Sentences like "She will go to France or Spain, or perhaps to Germany or France." appear formally redundant, yet become acceptable in contexts such as "Mary will go to a philosophy program in France or Spain, or a mathematics program in Germany or France." While this phenomenon has typically been analyzed using symbolic formal representations, we aim to provide an account grounded in artificial neural mechanisms. We first present new behavioral evidence from humans and large language models demonstrating the robustness of this apparent non-redundancy across contexts. We then show that, in language models, redundancy avoidance arises from two interacting mechanisms: models learn to bind contextually relevant information to repeated lexical items, and Transformer induction heads selectively attend to these context-licensed representations. We argue that this neural explanation sheds light on the mechanisms underlying context-sensitive semantic interpretation, and that it complements existing symbolic analyses.
This paper presents a comprehensive dataset of doctoral theses defended in France between 1985 and 2025, constructed from multiple national academic metadata sources. The dataset is primarily based on data from the French national thesis platform and is enriched using additional authority and bibliographic databases to improve data quality, completeness, and interoperability. The data production pipeline includes the aggregation of heterogeneous sources, the correction of inconsistent identifiers, the enrichment of person and institution records, and the construction of derived variables describing academic careers, jury participation, institutional affiliations, and thesis characteristics. Additional identifiers from major academic repositories and library catalogues are integrated to facilitate linkage with external data sources and future dataset extensions. The resulting dataset provides structured information at the thesis, individual, and institutional levels, enabling both descriptive and relational analyses. This resource is particularly suited for research on doctoral education, academic networks, supervision practices, jury composition, institutional collaboration, and th
Rapid urbanization and growing urban populations worldwide present significant challenges for cities, including increased traffic congestion and air pollution. Effective strategies are needed to manage traffic volumes and reduce emissions. In practice, traditional traffic flow simulations are used to test those strategies. However, high computational intensity usually limits their applicability in investigating a magnitude of different scenarios to evaluate best policies. This paper introduces an innovative approach to assess the effects of traffic policies using Graph Neural Networks (GNN). By incorporating complex transport network structures directly into the neural network, this approach could enable rapid testing of various policies without the delays associated with traditional simulations. We provide a proof of concept that GNNs can learn and predict changes in car volume resulting from capacity reduction policies. We train a GNN model based on a training set generated with a MATSim simulation for Paris, France. We analyze the model's performance across different road types and scenarios, finding that the GNN is generally able to learn the effects on edge-based traffic volum
On October 2nd, 2020, under the combined effect of the winter Alex storm formed off the Brittany coast, and a strong Mediterranean episode, very intensive rainfalls affected in the south eastern France, both Roya and V{é}subie catchments (locally up to 600 mm in 24h). This paroxysmal event with a heavy human toll (10 dead, 8 missing) generated extreme flash floods over a large part of the hydrographic network. The result is an almost generalized fluvial metamorphosis of rivers, from sinuous single-thread channels to braided channels. The characterization of morphological effects of these floods is based on a diachronic aerial picture analysis highlighting a strong increase of the active channel width (up to 900%) reaching -- or even pushing back in few sectors -- front limits of the valley bottom. In the V{é}subie, the 2D morphological effect of the Alex storm was 10 times higher than that of the 100-yrs return period flood of November 1997. Comparison of digital terrain models (DEM) before- and after-flood also allows us to foresee the altitudinal variations (erosion/deposition) that affected beds and their riverine margins. The analysis of the impacts caused by these floods chang
We present Paris 2.0, the first video generation model pre-trained through decentralized computation. Its training recipe builds upon Paris 1.0 (arXiv:2510.03434), the first ever open-weight Decentralized Diffusion Model (DDM), which showed that image generation can be trained without a monolithic GPU cluster. However, temporally coherent video generation had remained an open problem under decentralized training, and Paris 2.0 closes it. In low-resolution text-to-video training, against a monolithic model trained on the same data under a matched total compute budget, Paris 2.0 cuts Frechet Video Distance (FVD) from 561.04 to 279.01, a ~2.0x improvement, and lifts CLIP text-video similarity and aesthetic score.
