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Platform Information Provision: Evidence from an Online Auction Platform
No full textDigital platforms have reduced search costs, fostering niche product markets. However, these markets often suffer from limited and asymmetric information due to a lack of consumer feedback, risking market failure. This paper examines Catawiki's solution, where over 240 experts provide value estimates for rare collectibles being auctioned on a digital platform. Using data from 57,000 listings, we analyze the impact of these estimates on final prices and seller behavior. By leveraging both minimum and maximum expert estimates, we isolate the effect of increasing the maximum estimate while holding the minimum estimate fixed. Our findings indicate that higher expert estimates increase final bidden prices, suggesting buyer trust. Sellers also adjust their behavior by setting fewer reserve prices for items with high estimates, leading to more bids. Despite potential conflicts of interest stemming from the platform's dual role as matchmaker and advisor, our results show that expert estimates are influential even when potentially overinflated. This study underscores the critical role of platform-provided information in enhancing market efficiency
Reduced carbon outflow from a Floridian mangrove estuary up to two years after a hurricane
No full textInternational audienceMangrove ecosystems are our most carbon rich forests. They play a vital role in regulating carbon fluxes to the ocean (outwelling). These ecosystems are increasingly threatened by degradation. Here we present a 5-year long timeseries from 2014 to 2019 of dissolved organic and inorganic carbon outwelling from the Everglades National Park (Florida, US). The data includes a category 3-4 hurricane in 2017. Our results reveal a substantial and sustained decrease in both organic and inorganic carbon outwelling. Both remain low for up to two years following the hurricane. The mangrove estuarine contribution decreases compared to that of the marsh upstream. The proposed mechanisms are increased outwelling during the hurricane and extreme tree mortality limiting root respiration. With more intense hurricanes in the future, carbon outwelling risks being permanently lowered, which could alter the buffering capacity of coastal ocean acidification depending on the ratio of carbon dioxide within dissolved inorganic carbon
ClimarisQ: What can we learn from playing a game for climate education?
No full textInternational audienceClimarisQ is both a web and mobile game developed by the Institut Pierre-Simon Laplace to support climate change communication through interactive decision-making. This paper presents an exploratory evaluation of the game based on a post-play questionnaire completed by 77 users. Respondents rated ClimarisQ positively in terms of usability and scientific credibility. Self-reported outcomes indicate that the game supported reflection on the complexity, trade-offs, and uncertainty of climate-related decision-making, rather than the acquisition of factual knowledge, particularly among users with prior expertise. The respondent group was predominantly composed of educated and climate-aware adults, which limits generalization to other audiences. Beyond the questionnaire, the game has been tested in dozens of facilitated sessions with thousands of non-specialist participants, with consistently positive feedback. These results suggest that ClimarisQ can function as a complementary tool for climate education and outreach, especially when used in facilitated settings that encourage discussion and interpretation
Estimating the True Distribution of Data Collected with Randomized Response
No full textInternational audienceRandomized Response (RR) is a protocol designed to collect and analyze categorical data with local differential privacy guarantees. It has been used as a building block of mechanisms deployed by Big Tech companies to collect app or web users' data. Each user reports an automatic random alteration of their true value to the analytics server, which then estimates the histogram of the true unseen values of all users using a debiasing rule to compensate for the added randomness. A known issue is that the standard debiasing rule can yield a vector with negative values (which can not be interpreted as a histogram), and there is no consensus on the best fix. An elegant but slow solution is the Iterative Bayesian Update algorithm (IBU), which converges to the Maximum Likelihood Estimate (MLE) as the number of iterations goes to infinity. This paper bypasses IBU by providing a simple formula for the exact MLE of RR and compares it with other estimation methods experimentally to help practitioners decide which one to use
A new surrogate microstructure generator for porous materials with applications to the buffer layer of TRISO nuclear fuel particles
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Comparative physicochemical study of dielectric barrier discharge and post-discharge plasmas to treat non-small cell lung carcinoma in murine models
No full textInternational audienceWhile cold atmospheric plasmas (CAPs) are increasingly explored for cancer therapy, it remains unclear how distinct device configurations translate into differences in tissue coupling, safety, and therapeutic efficacy. To address this gap, a comparative evaluation of the two following CAP sources has been conducted: the ORJET (atmospheric pressure plasma jet in outer ring electrode configuration) and the PoDBD (post-discharge delivered by a dielectric barrier device with a grounded-mesh electrode). Electrical behavior is quantified on an equivalent electrical human body model, while optical emission spectroscopy and surface-oxidation assays are achieved on transdermal membranes and polyethylene substrates to characterize the nature and diffusion of plasma-generated reactive species. Thermal safety is examined in mice through real-time temperature monitoring and histological analysis while antitumor efficacy is determined in a syngeneic model of non-small cell lung cancer (NSCLC) treated five times. The two devices display fundamentally different modes of tissue coupling: ORJET delivers localized interfacial electric field while PoDBD exposes tissue solely to reactive oxygen and nitrogen species-rich post-discharge. Despite these differences, both generate similar reactive-species signatures, preserve tissue integrity when operated within safe thermal limits, and significantly slow tumor progression compared with controls, with no difference between devices. These findings indicate that therapeutic activity arises predominantly from reactive-species chemistry rather than electrical coupling, supporting the applicability of diverse CAP technologies for oncological treatment
