Miracle-dz/UniFAR-FTUs huggingface dataset:
Communication, Credit Provision and Loan Repayment: Evidence from a Person-to-Person Lending Experiment

We conduct a laboratory experiment to study whether pre-play communication mitigates opportunistic behavior in a person-to-person lending context. We implement a trust game in which the investment income of the borrower (second-mover) is uncertain and not revealed to the lender (first mover). In this "hidden action" condition lenders cannot distinguish strategic defaults from forced defaults. We compare a treatment in which the borrower can send pre-play text messages to the lender to a treatment without such communication. We find that communication does not have a significant positive effect on credit volumes or repayment rates in this hidden action condition. We compare these findings to the effect of communication in a baseline condition in which borrower income is deterministic and strategic defaults cannot be hidden from lenders. In this baseline condition, communication leads to higher credit volumes and repayment rates implying higher payoffs for both lenders and borrowers. Comparing borrower communication and behavior in the hidden action condition to the baseline condition we find that borrowers are much more likely to renege on promises to repay in the hidden action treatment.
Learning Sparse Masks for Efficient Image Super-Resolution

Current CNN-based super-resolution (SR) methods process all locations equally with computational resources being uniformly assigned in space. However, since highfrequency details mainly lie around edges and textures, less computational resources are required for those flat regions. Therefore, existing CNN-based methods involve much redundant computation in flat regions, which increases their computational cost and limits the applications on mobile devices. To address this limitation, we develop an SR network (SMSR) to learn sparse masks to prune redundant computation conditioned on the input image. Within our SMSR, spatial masks learn to identify "important" locations while channel masks learn to mark redundant channels in those "unimportant" regions. Consequently, redundant computation can be accurately located and skipped while maintaining comparable performance. It is demonstrated that our SMSR achieves state-of-the-art performance with 41%/33%/27% FLOPs being reduced for x2/3/4 SR.
Modelling the Delayed Nonlinear Fiber Response in Coherent Optical Communications

Fiber nonlinearities, that lead to nonlinear signal distortion, are normally considered to be the result of an instantaneous material response with respect to the optical field. However, in addition to an instantaneous part, the nonlinear fiber response includes a delayed contribution, referred to as the Raman response. The imaginary part of its Fourier transform, referred to as the Raman gain spectrum, leads to inter-channel stimulated Raman scattering (ISRS). ISRS is a nonlinear effect that redistributes optical power from high to lower frequencies during propagation. However, as the nonlinear fiber response is causal, the Raman spectrum obeys the Kramers–Kronig relations resulting in the real part of the complex valued Raman spectrum. While the impact of the imaginary part (i.e. ISRS) is well studied, the direct implications of its associated real part on the nonlinear distortions are unexplored. In this work, a theory is proposed to analytically quantify the impact of the real Raman spectrum on the nonlinear interference power (NLI), which models nonlinear distortion as noise in optical transmission. Starting from a generalized Manakov equation, an extension of the ISRS Gaussian Noise (GN) model is derived to include the real part of the Raman spectrum and, thus, to account for the complete nonlinear Raman response. Accurate integral expressions are derived and approximations in closed-form are proposed. Different formulations for the case of single -and dual polarized signals are derived and novel analytical approximations of the real Raman spectrum are proposed. Moreover, it is shown that the impact of the real Raman spectrum can be considered as a multiplicative factor to the instantaneous nonlinear distortions depending on the frequency separation of the interacting frequencies. A simple functional form is derived to assess the multiplicative impact of the real Raman spectrum on the NLI. The proposed theory is validated by numerical simulations over C-and C+L band, using experimentally measured fiber data.
Galerkin approximations for the optimal control of nonlinear delay differential equations

Optimal control problems of nonlinear delay differential equations (DDEs) are considered for which we propose a general Galerkin approximation scheme built from Koornwinder polynomials. Error estimates for the resulting Galerkin-Koornwinder approximations to the optimal control and the value function, are derived for a broad class of cost function-als and nonlinear DDEs. The approach is illustrated on a delayed logistic equation set not far away from its Hopf bifurcation point in the parameter space. In this case, we show that low-dimensional controls for a standard quadratic cost functional can be efficiently computed from Galerkin-Koornwinder approximations to reduce at a nearly optimal cost the oscillation amplitude displayed by the DDE's solution. Optimal controls computed from the Pontryagin's maximum principle (PMP) and the Hamilton-Jacobi-Bellman equation (HJB) associated with the corresponding ODE systems, are shown to provide numerical solutions in good agreement. It is finally argued that the value function computed from the corresponding reduced HJB equation provides a good approximation of that obtained from the full HJB equation.
Better Set Representations For Relational Reasoning