The SIR evolutionary model predicts too sharp a decrease of the fractions of people infected with COVID-19 in France after the start of the national lockdown, compared to what is observed. I fit the daily hospital data: arrivals in regular and critical care units, releases and deaths, using extended SEIR models. These involve ratios of evolutionary timescales to branching fractions, assumed uniform throughout a country, and the basic reproduction number, $R_0$, before and during the national lockdown, for each region of France. The joint-region Bayesian analysis allows precise evaluations of the time/fraction ratios and pre-hospitalized fractions. The hospital data are well fit by the models, except the arrivals in critical care, which decrease faster than predicted, indicating better treatment over time. Averaged over France, the analysis yields $R_0$= 3.4$\pm$0.1 before the lockdown and 0.65$\pm$0.04 (90% c.l.) during the lockdown, with small regional variations. On 11 May 2020, the Infection Fatality Rate in France was 4 $\pm$1% (90% c.l.), while the Feverish vastly outnumber the Asymptomatic, contrary to the early phases. Without the lockdown nor social distancing, over 2 milli
As a form of "small A", quantile machine learning is used to forecast diurnal and nocturnal $Q(.90)$ air temperatures for Paris, France from late spring through the summer months of 2021. The data are provided by the Paris-Montsouris weather station. Rather than trying to directly anticipate the onset and cessation of reported heat waves, Q(.90) values are estimated. The 90th percentile is chosen so that exceedances represent relatively rare and extreme conditions. Predictors include eight routinely available indicators of weather conditions, lagged by 14 days. Using holdout data, the temperature forecasts are produced two weeks in advance. Adaptive conformal prediction regions are computed that, under exchangeability, provide provably valid finite-sample coverage of forecasting uncertainty. For both diurnal and nocturnal temperatures, forecasting accuracy in the holdout data is promising, and sound measures of uncertainty are coupled with a novel decision-making framework. Benefits for policy and practice follow.
In this paper, gradient boosting is used to forecast the Q(.95) values of air temperature and the Steadman Heat Index. Paris, France during late the spring and summer months is the major focus. Predictors and responses are drawn from the Paris-Montsouris weather station for the years 2018 through 2024. Q(.95) values are used because of interest in summer heat that is statistically rare and extreme. The data are curated as a multiple time series for each year. Predictors include seven routinely collected indicators of weather conditions. They each are lagged by 14 days such that temperature and heat index forecasts are provided two weeks in advance. Forecasting uncertainty is addressed with conformal prediction regions. Forecasting accuracy is promising. Cairo, Egypt is a second location using data from the weather station at the Cairo Internal Airport over the same years and months. Cairo is a more challenging setting for temperature forecasting because its desert climate can create abrupt and erratic temperature changes. Yet, there is some progress forecasting record-setting hot days.
Dwarf spheroidal galaxies (dSphs) are excellent targets for indirect dark matter (DM) searches using gamma-ray telescopes because they are thought to have high DM content and a low astrophysical background. The sensitivity of these searches is improved by combining the observations of dSphs made by different gamma-ray telescopes. We present the results of a combined search by the most sensitive currently operating gamma-ray telescopes, namely: the satellite-borne Fermi-LAT telescope; the ground-based imaging atmospheric Cherenkov telescope arrays H.E.S.S., MAGIC, and VERITAS; and the HAWC water Cherenkov detector. Individual datasets were analyzed using a common statistical approach. Results were subsequently combined via a global joint likelihood analysis. We obtain constraints on the velocity-weighted cross section $\langle σ\mathit{v} \rangle$ for DM self-annihilation as a function of the DM particle mass. This five-instrument combination allows the derivation of up to 2-3 times more constraining upper limits on $\langle σ\mathit{v} \rangle$ than the individual results over a wide mass range spanning from 5 GeV to 100 TeV. Depending on the DM content modeling, the 95% confidence
This contribution presents a poll undertaken at the beginning of 2012, and addressed to every doctor in astronomy who obtained his/her degree in France. Its goal is to motivate the French astronomical community to think and discuss about what should be the training of PhDs, and what should be its objective. Further discussions and reactions can be posted e.g. on http://docastro.blogspot.fr/. A worrying results from the poll is that the majority of the participants would not encourage a young student to start a thesis in astronomy. The main reasons for this fact may be the high pressure on astronomy positions and the little interest a doctorate has for other careers in France. I suggest we either have to modify our formations or reduce the number of thesis starting each year in astronomy.
An account of the careers of the five women who completed a doctorate in mathematics in France before 1960 and became internationally known scientists, followed by a more general description of the place of women on the mathematical scene in France between 1930 and 1960.
In Interwar France, Henri Villat became the true leader of theoretical researches on fluid mechanics. Most of his original work was done before the First World War; it was highly theoretical and its applicability was questioned. After having organized the first post-WWI International Congress of Mathematicians in 1920, Villat became the editor of the famous Journal de mathématiques pure et appliqués and the director of the influential book series "Mémorial des sciences mathématiques." From 1929 on, he held the fluid mechanics chair established by the Air Ministry at the Sorbonne in Paris and was heading the government's critical effort invested in fluid mechanics. However, while both his wake theory and his turbulence theory seemingly had little success outside France or in the aeronautical industry (except in the eyes of his students), applied mathematics was despised by the loud generation of Bourbaki mathematicians coming of age in the mid 1930s. How are we to understand the contrasted assessments one can make of Villat's place in the history of fluid mechanics?
