22,800 results on '"P. Arnaud"'
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102. Examining the Effects of Socioeconomic Status Indicators on the Association between Growth Mindset and Sense of Belonging to School
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Julien Bakchich, Nele Claes, Arnaud Carré, and Annique Smeding
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In school settings, students' mindset about intelligence (i.e., fixed versus growth mindset) and their sense of belonging to school (SBS) have both been shown to predict academic attainment. However, these constructs have rarely been examined together although both were found to be impacted by students' socioeconomic status (SES). Across the literature, findings are inconsistent concerning this moderating effect of SES. In the present preregistered study, we used data from the French sample of the Programme for International Student Assessment 2018 (PISA; N = 6308) to examine whether growth mindset positively predicted SBS and whether this association was moderated by students' SES. Results showed that growth mindset was positively associated with SBS. On the confirmatory linear regression analyses, we found no moderation effect of any of the SES indicators on the association between growth mindset and SBS. However, pre-registered supplementary multigroup analyses showed descriptively that this association was stronger for high than for low SES students and notably when SES indicators concerned family financial resources. Limitations of this research and perspectives for future studies are discussed, with a focus on why the literature should care about the different meanings and consequences of SES indicators.
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- 2024
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103. Cooperative Learning Reduces the Gender Gap in Perceived Social Competences: A Large-Scale Nationwide Longitudinal Experiment
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Ocyna Rudmann, Anatolia Batruch, Emilio Paolo Visintin, Nicolas Sommet, Pascal Bressoux, Céline Darnon, Marinette Bouet, Marco Bressan, Genavee Brown, Carlos Cepeda, Anthony Cherbonnier, Marie Demolliens, Anne-Laure De Place, Olivier Desrichard, Théo Ducros, Luc Goron, Brivael Hemon, Pascal Huguet, Eric Jamet, Ruben Martinez, Vincent Mazenod, Nathalie Mella, Estelle Michinov, Nicolas Michinov, Nana Ofosu, Pascal Pansu, Laurine Peter, Benoit Petitcollot, Celine Poletti, Isabelle Régner, Mathilde Riant, Anais Robert, Camille Sanrey, Arnaud Stanczak, Farouk Toumani, Simon Vilmin, Eva Vives, and Fabrizio Butera
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Considering the evolving and unpredictable job market, adaptability is an important skill for young adults. Such adaptability implies that schools need to teach key social competences, like communication, collaboration, or problem-solving. In this area, a gender gap has consistently been found, showing that boys display social competences less than girls. A large-scale nationwide multilab longitudinal experiment--the ProFAN project--was conducted in France among more than 10,000 vocational high-school students. Its primary goal was to develop and test an intervention promoting a range of psychological and psychosocial variables in vocational high schools, including social competences. This 2-year long, three-wave field experiment compared the effects of a cooperative learning method--the jigsaw classroom, that entails positive goal and resource interdependence--to two control conditions: one that involves cooperation with resource independence, and the other that remains business-as-usual. This article focuses on the differential development of perceived social competences of adolescent boys and girls over time, comparing the three pedagogical methods. Results of longitudinal multilevel modeling replicate the gender gap in perceived social competences and show that this gap widens with time. However, and most importantly, the analyses revealed that such widening of the gender gap was greater in the two control conditions than in the jigsaw condition, in which the evolution of boys' and girls' perceptions of social competences remained similar over time. Contributions to the understanding of the development and teaching of social competences in education settings are discussed.
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- 2024
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104. The Social Class Test Gap: A Worldwide Investigation of the Role of Academic Anxiety and Income Inequality in Standardized Test Score Disparities
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Nele Claes, Annique Smeding, Arnaud Carré, and Nicolas Sommet
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We conducted three preregistered studies using the Organization for Economic Co-operation and Development Programme for International Student Assessment (PISA) data to provide a worldwide estimation of the standardized test gap between students from lower and higher social classes. We investigated: (a) the degree to which academic anxiety contributes to this gap and (b) the role of country-level income inequality in widening this gap. In Study 1, we used PISA 2003 data (250,000+ students from 41 countries) and demonstrated that anxiety accounts for approximately one-fifth of the performance gap between students with less educated parents and those with more educated parents. Unexpectedly, the social class test gap was weaker in more unequal countries than in more equal countries. In Studies 2a and 2b, we used the PISA 2012 and 2015 data (totaling over a million students from 65 countries and 72 countries, respectively) and differentiated the cultural dimension (parental education, cultural capital) and the economic dimension (economic capital) of social class. Regardless of the dimension, anxiety again accounted for between one-tenth and one-fifth of the performance gap between students from lower and higher social classes. Moreover, (a) the culturally based social class achievement gap was weaker in more unequal than in more equal countries, and (b) the economically based social class achievement gap was larger in more unequal than in more equal countries. Unexpectedly, we also find a robust association between national income inequality and academic anxiety across all three studies. Results are discussed in relation to the multidimensionality of social class and literature on the psychology of income inequality.
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- 2024
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105. A 50-year analysis of hydrological trends and processes in a Mediterranean catchment
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N. Folton, E. Martin, P. Arnaud, P. L'Hermite, and M. Tolsa
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Technology ,Environmental technology. Sanitary engineering ,TD1-1066 ,Geography. Anthropology. Recreation ,Environmental sciences ,GE1-350 - Abstract
The Réal Collobrier hydrological observatory in south-eastern France, managed by Irstea since 1966, constitutes a benchmark site for regional hydro-climatology. Because of the dense network of stream gauges and rain gauges available, this site provides a unique opportunity to evaluate long-term hydro-meteorological Mediterranean trends. The main catchment (70 km2) and its sub-catchments are located in the Massif des Maures of south-eastern France, close to the Mediterranean coast. The vegetation is composed of forest mainly calcified on crystalline soils (maquis of heath, cork-oak, maritime pine and chestnut). Direct human influence has been negligible over the past 50 years. The land use and land cover has remained almost unchanged, with the notable exception of a wildfire in 1990 that impacted a small sub-catchment. Therefore changes in the hydrological response of the catchments are caused by changes in climate and/or physical conditions. This study investigates changes in observational data using up to 50-year daily series of precipitation and streamflow. The analysis used several climate indices describing distinct modes of variability, at inter-annual and seasonal timescales. Trends were assessed by the Mann–Kendall method. The analysis also used hydrological indices describing drought events based on daily data for a description of low flows, in particular in terms of timing and severity. The analysis shows that there is a marked tendency towards a decrease in the water resources of the Réal Collobrier catchment in response to climate trends, with a consistent increase in drought severity and duration. But the changes are variable among the sub-catchments.
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- 2019
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106. ZeroCAL: Eliminating Carbon Dioxide Emissions from Limestones Decomposition to Decarbonize Cement Production.
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Leão, Adriano, Collin, Marie, Ghodkhande, Swarali, Bouissonnié, Arnaud, Chen, Xin, Malin, Benjamin, Liu, Yiming, Hovey, Geanna, Govindhakannan, Jagannathan, Plante, Erika, Jassby, David, Gädt, Torben, Corsini, Lorenzo, Simonetti, Dante, Rosner, Fabian, and Sant, Gaurav
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Limestone (calcite, CaCO3) is an abundant and cost-effective source of calcium oxide (CaO) for cement and lime production. However, the thermochemical decomposition of limestone (∼800 °C, 1 bar) to produce lime (CaO) results in substantial carbon dioxide (CO2(g)) emissions and energy use, i.e., ∼1 tonne [t] of CO2 and ∼1.4 MWh per t of CaO produced. Here, we describe a new pathway to use CaCO3 as a Ca source to make hydrated lime (portlandite, Ca(OH)2) at ambient conditions (p, T)-while nearly eliminating process CO2(g) emissions (as low as 1.5 mol. % of the CO2 in the precursor CaCO3, equivalent to 9 kg of CO2(g) per t of Ca(OH)2)-within an aqueous flow-electrolysis/pH-swing process that coproduces hydrogen (H2(g)) and oxygen (O2(g)). Because Ca(OH)2 is a zero-carbon precursor for cement and lime production, this approach represents a significant advancement in the production of zero-carbon cement. The Zero CArbon Lime (ZeroCAL) process includes dissolution, separation/recovery, and electrolysis stages according to the following steps: (Step 1) chelator (e.g., ethylenediaminetetraacetic acid, EDTA)-promoted dissolution of CaCO3 and complexation of Ca2+ under basic (>pH 9) conditions, (Step 2a) Ca enrichment and separation using nanofiltration (NF), which allows separation of the Ca-EDTA complex from the accompanying bicarbonate (HCO3 -) species, (Step 2b) acidity-promoted decomplexation of Ca from EDTA, which allows near-complete chelator recovery and the formation of a Ca-enriched stream, and (Step 3) rapid precipitation of Ca(OH)2 from the Ca-enriched stream using electrolytically produced alkalinity. These reactions can be conducted in a seawater matrix yielding coproducts including hydrochloric acid (HCl) and sodium bicarbonate (NaHCO3), resulting from electrolysis and limestone dissolution, respectively. Careful analysis of the reaction stoichiometries and energy balances indicates that approximately 1.35 t of CaCO3, 1.09 t of water, 0.79 t of sodium chloride (NaCl), and ∼2 MWh of electrical energy are required to produce 1 t of Ca(OH)2, with significant opportunity for process intensification. This approach has major implications for decarbonizing cement production within a paradigm that emphasizes the use of existing cement plants and electrification of industrial operations, while also creating approaches for alkalinity production that enable cost-effective and scalable CO2 mineralization via Ca(OH)2 carbonation.
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- 2024
107. CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed.
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Hwang, Wonmuk, Austin, Steven, Blondel, Arnaud, Boittier, Eric, Boresch, Stefan, Buck, Matthias, Buckner, Joshua, Caflisch, Amedeo, Chang, Hao-Ting, Cheng, Xi, Choi, Yeol, Chu, Jhih-Wei, Crowley, Michael, Cui, Qiang, Damjanovic, Ana, Deng, Yuqing, Devereux, Mike, Ding, Xinqiang, Feig, Michael, Gao, Jiali, Glowacki, David, Gonzales, James, Hamaneh, Mehdi, Harder, Edward, Hayes, Ryan, Huang, Jing, Huang, Yandong, Hudson, Phillip, Im, Wonpil, Islam, Shahidul, Jiang, Wei, Jones, Michael, Käser, Silvan, Kearns, Fiona, Kern, Nathan, Klauda, Jeffery, Lazaridis, Themis, Lee, Jinhyuk, Lemkul, Justin, Liu, Xiaorong, Luo, Yun, MacKerell, Alexander, Major, Dan, Meuwly, Markus, Nam, Kwangho, Nilsson, Lennart, Ovchinnikov, Victor, Paci, Emanuele, Park, Soohyung, Pastor, Richard, Pittman, Amanda, Post, Carol, Prasad, Samarjeet, Pu, Jingzhi, Qi, Yifei, Rathinavelan, Thenmalarchelvi, Roe, Daniel, Roux, Benoit, Rowley, Christopher, Shen, Jana, Simmonett, Andrew, Sodt, Alexander, Töpfer, Kai, Upadhyay, Meenu, van der Vaart, Arjan, Vazquez-Salazar, Luis, Venable, Richard, Warrensford, Luke, Woodcock, H, Wu, Yujin, Brooks, Charles, Brooks, Bernard, and Karplus, Martin
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Quantum Theory ,Molecular Dynamics Simulation ,Software - Abstract
Since its inception nearly a half century ago, CHARMM has been playing a central role in computational biochemistry and biophysics. Commensurate with the developments in experimental research and advances in computer hardware, the range of methods and applicability of CHARMM have also grown. This review summarizes major developments that occurred after 2009 when the last review of CHARMM was published. They include the following: new faster simulation engines, accessible user interfaces for convenient workflows, and a vast array of simulation and analysis methods that encompass quantum mechanical, atomistic, and coarse-grained levels, as well as extensive coverage of force fields. In addition to providing the current snapshot of the CHARMM development, this review may serve as a starting point for exploring relevant theories and computational methods for tackling contemporary and emerging problems in biomolecular systems. CHARMM is freely available for academic and nonprofit research at https://academiccharmm.org/program.