Incorporating relational reasoning into neural networks has greatly expanded their capabilities and scope. One defining trait of relational reasoning is that it operates on a set of entities, as opposed to standard vector representations. Existing end-to-end approaches typically extract entities from inputs by directly interpreting the latent feature representations as a set. We show that these approaches do not respect set permutational invariance and thus have fundamental representational limitations. To resolve this limitation, we propose a simple and general network module called a Set Refiner Network (SRN). We first use synthetic image experiments to demonstrate how our approach effectively decomposes objects without explicit supervision. Then, we insert our module into existing relational reasoning models and show that respecting set invariance leads to substantial gains in prediction performance and robustness on several relational reasoning tasks.
Approximate Gradient Coding With Optimal Decoding

Gradient codes use data replication to mitigate the effect of straggling machines in distributed machine learning. Approximate gradient codes consider codes where the data replication factor is too low to recover the full gradient exactly. Our work is motivated by the challenge of designing approximate gradient codes that simultaneously work well in both the adversarial and random straggler models. We introduce novel approximate gradient codes based on expander graphs. We analyze the decoding error both for random and adversarial stragglers, when optimal decoding coefficients are used. With random stragglers, our codes achieve an error to the gradient that decays exponentially in the replication factor. With adversarial stragglers, the error is smaller than any existing code with similar performance in the random setting. We prove convergence bounds in both settings for coded gradient descent under standard assumptions. With random stragglers, our convergence rate improves upon rates obtained via black-box approaches. With adversarial stragglers, we show that gradient descent converges down to a noise floor that scales linearly with the adversarial error to the gradient. We demonstrate empirically that our codes achieve near-optimal error with random stragglers and converge faster than algorithms that do not use optimal decoding coefficients.
Uncovering the Link between Self-control, Age, and Psychological Maladjustment among Spanish Adolescents and Young Adults

espanolEste estudio explora las tendencias evolutivas en el autocontrol y su relacion con el desajuste psicologico en adolescentes y jovenes adultos. La muestra estuvo compuesta por 1,600 adolescentes y jovenes adultos espanoles de entre 12 y 34 anos (M = 22.28, DT = 6.94) seleccionados aleatoriamente. Se administraron medidas de autocontrol, adiccion a internet y sintomas de ansiedad-depresion. El sexo y la edad fueron utilizados como variables de control. Los resultados indicaron que los adolescentes con edades comprendidas entre los 15 y 19 anos mostraron las puntuaciones mas bajas en autocontrol, mientras que los participantes de mas edad puntuaron mas alto. Puntuaciones bajas en autocontrol estuvieron significativamente asociadas con mayor consumo de sustancias, mas sintomas de ansiedad-depresion y puntuaciones mas elevadas en la escala de adiccion a internet. Ademas, los participantes con puntuaciones mas altas en la escala de adiccion puntuaron mas bajo en autocontrol. Estos resultados evidencian las relaciones entre el autocontrol y algunos problemas emocionales y conductuales y sugieren la importancia de promover el autocontrol para prevenir la adiccion a internet, el consumo de sustancias y los problemas de ansiedad-depresion en adolescentes y jovenes adultos. EnglishThis study explores the relationships of self-control with age and psychological maladjustment in adolescents and young adults. The sample consisted of 1,600 randomly selected Spanish adolescents and young adults, aged 12-34 years (M = 22.28, SD = 6.94). Measures of self-control, Internet addiction, and anxiety-depressive symptoms were administered. Control variables included age and sex. Results indicated that adolescents between 15 and 19 years old showed the lowest self-control scores, while older participants showed greater scores. Low scores in self-control were significantly associated with greater substance consumption, more anxiety-depression symptoms, and higher scores on the Internet addiction scale. Also, participants with high scores on the addiction scale scored lower on self-control. These results evidence associations between self-control and some emotional-behavioral problems, and suggest the importance of promoting self-control to prevent Internet addiction, substance consumption, and anxiety-depression in adolescents and young adults.
Energy Partitioning and Horizontal Dispersion in a Stratified Rotating Lake

