Miracle-dz/UniFAR-FTUs huggingface dataset:
Determination of the nature and reactivity of copper sites in Cu–TiO2 catalysts

XPS, temperature programmed reduction (TPR) and FTIR spectroscopy of adsorbed CO and the hydrogenation of crotonaldehyde were used to study the nature of the surface species present for two Cu–TiO2 catalysts (2 and 5% Cu) subjected to reduction in hydrogen at 523, 623 and 723 K. Despite TPR and XPS evidence for complete reduction, IR studies gave clear evidence for the presence of surface Cu(II) ions and two distinct types of Cu(I) sites. The large absorption coefficient for CO on the latter meant that spectra were dominated by bands due to CO on Cu+ even though these ions made up less than an estimated 10% of the total exposed copper sites. Exposed Cu+ remained after re-oxidation treatment, at 623 K in oxygen but this treatment, when followed by 523 K reduction, did not recover Cu(0) sites lost by high temperature reduction, indicating that sintering rather than encapsulation by TiOx was responsible for loss of copper surface area.
To Join the Shortest or the Longest Queue? Inferring Service Quality from Queue Lengths

A classical example that illustrates how observed customer behavior impacts other customers’ decisions is the selection of a restaurant whose quality is unsure: Customers often choose a busier restaurant despite additional waiting time they have to incur, inferring that other customers in that restaurant know something that they do not know. When choosing between different service providers of which the quality is common knowledge, customers trade off differences in expected waiting times for services with differences in service value when deciding from which firm to purchase the service. In the case that the quality of the service provider is not common knowledge, but customers have some private information about the service quality, observed behavior of other customers may complement one’s private information. In an environment where congestion occurs, queues are an indication of the other customers’ choices. Rational customers take thus congestion levels or queue lengths at two competing service providers into account when making a purchasing decision. It may even be the case that customers ignore their private information and make a decision solely based on the observed queue lengths. In the economics literature, this has been referred to as ’herding’. In this paper, we introduce herding behavior of customers, in a two-server queuing model. By means of a simple model, we study how two service providers split the market. Customers arrive according to a Poisson process to the market. Service times are exponentially distributed with the same rate at each service provider. Upon arrival customers receive some private, but, imperfect information about which server provides better quality. In addition, customers observe the number of other customers queuing at each server. Based on both private and queue length information customers decide which queue to join or whether to balk. We find that it may be rational in equilibrium to ignore private information and purchase from the service provider with the longer queue. Our model allows better understanding of how the queue length impacts consumer choice for products or services with quality uncertainty.
Auditing Government AI: How to assess ethical vulnerability in machine learning

Governments are increasingly using algorithmic systems to make important decisions that impact large populations. Governments struggle to thoroughly audit, monitor, regulate, and evaluate these systems, leaving them vulnerable to ethical infractions such as biased outcomes, disparate impacts on different populations, and solutions that are unexplainable and unaccountable to citizens. This paper outlines a tool that quickly assesses the vulnerability of a machine learning project to ethical infractions by translating data scientist’s technical expertise into a high-level risk score. This score allows governments to quickly identify high risk projects across their portfolio. As a result, they can appropriately allocate resources, continuous monitoring, governance and audit cycles to regulate and mitigate ethical concerns.
Singlehood in contemporary Japan: Rating, dating, and waiting for a good match.

BACKGROUND Late age at marriage and rising rates of singlehood increasingly characterize East Asian societies. For Japan, these are major contributors to the very low birth rate. OBJECTIVE We analyze two unique data sets: dating records covering a two-year period from one of Japan's largest marriage agencies and in-depth interviews with 30 highly-educated Japanese singles. The longitudinal nature of the quantitative data allows us to test hypotheses about how single men's and women's preferences for partners' characteristics adjust over time. The qualitative data provides a more fine-grained look at Japanese singles' partner preferences. METHODS We employ fixed-effects regression models to analyze Japanese men's and women's preferences for the relative and absolute education, income, and age of potential marriage partners. RESULTS Both the quantitative and qualitative data suggest that Japanese women continue to highly value men's income-earning capacity. Men, in contrast, value a partner with moderate income-earning potential. Women's and men's preferences for partner's education are somewhat weaker, and women broaden their educational preference over time. CONCLUSION Japanese men's and women's preferences for a potential partner's characteristics are largely consistent with Becker's theory of gender-role specialization. But we also find evidence consistent with Oppenheimer's expectation that men are coming to value women's income-earning capacity more highly than in the past. CONTRIBUTION We use a unique Japanese data set featuring dating records over a two-year period to examine the appropriateness of theories of marital sorting proposed by Becker and Oppenheimer. Our quantitative analysis is complemented by in-depth interviews with Japanese singles.
MOREAU ENVELOPE OF SUPREMUM FUNCTIONS WITH