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- 2024
108. AI-Enhanced Prediction of Aortic Stenosis Progression: Insights From the PROGRESSA Study.
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Sanabria, Melissa, Tastet, Lionel, Pelletier, Simon, Leclercq, Mickael, Ohl, Louis, Hermann, Lara, Mattei, Pierre-Alexandre, Precioso, Frederic, Coté, Nancy, Pibarot, Philippe, and Droit, Arnaud
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aortic stenosis ,deep learning ,machine learning ,risk prediction - Abstract
BACKGROUND: Aortic valve stenosis (AS) is a progressive chronic disease with progression rates that vary in patients and therefore difficult to predict. OBJECTIVES: The aim of this study was to predict the progression of AS using comprehensive and longitudinal patient data. METHODS: Machine and deep learning algorithms were trained on a data set of 303 patients enrolled in the PROGRESSA (Metabolic Determinants of the Progression of Aortic Stenosis) study who underwent clinical and echocardiographic follow-up on an annual basis. Performance of the models was measured to predict disease progression over long (next 5 years) and short (next 2 years) terms and was compared to a standard clinical model with usually used features in clinical settings based on logistic regression. RESULTS: For each annual follow-up visit including baseline, we trained various supervised learning algorithms in predicting disease progression at 2- and 5-year terms. At both terms, LightGBM consistently outperformed other models with the highest average area under curves across patient visits (0.85 at 2 years, 0.83 at 5 years). Recurrent neural network-based models (Gated Recurrent Unit and Long Short-Term Memory) and XGBoost also demonstrated strong predictive capabilities, while the clinical model showed the lowest performance. CONCLUSIONS: This study demonstrates how an artificial intelligence-guided approach in clinical routine could help enhance risk stratification of AS. It presents models based on multisource comprehensive data to predict disease progression and clinical outcomes in patients with mild-to-moderate AS at baseline.
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- 2024
109. Glutathione peroxidase 3 is a potential biomarker for konzo.
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Bramble, Matthew, Fourcassié, Victor, Vashist, Neerja, Roux-Dalvai, Florence, Zhou, Yun, Bumoko, Guy, Kasendue, Michel, Spencer, DAndre, Musasa Hanshi-Hatuhu, Hilaire, Kambale-Mastaki, Vincent, Manalo, Rafael, Mohammed, Aliyah, McIlwain, David, Cunningham, Gary, Summar, Marshall, Boivin, Michael, Caldovic, Ljubica, Vilain, Eric, Mumba-Ngoyi, Dieudonne, Tshala-Katumbay, Desire, and Droit, Arnaud
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Humans ,Biomarkers ,Glutathione Peroxidase ,Female ,Male ,Adult ,Child ,Young Adult ,Cohort Studies ,Middle Aged ,Adolescent - Abstract
Konzo is a neglected paralytic neurological disease associated with food (cassava) poisoning that affects the worlds poorest children and women of childbearing ages across regions of sub-Saharan Africa. Despite understanding the dietary factors that lead to konzo, the molecular markers and mechanisms that trigger this disease remain unknown. To identify potential protein biomarkers associated with a disease status, plasma was collected from two independent Congolese cohorts, a discovery cohort (n = 60) and validation cohort (n = 204), sampled 10 years apart and subjected to multiple high-throughput assays. We identified that Glutathione Peroxidase 3 (GPx3), a critical plasma-based antioxidant enzyme, was the sole protein examined that was both significantly and differentially abundant between affected and non-affected participants in both cohorts, with large reductions observed in those affected with konzo. Our findings raise the notion that reductions in key antioxidant mechanisms may be the biological risk factor for the development of konzo, particularly those mediated through pathways involving the glutathione peroxidase family.
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- 2024
110. Harmonizing tau positron emission tomography in Alzheimer's disease: The CenTauR scale and the joint propagation model
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Leuzy, Antoine, Raket, Lars Lau, Villemagne, Victor L, Klein, Gregory, Tonietto, Matteo, Olafson, Emily, Baker, Suzanne, Saad, Ziad S, Bullich, Santiago, Lopresti, Brian, Bohorquez, Sandra Sanabria, Boada, Mercè, Betthauser, Tobey J, Charil, Arnaud, Collins, Emily C, Collins, Jessica A, Cullen, Nicholas, Gunn, Roger N, Higuchi, Makoto, Hostetler, Eric, Hutchison, R Matthew, Iaccarino, Leonardo, Insel, Philip S, Irizarry, Michael C, Jack, Clifford R, Jagust, William J, Johnson, Keith A, Johnson, Sterling C, Karten, Yashmin, Marquié, Marta, Mathotaarachchi, Sulantha, Mintun, Mark A, Ossenkoppele, Rik, Pappas, Ioannis, Petersen, Ronald C, Rabinovici, Gil D, Rosa‐Neto, Pedro, Schwarz, Christopher G, Smith, Ruben, Stephens, Andrew W, Whittington, Alex, Carrillo, Maria C, Pontecorvo, Michael J, Haeberlein, Samantha Budd, Dunn, Billy, Kolb, Hartmuth C, Sivakumaran, Sudhir, Rowe, Christopher C, Hansson, Oskar, and Doré, Vincent
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Biological Psychology ,Psychology ,Biomedical Imaging ,Neurodegenerative ,Alzheimer's Disease ,Brain Disorders ,Dementia ,Neurosciences ,Acquired Cognitive Impairment ,Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD) ,Bioengineering ,Aging ,Neurological ,Alzheimer Disease ,Humans ,Positron-Emission Tomography ,tau Proteins ,Brain ,Male ,Female ,Aged ,Cohort Studies ,Radiopharmaceuticals ,Models ,Statistical ,[F-18]Flortaucipir ,[F-18]RO948 ,[F-18]MK-6240 ,[F-18]GTP1 ,[F-18]PI-2620 ,Alzheimer's disease ,C-Path ,CenTauR ,Centiloid ,CPAD ,head-to-head ,Imaging ,PET ,standardization ,tau ,C‐Path ,[18F]Flortaucipir ,[18F]GTP1 ,[18F]MK‐6240 ,[18F]PI‐2620 ,[18F]RO948 ,head‐to‐head ,Clinical Sciences ,Geriatrics ,Clinical sciences ,Biological psychology - Abstract
IntroductionTau-positron emission tomography (PET) outcome data of patients with Alzheimer's disease (AD) cannot currently be meaningfully compared or combined when different tracers are used due to differences in tracer properties, instrumentation, and methods of analysis.MethodsUsing head-to-head data from five cohorts with tau PET radiotracers designed to target tau deposition in AD, we tested a joint propagation model (JPM) to harmonize quantification (units termed "CenTauR" [CTR]). JPM is a statistical model that simultaneously models the relationships between head-to-head and anchor point data. JPM was compared to a linear regression approach analogous to the one used in the amyloid PET Centiloid scale.ResultsA strong linear relationship was observed between CTR values across brain regions. Using the JPM approach, CTR estimates were similar to, but more accurate than, those derived using the linear regression approach.DiscussionPreliminary findings using the JPM support the development and adoption of a universal scale for tau-PET quantification.HighlightsTested a novel joint propagation model (JPM) to harmonize quantification of tau PET. Units of common scale are termed "CenTauRs". Tested a Centiloid-like linear regression approach. Using five cohorts with head-to-head tau PET, JPM outperformed linearregressionbased approach. Strong linear relationship was observed between CenTauRs values across brain regions.
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- 2024
111. Interacting internal waves explain global patterns of interior ocean mixing.
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Dematteis, Giovanni, Le Boyer, Arnaud, Pollmann, Friederike, Polzin, Kurt, Alford, Matthew, Whalen, Caitlin, and Lvov, Yuri
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Across the stable density stratification of the abyssal ocean, deep dense water is slowly propelled upward by sustained, though irregular, turbulent mixing. The resulting mean upwelling determines large-scale oceanic circulation properties like heat and carbon transport. In the ocean interior, this turbulent mixing is caused mainly by breaking internal waves: generated predominantly by winds and tides, these waves interact nonlinearly, transferring energy downscale, and finally become unstable, break and mix the water column. This paradigm, long parameterized heuristically, still lacks full theoretical explanation. Here, we close this gap using wave-wave interaction theory with input from both localized and global observations. We find near-ubiquitous agreement between first-principle predictions and observed mixing patterns in the global ocean interior. Our findings lay the foundations for a wave-driven mixing parameterization for ocean general circulation models that is entirely physics-based, which is key to reliably represent future climate states that could differ substantially from todays.
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- 2024
112. Thermal and microclimatic behavior of OASIS schoolyard paving materials
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Karam, Ghid, Chanial, Maïlys, Grados, Arnaud, Hendel, Martin, and Royon, Laurent
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Physics - Physics and Society ,Physics - Atmospheric and Oceanic Physics - Abstract
As part of its Resilience Strategy, the City of Paris' OASIS program aims to contribute ot its adaptation to heatwaves and climate change by transforming schoolyards into climate shelters, namely via desealing and greening. In this context, a variety of alternative pavement materials have been proposed to replace the initial schoolyard pavement, composed of an asphalt sidewalk structure. In the context of the EU-funded ERDF UIA OASIS project, the thermal performance of these alternative materials and their impact in terms of urban cooling was explored. To this aim, five samples of reference and innovative schoolyard pavements were studied in the lab under heat-wave conditions. Alternative green, biosourced, recycled and reflective pavement solutions were compared to standard fine-aggregate asphalt concrete. Their performance was evaluated with regards to their contribution to the urban heat island phenomenon and to pedestrian heat stress, account for the typical use schedule of schoolyards. Green and biosourced materials were found to perform well for both indicators, while the standard and recycled solutions had poor UHI performance but had limited negative effects on daytime heat stress. The reflective pavement had better UHI performance but had high radiosity during daytime which can negatively affect pedestrian heat stress.
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- 2024
113. On the Effects of Smoothing Rugged Landscape by Different Toy Problems: A Case Study on UBQP
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Wang, Wei, Shi, Jialong, Sun, Jianyong, Liefooghe, Arnaud, Zhang, Qingfu, and Fan, Ye
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Mathematics - Optimization and Control - Abstract
The hardness of the Unconstrained Binary Quadratic Program (UBQP) problem is due its rugged landscape. Various algorithms have been proposed for UBQP, including the Landscape Smoothing Iterated Local Search (LSILS). Different from other UBQP algorithms, LSILS tries to smooth the rugged landscape by building a convex combination of the original UBQP and a toy UBQP. In this paper, our study further investigates the impact of smoothing rugged landscapes using different toy UBQP problems, including a toy UBQP with matrix ^Q1 (construct by "+/-1"), a toy UBQP with matrix ^Q2 (construct by "+/-i") and a toy UBQP with matrix ^Q3 (construct randomly). We first assess the landscape flatness of the three toy UBQPs. Subsequently, we test the efficiency of LSILS with different toy UBQPs. Results reveal that the toy UBQP with ^Q1 (construct by "+/-1") exhibits the flattest landscape among the three, while the toy UBQP with ^Q3 (construct randomly) presents the most non-flat landscape. Notably, LSILS using the toy UBQP with ^Q2 (construct by "+/-i") emerges as the most effective, while ^Q3 (construct randomly) has the poorest result. These findings contribute to a detailed understanding of landscape smoothing techniques in optimizing UBQP.