Abstract The response of a stratified rotating basin to the release of a linearly tilted interface is derived. This case is compared with a uniformly forced basin in the two limits when the duration of the forcing is much greater than the period of the dominant internal waves and when it is much smaller. Energy partitioning is studied as a function of the Burger number S (relative importance of stratification versus rotation), showing the dominance of a geostrophic component over the wave field for low S. Trajectories are integrated numerically, revealing the Stokes drift of the waves to be always cyclonic. Transport properties are classified in terms of S and the Wedderburn number W (relative importance of the disturbance versus stratification). The geostrophic flow is the main source of advection, but only the waves allow particles to break the barrier to transport between the two geostrophic gyres, ultimately leading to stretching and folding. For low values of W, advection can become chaotic. Conserva.
Magnetic configuration and plasma start-up in the WEST tokamak

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Magnetic configuration and plasma start-up in the WEST tokamak E. Nardon, Holger Heumann, J. Artaud, J. Bucalossi, D Douai, N. Fedorczak, T. Loarer, P. Moreau, R. Nouailletas, C. Reux, et al.
The Influence of Fundamental Factors and Systematic Risk to Stock Prices on Companies Listed in the Indonesian Stock Exchange

The stock price is one indicator of the successful management of the company, if the stock price of a company always increases, the investor or prospective investor considers that the company succeeded in managing their business. The investor or prospective investor confidence is very beneficial for emitter, as more and more people who believe in the issuer's willingness to invest in listed companies stronger Recently, we revealed the influence of fundamental factors and systematic risk simultaneously and partially on stock price at Company Registered in LQ45 Index Period 2011-2015 In this study, the data analysis model used is the test panel data regression (pool) which is a combination of cross section with the timeseries data. The results of this study indicated that simultaneously there were significant influence between the Price Earnings Ratio (PER), Earning per Share (EPS), Net Profit Margin (NPM), Price to Book Value (PBV), and Risk Systematic on stock prices on companies listed in LQ45 Index 2011-2015. Partially, Price Earnings Ratio (PER), Earning per Share (EPS), Net Profit Margin (NPM), Price to Book Value (PBV), and Systematic Risks have significant effect on stock prices.
Markets, Banks, and Shadow Banks

We analyze the effect of bank capital requirements on the structure and risk of a financial system where markets, regulated banks, and shadow banks coexist. Banks face a moral hazard problem in screening entrepreneurs' projects, and they choose whether to be regulated or not. If regulated, a supervisor certifies their capital; if not, they have to rely on more expensive private certification. Under both risk-insensitive and risk-sensitive requirements, safer entrepreneurs borrow from the market and riskier entrepreneurs borrow from banks. But risk-insensitive (sensitive) requirements are especially costly for relatively safe (risky) entrepreneurs, which may shift from regulated to shadow banks.
Relationship of total bacterial and somatic cell counts with milk production and composition – multivariate analysis

This study was realized to evaluate the monthly production, composition and quality of milk (total and defatted dry extract, lactose, fat and protein) and their relation to somatic cell count (SCC) and total bacterial count (TBC) using multivariate statistical analyses. The data are from a dairy farm for the period of two years (from January 2015 to December 2016). The SCC and TBC variables were transformed to somatic cell score (SCS) and log10 (LogTBC). Factor analysis, discriminant analysis and cluster analysis were used. Through factor analysis, it was found two factors that together explained 69.5% of the total data variation. The first factor represented the inverse relationship between lactose versus fat and protein content, while the second factor represented the inverse relationship among monthly milk yield versus SCS and LogTBC. The discriminant analysis identified that lactose and protein contents and SCS were the variables that had the greatest participation in the separation of the groups formed by the cluster analysis. The groups differed mainly by the monthly production of milk, composition and SCS. Finally, there are important multivariate relations between the variables milk production, composition and quality.
The Part-Time Wage Gap across the Wage Distribution

Part-time work has vastly expanded in most OECD labor markets during the last decades. At the same time, full- and part-time wages have grown increasingly apart, leading to a substantial raw part-time wage penalty. Using quantile regression methods, this paper analyses the female part-time wage gap across the wage distribution and over time (1990-2009), while controlling for sample selection into full- and part-time employment. The estimated part-time gap is much lower than the raw gap. Nonetheless, a persistent part-time penalty is found for the lowest wage quartile. The wide divide between the observed and estimated part-time gap is due to strong positive selection into full-time work, which increases over time. On the contrary, sample selection into part-time employment goes from being positive at the beginning of the 1990s to disappearing by the end of the 2000s, even turning negative at the lower end of the distribution. An exploration of potential mechanisms reveals a large prevalence of job mismatch at the lower end of the part-time wage distribution as well as rising differences in the distribution of job tasks between full- and part-time employment.
Monetary analysis : price and financial stability