This note examines financial distributions to competing teams at the end of the most famous multiple stage professional (male) bicyclist race, TOUR DE FRANCE. A rank-size law (RSL) is calculated for the team financial gains. The RSL is found to be hyperbolic with a surprisingly simple decay exponent (about equal to -1). Yet, the financial gain distributions unexpectedly do not obey Pareto principle of factor sparsity. Next, several (8) inequality indices are considered : the Entropy, the Hirschman-Herfindahl, Theil, Pietra-Hoover, Gini, Rosenbluth indices, the Coefficient of Variation and the Concentration Index are calculated for outlining diversity measures. The connection between such indices and their concentration aspects meanings are presented as support of the RSL findings. The results emphasize that the sum of skills and team strategies are effectively contributing to the financial gains distributions. From theoretical and practical points of view, the findings suggest that one should investigate other "long multiple stage races" and rewarding rules. Indeed, money prize rules coupling to stage difficulty might influence and maybe enhance (or deteriorate) purely sportive asp
We present Paris, the first publicly released diffusion model pre-trained entirely through decentralized computation. Paris demonstrates that high-quality text-to-image generation can be achieved without centrally coordinated infrastructure. Paris is open for research and commercial use. Paris required implementing our Distributed Diffusion Training framework from scratch. The model consists of 8 expert diffusion models (129M-605M parameters each) trained in complete isolation with no gradient, parameter, or intermediate activation synchronization. Rather than requiring synchronized gradient updates across thousands of GPUs, we partition data into semantically coherent clusters where each expert independently optimizes its subset while collectively approximating the full distribution. A lightweight transformer router dynamically selects appropriate experts at inference, achieving generation quality comparable to centrally coordinated baselines. Eliminating synchronization enables training on heterogeneous hardware without specialized interconnects. Empirical validation confirms that Paris's decentralized training maintains generation quality while removing the dedicated GPU cluster
This research focuses on the study of relationships between public and private equity investors in France. In this regard, we need to apprehend the formal or informal nature of interactions that can sometimes take place within traditional innovation networks (Djellal \& Gallouj, 2018). For this, our article mobilizes a public-private partnerships approach (PPPs) and the resource-based view theory. These perspectives emphasize the complementary role of disciplinary and incentive mechanisms as well as the exchange of specific resources as levers for value creation. Moreover, these orientations crossed with the perspective of a hybrid form of co-investment allow us to build a coherent and explanatory framework of the mixed syndication phenomenon. Our methodology is based on a qualitative approach with an interpretative aim, which includes twenty-seven semi-structured interviews. These data were subjected to a thematic content analysis using Nvivo software. The results suggest that the relationships between public and private Venture capitalists (VCs) of a formal or informal nature, more specifically in a syndication context, at a national or regional level, are representative of a
This paper concerns the emergence of modern mathematical statistics in France after the First World War. Emile Borel's achievements are presented, and especially his creation of two institutions where mathematical statistics was developed: the {\it Statistical Institute of Paris University}, (ISUP) in 1922 and above all the {\it Henri Poincaré Institute} (IHP) in 1928. At the IHP, a new journal {\it Annales de l'Institut Henri Poincaré} was created in 1931. We discuss the first papers in that journal dealing with mathematical statistics.
Urban decarbonization is one of the pillars for strategies to achieve carbon neutrality around the world. However, the current speed of urban decarbonization is insufficient to keep pace with efforts to achieve this goal. Rooftop PVs integrated with electric vehicles (EVs) as battery is a promising technology capable to supply CO2-free, affordable, and dispatchable electricity in urban environments (SolarEV City Concept). Here, we evaluated Paris, France for the decarbonization potentials of rooftop PV + EV in comparison to the surrounding suburban area Ile-de-France and Kyoto, Japan. We assessed various scenarios by calculating the energy sufficiency, self-consumption, self-sufficiency, cost savings, and CO2 emission reduction of the PV + EV system or PV only system. The combination of EVs with PVs by V2H or V2B systems at the city or region level was found to be more effective in Ile-de-France than in Paris suggesting that SolarEV City is more effective for geographically larger area including Paris. If implemented at a significant scale, they can add substantial values to rooftop PV economics and keep a high self-consumption and self-sufficiency, which also allows bypassing the
In patent prosecution, timely and effective responses to Office Actions (OAs) are crucial for securing patents. However, past automation and artificial intelligence research have largely overlooked this aspect. To bridge this gap, our study introduces the Patent Office Action Response Intelligence System (PARIS) and its advanced version, the Large Language Model (LLM) Enhanced PARIS (LE-PARIS). These systems are designed to enhance the efficiency of patent attorneys in handling OA responses through collaboration with AI. The systems' key features include the construction of an OA Topics Database, development of Response Templates, and implementation of Recommender Systems and LLM-based Response Generation. To validate the effectiveness of the systems, we have employed a multi-paradigm analysis using the USPTO Office Action database and longitudinal data based on attorney interactions with our systems over six years. Through five studies, we have examined the constructiveness of OA topics (studies 1 and 2) using topic modeling and our proposed Delphi process, the efficacy of our proposed hybrid LLM-based recommender system tailored for OA responses (study 3), the quality of generate
Here the orientation of the Gothic cathedrals in France is discussed and investigated using the satellite maps. Except a few of them, these buildings have the apse facing the rising sun, according to a practice adopted during the middle age.