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- 2024
114. Evaluating Human Trajectory Prediction with Metamorphic Testing
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Spieker, Helge, Belmecheri, Nassim, Gotlieb, Arnaud, and Lazaar, Nadjib
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Computer Science - Software Engineering ,Computer Science - Artificial Intelligence - Abstract
The prediction of human trajectories is important for planning in autonomous systems that act in the real world, e.g. automated driving or mobile robots. Human trajectory prediction is a noisy process, and no prediction does precisely match any future trajectory. It is therefore approached as a stochastic problem, where the goal is to minimise the error between the true and the predicted trajectory. In this work, we explore the application of metamorphic testing for human trajectory prediction. Metamorphic testing is designed to handle unclear or missing test oracles. It is well-designed for human trajectory prediction, where there is no clear criterion of correct or incorrect human behaviour. Metamorphic relations rely on transformations over source test cases and exploit invariants. A setting well-designed for human trajectory prediction where there are many symmetries of expected human behaviour under variations of the input, e.g. mirroring and rescaling of the input data. We discuss how metamorphic testing can be applied to stochastic human trajectory prediction and introduce the Wasserstein Violation Criterion to statistically assess whether a follow-up test case violates a label-preserving metamorphic relation., Comment: MET'24: 9th ACM International Workshop on Metamorphic Testing
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- 2024
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115. Communicating the gravitational-wave discoveries of the LIGO-Virgo-KAGRA Collaboration
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Middleton, Hannah, Berry, Christopher P L, Arnaud, Nicolas, Blair, David, Bondell, Jacqueline, Bonino, Alice, Bonne, Nicolas, Chatterjee, Debarati, Chaty, Sylvain, Colloms, Storm, Cominsky, Lynn, Conti, Livia, Cordero-Carrión, Isabel, Coyne, Robert, Doctor, Zoheyr, Freise, Andreas, Geller, Aaron, Green, Anna C, Gupta, Jen, Holz, Daniel, Katzman, William, Kaur, Jyoti, Keitel, David, Key, Joey Shapiro, Kijbunchoo, Nutsinee, Knox, Carl, Krawczyk, Coleman, Lang, Ryan N, Larson, Shane L, Milde, Susanne, Napolano, Vincenzo, North, Chris, Rieger, Sascha, Rossi, Giada, Shinkai, Hisaaki, Simonnet, Aurore, and Spencer, Andrew
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Astrophysics - Instrumentation and Methods for Astrophysics ,Astrophysics - High Energy Astrophysical Phenomena ,General Relativity and Quantum Cosmology ,Physics - Physics Education ,Physics - Popular Physics - Abstract
The LIGO-Virgo-KAGRA (LVK) Collaboration has made breakthrough discoveries in gravitational-wave astronomy, a new field that provides a different means of observing our Universe. Gravitational-wave discoveries are possible thanks to the work of thousands of people from across the globe working together. In this article, we discuss the range of engagement activities used to communicate LVK gravitational-wave discoveries and the stories of the people behind the science, using the activities surrounding the release of the third Gravitational-Wave Transient Catalog as a case study., Comment: 15 pages, 7 figures, published in JCOM: https://doi.org/10.22323/2.23070803
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- 2024
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116. Swift-BAT GUANO follow-up of gravitational-wave triggers in the third LIGO-Virgo-KAGRA observing run
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Raman, Gayathri, Ronchini, Samuele, Delaunay, James, Tohuvavohu, Aaron, Kennea, Jamie A., Parsotan, Tyler, Ambrosi, Elena, Bernardini, Maria Grazia, Campana, Sergio, Cusumano, Giancarlo, D'Ai, Antonino, D'Avanzo, Paolo, D'Elia, Valerio, De Pasquale, Massimiliano, Dichiara, Simone, Evans, Phil, Hartmann, Dieter, Kuin, Paul, Melandri, Andrea, O'Brien, Paul, Osborne, Julian P., Page, Kim, Palmer, David M., Sbarufatti, Boris, Tagliaferri, Gianpiero, Troja, Eleonora, Abac, A. G., Abbott, R., Abe, H., Abouelfettouh, I., Acernese, F., Ackley, K., Adamcewicz, C., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Al-Shammari, S., Altin, P. A., Alvarez-Lopez, S., Amato, A., Amez-Droz, L., Amorosi, A., Amra, C., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Anglin, J., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Aubin, F., AultONeal, K., Avallone, G., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Bai, Y., Baier, J. G., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Barthelmy, S. D., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Bazzan, M., Bécsy, B., Bedakihale, V. 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K., Obayashi, K., Oberling, J., O'Dell, J., Oertel, M., Offermans, A., Oganesyan, G., Oh, J. J., Oh, K., Oh, S. H., O'Hanlon, T., Ohashi, M., Ohkawa, M., Ohme, F., Ohta, H., Oliveira, A. S., Oliveri, R., Oloworaran, V., O'Neal, B., Oohara, K., O'Reilly, B., Ormsby, N. D., Orselli, M., O'Shaughnessy, R., Oshima, Y., Oshino, S., Ossokine, S., Osthelder, C., Ottaway, D. J., Ouzriat, A., Overmier, H., Owen, B. J., Pace, A. E., Pagano, R., Page, M. A., Pai, A., Pai, S. A., Pal, A., Pal, S., Palaia, M. A., Palashov, O., Pálfi, M., Palma, P. P., Palomba, C., Pan, K. C., Panda, P. K., Panebianco, L., Pang, P. T. H., Pannarale, F., Pant, B. C., Panther, F. H., Panzer, C. D., Paoletti, F., Paoli, A., Paolone, A., Papalexakis, E. E., Papalini, L., Papigkiotis, G., Parisi, A., Park, J., Parker, W., Pascale, G., Pascucci, D., Pasqualetti, A., Passaquieti, R., Passuello, D., Patane, O., Patel, M., Pathak, D., Pathak, M., Patra, A., Patricelli, B., Patron, A. S., Paul, S., Payne, E., Pearce, T., Pedraza, M., Pegna, R., Pele, A., Arellano, F. E. Peña, Penn, S., Penuliar, M. D., Perego, A., Pereira, A., Perez, J. J., Périgois, C., Perkins, C. C., Perna, G., Perreca, A., Perret, J., Perriès, S., Perry, J. W., Pesios, D., Petrillo, C., Pfeiffer, H. P., Pham, H., Pham, K. A., Phukon, K. S., Phurailatpam, H., Piccinni, O. J., Pichot, M., Piendibene, M., Piergiovanni, F., Pierini, L., Pierra, G., Pierro, V., Pietrzak, M., Pillas, M., Pilo, F., Pinard, L., Pineda-Bosque, C., Pinto, I. M., Pinto, M., Piotrzkowski, B. J., Pirello, M., Pitkin, M. D., Placidi, A., Placidi, E., Planas, M. L., Plastino, W., Poggiani, R., Polini, E., Pompili, L., Poon, J., Porcelli, E., Portell, J., Porter, E. K., Posnansky, C., Poulton, R., Powell, J., Pracchia, M., Pradhan, B. K., Pradier, T., Prajapati, A. K., Prasai, K., Prasanna, R., Prasia, P., Pratten, G., Principe, M., Prodi, G. A., Prokhorov, L., Prosposito, P., Prudenzi, L., Puecher, A., Pullin, J., Punturo, M., Puosi, F., Puppo, P., Pürrer, M., Qi, H., Qin, J., Quéméner, G., Quetschke, V., Quigley, C., Quinonez, P. J., Quitzow-James, R., Raab, F. J., Raaijmakers, G., Radulesco, N., Raffai, P., Rail, S. X., Raja, S., Rajan, C., Rajbhandari, B., Ramirez, D. S., Ramirez, K. E., Vidal, F. A. Ramis, Ramos-Buades, A., Rana, D., Randel, E., Ranjan, S., Rapagnani, P., Ratto, B., Rawat, S., Ray, A., Raymond, V., Razzano, M., Read, J., Payo, M. Recaman, Regimbau, T., Rei, L., Reid, S., Reid, S. W., Reitze, D. H., Relton, P., Renzini, A., Rettegno, P., Revenu, B., Reza, A., Rezac, M., Rezaei, A. S., Ricci, F., Ricci, M., Richards, D., Richardson, C. J., Richardson, J. W., Rijal, A., Riles, K., Riley, H. K., Rinaldi, S., Rittmeyer, J., Robertson, C., Robinet, F., Robinson, M., Rocchi, A., Rolland, L., Rollins, J. G., Romanelli, M., Romano, A. E., Romano, R., Romero, A., Romero-Shaw, I. M., Romie, J. H., Roocke, T. J., Rosa, L., Rosauer, T. J., Rose, C. A., Rosińska, D., Ross, M. P., Rossello, M., Rowan, S., Roy, S. K., Roy, S., Rozza, D., Ruggi, P., Morales, E. Ruiz, Ruiz-Rocha, K., Sachdev, S., Sadecki, T., Sadiq, J., Saffarieh, P., Sah, M. R., Saha, S. S., Sainrat, T., Menon, S. Sajith, Sakai, K., Sakellariadou, M., Sako, T., Sakon, S., Salafia, O. S., Salces-Carcoba, F., Salconi, L., Saleem, M., Salemi, F., Sallé, M., Salvador, S., Sanchez, A., Sanchez, E. J., Sanchez, J. H., Sanchez, L. E., Sanchis-Gual, N., Sanders, J. R., Sänger, E. M., Saravanan, T. R., Sarin, N., Sasli, A., Sassi, P., Sassolas, B., Satari, H., Sato, R., Sato, S., Sato, Y., Sauter, O., Savage, R. L., Sawada, T., Sawant, H. L., Sayah, S., Schaetzl, D., Scheel, M., Scheuer, J., Schiworski, M. G., Schmidt, P., Schmidt, S., Schnabel, R., Schneewind, M., Schofield, R. M. S., Schouteden, K., Schuler, H., Schulte, B. W., Schutz, B. F., Schwartz, E., Scott, J., Scott, S. M., Seetharamu, T. C., Seglar-Arroyo, M., Sekiguchi, Y., Sellers, D., Sengupta, A. S., Sentenac, D., Seo, E. G., Seo, J. W., Sequino, V., Sergeev, A., Serra, M., Servignat, G., Setyawati, Y., Shaffer, T., Shah, U. S., Shahriar, M. S., Shaikh, M. A., Shams, B., Shao, L., Sharma, A. K., Sharma, P., Sharma-Chaudhary, S., Shawhan, P., Shcheblanov, N. S., Shen, B., Shikano, Y., Shikauchi, M., Shimode, K., Shinkai, H., Shiota, J., Shoemaker, D. H., Shoemaker, D. M., Short, R. W., ShyamSundar, S., Sider, A., Siegel, H., Sieniawska, M., Sigg, D., Silenzi, L., Simmonds, M., Singer, L. P., Singh, A., Singh, D., Singh, M. K., Singha, A., Sintes, A. M., Sipala, V., Skliris, V., Slagmolen, B. J. J., Slaven-Blair, T. J., Smetana, J., Smith, J. R., Smith, L., Smith, R. J. E., Smith, W. J., Soldateschi, J., Somala, S. N., Somiya, K., Soni, K., Soni, S., Sordini, V., Sorrentino, F., Sorrentino, N., Soulard, R., Souradeep, T., Southgate, A., Sowell, E., Spagnuolo, V., Spencer, A. P., Spera, M., Spinicelli, P., Srivastava, A. K., Stachurski, F., Steer, D. A., Steinlechner, J., Steinlechner, S., Stergioulas, N., Stevens, P., StPierre, M., Strang, L. C., Stratta, G., Strong, M. D., Strunk, A., Sturani, R., Stuver, A. L., Suchenek, M., Sudhagar, S., Sueltmann, N., Sullivan, A. G., Sullivan, K. D., Sun, L., Sunil, S., Sur, A., Suresh, J., Sutton, P. J., Suzuki, Takamasa, Suzuki, Takanori, Swinkels, B. L., Syx, A., Szczepańczyk, M. J., Szewczyk, P., Tacca, M., Tagoshi, H., Tait, S. C., Takahashi, H., Takahashi, R., Takamori, A., Takatani, K., Takeda, H., Takeda, M., Talbot, C. J., Talbot, C., Tamaki, M., Tamanini, N., Tanabe, D., Tanaka, K., Tanaka, S. J., Tanaka, T., Tanasijczuk, A. J., Tang, D., Tanioka, S., Tanner, D. B., Tao, L., Tapia, R. D., Martín, E. N. Tapia San, Tarafder, R., Taranto, C., Taruya, A., Tasson, J. D., Teloi, M., Tenorio, R., Themann, H., Theodoropoulos, A., Thirugnanasambandam, M. P., Thomas, L. M., Thomas, M., Thomas, P., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tissino, J., Tiwari, A., Tiwari, Shubhanshu, Tiwari, Srishti, Tiwari, V., Todd, M. R., Toivonen, A. M., Toland, K., Tolley, A. E., Tomaru, T., Tomita, K., Tomura, T., Tong-Yu, C., Toriyama, A., Toropov, N., Torres-Forné, A., Torrie, C. I., Toscani, M., Melo, I. Tosta e, Tournefier, E., Trani, A. A., Trapananti, A., Travasso, F., Traylor, G., Trenado, J., Trevor, M., Tringali, M. C., Tripathee, A., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. J., Tsang, T. T. L., Tso, R., Tsuchida, S., Tsukada, L., Tsutsui, T., Turbang, K., Turconi, M., Turski, C., Ubach, H., Ubhi, A. S., Uchikata, N., Uchiyama, T., Udall, R. P., Uehara, T., Ueno, K., Unnikrishnan, C. S., Ushiba, T., Utina, A., Vacatello, M., Vahlbruch, H., Vaidya, N., Vajente, G., Vajpeyi, A., Valdes, G., Valencia, J., Valentini, M., Vallejo-Peña, S. A., Vallero, S., Valsan, V., van Bakel, N., van Beuzekom, M., van Dael, M., Brand, J. F. J. van den, Broeck, C. Van Den, Vander-Hyde, D. C., van der Sluys, M., Van de Walle, A., van Dongen, J., Vandra, K., van Haevermaet, H., van Heijningen, J. V., Vanosky, J., van Putten, M. H. P. M., van Ranst, Z., van Remortel, N., Vardaro, M., Vargas, A. F., Varma, V., Vasúth, M., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venikoudis, S., Venneberg, J., Verdier, P., Verkindt, D., Verma, B., Verma, P., Verma, Y., Vermeulen, S. M., Veske, D., Vetrano, F., Veutro, A., Vibhute, A. M., Viceré, A., Vidyant, S., Viets, A. D., Vijaykumar, A., Vilkha, A., Villa-Ortega, V., Vincent, E. T., Vinet, J. -Y., Viret, S., Virtuoso, A., Vitale, S., Vocca, H., Voigt, D., von Reis, E. R. G., von Wrangel, J. S. A., Vyatchanin, S. P., Wade, L. E., Wade, M., Wagner, K. J., Walet, R. C., Walker, M., Wallace, G. S., Wallace, L., Wang, H., Wang, J. Z., Wang, W. H., Wang, Z., Waratkar, G., Ward, R. L., Warner, J., Was, M., Washimi, T., Washington, N. Y., Watarai, D., Wayt, K. E., Weaver, B., Weaving, C. R., Webster, S. A., Weinert, M., Weinstein, A. J., Weiss, R., Weller, C. M., Weller, R. A., Wellmann, F., Wen, L., Weßels, P., Wette, K., Whelan, J. T., White, D. D., Whiting, B. F., Whittle, C., Wildberger, J. B., Wilk, O. S., Wilken, D., Willetts, K., Williams, D., Williams, M. J., Williams, N. S., Willis, J. L., Willke, B., Wils, M., Wipf, C. C., Woan, G., Woehler, J., Wofford, J. K., Wolfe, N. E., Wong, D., Wong, H. T., Wong, H. W. Y., Wong, I. C. F., Wright, J. L., Wright, M., Wu, C., Wu, D. S., Wu, H., Wysocki, D. M., Xiao, L., Xu, V. A., Xu, Y., Yadav, N., Yamamoto, H., Yamamoto, K., Yamamoto, M., Yamamoto, T. S., Yamamoto, T., Yamamura, S., Yamazaki, R., Yan, S., Yan, T., Yang, F. W., Yang, F., Yang, K. Z., Yang, L. -C., Yang, Y., Yarbrough, Z., Yeh, S. -W., Yelikar, A. B., Yeung, S. M. C., Yin, X., Yokoyama, J., Yokozawa, T., Yoo, J., Yu, H., Yuzurihara, H., Zadrożny, A., Zannelli, A. J., Zanolin, M., Zeeshan, M., Zelenova, T., Zendri, J. -P., Zeoli, M., Zerrad, M., Zevin, M., Zhang, A. C., Zhang, J., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhong, S., Zhou, R., Zhu, Z. -H., Zimmerman, A. B., Zucker, M. E., and Zweizig, J.