In a world with self-generated, endogenous risk and time-varying risk premia, price stability and financial stability are inseparable. A monetary analysis based on the distribution of liquidity mismatch across sectors provides valuable information about the build-up of vulnerabilities in tranquil times and helps to identify balance sheet impaired sectors in volatile times. When the monetary transmission mechanism becomes “sectorally impaired”, monetary policy action disproportionally favours issuers of government and large corporation debt over small and medium-sized enterprises (SMEs). Reviving a prudently designed asset-backed securitisation market for SME and consumer loans would alleviate this discrepancy and establish a pan-European intermediation market.
Inequality, Finance and Pro-Poor Investment in Africa

This study complements existing literature by investigating how investment-driven finance affects inequality in Africa. The empirical evidence is based on restricted and unrestricted Two-Stage Least Squares and a pre-crisis periodicity (1980-2002). Inequality is measured with estimated household income inequality whereas financial development is proxied with financial depth (money supply and liquid liabilities), financial efficiency (at banking and financial system levels), financial activity (from banking and financial system perspectives) and financial size. The findings show that with the exception of foreign investment, financial dynamics of depth, efficiency, activity and size enhance equalizing income-distribution through domestic, private and public investment channels. Policy implications are discussed with particular emphasis on improving inclusive development for the post-2015 sustainable development agenda. Notably, in the current transition from Millennium Development Goals (MDGs) to Sustainable Development Goals (SDGs), mobilizing domestic resources for investment purposes may have greater inclusive benefits than overly reliance on foreign sources of capital.
Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes

Determinantal point processes (DPPs) have attracted significant attention from the machine learning community for their ability to model subsets drawn from a large collection of items. Recent work shows that nonsymmetric DPP kernels have significant advantages over symmetric kernels in terms of modeling power and predictive performance. However, the nonsymmetric kernel learning algorithm from prior work has computational complexity that is cubic in the size of the DPP ground set, from which subsets are drawn, making it impractical to use at large scales. In this work, we propose a new decomposition for nonsymmetric DPP kernels that induces linear-time complexity for learning and approximate maximum a posteriori (MAP) inference. We also prove a lower bound on the quality of this MAP approximation. Through evaluation on real-world datasets, we show that our new decomposition not only scales better, but also matches or exceeds the predictive performance of prior work.
Noise or Signal: The Role of Image Backgrounds in Object Recognition

We assess the tendency of state-of-the-art object recognition models to depend on signals from image backgrounds. We create a toolkit for disentangling foreground and background signal on ImageNet images, and find that (a) models can achieve non-trivial accuracy by relying on the background alone, (b) models often misclassify images even in the presence of correctly classified foregrounds--up to 87.5% of the time with adversarially chosen backgrounds, and (c) more accurate models tend to depend on backgrounds less. Our analysis of backgrounds brings us closer to understanding which correlations machine learning models use, and how they determine models' out of distribution performance.
The dependency of Type Ia Supernova parameters on host galaxy morphology for the Pantheon cosmological sample

Type Ia Supernovae (SNe Ia) are widely used to measure distances in the Universe. Despite the recent progress achieved in SN Ia standardisation, the Hubble diagram still shows some remaining intrinsic dispersion. The remaining scatter in supernova luminosity could be due to the environmental effects that are not yet accounted for by the current standardisation methods. In this work, we compare the local and global colour $(U-V)$, the local star formation rate, and the host stellar mass to the host galaxy morphology. The observed trends suggest that the host galaxy morphology is a good parameter to characterize the SN Ia environment. Therefore, we study the influence of host galaxy morphology on light-curve parameters of SNe Ia for the Pantheon cosmological supernova sample. We determine the Hubble morphological type of host galaxies for a sub-sample of 330 SNe Ia. We confirm that the SALT2 stretch parameter $x_1$ depends on the host morphology with the $p$-value $\sim10^{-14}$. The supernovae with lower stretch value are hosted mainly by elliptical and lenticular galaxies. No correlation for the SALT2 colour parameter $c$ is found. We also examine Hubble diagram residuals for supernovae hosted by the "Early-type" and "Late-type" morphological groups of galaxies. The analysis reveals that the mean distance modulus residual in early-type galaxies is smaller than the one in late-type galaxies, which means that early-type galaxies contain brighter supernovae. However, we do not observe any difference in the residual dispersion for these two morphological groups. The obtained results are in the line with other analyses showing environmental dependence of SN Ia light-curve parameters and luminosity. We confirm the importance of including a host galaxy parameter into the standardisation procedure of SNe Ia for further cosmological studies.