In this paper, we investigate the Moreau envelope of the supremum of a family 5 of convex, proper, and lower semicontinuous functions. Under mild assumptions, we prove that 6 the Moreau envelope of a supremum is the supremum of Moreau envelopes, which allows us to ap7 proximate possibly nonsmooth supremum functions by smooth functions that are also the suprema 8 of functions. Consequently, we propose and study approximated optimization problems from infi9 nite and stochastic programming for which we obtain zero-duality results and optimality conditions 10 without the verification of constraint qualification conditions. 11
Numerical Analysis of the Influence of Waviness on the Film Thickness of a Circular EHL Contact

Surface roughness and/or surface imperfections are well known to significantly affect the performance of concentrated contacts. Any deviation from the smooth surface will act as a stress raiser for itself (bump) or of its neighborhood (dent), and will therefore reduce the fatigue life of the component it is part of. These imperfections can also act as initiation sites of other types of contact failure such as scuffing, when contact conditions such as load, speed and film thickness become more and more severe. With the help of increasing computer speeds and more efficient numerical techniques, a theoretical analysis of the failure of concentrated contacts becomes possible. The full answer will involve many aspects of the contact, including the generation of heat, thermal response of the lubricant and solids, non-Newtonian as well as surface chemistry effects. This paper concentrates on the way the lubricant film thickness is affected by waviness and tries to identify the locations and the conditions where the film thickness is minimal. The lubrication ofnonsmooth surfaces is a transient two-dimensional problem, which will be treated without any geometrical simplification. More precisely, this paper focuses on the influence of rolling speed and the slide-to-roll ratio on the film thickness separating a smooth surface and one with transverse waviness.
Does Culture Matter? Impact of Individualism and Uncertainty Avoidance on App Reviews

Mobile applications are often used by an international audience and therefore receive a high daily amount of user reviews from various countries. Previous work found evidence that app store reviews contain helpful information for software evolution processes. However, the cultural diversity of the reviews and its consequences on specific user feedback characteristics has only been researched to a limited extent so far. In this paper, we examine the influence of two cultural dimensions, Individualism and Uncertainty Avoidance on user feedback in Apple app store reviews written in different languages. For this purpose, we collected 647,141 reviews from eight countries and written in five languages over a period of six months. We then used manual content analysis and automated processing to examine a sample of 3,120 reviews. The results show that there is a statistically significant influence of Individualism and Uncertainty Avoidance on user feedback characteristics. The results of this study will help researchers and practitioners to reduce algorithm bias caused by less diversified training and test data and to raise awareness of the importance of analyzing diversified user feedback.
The Golden Age of the Mathematical Finance

This paper is devoted to show that the last quarter of the past century can be considered as the golden age of the Mathematical Finance. In this period the collaboration of great economists and the best generation of probabilists, most of them from the Strasbourg’s School led by Paul André Meyer, gave rise to the foundations of this discipline. They established the two fundamentals theorems of arbitrage theory, close formulas for options, the main modelling approaches and created the appropriate framework for the posterior development.
Bootstrapped Representation Learning on Graphs

Current state-of-the-art self-supervised learning methods for graph neural networks (GNNs) are based on contrastive learning. As such, they heavily depend on the construction of augmentations and negative examples. For example, on the standard PPI benchmark, increasing the number of negative pairs improves performance, thereby requiring computation and memory cost quadratic in the number of nodes to achieve peak performance. Inspired by BYOL , a recently introduced method for self-supervised learning that does not require negative pairs, we present Bootstrapped Graph Latents, BGRL , a self-supervised graph representation method that gets rid of this potentially quadratic bottleneck. BGRL outperforms or matches the previous unsupervised state-of-the-art results on several established benchmark datasets. Moreover, it enables the effective usage of graph attentional (GAT) encoders, allowing us to further improve the state of the art. In particular on the PPI dataset, using GAT as an encoder we achieve state-of-the-art 70.49% Micro-F1, us-ing the linear evaluation protocol. On all other datasets under consideration, our model is competitive with the equivalent supervised GNN results, often exceeding them.
The fractal structure of soil aggregates: its measurement and interpretation