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Astrophysics - High Energy Astrophysical Phenomena ,General Relativity and Quantum Cosmology - Abstract
We present results from a search for X-ray/gamma-ray counterparts of gravitational-wave (GW) candidates from the third observing run (O3) of the LIGO-Virgo-KAGRA (LVK) network using the Swift Burst Alert Telescope (Swift-BAT). The search includes 636 GW candidates received in low latency, 86 of which have been confirmed by the offline analysis and included in the third cumulative Gravitational-Wave Transient Catalogs (GWTC-3). Targeted searches were carried out on the entire GW sample using the maximum--likelihood NITRATES pipeline on the BAT data made available via the GUANO infrastructure. We do not detect any significant electromagnetic emission that is temporally and spatially coincident with any of the GW candidates. We report flux upper limits in the 15-350 keV band as a function of sky position for all the catalog candidates. For GW candidates where the Swift-BAT false alarm rate is less than 10$^{-3}$ Hz, we compute the GW--BAT joint false alarm rate. Finally, the derived Swift-BAT upper limits are used to infer constraints on the putative electromagnetic emission associated with binary black hole mergers., Comment: 50 pages, 10 figures, 4 tables
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- 2024
117. Foundation Models for the Electric Power Grid
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Hamann, Hendrik F., Brunschwiler, Thomas, Gjorgiev, Blazhe, Martins, Leonardo S. A., Puech, Alban, Varbella, Anna, Weiss, Jonas, Bernabe-Moreno, Juan, Massé, Alexandre Blondin, Choi, Seong, Foster, Ian, Hodge, Bri-Mathias, Jain, Rishabh, Kim, Kibaek, Mai, Vincent, Mirallès, François, De Montigny, Martin, Ramos-Leaños, Octavio, Suprême, Hussein, Xie, Le, Youssef, El-Nasser S., Zinflou, Arnaud, Belyi, Alexander J., Bessa, Ricardo J., Bhattarai, Bishnu Prasad, Schmude, Johannes, and Sobolevsky, Stanislav
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Computer Science - Computational Engineering, Finance, and Science ,Electrical Engineering and Systems Science - Systems and Control - Abstract
Foundation models (FMs) currently dominate news headlines. They employ advanced deep learning architectures to extract structural information autonomously from vast datasets through self-supervision. The resulting rich representations of complex systems and dynamics can be applied to many downstream applications. Therefore, FMs can find uses in electric power grids, challenged by the energy transition and climate change. In this paper, we call for the development of, and state why we believe in, the potential of FMs for electric grids. We highlight their strengths and weaknesses amidst the challenges of a changing grid. We argue that an FM learning from diverse grid data and topologies could unlock transformative capabilities, pioneering a new approach in leveraging AI to redefine how we manage complexity and uncertainty in the electric grid. Finally, we discuss a power grid FM concept, namely GridFM, based on graph neural networks and show how different downstream tasks benefit., Comment: Major equal contributors: H.F.H., T.B., B.G., L.S.A.M., A.P., A.V., J.W.; Significant equal contributors: J.B., A.B.M., S.C., I.F., B.H., R.J., K.K., V.M., F.M., M.D.M., O.R., H.S., L.X., E.S.Y., A.Z.; Other equal contributors: A.J.B., R.J.B., B.P.B., J.S., S.S; Lead contact: H.F.H
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- 2024
118. Dirichlet process mixture model based on topologically augmented signal representation for clustering infant vocalizations
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Bonafos, Guillem, Bourot, Clara, Pudlo, Pierre, Freyermuth, Jean-Marc, Reboul, Laurence, Tronçon, Samuel, and Rey, Arnaud
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Statistics - Applications ,Computer Science - Sound ,Electrical Engineering and Systems Science - Audio and Speech Processing ,Statistics - Machine Learning - Abstract
Based on audio recordings made once a month during the first 12 months of a child's life, we propose a new method for clustering this set of vocalizations. We use a topologically augmented representation of the vocalizations, employing two persistence diagrams for each vocalization: one computed on the surface of its spectrogram and one on the Takens' embeddings of the vocalization. A synthetic persistent variable is derived for each diagram and added to the MFCCs (Mel-frequency cepstral coefficients). Using this representation, we fit a non-parametric Bayesian mixture model with a Dirichlet process prior to model the number of components. This procedure leads to a novel data-driven categorization of vocal productions. Our findings reveal the presence of 8 clusters of vocalizations, allowing us to compare their temporal distribution and acoustic profiles in the first 12 months of life.
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- 2024
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119. Observations on the hex clusters of the Spectre tilings
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Chéritat, Arnaud
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Mathematics - Combinatorics ,Mathematics - Metric Geometry ,52C20 (Primary) 05B45 (Secondary) - Abstract
Decorating the Spectre tile with hexagons reveals triangular hexagonal clusters whose structure we study. In the process we reprove that the Spectre tilings exist and are uniquely hierarchical. The proof is not computer-assisted., Comment: 136 pages, 238 figures
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- 2024
120. Derivation of stochastic models for coastal waves
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Debussche, Arnaud, Mémin, Étienne, and Moneyron, Antoine
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Mathematics - Probability - Abstract
In this paper, we consider a stochastic nonlinear formulation of classical coastal waves models under location uncertainty (LU). In the formal setting investigated here, stochastic versions of the Serre-Green- Nagdi, Boussinesq and classical shallow water wave models are obtained through an asymptotic expansion, which is similar to the one operated in the deterministic setting. However, modified advection terms emerge, together with advection noise terms. These terms are well-known features arising from the LU formalism, based on momentum conservation principle., Comment: Corrected minor typos compared to first version
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- 2024
121. Some properties of a non-hydrostatic stochastic oceanic primitive equations model
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Debussche, Arnaud, Mémin, Étienne, and Moneyron, Antoine
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Mathematics - Probability ,Mathematical Physics - Abstract
In this paper, we study how relaxing the classical hydrostatic balance hypothesis affects theoretical aspects of the LU primitive equations well-posedness. We focus on models that sit between incompressible 3D LU Navier-Stokes equations and standard LU primitive equations, aiming for numerical manageability while capturing non-hydrostatic phenomena. Our main result concerns the well-posedness of a specific stochastic interpretation of the LU primitive equations. This holds with rigid-lid type boundary conditions, and when the horizontal component of noise is independent of z. In fact these conditions can be related to the dynamical regime in which the primitive equations remain valid. Moreover, under these conditions, we show that the LU primitive equations solution tends toward the one of the deterministic primitive equations for a vanishing noise, thus providing a physical coherence to the LU stochastic model.
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- 2024
122. Investigation of the rotational spectrum of CH$_3$$^{17}$OH and its tentative detection toward Sagittarius B2(N)
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Müller, Holger S. P., Ilyushin, Vadim V., Belloche, Arnaud, Lewen, Frank, and Schlemmer, Stephan
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Astrophysics - Astrophysics of Galaxies ,Astrophysics - Solar and Stellar Astrophysics ,Physics - Chemical Physics - Abstract
Methanol is an abundant molecule in space. The column density of CH$_3^{18}$OH is in some star-forming regions so high that the search for CH$_3^{17}$OH is promising. But only very few transition frequencies of CH$_3^{17}$OH with a microwave accuracy have been published thus far. We recorded the rotational spectrum of CH$_3^{17}$OH between 38 and 1095 GHz employing a methanol sample enriched in $^{17}$O to 20\%. A torsion-rotation Hamiltonian model based on the rho-axis method was employed to fit the data, as in our previous studies. We searched for rotational transitions of CH$_3^{17}$OH in the imaging spectral line survey ReMoCA obtained with the Atacama Large Millimeter/submillimeter Array (ALMA) toward the high-mass star-forming region Sgr B2(N). The observed spectra were modeled under the assumption of local thermodynamic equilibrium (LTE). The assignments cover $0 \le J \le 45$, $K_a \le 16$, and mainly the $v_ t = 0$ and 1 torsional states. The Hamiltonian model describes our data well. The model was applied to derive a line list for radio-astronomical observations. We report a tentative detection of CH$_3^{17}$OH along with secure detections of the more abundant isotopologs of methanol toward Sgr B2(N2b). The derived column densities yield isotopic ratios $^{12}$C/$^{13}$C = 25, $^{16}$O/$^{18}$O = 240, and $^{18}$O/$^{17}$O = 3.3, which are consistent with values found earlier for other molecules in Sgr B2. The agreement between the $^{18}$O/$^{17}$O isotopic ratio that we obtained for methanol and the $^{18}$O/$^{17}$O ratios reported in the past for other molecules in Sgr B2(N) strongly supports our tentative interstellar identification of CH$_3^{17}$OH. The accuracy of the derived line list is sufficient for further radio astronomical searches for this methanol isotopolog toward other star-forming regions., Comment: 16 pages, including tables, figures, references and appendices. Abstract slightly shortened. Astronomy and Astrophysics, accepted for publication
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- 2024
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123. Enumeration of minimal transversals of hypergraphs of bounded VC-dimension
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Mary, Arnaud
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Mathematics - Combinatorics ,Computer Science - Computational Complexity ,Computer Science - Discrete Mathematics ,Computer Science - Data Structures and Algorithms - Abstract
We consider the problem of enumerating all minimal transversals (also called minimal hitting sets) of a hypergraph $\mathcal{H}$. An equivalent formulation of this problem known as the \emph{transversal hypergraph} problem (or \emph{hypergraph dualization} problem) is to decide, given two hypergraphs, whether one corresponds to the set of minimal transversals of the other. The existence of a polynomial time algorithm to solve this problem is a long standing open question. In \cite{fredman_complexity_1996}, the authors present the first sub-exponential algorithm to solve the transversal hypergraph problem which runs in quasi-polynomial time, making it unlikely that the problem is (co)NP-complete. In this paper, we show that when one of the two hypergraphs is of bounded VC-dimension, the transversal hypergraph problem can be solved in polynomial time, or equivalently that if $\mathcal{H}$ is a hypergraph of bounded VC-dimension, then there exists an incremental polynomial time algorithm to enumerate its minimal transversals. This result generalizes most of the previously known polynomial cases in the literature since they almost all consider classes of hypergraphs of bounded VC-dimension. As a consequence, the hypergraph transversal problem is solvable in polynomial time for any class of hypergraphs closed under partial subhypergraphs. We also show that the proposed algorithm runs in quasi-polynomial time in general hypergraphs and runs in polynomial time if the conformality of the hypergraph is bounded, which is one of the few known polynomial cases where the VC-dimension is unbounded.
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- 2024
124. An Energy Stable Nonlinear Incompressible Multi-Phase Flow Formulation
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Nordström, Jan and Malan, Arnaud. G.
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Mathematics - Analysis of PDEs ,Mathematics - Numerical Analysis ,65M12 ,G.1.8 - Abstract
We show that a reformulation of the governing equations for incompressible multi-phase flow in the volume of fluid setting leads to a well defined energy rate. Weak nonlinear inflow-outflow and solid wall boundary conditions complement the development and lead to an energy estimate in terms of external data. The new formulation combined with summation-by-parts operators lead to provably nonlinear energy stability.
