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“Write your model almost as you would on paper and Michel will take care of the rest!” Michel Juillard’s contribution to macroeconomics in historical perspective
No full textInternational audienceIn this article, we document Michel Juillard’s contribution to macroeconomics. Best known as the creator of the computer package Dynare, Juillard’s impact extends far beyond software development. We trace his training and career from his first encounter with computers in high school through his ongoing work on Dynare. His contribution to macroeconomics, we argue, is threefold: intellectual (devising algorithms and addressing specific computational problems for a class of models), technical (writing code and developing a computer package), and institutional (establishing and maintaining the governance structures that ensure Dynare’s sustainability as a digital commons). Juillard’s career highlights broader questions about adapting Ostrom’s framework to digital commons development, the principles that govern software development, and the place computational economics should occupy in the history of macroeconomics
Unraveling energy flow mechanisms in semiconductors by ultrafast spectroscopy: Germanium as a case study
No full textInternational audienceSemiconductor materials are the foundation of modern electronics, and their functionality is dictated by the interactions between fundamental excitations occurring on (sub-)picosecond timescales. Using time-resolved Raman spectroscopy and transient reflectivity measurements, we shed light on the ultrafast dynamics in germanium. We observe an increase in the optical phonon temperature in the first few picoseconds, driven by the energy transfer from photoexcited holes, and the subsequent decay into acoustic phonons through anharmonic coupling. Moreover, the temperature, Raman frequency, and linewidth of this phonon mode show strikingly different decay dynamics. This difference was ascribed to the local thermal strain generated by the ultrafast excitation. We also observe Brillouin oscillations, given by a strain pulse traveling through germanium, whose damping is correlated to the optical phonon mode. These findings, supported by density functional theory and molecular dynamics simulations, provide a better understanding of the energy dissipation mechanisms in semiconductors
Search for low-mass hidden-valley dark showers with non-prompt muon pairs in proton-proton collisions at = 13 TeV
No full textInternational audienceA search for signatures of a dark analog to quantum chromodynamics is performed. The analysis targets long-lived dark mesons that decay into standard-model particles, with a high branching fraction of the dark mesons decaying into muons. The dark mesons are formed by the hadronisation of dark partons, which are produced by a decay of the Higgs boson. The search is performed using a data set corresponding to an integrated luminosity of 41.6 fb, which was collected in proton-proton collisions at = 13 TeV by the CMS experiment at the CERN LHC in 2018 using non-prompt muon triggers. The search is based on resonant muon pair signatures. Machine-learning techniques are employed in the analysis, utilising boosted decision trees to discriminate between signal and background. No significant excess is observed above the standard model expectation. Upper limits on the branching fraction of the Higgs boson decaying to dark partons are determined to be as low as 10 at 95% confidence level, surpassing and extending the existing limits on models with dark mesons for mean proper decay lengths of less than 500 mm and for masses down to 0.3 GeV. First limits are set for extended dark-shower models with two dark flavours that contain dark photons, probing their masses down to 0.33 GeV
Turning a solar cell into a catalyst: (Ag,Cu)(In,Ga)Se<sub>2</sub> p–n junction enabling ambient dry reforming of methane
No full textInternational audiencePhotocatalysis driven by solar energy offers a sustainable alternative to thermocatalysis for methane valorization, however large-scale deployment remains limited by catalyst efficiency and scalability. Meanwhile, photovoltaic technologies, though highly developed for electricity generation, still face challenges in costly energy storage and underutilized potential in direct solar-to-chemical energy conversion. In this context, CIGS thin-film solar cells emerge as promising candidates for photocatalytic applications due to their strong light absorption, tunable electronic properties, and industrial scalability. In the present work, we use a thin-film CIGS solar cell plates, re-designed as a monolithic photocatalyst, to drive DRM under ambient conditions. A 2 µm ptype (Ag,Cu)(In,Ga)Se2 (ACIGS) absorber deposited on a soda-lime glass/Mo substrate is overcoated with an ntype CdS layer, forming a p-n junction that couples strong light absorption with built-in charge separation. Under irradiation, ACIGS/CdS plates produce > 2 mmol g cat -1 syngas with ≈ 85 % CO selectivity at ambient conditions, without any external electric power or thermal input. Mechanistic evidence indicates deep CH4 dissociation to surface carbon and hydrogen, with subsequent CO2 reduction by surface carbon to CO. The catalytic plates are air-regenerable under light and exhibit notable stability. Turning a solar-cell design into the catalytic junction tackles efficiency and manufacturing hurdles for CH4/CO2 conversion. Because CIGS and CdS processes already exist at industrial scale, this approach provides a practical route to deployable solar chemical hardware; further gains are expected from junction optimization and selective co-catalysts
Water Isotope Model Intercomparison Project (WisoMIP): Present‐Day Climate
No full textInternational audienceAbstract We present the first results of the Water Isotope Model Intercomparison Project (WisoMIP), with Phase 1 focused on modern simulations (1979–2023) from a suite of isotope‐enabled atmospheric general circulation models nudged to ERA5 reanalyzes. Water sources, mixing, and rainout history influence the isotopic composition of vapor and precipitation, making these simulations powerful tools for tracing the global water cycle. By prescribing identical winds, sea surface temperatures, and sea ice conditions, we isolate differences in water isotope behavior across models, controlling for variability in atmospheric dynamics and mean climate. Our analyses show that the ensemble mean best matches observations, as individual model errors cancel out to yield a more accurate representation of Earth's isotope distributions. We also evaluate trends and responses to major climate modes during the recent warming period, highlighting regional and temporal sensitivities in the isotope signals. These diagnostics extend beyond traditional model evaluation metrics (e.g., temperature, precipitation) to reveal uncertainties in physical processes and guide improvements in model parameterizations. The resulting modern nudged ensemble data set serves as a benchmark for isotope‐enabled model development, satellite product comparison, and understanding of water cycle changes in a warming climate. Given its standardized design and broad participation, WisoMIP provides a valuable “isotope reanalysis” product for applications ranging from paleoclimate reconstruction to model tuning. Our work demonstrates the importance of coordinated isotope model evaluation in advancing the use of water isotopes as a diagnostic tool in climate science