SUMMARY The theory of fractal geometry is presented with reference to soil structure. Recent work on relating fractal structure to pore structure in soils is reviewed. It is suggested that the connection made in previous work between the fractal dimension and soil moisture retention curves is based on simplified assumptions that complicate the interpretation of results. A simple method for estimating the fractal dimension, D, of natural aggregates which circumvents some of these assumptions is presented. Preliminary results of aggregates from soils under different management systems show that, for the soils examined, D ranged from 2.75 to 2.93. The use of D to quantify heterogeneity in soil is explored.
Barrow entropy corrections to Friedmann equations

Inspired by the Covid-19 virus structure, Barrow argued that quantum-gravitational effects may introduce intricate, fractal features on the black hole horizon [Phys. Lett. B 808 (2020) 135643]. In this viewpoint, black hole entropy no longer obeys the area law and instead it can be given by S ∼ A , where the exponent δ ranges 0 ≤ δ ≤ 1, and indicates the amount of the quantumgravitational deformation effects. Based on this, and using the deep connection between gravity and thermodynamics, we disclose the effects of the Barrow entropy on the cosmological equations. For this purpose, we start from the first law of thermodynamics, dE = TdS + WdV , on the apparent horizon of the Friedmann-Robertson-Walker (FRW) Universe, and derive the corresponding modified Friedmann equations by assuming the entropy associated with the apparent horizon has the form of Barrow entropy. We also examine the validity of the generalized second law of thermodynamics for the Universe enclosed by the apparent horizon. Finally, we employ the emergence scenario of gravity and extract the modified Friedmann equation in the presence of Barrow entropy which coincide with one obtained from the first law of thermodynamics. When δ = 0, the results of standard cosmology are deduced.
Computing Betweenness Centrality in Link Streams

Betweeness centrality is one of the most important concepts in graph analysis. It was recently extended to link streams, a graph generalization where links arrive over time. However, its computation raises non-trivial issues, due in particular to the fact that time is considered as continuous. We provide here the first algorithms to compute this generalized betweenness centrality, as well as several companion algorithms that have their own interest. They work in polynomial time and space, we illustrate them on typical examples, and we provide an implementation.
Downstream Depolarization in the Sausage Relic: A 1–4 GHz Very Large Array Study

Radio relics are elongated sources related to shocks driven by galaxy cluster merger events. Although these objects are highly polarized at GHz frequencies (≳20%), high-resolution studies of their polarization properties are still lacking. We present the first high-resolution and high-sensitivity polarimetry study of the merging galaxy cluster CIZA J2242.8+5301 in the 1–4 GHz frequency band. We use the QU-fitting approach to model the Stokes I, Q, and U emission, obtaining best-fit intrinsic polarization fraction (p 0), intrinsic polarization angle (χ 0), rotation measure (RM), and wavelength-dependent depolarization (σ RM) maps of the cluster. Our analysis focuses on the northern relic (RN). For the first time in a radio relic, we observe a decreasing polarization fraction in the downstream region. Our findings are possibly explained by geometrical projections and/or by decreasing of the magnetic field anisotropy toward the cluster center. From the amount of depolarization of the only detected background radio galaxy, we estimate a turbulent magnetic field strength of B turb ∼ 5.6 μG in the relic. Finally, we observe RM fluctuations of about 30 rad m−2 around the median value of 140.8 rad m−2 at the relic position.
Disturbing Reinforcement Learning Agents with Corrupted Rewards

Reinforcement Learning (RL) algorithms have led to recent successes in solving complex games, such as Atari or Starcraft, and to a huge impact in real-world applications, such as cybersecurity or autonomous driving. In the side of the drawbacks, recent works have shown how the performance of RL algorithms decreases under the influence of soft changes in the reward function. However, little work has been done about how sensitive these disturbances are depending on the aggressiveness of the attack and the learning exploration strategy. In this paper, we propose to fill this gap in the literature analyzing the effects of different attack strategies based on reward perturbations, and studying the effect in the learner depending on its exploration strategy. In order to explain all the behaviors, we choose a sub-class of MDPs: episodic, stochastic goal-only-rewards MDPs, and in particular, an intelligible grid domain as a benchmark. In this domain, we demonstrate that smoothly crafting adversarial rewards are able to mislead the learner, and that using low exploration probability values, the policy learned is more robust to corrupt rewards. Finally, in the proposed learning scenario, a counterintuitive result arises: attacking at each learning episode is the lowest cost attack strategy.
Proximal and Federated Random Reshuffling