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- 2024
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125. Distance to Transitivity: New Parameters for Taming Reachability in Temporal Graphs
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Casteigts, Arnaud, Morawietz, Nils, and Wolf, Petra
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Computer Science - Computational Complexity - Abstract
A temporal graph is a graph whose edges only appear at certain points in time. Reachability in these graphs is defined in terms of paths that traverse the edges in chronological order (temporal paths). This form of reachability is neither symmetric nor transitive, the latter having important consequences on the computational complexity of even basic questions, such as computing temporal connected components. In this paper, we introduce several parameters that capture how far a temporal graph $\mathcal{G}$ is from being transitive, namely, \emph{vertex-deletion distance to transitivity} and \emph{arc-modification distance to transitivity}, both being applied to the reachability graph of $\mathcal{G}$. We illustrate the impact of these parameters on the temporal connected component problem, obtaining several tractability results in terms of fixed-parameter tractability and polynomial kernels. Significantly, these results are obtained without restrictions of the underlying graph, the snapshots, or the lifetime of the input graph. As such, our results isolate the impact of non-transitivity and confirm the key role that it plays in the hardness of temporal graph problems.
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- 2024
126. Domain Adaptation of Echocardiography Segmentation Via Reinforcement Learning
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Judge, Arnaud, Judge, Thierry, Duchateau, Nicolas, Sandler, Roman A., Sokol, Joseph Z., Bernard, Olivier, and Jodoin, Pierre-Marc
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Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Artificial Intelligence ,Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning - Abstract
Performance of deep learning segmentation models is significantly challenged in its transferability across different medical imaging domains, particularly when aiming to adapt these models to a target domain with insufficient annotated data for effective fine-tuning. While existing domain adaptation (DA) methods propose strategies to alleviate this problem, these methods do not explicitly incorporate human-verified segmentation priors, compromising the potential of a model to produce anatomically plausible segmentations. We introduce RL4Seg, an innovative reinforcement learning framework that reduces the need to otherwise incorporate large expertly annotated datasets in the target domain, and eliminates the need for lengthy manual human review. Using a target dataset of 10,000 unannotated 2D echocardiographic images, RL4Seg not only outperforms existing state-of-the-art DA methods in accuracy but also achieves 99% anatomical validity on a subset of 220 expert-validated subjects from the target domain. Furthermore, our framework's reward network offers uncertainty estimates comparable with dedicated state-of-the-art uncertainty methods, demonstrating the utility and effectiveness of RL4Seg in overcoming domain adaptation challenges in medical image segmentation., Comment: 9 pages
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- 2024
127. Numerical simulations for the SAXO+ upgrade: Performance analysis of the adaptive optics system
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Goulas, Charles, Galicher, Raphaël, Vidal, Fabrice, Mazoyer, Johan, Ferreira, Florian, Sevin, Arnaud, Boccaletti, Anthony, Gendron, Eric, Béchet, Clémentine, Tallon, Michel, Langlois, Maud, Kulcsár, Caroline, Raynaud, Henri-François, Galland, Nicolas, Schreiber, Laura, Dinis, Isaac Bernardino, Wildi, François, Chauvin, Gaël, and Milli, Julien
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Astrophysics - Instrumentation and Methods for Astrophysics - Abstract
SPHERE, operating at the VLT since 2014, is currently one of the high-contrast instruments with a higher performance. Its adaptive optics system, known as SAXO, will be upgraded to SAXO+, which features the addition of a second stage of adaptive optics. This stage will use a near-infrared pyramid wavefront sensor to record images of fainter exoplanets around redder stars. In this work, we compare the performance of SAXO and SAXO+. We look for the optimal values of the key system parameters of SAXO+ for various science cases and turbulence conditions. We performed numerical simulations using COMPASS, an end-to-end adaptive optics simulation tool. We simulated perfect coronagraph images of an on-axis point source, and we minimized the residual starlight intensity between 3 and 5 ${\lambda}/D$ as a performance criterion. The explored parameter space includes science cases, turbulence conditions, and key system parameters. In every science case and turbulence condition, SAXO+ reduces the residual starlight intensity inside the correction zone of the second stage by a factor of ten compared to SAXO. The optimal first stage gain is lower for SAXO+ than for SAXO alone. We quantified the gain in performance of SAXO+ when changing the second stage frequency from 2 kHz to 3 kHz, and we conclude that 2 kHz may be sufficient for most realistic conditions. We give the optimal first stage gain as well as the first and second stage frequencies for every seeing, coherence time, and science case. Finally, we find that a 2 ${\lambda_{\mathrm{WFS}}}/D$ pyramid modulation radius is a good trade-off between performance and robustness against varying turbulence conditions. This study shows that the future SAXO+ system will outperform the current SAXO system in all studied cases., Comment: 14 pages, 11 figures
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- 2024
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128. Homomorphisms and Embeddings of STRIPS Planning Models
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Lequen, Arnaud, Cooper, Martin C., and Maris, Frédéric
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Computer Science - Artificial Intelligence - Abstract
Determining whether two STRIPS planning instances are isomorphic is the simplest form of comparison between planning instances. It is also a particular case of the problem concerned with finding an isomorphism between a planning instance $P$ and a sub-instance of another instance $P_0$ . One application of such a mapping is to efficiently produce a compiled form containing all solutions to P from a compiled form containing all solutions to $P_0$. We also introduce the notion of embedding from an instance $P$ to another instance $P_0$, which allows us to deduce that $P_0$ has no solution-plan if $P$ is unsolvable. In this paper, we study the complexity of these problems. We show that the first is GI-complete, and can thus be solved, in theory, in quasi-polynomial time. While we prove the remaining problems to be NP-complete, we propose an algorithm to build an isomorphism, when possible. We report extensive experimental trials on benchmark problems which demonstrate conclusively that applying constraint propagation in preprocessing can greatly improve the efficiency of a SAT solver.
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- 2024
129. Turbulent homeomorphisms and the topological snail
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Dehove, Arnaud
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Mathematics - Dynamical Systems ,37B40, 37B55 ,A.0 - Abstract
The topological snail is a geometric universal plane object, described by matrices in the projective special linear group with integer coefficients. It has many nice properties : in the case of three points, it natually defines a representation of the mapping-class group. From a dynamical point of view, it gives a description of the set of fixed points and periodic obits of turbulent homeomorphims in the plane. Those homeomorphisms are the truly complicated one. They have many fixed points and periodic orbits, as much as the trace of the turbulence matrix that caracterizes their action on a finite invariant set. The topological snail generally gives a map of the fixed points and periodic orbits of a such homeomorphims. One can compute their indexes and turbulent topological types, and different Nielsen's classes of fixed points naturally give a minoration of the topological entropy of the considered homeomorphisms by the logarithm of the spectral radius of the turbulence matrix. One even get a dynamical interpretation and proof of the extension of the Fermat' theorem to matrices. I first described the topological snail and its properties at my conference the 31st of may 2024 in Paris, in the case of a purely turbulent homeomorphism, with an invariant set of three points.
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- 2024
130. Phase-Bounded Broadcast Networks over Topologies of Communication
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Guillou, Lucie, Sangnier, Arnaud, and Sznajder, Nathalie
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Computer Science - Logic in Computer Science ,Computer Science - Multiagent Systems - Abstract
We study networks of processes that all execute the same finite state protocol and that communicate through broadcasts. The processes are organized in a graph (a topology) and only the neighbors of a process in this graph can receive its broadcasts. The coverability problem asks, given a protocol and a state of the protocol, whether there is a topology for the processes such that one of them (at least) reaches the given state. This problem is undecidable. We study here an under-approximation of the problem where processes alternate a bounded number of times $k$ between phases of broadcasting and phases of receiving messages. We show that, if the problem remains undecidable when $k$ is greater than 6, it becomes decidable for $k=2$, and EXPSPACE-complete for $k=1$. Furthermore, we show that if we restrict ourselves to line topologies, the problem is in $P$ for $k=1$ and $k=2$., Comment: long version of a paper accepted to appear at CONCUR 2024
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- 2024
131. Coupled Input-Output Dimension Reduction: Application to Goal-oriented Bayesian Experimental Design and Global Sensitivity Analysis
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Chen, Qiao, Arnaud, Elise, Baptista, Ricardo, and Zahm, Olivier
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Statistics - Machine Learning ,Computer Science - Machine Learning ,Mathematics - Statistics Theory ,65D40, 62F15, 62K05 - Abstract
We introduce a new method to jointly reduce the dimension of the input and output space of a high-dimensional function. Choosing a reduced input subspace influences which output subspace is relevant and vice versa. Conventional methods focus on reducing either the input or output space, even though both are often reduced simultaneously in practice. Our coupled approach naturally supports goal-oriented dimension reduction, where either an input or output quantity of interest is prescribed. We consider, in particular, goal-oriented sensor placement and goal-oriented sensitivity analysis, which can be viewed as dimension reduction where the most important output or, respectively, input components are chosen. Both applications present difficult combinatorial optimization problems with expensive objectives such as the expected information gain and Sobol indices. By optimizing gradient-based bounds, we can determine the most informative sensors and most sensitive parameters as the largest diagonal entries of some diagnostic matrices, thus bypassing the combinatorial optimization and objective evaluation.
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- 2024
132. The Kinetics Observer: A Tightly Coupled Estimator for Legged Robots
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Demont, Arnaud, Benallegue, Mehdi, Benallegue, Abdelaziz, Gergondet, Pierre, Dallard, Antonin, Cisneros, Rafael, Murooka, Masaki, and Kanehiro, Fumio
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Computer Science - Robotics - Abstract
In this paper, we propose the "Kinetics Observer", a novel estimator addressing the challenge of state estimation for legged robots using proprioceptive sensors (encoders, IMU and force/torque sensors). Based on a Multiplicative Extended Kalman Filter, the Kinetics Observer allows the real-time simultaneous estimation of contact and perturbation forces, and of the robot's kinematics, which are accurate enough to perform proprioceptive odometry. Thanks to a visco-elastic model of the contacts linking their kinematics to the ones of the centroid of the robot, the Kinetics Observer ensures a tight coupling between the whole-body kinematics and dynamics of the robot. This coupling entails a redundancy of the measurements that enhances the robustness and the accuracy of the estimation. This estimator was tested on two humanoid robots performing long distance walking on even terrain and non-coplanar multi-contact locomotion.
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- 2024
133. Four microlensing giant planets detected through signals produced by minor-image perturbations
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Han, Cheongho, Bond, Ian A., Lee, Chung-Uk, Gould, Andrew, Albrow, Michael D., Chung, Sun-Ju, Hwang, Kyu-Ha, Jung, Youn Kil, Ryu, Yoon-Hyun, Shvartzvald, Yossi, Shin, In-Gu, Yee, Jennifer C., Yang, Hongjing, Zang, Weicheng, Cha, Sang-Mok, Kim, Doeon, Kim, Dong-Jin, Kim, Seung-Lee, Lee, Dong-Joo, Lee, Yongseok, Park, Byeong-Gon, Pogge, Richard W., Abe, Fumio, Bando, Ken, Barry, Richard, Bennett, David P., Bhattacharya, Aparna, Fujii, Hirosame, Fukui, Akihiko, Hamada, Ryusei, Hamasaki, Shunya Hamada Naoto, Hirao, Yuki, Silva, Stela Ishitani, Itow, Yoshitaka, Kirikawa, Rintaro, Koshimoto, Naoki, Matsubara, Yutaka, Miyazaki, Shota, Muraki, Yasushi, Nagai, Tutumi, Nunota, Kansuke, Olmschenk, Greg, Ranc, Clément, Rattenbury, Nicholas J., Satoh, Yuki, Sumi, Takahiro, Suzuki, Daisuke, Tomoyoshi, Mio, Tristram, Paul J., Vandorou, Aikaterini, Yama, Hibiki, Yamashita, Kansuke, Bachelet, Etienne, Rota, Paolo, Bozza, Valerio, Zielinski, Paweł, Street, Rachel A., Tsapras, Yiannis, Hundertmark, Markus, Wambsganss, Joachim, Wyrzykowski, Łukasz, Jaimes, Roberto Figuera, Cassan, Arnaud, Dominik, Martin, Rybicki, Krzysztof A., and Rabus, Markus
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Astrophysics - Earth and Planetary Astrophysics ,Astrophysics - Astrophysics of Galaxies ,Astrophysics - Instrumentation and Methods for Astrophysics ,Astrophysics - Solar and Stellar Astrophysics - Abstract
We investigated the nature of the anomalies appearing in four microlensing events KMT-2020-BLG-0757, KMT-2022-BLG-0732, KMT-2022-BLG-1787, and KMT-2022-BLG-1852. The light curves of these events commonly exhibit initial bumps followed by subsequent troughs that extend across a substantial portion of the light curves. We performed thorough modeling of the anomalies to elucidate their characteristics. Despite their prolonged durations, which differ from the usual brief anomalies observed in typical planetary events, our analysis revealed that each anomaly in these events originated from a planetary companion located within the Einstein ring of the primary star. It was found that the initial bump arouse when the source star crossed one of the planetary caustics, while the subsequent trough feature occurred as the source traversed the region of minor image perturbations lying between the pair of planetary caustics. The estimated masses of the host and planet, their mass ratios, and the distance to the discovered planetary systems are $(M_{\rm host}/M_\odot, M_{\rm planet}/M_{\rm J}, q/10^{-3}, \dl/{\rm kpc}) = (0.58^{+0.33}_{-0.30}, 10.71^{+6.17}_{-5.61}, 17.61\pm 2.25,6.67^{+0.93}_{-1.30})$ for KMT-2020-BLG-0757, $(0.53^{+0.31}_{-0.31}, 1.12^{+0.65}_{-0.65}, 2.01 \pm 0.07, 6.66^{+1.19}_{-1.84})$ for KMT-2022-BLG-0732, $(0.42^{+0.32}_{-0.23}, 6.64^{+4.98}_{-3.64}, 15.07\pm 0.86, 7.55^{+0.89}_{-1.30})$ for KMT-2022-BLG-1787, and $(0.32^{+0.34}_{-0.19}, 4.98^{+5.42}_{-2.94}, 8.74\pm 0.49, 6.27^{+0.90}_{-1.15})$ for KMT-2022-BLG-1852. These parameters indicate that all the planets are giants with masses exceeding the mass of Jupiter in our solar system and the hosts are low-mass stars with masses substantially less massive than the Sun., Comment: 10 pages, 12 figures, 7 tables
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- 2024
134. Forest-skein groups III: simplicity
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Brothier, Arnaud and Seelig, Ryan
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Mathematics - Group Theory ,Mathematics - Dynamical Systems - Abstract
An Ore forest-skein category provides three forest-skein groups equipped with a powerful diagrammatic calculus analogous to Richard Thompson's groups F,T,V. We investigate when forest-skein groups have simple derived subgroups and establish two characterisations: a dynamical one and a categorical one. We then construct two classes of examples. The first associates two finitely presented simple groups to every finite binary tree and the second associates two simple groups to every n-ary Higman-Thompson group., Comment: 46 pages, 22 figures
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- 2024
135. Influence of Orbit and Mass Constraints on Reflected Light Characterization of Directly Imaged Rocky Exoplanets
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Salvador, Arnaud, Robinson, Tyler D., Fortney, Jonathan J., and Marley, Mark S.
