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    22122 research outputs found

    Jigmé Lingpa ('Jigs med gling pa, 1730–1798)

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    Toward Numerically Exact Computation of Conductivity in the Thermodynamic Limit of Interacting Lattice Models

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    5 pages, 3 figures + supplemental material, 13 pages, 16 figuresInternational audienc

    Hidden entanglement in twin beams generated through optical parametric amplification in hot alkali atoms

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    International audienceProper characterization of quantum correlations in a multimode optical state is critical for applications in quantum information science; however, the most common entanglement measurements can lead to an incomplete state reconstruction. This is the case for the ubiquitous spectral measurement of field quadratures for which a full characterization of the quantum correlations between optical beams is not possible. We demonstrate this effect in twin beams generated through parametric amplification by four-wave mixing in hot rubidium vapor, showing the role of a frequency dependent gain response. We implement a resonator-based measurement that reveals entanglement between beams that is otherwise hidden by usual spectral measurements. Additionally, this system shows how the phase shifts between the carrier and the sidebands on the involved fields affect the observation of entanglement for different entanglement witnesses, demonstrating the relevance of making a complete state tomography

    A class of short-term models for the oil industry addressing speculative storage

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    This is a work in progress. The aim is to propose a plausible mechanism for the short term dynamics of the oil market based on the interaction of economic agents. This is a theoretical research which by no means aim at describing all the aspects of the oil market. In particular, we use the tools and terminology of game theory, but we do not claim that this game actually exists in the real world. In parallel, we are currently studying and calibrating a long term model for the oil industry, which addresses the interactions of a monopolists with a competitive fringe of small producers. It is the object of another paper that will be available soon. The present premiminary version does not contain all the economic arguments and all the connections with our long term model. It mostly addresses the description of the model, the equations and numerical simulations focused on the oil industry short term dynamics. A more complete version will be available soon

    Constantinople occupée, 1918-1923: Une histoire culturelle

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    International audienc

    Tracking solid electrolyte interphase dynamics using operando fibre-optic infra-red spectroscopy and multivariate curve regression

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    International audienceAs batteries drive the transition to electrified transportation and energy systems, ensuring their quality, reliability, lifetime, and safety is crucial. While the solid electrolyte interphase (SEI) is known to govern these performance characteristics, its dynamic nature makes understanding its nucleation, growth, and composition an ambitious, yet elusive aspiration. This work employs chalcogenide fibres embedded in negative electrode materials for operando Infra-red Fibre-optic Evanescent Wave Spectroscopy (IR-FEWS), combined with Multivariate Curve Resolution by Alternating Least Squares (MCR-ALS) algorithms for spectra analysis. By establishing molecular fingerprints that can be used to identify reaction products, IR-FEWS combined with MCR-ALS enables improved understanding of SEI evolution during cell formation with notable differences stemming from electrolyte or anode material. For example, despite operating at an elevated potential, lithium titanate's SEI has intrinsic instability, evidenced by continued carbonate formation. This approach leads the hunt for the SEI down a new path, giving empirical formulations theoretical roots

    Viral and immune dynamics of genital human papillomavirus infections in young women with high temporal resolution

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    International audienceHuman papillomavirus (HPV) infections drive one in 20 new cancer cases, exerting a particularly high burden on women. Most anogenital HPV infections are cleared in less than two years, but the underlying mechanisms that favour persistence in around 10% of women remain largely unknown. Notwithstanding, it is precisely this information that is crucial for improving treatment, screening, and vaccination strategies. To understand viral and immune dynamics in non-persisting HPV infections, we set up an observational longitudinal cohort study with frequent on-site visits for biological sample collection. We enrolled 189 women aged from 18 to 25 and living in the area of Montpellier (France) between 2016 and 2020. We performed 974 on-site visits for a total of 1,619 months of follow-up. We collected data on virus load, local immune cell populations, local concentrations of cytokines, and circulating antibody titres. Using hierarchical Bayesian statistical modelling to simultaneously analyse the data from 164 HPV infections from 76 participants, we show that in two months after infection, HPV viral load in non-persisting infections reaches a plateau that lasts on average for 13 to 20 months (95% credibility interval) and is then followed by a rapid clearance phase. This first description of the dynamics of HPV infections comes with the identification of immune correlates associated with infection clearance, especially gamma-delta T cells and CXCL10 concentration. A limitation of this study on HPV kinetics is that many infection follow-ups are censored. Furthermore, some immune cell populations are difficult to label because cervical immunity is less well characterised than systemic immunity. These results open new perspectives for understanding the frontier between acute and chronic infections, and for controlling HPV-associated diseases, as well as for research on human cancers of infectious origin. Trial Registration: This trial was registered is registered at ClinicalTrials.gov under the ID NCT02946346 . This study has been approved by the Comité de Protection des Personnes (CPP) Sud Méditerranée I (reference number 2016-A00712-49); by the Comité Consultatif sur le Traitement de l’Information en matière de Recherche dans le domaine de la Santé (reference number 16.504); by the Commission Nationale Informatique et Libertés (reference number MMS/ABD/ AR1612278, decision number DR-2016–488), by the Agence Nationale de Sécurité du Médicament et des Produits de Santé (reference 20160072000007)

    Learning interactions between Rydberg atoms

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    19 pages, 11 figuresInternational audienceQuantum simulators have the potential to solve quantum many-body problems that are beyond the reach of classical computers, especially when they feature long-range entanglement. To fulfill their prospects, quantum simulators must be fully controllable, allowing for precise tuning of the microscopic physical parameters that define their implementation. We consider Rydberg-atom arrays, a promising platform for quantum simulations. Experimental control of such arrays is limited by the imprecision on the optical tweezers positions when assembling the array, hence introducing uncertainties in the simulated Hamiltonian. In this work, we introduce a scalable approach to Hamiltonian learning using graph neural networks (GNNs). We employ the Density Matrix Renormalization Group (DMRG) to generate ground-state snapshots of the transverse field Ising model realized by the array, for many realizations of the Hamiltonian parameters. Correlation functions reconstructed from these snapshots serve as input data to carry out the training. We demonstrate that our GNN model has a remarkable capacity to extrapolate beyond its training domain, both regarding the size and the shape of the system, yielding an accurate determination of the Hamiltonian parameters with a minimal set of measurements. We prove a theorem establishing a bijective correspondence between the correlation functions and the interaction parameters in the Hamiltonian, which provides a theoretical foundation to our learning algorithm. Our work could open the road to feedback control of the positions of the optical tweezers, hence providing a decisive improvement of analog quantum simulators

    Avoiding Signature Avoidance in ML Modules with Zippers

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