Random Reshuffling (RR), also known as Stochastic Gradient Descent (SGD) without replacement, is a popular and theoretically grounded method for finite-sum minimization. We propose two new algorithms: Proximal and Federated Random Reshuffing (ProxRR and FedRR). The first algorithm, ProxRR, solves composite convex finite-sum minimization problems in which the objective is the sum of a (potentially non-smooth) convex regularizer and an average of $n$ smooth objectives. We obtain the second algorithm, FedRR, as a special case of ProxRR applied to a reformulation of distributed problems with either homogeneous or heterogeneous data. We study the algorithms' convergence properties with constant and decreasing stepsizes, and show that they have considerable advantages over Proximal and Local SGD. In particular, our methods have superior complexities and ProxRR evaluates the proximal operator once per epoch only. When the proximal operator is expensive to compute, this small difference makes ProxRR up to $n$ times faster than algorithms that evaluate the proximal operator in every iteration. We give examples of practical optimization tasks where the proximal operator is difficult to compute and ProxRR has a clear advantage. Finally, we corroborate our results with experiments on real data sets.
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation

We consider training models on private data that are distributed across user devices. To ensure privacy, we add on-device noise and use secure aggregation so that only the noisy sum is revealed to the server. We present a comprehensive end-toend system, which appropriately discretizes the data and adds discrete Gaussian noise before performing secure aggregation. We provide a novel privacy analysis for sums of discrete Gaussians and carefully analyze the effects of data quantization and modular summation arithmetic. Our theoretical guarantees highlight the complex tension between communication, privacy, and accuracy. Our extensive experimental results demonstrate that our solution is essentially able to match the accuracy to central differential privacy with less than 16 bits of precision per value.
Continuous Learning in Neural Machine Translation using Bilingual Dictionaries

While recent advances in deep learning led to significant improvements in machine translation, neural machine translation is often still not able to continuously adapt to the environment. For humans, as well as for machine translation, bilingual dictionaries are a promising knowledge source to continuously integrate new knowledge. However, their exploitation poses several challenges: The system needs to be able to perform one-shot learning as well as model the morphology of source and target language. In this work, we proposed an evaluation framework to assess the ability of neural machine translation to continuously learn new phrases. We integrate one-shot learning methods for neural machine translation with different word representations and show that it is important to address both in order to successfully make use of bilingual dictionaries. By addressing both challenges we are able to improve the ability to translate new, rare words and phrases from 30% to up to 70%. The correct lemma is even generated by more than 90%.
Heat current and entropy production rate in local non-Markovian quantum dynamics of global Markovian evolution

We examine the elements of the balance equation of entropy in open quantum evolutions and their response as we go from a Markovian to a non-Markovian situation. In particular, we look at the heat current and entropy production rate in the non-Markovian reduced evolution, as well as a Markovian limit of the same, experienced by one of two interacting systems immersed in a Markovian bath. The analysis naturally leads us to define a heat current deficit and an entropy production rate deficit, which are differences between the global and local versions of the corresponding quantities. The investigation leads, in certain cases, to a complementarity of the time-integrated heat current deficit and the relative entropy of entanglement between the two systems.
DeepF0: End-To-End Fundamental Frequency Estimation for Music and Speech Signals

We propose a novel pitch estimation technique called DeepF0, which leverages the available annotated data to directly learns from the raw audio in a data-driven manner. f0 estimation is important in various speech processing and music information retrieval applications. Existing deep learning models for pitch estimations have relatively limited learning capabilities due to their shallow receptive field. The proposed model addresses this issue by extending the receptive field of a network by introducing the dilated convolutional blocks into the network. The dilation factor increases the network receptive field exponentially without increasing the parameters of the model exponentially. To make the training process more efficient and faster, DeepF0 is augmented with residual blocks with residual connections. Our empirical evaluation demonstrates that the proposed model outperforms the baselines in terms of raw pitch accuracy and raw chroma accuracy even using 77.4% fewer network parameters. We also show that our model can capture reasonably well pitch estimation even under the various levels of accompaniment noise.