Characterization and forecast of global influenza subtype dynamics
No full textInternational audienceAbstract The subtype composition of seasonal influenza waves varies in space and time. Influenza subtypes A/H1N1, A/H3N2 and B tend to have different impacts on population groups; therefore, understanding the drivers of their cocirculation and anticipating their composition is important for epidemic preparedness. FluNet provides data on influenza specimens by subtype for more than 150 countries. However, owing to surveillance variations across countries, global analyses usually focus on subtype compositions, a kind of data difficult to treat with advanced statistical methods. We used compositional data analysis to circumvent the problem and study trajectories of annual subtype compositions of countries. Here we first examine global trends from 2000 to 2023. We identify a few seasons which stood out for the strong within-country subtype dominance due to either a new virus/clade taking over (2003/2004 season, A/H1N1pdm pandemic) or subtypes’ spatial segregation (coronavirus disease 2019 pandemic). Second, we show that geographical factors, most notably international mobility, concurred in shaping countries’ composition trajectories between 2010 and 2019. Trajectories clustered in two macroregions characterized by subtype alternation versus persistent mixing. Finally, we define five algorithms for forecasting the next year’s composition and found that incorporating the global history of subtype composition in a Bayesian hierarchical vector autoregressive model improved predictions compared with naive methods. The joint analysis of spatiotemporal dynamics of influenza subtypes worldwide reveals a hidden structure in subtype circulation that can be used to improve predictions of the subtype composition of next year’s epidemic according to place
Simple generators of rational function fields
No full textConsider a subfield of the field of rational functions in several indeterminates. We present an algorithm that, given a set of generators of such a subfield, finds a simple generating set. We provide an implementation of the algorithm and show that it improves upon the state of the art both in efficiency and the quality of the results. Furthermore, we demonstrate the utility of simplified generators through several case studies from different application domains, such as structural parameter identifiability. The main algorithmic novelties include performing only partial Gröbner basis computation via sparse interpolation and efficient search for polynomials of a fixed degree in a subfield of the rational function field
Diffusion-based Annealed Boltzmann Generators : benefits, pitfalls and hopes
No full textSampling configurations at thermodynamic equilibrium is a central challenge in statistical physics. Boltzmann Generators (BGs) tackle it by combining a generative model with a Monte Carlo (MC) correction step to obtain asymptotically unbiased samples from an unnormalized target. Most current BGs use classic MC mechanisms such as importance sampling, which both require tractable likelihoods from the backbone model and scale poorly in high-dimensional, multi-modal targets. We study BGs built on annealed Monte Carlo (aMC), which is designed to overcome these limitations by bridging a simple reference to the target through a sequence of intermediate densities. Diffusion models (DMs) are powerful generative models and have already been incorporated into aMC-based recalibration schemes via the diffusion-induced density path, making them appealing backbones for aMC-BGs. We provide an empirical meta-analysis of DM-based aMC-BGs on controlled multi-modal Gaussian mixtures (varying mode separation, number of modes, and dimension), explicitly disentangling inference effects from learning effects by comparing (i) a perfectly learned DM and (ii) a DM trained from data. Even with a perfect DM, standard integrations using only first-order stochastic denoising kernels fail systematically, whereas second-order denoising kernels can substantially improve performance when covariance information is available. We further propose a deterministic aMC integration based on first-order transport maps derived from DMs, which outperforms the stochastic first-order variant at higher computational cost. Finally, in the learned-DM setting, all DM-aMC variants struggle to produce accurate BGs; we trace the main bottleneck to inaccurate DM log-density estimation
Runtime Analysis of the Compact Genetic Algorithm on the LeadingOnes Benchmark
No full textInternational audienceThe compact genetic algorithm (cGA) is one of the simplest estimation-of-distribution algorithms (EDAs). Next to the univariate marginal distribution algorithm (UMDA)another simple EDA-, the cGA has been subject to extensive mathematical runtime analyses, often showcasing a similar or even superior performance to competing approaches. Surprisingly though, up to date and in contrast to the UMDA and many other heuristics, we lack a rigorous runtime analysis of the cGA on the LEADINGONES benchmark-one of the most studied theory benchmarks in the domain of evolutionary computation.We fill this gap in the literature by conducting a formal runtime analysis of the cGA on LEADINGONES. For the cGA's single parameter-called the hypothetical population size-at least polylogarithmically larger than the problem size, we prove that the cGA samples the optimum of LEADINGONES with high probability within a number of function evaluations quasi-linear in the problem size and linear in the hypothetical population size. For the best hypothetical population size, our result matches, up to polylogarithmic factors, the typical quadratic runtime that many randomized search heuristics exhibit on LEADINGONES. Our analysis exhibits some noteworthy differences in the working principles of the two algorithms which were not visible in previous works