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Astrophysics - Earth and Planetary Astrophysics ,Astrophysics - Instrumentation and Methods for Astrophysics ,Physics - Geophysics - Abstract
Survey strategies for upcoming exoplanet direct imaging missions have considered varying assumptions of prior knowledge. Precursor radial velocity surveys could have detected nearby exo-Earths and provided prior orbit and mass constraints. Alternatively, a direct imaging mission performing astrometry could yield constraints on orbit and phase angle of target planets. Understanding the impact of prior mass and orbit information on planetary characterization is crucial for efficiently recognizing habitable exoplanets. To address this question, we use a reflected-light retrieval tool to infer the atmospheric and bulk properties of directly imaged Earth-analogs while considering varying levels of prior information and signal-to-noise ratio (SNR). Because of the strong correlation between the orbit-related parameters and the planetary radius, prior information on the orbital distance and planetary phase yield tight constraints on the planetary radius: from $R_{\rm{p}}=2.95^{+2.69}_{-1.95}~R_{\oplus}$ without prior knowledge, to $R_{\rm{p}}=1.01^{+0.33}_{-0.19}~R_{\oplus}$ with prior determination of the orbit for $\rm{SNR}=20$ in the visible/near-infrared spectral range, thus allowing size determination from reflected light observations. However, additional knowledge of planet mass does not notably enhance radius ($R_{\rm{p}}=0.98^{+0.17}_{-0.14}~R_{\oplus}$) or atmospheric characterization. Also, prior knowledge of the mass alone does not yield a tight radius constraint ($R_{\rm{p}}=1.64^{+1.29}_{-0.80}~R_{\oplus}$) nor improves atmospheric composition inference. By contrast, because of its sensitivity to gas column abundance, detecting a Rayleigh scattering slope or bounding Rayleigh opacity helps to refine gas mixing ratio inferences without requiring prior mass knowledge. Overall, apart from radius determination, increasing the SNR is more beneficial than additional prior observations., Comment: Accepted for publication in ApJL
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- 2024
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136. The PLATO Mission
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Rauer, Heike, Aerts, Conny, Cabrera, Juan, Deleuil, Magali, Erikson, Anders, Gizon, Laurent, Goupil, Mariejo, Heras, Ana, Lorenzo-Alvarez, Jose, Marliani, Filippo, Martin-Garcia, César, Mas-Hesse, J. Miguel, O'Rourke, Laurence, Osborn, Hugh, Pagano, Isabella, Piotto, Giampaolo, Pollacco, Don, Ragazzoni, Roberto, Ramsay, Gavin, Udry, Stéphane, Appourchaux, Thierry, Benz, Willy, Brandeker, Alexis, Güdel, Manuel, Janot-Pacheco, Eduardo, Kabath, Petr, Kjeldsen, Hans, Min, Michiel, Santos, Nuno, Smith, Alan, Suarez, Juan-Carlos, Werner, Stephanie C., Aboudan, Alessio, Abreu, Manuel, a, Lorena Acu, Adams, Moritz, Adibekyan, Vardan, Affer, Laura, Agneray, François, Agnor, Craig, Børsen-Koch, Victor Aguirre, Ahmed, Saad, Aigrain, Suzanne, Al-Bahlawan, Ashraf, Gil, M de los Angeles Alcacera, Alei, Eleonora, Alencar, Silvia, Alexander, Richard, Alfonso-Garzón, Julia, Alibert, Yann, Prieto, Carlos Allende, Almeida, Leonardo, Sobrino, Roi Alonso, Altavilla, Giuseppe, Althaus, Christian, Trujillo, Luis Alonso Alvarez, Amarsi, Anish, Eiff, Matthias Ammler-von, Amôres, Eduardo, Andrade, Laerte, Antoniadis-Karnavas, Alexandros, António, Carlos, del Moral, Beatriz Aparicio, Appolloni, Matteo, Arena, Claudio, Armstrong, David, Aliaga, Jose Aroca, Asplund, Martin, Audenaert, Jeroen, Auricchio, Natalia, Avelino, Pedro, Baeke, Ann, Baillié, Kevin, Balado, Ana, Balagueró, Pau Ballber, Balestra, Andrea, Ball, Warrick, Ballans, Herve, Ballot, Jerome, Barban, Caroline, Barbary, Gaële, Barbieri, Mauro, Forteza, Sebasti Barceló, Barker, Adrian, Barklem, Paul, Barnes, Sydney, Navascues, David Barrado, Barragan, Oscar, Baruteau, Clément, Basu, Sarbani, Baudin, Frederic, Baumeister, Philipp, Bayliss, Daniel, Bazot, Michael, Beck, Paul G., Bedding, Tim, Belkacem, Kevin, Bellinger, Earl, Benatti, Serena, Benomar, Othman, Bérard, Diane, Bergemann, Maria, Bergomi, Maria, Bernardo, Pierre, Biazzo, Katia, Bignamini, Andrea, Bigot, Lionel, Billot, Nicolas, Binet, Martin, Biondi, David, Biondi, Federico, Birch, Aaron C., Bitsch, Bertram, Ceballos, Paz Victoria Bluhm, Bódi, Attila, Bognár, Zsófia, Boisse, Isabelle, Bolmont, Emeline, Bonanno, Alfio, Bonavita, Mariangela, Bonfanti, Andrea, Bonfils, Xavier, Bonito, Rosaria, Bonomo, Aldo Stefano, Börner, Anko, Saikia, Sudeshna Boro, Martín, Elisa Borreguero, Borsa, Francesco, Borsato, Luca, Bossini, Diego, Bouchy, Francois, Boué, Gwenaël, Boufleur, Rodrigo, Boumier, Patrick, Bourrier, Vincent, Bowman, Dominic M., Bozzo, Enrico, Bradley, Louisa, Bray, John, Bressan, Alessandro, Breton, Sylvain, Brienza, Daniele, Brito, Ana, Brogi, Matteo, Brown, Beverly, Brown, David J. A., Brun, Allan Sacha, Bruno, Giovanni, Bruns, Michael, Buchhave, Lars A., Bugnet, Lisa, Buldgen, Gaël, Burgess, Patrick, Busatta, Andrea, Busso, Giorgia, Buzasi, Derek, Caballero, José A., Cabral, Alexandre, Gomez, Juan-Francisco Cabrero, Calderone, Flavia, Cameron, Robert, Cameron, Andrew, Campante, Tiago, Gestal, Néstor Campos, Martins, Bruno Leonardo Canto, Cara, Christophe, Carone, Ludmila, Carrasco, Josep Manel, Casagrande, Luca, Casewell, Sarah L., Cassisi, Santi, Castellani, Marco, Castro, Matthieu, Catala, Claude, Fernández, Irene Catalán, Catelan, Márcio, Cegla, Heather, Cerruti, Chiara, Cessa, Virginie, Chadid, Merieme, Chaplin, William, Charpinet, Stephane, Chiappini, Cristina, Chiarucci, Simone, Chiavassa, Andrea, Chinellato, Simonetta, Chirulli, Giovanni, Christensen-Dalsgaard, Jørgen, Church, Ross, Claret, Antonio, Clarke, Cathie, Claudi, Riccardo, Clermont, Lionel, Coelho, Hugo, Coelho, Joao, Cogato, Fabrizio, Colomé, Josep, Condamin, Mathieu, García, Fernando Conde, Conseil, Simon, Corbard, Thierry, Correia, Alexandre C. M., Corsaro, Enrico, Cosentino, Rosario, Costes, Jean, Cottinelli, Andrea, Covone, Giovanni, Creevey, Orlagh L., Crida, Aurelien, Csizmadia, Szilard, Cunha, Margarida, Curry, Patrick, da Costa, Jefferson, da Silva, Francys, Dalal, Shweta, Damasso, Mario, Damiani, Cilia, Damiani, Francesco, Chagas, Maria Liduina das, Davies, Melvyn, Davies, Guy, Davies, Ben, Davison, Gary, de Almeida, Leandro, de Angeli, Francesca, de Barros, Susana Cristina Cabral, Leão, Izan de Castro, de Freitas, Daniel Brito, de Freitas, Marcia Cristina, De Martino, Domitilla, de Medeiros, José Renan, de Paula, Luiz Alberto, Gómez, Álvaro de Pedraza, de Plaa, Jelle, De Ridder, Joris, Deal, Morgan, Decin, Leen, Deeg, Hans, Innocenti, Scilla Degl, Deheuvels, Sebastien, del Burgo, Carlos, Del Sordo, Fabio, Delgado-Mena, Elisa, Demangeon, Olivier, Denk, Tilmann, Derekas, Aliz, Desert, Jean-Michel, Desidera, Silvano, Dexet, Marc, Di Criscienzo, Marcella, Di Giorgio, Anna Maria, Di Mauro, Maria Pia, Rial, Federico Jose Diaz, Díaz-García, José-Javier, Dima, Marco, Dinuzzi, Giacomo, Dionatos, Odysseas, Distefano, Elisa, Nascimento Jr., Jose-Dias do, Domingo, Albert, D'Orazi, Valentina, Dorn, Caroline, Doyle, Lauren, Duarte, Elena, Ducellier, Florent, Dumaye, Luc, Dumusque, Xavier, Dupret, Marc-Antoine, Eggenberger, Patrick, Ehrenreich, David, Eigmüller, Philipp, Eising, Johannes, Emilio, Marcelo, Eriksson, Kjell, Ermocida, Marco, Giribaldi, Riano Isidoro Escate, Eschen, Yoshi, ez, Lucía Espinosa Yá, Estrela, In s, Evans, Dafydd Wyn, Fabbian, Damian, Fabrizio, Michele, Faria, João Pedro, Farina, Maria, Farinato, Jacopo, Feliz, Dax, Feltzing, Sofia, Fenouillet, Thomas, Fernández, Miguel, Ferrari, Lorenza, Ferraz-Mello, Sylvio, Fialho, Fabio, Fienga, Agnes, Figueira, Pedro, Fiori, Laura, Flaccomio, Ettore, Focardi, Mauro, Foley, Steve, Fontignie, Jean, Ford, Dominic, Fornazier, Karin, Forveille, Thierry, Fossati, Luca, Franca, Rodrigo de Marca, da Silva, Lucas Franco, Frasca, Antonio, Fridlund, Malcolm, Furlan, Marco, Gabler, Sarah-Maria, Gaido, Marco, Gallagher, Andrew, Sempere, Paloma I. Gallego, Galli, Emanuele, García, Rafael A., Hernández, Antonio García, Munoz, Antonio Garcia, García-Vázquez, Hugo, Haba, Rafael Garrido, Gaulme, Patrick, Gauthier, Nicolas, Gehan, Charlotte, Gent, Matthew, Georgieva, Iskra, Ghigo, Mauro, Giana, Edoardo, Gill, Samuel, Girardi, Leo, Winter, Silvia Giuliatti, Giusi, Giovanni, da Silva, João Gomes, Zazo, Luis Jorge Gómez, Gomez-Lopez, Juan Manuel, Hernández, Jonay Isai González, Murillo, Kevin Gonzalez, Melchor, Alejandro Gonzalo, Gorius, Nicolas, Gouel, Pierre-Vincent, Goulty, Duncan, Granata, Valentina, Grenfell, John Lee, bach, Denis Grie, Grolleau, Emmanuel, Grouffal, Salomé, Grziwa, Sascha, Guarcello, Mario Giuseppe, Gueguen, Lo c, Guenther, Eike Wolf, Guilhem, Terrasa, Guillerot, Lucas, Guillot, Tristan, Guiot, Pierre, Guterman, Pascal, Gutiérrez, Antonio, Gutiérrez-Canales, Fernando, Hagelberg, Janis, Haldemann, Jonas, Hall, Cassandra, Handberg, Rasmus, Harrison, Ian, Harrison, Diana L., Hasiba, Johann, Haswell, Carole A., Hatalova, Petra, Hatzes, Artie, Haywood, Raphaelle, Hébrard, Guillaume, Heckes, Frank, Heiter, Ulrike, Hekker, Saskia, Heller, René, Helling, Christiane, Helminiak, Krzysztof, Hemsley, Simon, Heng, Kevin, Herbst, Konstantin, Hermans, Aline, Hermes, JJ, Torres, Nadia Hidalgo, Hinkel, Natalie, Hobbs, David, Hodgkin, Simon, Hofmann, Karl, Hojjatpanah, Saeed, Houdek, Günter, Huber, Daniel, Huesler, Joseph, Hui-Bon-Hoa, Alain, Huygen, Rik, Huynh, Duc-Dat, Iro, Nicolas, Irwin, Jonathan, Irwin, Mike, Izidoro, André, Jacquinod, Sophie, Jannsen, Nicholas Emborg, Janson, Markus, Jeszenszky, Harald, Jiang, Chen, Mancebo, Antonio José