Online Markov Decision Processes with Terminal Law Constraints
No full textTraditional reinforcement learning usually assumes either episodic interactions with resets or continuous operation to minimize average or cumulative loss. While episodic settings have many theoretical results, resets are often unrealistic in practice. The infinite-horizon setting avoids this issue but lacks non-asymptotic guarantees in online scenarios with unknown dynamics. In this work, we move towards closing this gap by introducing a reset-free framework called the periodic framework, where the goal is to find periodic policies: policies that not only minimize cumulative loss but also return the agents to their initial state distribution after a fixed number of steps. We formalize the problem of finding optimal periodic policies and identify sufficient conditions under which it is well-defined for tabular Markov decision processes. To evaluate algorithms in this framework, we introduce the periodic regret, a measure that balances cumulative loss with the terminal law constraint. We then propose the first algorithms for computing periodic policies in two multi-agent settings and show they achieve sublinear periodic regret of order Õ(T 3/4 ). This provides the first non-asymptotic guarantees for reset-free learning in the setting of M homogeneous agents, for M > 1
Hematopoiesis as a continuum: from stochastic compartmental model to hydrodynamic limit
No full textInternational audienceWe consider a multiscale stochastic compartmental model with three types of cells (stem cells, immature cells and mature cells) which combines cell proliferation and cell differentiation. We derive a hydrodynamic limit when the number of immature compartments goes to infinity obtaining a partial differential equations system with boundary conditions, modelling hematopoiesis as a continuum. We assume that proliferation and differentiation are regulated and let the corresponding rates depend on the number of mature cells. This leads us to model the dynamics of the population by a Markov process in continuous time and discrete space, which does not satisfy the branching property. We prove the convergence in law of the stem and mature cells population size processes and of the empirical measures of the immature cells dynamics, conveniently rescaled, to the unique triplet involving coupled functions and a measure, which are solutions of a deterministic measure valued equation with boundary dynamics. The cell differentiation induces a transport term in space and the main difficulty comes from the boundary effects coming from stem and mature cells. We also prove that the limiting measure admits at each time a density with respect to Lebesgue measure and can be characterized as solution of a partial differential equation
A Bayesian System with Neuron Clocks for Biosignal Classification
No full textA compact Bayesian system for end-to-end inference that uses time-encoded probabilistic computing is presented. The architecture integrates (1) a neuron clocking scheme for timing sequential phases of the system, (2) a feature-extraction module based on rate-coding information with neuron circuits, (3) a circuit able to extract likelihoods from a probability distribution and then calculate Bayes' rule and (4) a race-to-threshold winner-take-all circuit that can drive downstream actuation circuits. All circuits were implemented and simulated at the transistor level in TSMC 130 nm CMOS using a 1.0 V supply. The system was benchmarked using a two-category sleep-stage classification task, and achieved an accuracy of 80.8%, which closely matches the 81.5% result of an equivalent inference in software. The complete architecture uses less than 150 transistors, making it suitable for ultra-lowpower edge biosignal processing
Designing RNA structures while maximizing stacking interactions
No full textInverse folding is a classic problem in RNA bioinformatics, crucial for the design of functional synthetic RNAs, and consists in finding a sequence that uniquely folds into a target secondary structure with respect to energy minimization. In a simple base pairs maximization model (maxBPs), Bonnet et al. showed that a mildly constrained version of inverse folding is NP-hard. By contrast, a linear-time exact algorithm was proposed for maxBPs inverse folding, when restricted to input structures with helices of size 3 + . However, the maxBPs model artificially induces drastic limitations on the set of designable structures, forbidding the design of well-known RNA families. In this work, we consider a more realistic energy model accounting for base pairs stacks, a major contributor to RNA stability, and study inverse folding in a stacks maximization model (maxStacks). We propose a linear-time algorithm for maxStacks inverse folding, restricted to helices consisting of at least ⌈log 3.56 (∆) + 6.2⌉ base pairs, where ∆ is the largest degree of a loop in the target structure. Our approach relies on the introduction of the locked property, a concept which in particular induces a unique matching of intervals involved in helices, and represents a sufficient condition for a sequence to represent a solution of maxStacks inverse folding. In particular, our algorithm implies the designability of structures featuring loops of arbitrary degree ∆ in the maxStacks model, something that was intrinsically impossible in the maxBPs model beyond ∆ > 4. Moreover, the locked property can be generalized to target structures and competing structure which include crossing base pairs (aka called general pseudoknots). In this challenging setting, we obtain a linear-time algorithm for maxStacks inverse folding, restricted to structures with helix sizes at least ⌈log 3.56 (m) + 6.2⌉, where m is the number of helices. This result is surprising since validating designs requires solving the RNA folding with general pseudoknots, a problem known to be NP-hard in the maxStacks model
Synchronous vs Asynchronous Active Learning
No full textÉcole thématiqueThese slides are a short class summarizing the main result in synchronous and asynchronous optimization. The material covers batch acquisition criteria, their theoretical step-ahead pendants, and the asynchronous versions