions. Algorithmically, we propose an \emph{analytical Fourier accountant} that represents the \emph{complex} logarithm of $\phi$-functions symbolically and uses Gaussian quadrature for numerical computation. On several popular DP mechanisms and their subsampled counterparts, we demonstrate the flexibility and tightness of our approach in theory and experiments.