Jimenez, Jofre, Paula, Johansen, Anders, Johnston, Cole, Jones, Geraint, Kallinger, Thomas, Kálmán, Szilárd, Kanitz, Thomas, Karjalainen, Marie, Karjalainen, Raine, Karoff, Christoffer, Kawaler, Steven, Kawata, Daisuke, Keereman, Arnoud, Keiderling, David, Kennedy, Tom, Kenworthy, Matthew, Kerschbaum, Franz, Kidger, Mark, Kiefer, Flavien, Kintziger, Christian, Kislyakova, Kristina, Kiss, László, Klagyivik, Peter, Klahr, Hubert, Klevas, Jonas, Kochukhov, Oleg, Köhler, Ulrich, Kolb, Ulrich, Koncz, Alexander, Korth, Judith, Kostogryz, Nadiia, Kovács, Gábor, Kovács, József, Kozhura, Oleg, Krivova, Natalie, Kucinskas, Arunas, Kuhlemann, Ilyas, Kupka, Friedrich, Laauwen, Wouter, Labiano, Alvaro, Lagarde, Nadege, Laget, Philippe, Laky, Gunter, Lam, Kristine Wai Fun, Lambrechts, Michiel, Lammer, Helmut, Lanza, Antonino Francesco, Lanzafame, Alessandro, Martiz, Mariel Lares, Laskar, Jacques, Latter, Henrik, Lavanant, Tony, Lawrenson, Alastair, Lazzoni, Cecilia, Lebre, Agnes, Lebreton, Yveline, Etangs, Alain Lecavelier des, Lee, Katherine, Leinhardt, Zoe, Leleu, Adrien, Lendl, Monika, Leto, Giuseppe, Levillain, Yves, Libert, Anne-Sophie, Lichtenberg, Tim, Ligi, Roxanne, Lignieres, Francois, Lillo-Box, Jorge, Linsky, Jeffrey, Liu, John Scige, Loidolt, Dominik, Longval, Yuying, Lopes, Ilídio, Lorenzani, Andrea, Ludwig, Hans-Guenter, Lund, Mikkel, Lundkvist, Mia Sloth, Luri, Xavier, Maceroni, Carla, Madden, Sean, Madhusudhan, Nikku, Maggio, Antonio, Magliano, Christian, Magrin, Demetrio, Mahy, Laurent, Maibaum, Olaf, Malac-Allain, LeeRoy, Malapert, Jean-Christophe, Malavolta, Luca, Maldonado, Jesus, Mamonova, Elena, Manchon, Louis, Manjón, Andres, Mann, Andrew, Mantovan, Giacomo, Marafatto, Luca, Marconi, Marcella, Mardling, Rosemary, Marigo, Paola, Marinoni, Silvia, Marques, rico, Marques, Joao Pedro, Marrese, Paola Maria, Marshall, Douglas, Perales, Silvia Martínez, Mary, David, Marzari, Francesco, Masana, Eduard, Mascher, Andrina, Mathis, Stéphane, Mathur, Savita, Vodopivec, Iris Martín, Figueiredo, Ana Carolina Mattiuci, Maxted, Pierre F. L., Mazeh, Tsevi, Mazevet, Stephane, Mazzei, Francesco, McCormac, James, McMillan, Paul, Menou, Lucas, Merle, Thibault, Meru, Farzana, Mesa, Dino, Messina, Sergio, Mészáros, Szabolcs, Meunier, Nadége, Meunier, Jean-Charles, Micela, Giuseppina, Michaelis, Harald, Michel, Eric, Michielsen, Mathias, Michtchenko, Tatiana, Miglio, Andrea, Miguel, Yamila, Milligan, David, Mirouh, Giovanni, Mitchell, Morgan, Moedas, Nuno, Molendini, Francesca, Molnár, László, Mombarg, Joey, Montalban, Josefina, Montalto, Marco, Monteiro, Mário J. P. F. G., Sánchez, Francisco Montoro, Morales, Juan Carlos, Morales-Calderon, Maria, Morbidelli, Alessandro, Mordasini, Christoph, Moreau, Chrystel, Morel, Thierry, Morello, Guiseppe, Morin, Julien, Mortier, Annelies, Mosser, Beno t, Mourard, Denis, Mousis, Olivier, Moutou, Claire, Mowlavi, Nami, Moya, Andrés, Muehlmann, Prisca, Muirhead, Philip, Munari, Matteo, Musella, Ilaria, Mustill, Alexander James, Nardetto, Nicolas, Nardiello, Domenico, Narita, Norio, Nascimbeni, Valerio, Nash, Anna, Neiner, Coralie, Nelson, Richard P., Nettelmann, Nadine, Nicolini, Gianalfredo, Nielsen, Martin, Niemi, Sami-Matias, Noack, Lena, Noels-Grotsch, Arlette, Noll, Anthony, Norazman, Azib, Norton, Andrew J., Nsamba, Benard, Ofir, Aviv, Ogilvie, Gordon, Olander, Terese, Olivetto, Christian, Olofsson, Göran, Ong, Joel, Ortolani, Sergio, Oshagh, Mahmoudreza, Ottacher, Harald, Ottensamer, Roland, Ouazzani, Rhita-Maria, Paardekooper, Sijme-Jan, Pace, Emanuele, Pajas, Miriam, Palacios, Ana, Palandri, Gaelle, Palle, Enric, Paproth, Carsten, Parro, Vanderlei, Parviainen, Hannu, Granado, Javier Pascual, Passegger, Vera Maria, Pastor-Morales, Carmen, Pätzold, Martin, Pedersen, May Gade, Hidalgo, David Pena, Pepe, Francesco, Pereira, Filipe, Persson, Carina M., Pertenais, Martin, Peter, Gisbert, Petit, Antoine C., Petit, Pascal, Pezzuto, Stefania, Pichierri, Gabriele, Pietrinferni, Adriano, Pinheiro, Fernando, Pinsonneault, Marc, Plachy, Emese, Plasson, Philippe, Plez, Bertrand, Poppenhaeger, Katja, Poretti, Ennio, Portaluri, Elisa, Portell, Jordi, de Mello, Gustavo Frederico Porto, Poyatos, Julien, Pozuelos, Francisco J., Moroni, Pier Giorgio Prada, Pricopi, Dumitru, Prisinzano, Loredana, Quade, Matthias, Quirrenbach, Andreas, Reina, Julio Arturo Rabanal, Soares, Maria Cristina Rabello, Raimondo, Gabriella, Rainer, Monica, Rodón, Jose Ramón, Ramón-Ballesta, Alejandro, Zapata, Gonzalo Ramos, Rätz, Stefanie, Rauterberg, Christoph, Redman, Bob, Redmer, Ronald, Reese, Daniel, Regibo, Sara, Reiners, Ansgar, Reinhold, Timo, Renie, Christian, Ribas, Ignasi, Ribeiro, Sergio, Ricciardi, Thiago Pereira, Rice, Ken, Richard, Olivier, Riello, Marco, Rieutord, Michel, Ripepi, Vincenzo, Rixon, Guy, Rockstein, Steve, Ortiz, José Ramón Rodón, Rodríguez, María Teresa Rodrigo, Amor, Alberto Rodríguez, Díaz, Luisa Fernanda Rodríguez, Garcia, Juan Pablo Rodriguez, Rodriguez-Gomez, Julio, Roehlly, Yannick, Roig, Fernando, Rojas-Ayala, Bárbara, Rolf, Tobias, Rørsted, Jakob Lysgaard, Rosado, Hugo, Rosotti, Giovanni, Roth, Olivier, Roth, Markus, Rousseau, Alex, Roxburgh, Ian, Roy, Fabrice, Royer, Pierre, Ruane, Kirk, Mastropasqua, Sergio Rufini, de Galarreta, Claudia Ruiz, Russi, Andrea, Saar, Steven, Saillenfest, Melaine, Salaris, Maurizio, Salmon, Sebastien, Saltas, Ippocratis, Samadi, Réza, Samadi, Aunia, Samra, Dominic, da Silva, Tiago Sanches, Carrasco, Miguel Andrés Sánchez, Santerne, Alexandre, Pé, Amaia Santiago, Santoli, Francesco, Santos, ngela R. G., Mesa, Rosario Sanz, Sarro, Luis Manuel, Scandariato, Gaetano, Schäfer, Martin, Schlafly, Edward, Schmider, François-Xavier, Schneider, Jean, Schou, Jesper, Schunker, Hannah, Schwarzkopf, Gabriel Jörg, Serenelli, Aldo, Seynaeve, Dries, Shan, Yutong, Shapiro, Alexander, Shipman, Russel, Sicilia, Daniela, sanmartin, Maria Angeles Sierra, Sigot, Axelle, Silliman, Kyle, Silvotti, Roberto, Simon, Attila E., Napoli, Ricardo Simoyama, Skarka, Marek, Smalley, Barry, Smiljanic, Rodolfo, Smit, Samuel, Smith, Alexis, Smith, Leigh, Snellen, Ignas, Sódor, Ádám, Sohl, Frank, Solanki, Sami K., Sortino, Francesca, Sousa, Sérgio, Southworth, John, Souto, Diogo, Sozzetti, Alessandro, Stamatellos, Dimitris, Stassun, Keivan, Steller, Manfred, Stello, Dennis, Stelzer, Beate, Stiebeler, Ulrike, Stokholm, Amalie, Storelvmo, Trude, Strassmeier, Klaus, Strøm, Paul Anthony, Strugarek, Antoine, Sulis, Sophia, vanda, Michal, Szabados, László, Szabó, Róbert, Szabó, Gyula M., Szuszkiewicz, Ewa, Talens, Geert Jan, Teti, Daniele, Theisen, Tom, Thévenin, Frédéric, Thoul, Anne, Tiphene, Didier, Titz-Weider, Ruth, Tkachenko, Andrew, Tomecki, Daniel, Tonfat, Jorge, Tosi, Nicola, Trampedach, Regner, Traven, Gregor, Triaud, Amaury, Trønnes, Reidar, Tsantaki, Maria, Tschentscher, Matthias, Turin, Arnaud, Tvaruzka, Adam, Ulmer, Bernd, Ulmer-Moll, Solène, Ulusoy, Ceren, Umbriaco, Gabriele, Valencia, Diana, Valentini, Marica, Valio, Adriana, Guijarro, Ángel Luis Valverde, Van Eylen, Vincent, Van Grootel, Valerie, van Kempen, Tim A., Van Reeth, Timothy, Van Zelst, Iris, Vandenbussche, Bart, Vasiliou, Konstantinos, Vasilyev, Valeriy, de Mascarenhas, David Vaz, Vazan, Allona, Nunez, Marina Vela, Velloso, Eduardo Nunes, Ventura, Rita, Ventura, Paolo, Venturini, Julia, Trallero, Isabel Vera, Veras, Dimitri, Verdugo, Eva, Verma, Kuldeep, Vibert, Didier, Martinez, Tobias Vicanek, Vida, Krisztián, Vigan, Arthur, Villacorta, Antonio, Villaver, Eva, Aparicio, Marcos Villaverde, Viotto, Valentina, Vorobyov, Eduard, Vorontsov, Sergey, Wagner, Frank W., Walloschek, Thomas, Walton, Nicholas, Walton, Dave, Wang, Haiyang, Waters, Rens, Watson, Christopher, Wedemeyer, Sven, Weeks, Angharad, Weingrill, Jörg, Weiss, Annita, Wendler, Belinda, West, Richard, Westerdorff, Karsten, Westphal, Pierre-Amaury, Wheatley, Peter, White, Tim, Whittaker, Amadou, Wickhusen, Kai, Wilson, Thomas, Windsor, James, Winter, Othon, Winther, Mark Lykke, Winton, Alistair, Witteck, Ulrike, Witzke, Veronika, Woitke, Peter, Wolter, David, Wuchterl, Günther, Wyatt, Mark, Yang, Dan, Yu, Jie, Sanchez, Ricardo Zanmar, Osorio, María Rosa Zapatero, Zechmeister, Mathias, Zhou, Yixiao, Ziemke, Claas, and Zwintz, Konstanze
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Astrophysics - Instrumentation and Methods for Astrophysics ,Astrophysics - Earth and Planetary Astrophysics ,Astrophysics - Solar and Stellar Astrophysics - Abstract
PLATO (PLAnetary Transits and Oscillations of stars) is ESA's M3 mission designed to detect and characterise extrasolar planets and perform asteroseismic monitoring of a large number of stars. PLATO will detect small planets (down to <2 R_(Earth)) around bright stars (<11 mag), including terrestrial planets in the habitable zone of solar-like stars. With the complement of radial velocity observations from the ground, planets will be characterised for their radius, mass, and age with high accuracy (5 %, 10 %, 10 % for an Earth-Sun combination respectively). PLATO will provide us with a large-scale catalogue of well-characterised small planets up to intermediate orbital periods, relevant for a meaningful comparison to planet formation theories and to better understand planet evolution. It will make possible comparative exoplanetology to place our Solar System planets in a broader context. In parallel, PLATO will study (host) stars using asteroseismology, allowing us to determine the stellar properties with high accuracy, substantially enhancing our knowledge of stellar structure and evolution. The payload instrument consists of 26 cameras with 12cm aperture each. For at least four years, the mission will perform high-precision photometric measurements. Here we review the science objectives, present PLATO's target samples and fields, provide an overview of expected core science performance as well as a description of the instrument and the mission profile at the beginning of the serial production of the flight cameras. PLATO is scheduled for a launch date end 2026. This overview therefore provides a summary of the mission to the community in preparation of the upcoming operational phases.
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- 2024
137. Simplified and Generalized Masked Diffusion for Discrete Data
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Shi, Jiaxin, Han, Kehang, Wang, Zhe, Doucet, Arnaud, and Titsias, Michalis K.
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Computer Science - Machine Learning ,Statistics - Machine Learning - Abstract
Masked (or absorbing) diffusion is actively explored as an alternative to autoregressive models for generative modeling of discrete data. However, existing work in this area has been hindered by unnecessarily complex model formulations and unclear relationships between different perspectives, leading to suboptimal parameterization, training objectives, and ad hoc adjustments to counteract these issues. In this work, we aim to provide a simple and general framework that unlocks the full potential of masked diffusion models. We show that the continuous-time variational objective of masked diffusion models is a simple weighted integral of cross-entropy losses. Our framework also enables training generalized masked diffusion models with state-dependent masking schedules. When evaluated by perplexity, our models trained on OpenWebText surpass prior diffusion language models at GPT-2 scale and demonstrate superior performance on 4 out of 5 zero-shot language modeling tasks. Furthermore, our models vastly outperform previous discrete diffusion models on pixel-level image modeling, achieving 2.75 (CIFAR-10) and 3.40 (ImageNet 64x64) bits per dimension that are better than autoregressive models of similar sizes. Our code is available at https://github.com/google-deepmind/md4., Comment: NeurIPS 2024. Code is available at: https://github.com/google-deepmind/md4
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- 2024
138. On determinantal point processes with nonsymmetric kernels
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Arnaud, Poinas
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Mathematics - Statistics Theory - Abstract
Determinantal point processes (DPPs for short) are a class of repulsive point processes. They have found some statistical applications to model spatial point pattern datasets with repulsion between close points. In the case of DPPs on finite sets, they are defined by a matrix called the DPP kernel which is usually assumed to be symmetric. While there are a few known examples of DPPs with nonsymmetric kernels, not much is known on how this affects their usual properties. In this paper, we demonstrate how to adapt the results on $P_0$ matrices to the DPP setting in order to get necessary and sufficient conditions for the well-definedness of DPPs with nonsymmetric kernels. We also generalize various common results on DPPs. We then show how to use these results to construct attractive couplings of regular DPPs with symmetric kernels in order to model spatial marked point patterns with repulsion between points of the same mark and attraction between points of different marks.
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- 2024
139. Reconstructing training data from document understanding models
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Dentan, Jérémie, Paran, Arnaud, and Shabou, Aymen
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Computer Science - Cryptography and Security - Abstract
Document understanding models are increasingly employed by companies to supplant humans in processing sensitive documents, such as invoices, tax notices, or even ID cards. However, the robustness of such models to privacy attacks remains vastly unexplored. This paper presents CDMI, the first reconstruction attack designed to extract sensitive fields from the training data of these models. We attack LayoutLM and BROS architectures, demonstrating that an adversary can perfectly reconstruct up to 4.1% of the fields of the documents used for fine-tuning, including some names, dates, and invoice amounts up to six-digit numbers. When our reconstruction attack is combined with a membership inference attack, our attack accuracy escalates to 22.5%. In addition, we introduce two new end-to-end metrics and evaluate our approach under various conditions: unimodal or bimodal data, LayoutLM or BROS backbones, four fine-tuning tasks, and two public datasets (FUNSD and SROIE). We also investigate the interplay between overfitting, predictive performance, and susceptibility to our attack. We conclude with a discussion on possible defenses against our attack and potential future research directions to construct robust document understanding models.
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- 2024
140. Atomic representations of R. Thompson's groups and Cuntz's algebra
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Brothier, Arnaud and Wijesena, Dilshan
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Mathematics - Group Theory ,Mathematics - Operator Algebras - Abstract
We continue to study Pythagorean unitary representation of Richard Thompson's groups $F,T,V$ and their extension to the Cuntz(-Dixmier) algebra. Any linear isometry from a Hilbert space to its direct sum square produces such. We focus on those arising from a finite-dimensional Hilbert space. We show that they decompose as a direct sum of a so-called diffuse part and an atomic part. We previously proved that the diffuse part is Ind-mixing: it does not contain induced representations of finite-dimensional ones. In this article, we fully describe the atomic part: it is a finite direct sum of irreducible monomial representations arising from a precise family of parabolic subgroups., Comment: This is a shorter and updated version of arXiv:2302.04458 where we only consider finite-dimensional P-modules. 24 pages, 4 figures
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- 2024
141. A low cosmic-ray ionisation rate in the prestellar core Ophiuchus/H-MM1. Mapping of the molecular ions ortho-H2D+, N2H+, and DCO+
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Harju, Jorma, Vastel, Charlotte, Sipilae, Olli, Redaelli, Elena, Caselli, Paola, Pineda, Jaime E., Belloche, Arnaud, and Wyrowski, Friedrich
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Astrophysics - Astrophysics of Galaxies - Abstract
(abridged) We have mapped the prestellar core H-MM1 in Ophiuchus in rotational lines of ortho-H2D+ (oH2D+), N2H+, and DCO+ at the wavelength 0.8 mm with the Large APEX sub-Millimeter Array (LAsMA) multibeam receiver of the Atacama Pathfinder EXperiment (APEX) telescope. We also ran a series of chemistry models to predict the abundance distributions of the observed molecules, and to estimate the effect of the cosmic-ray ionisation rate on their abundances. The three line maps show different distributions. The oH2D+ map is extended and outlines the general structure of the core, while N2H+ mainly shows the density maxima, and the DCO+ emission peaks are shifted towards one edge of the core where a region of enhanced desorption has been found previously. According to the chemical simulation, the fractional oH2D+ abundance remains relatively high in the centre of the core, and its column density correlates strongly with the cosmic-ray ionisation rate. Simulated line maps constrain the cosmic-ray ionisation rate per hydrogen molecule to be low, between 5e-18/s and 1e-17/s in the H-MM1 core. This estimate agrees with the gas temperature measured in the core. Modelling line emission of oH2D+ provides a straightforward method of determining the cosmic-ray ionisation rate in dense clouds, where the primary ion, H3+, is not observable., Comment: accepted for publication in Astronomy & Astrophysics
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- 2024
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142. Efficient Prior Calibration From Indirect Data
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Akyildiz, O. Deniz, Girolami, Mark, Stuart, Andrew M., and Vadeboncoeur, Arnaud
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Statistics - Machine Learning ,Computer Science - Machine Learning ,Statistics - Computation - Abstract
Bayesian inversion is central to the quantification of uncertainty within problems arising from numerous applications in science and engineering. To formulate the approach, four ingredients are required: a forward model mapping the unknown parameter to an element of a solution space, often the solution space for a differential equation; an observation operator mapping an element of the solution space to the data space; a noise model describing how noise pollutes the observations; and a prior model describing knowledge about the unknown parameter before the data is acquired. This paper is concerned with learning the prior model from data; in particular, learning the prior from multiple realizations of indirect data obtained through the noisy observation process. The prior is represented, using a generative model, as the pushforward of a Gaussian in a latent space; the pushforward map is learned by minimizing an appropriate loss function. A metric that is well-defined under empirical approximation is used to define the loss function for the pushforward map to make an implementable methodology. Furthermore, an efficient residual-based neural operator approximation of the forward model is proposed and it is shown that this may be learned concurrently with the pushforward map, using a bilevel optimization formulation of the problem; this use of neural operator approximation has the potential to make prior learning from indirect data more computationally efficient, especially when the observation process is expensive, non-smooth or not known. The ideas are illustrated with the Darcy flow inverse problem of finding permeability from piezometric head measurements.
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- 2024
143. On the origin of infrared bands attributed to tryptophan in Spitzer observations of IC 348
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Dhariwal, Aditya, Speak, Thomas H., Zeng, Linshan, Rashidi, Amirhossein, Moore, Brendan, Berné, Olivier, Remijan, Anthony J., Schroetter, Ilane, McGuire, Brett A., Rivilla, Víctor M., Belloche, Arnaud, Jørgensen, Jes K., Djuricanin, Pavle, Momose, Takamasa, and Cooke, Ilsa R.
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Astrophysics - Astrophysics of Galaxies ,Astrophysics - Solar and Stellar Astrophysics - Abstract
Infrared emission features toward interstellar gas of the IC 348 star cluster in Perseus have been recently proposed to originate from the amino acid tryptophan. The assignment was based on laboratory infrared spectra of tryptophan pressed into pellets, a method which is known to cause large frequency shifts compared to the gas phase. We assess the validity of the assignment based on the original Spitzer data as well as new data from JWST. In addition, we report new spectra of tryptophan condensed in para-hydrogen matrices to compare with the observed spectra. The JWST MIRI data do not show evidence for tryptophan, despite deeper integration toward IC 348. In addition, we show that several of the lines attributed to tryptophan are likely due to instrumental artifacts. This, combined with the new laboratory data, allows us to conclude that there is no compelling evidence for the tryptophan assignment.
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- 2024
144. ELG Spectroscopic Systematics Analysis of the DESI Data Release 1
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Yu, Jiaxi, Ross, Ashley J., Rocher, Antoine, Alves, Otávio, de Mattia, Arnaud, Forero-Sánchez, Daniel, Kneib, Jean-Paul, Krolewski, Alex, Lan, TingWen, Rashkovetskyi, Michael, Aguilar, Jessica Nicole, Ahlen, Steven, Bailey, Stephen, Brooks, David, Chaussidon, Edmond, Claybaugh, Todd, de la Macorra, Axel, Dey, Arjun, Dey, Biprateep, Doel, Peter, Fanning, Kevin, Forero-Romero, Jaime E., Gaztañaga, Enrique, Gontcho, Satya Gontcho A, Honscheid, Klaus, Howlett, Cullan, Juneau, Stephanie, Kisner, Theodore, Kremin, Anthony, Lambert, Andrew, Landriau, Martin, Guillou, Laurent Le, Levi, Michael E., Manera, Marc, Martini, Paul, Meisner, Aaron, Miquel, Ramon, Moustakas, John, Mueller, Eva-Maria, Muñoz-Gutiérrez, Andrea, Myers, Adam D., Nie, Jundan, Niz, Gustavo, Palanque-Delabrouille, Nathalie, Percival, Will J., Poppett, Claire, Prada, Francisco, Rezaie, Mehdi, Rossi, Graziano, Sanchez, Eusebio, Schlafly, Edward F., Schlegel, David, Schubnell, Michael, Seo, Hee-Jong, Sprayberry, David, Tarlé, Gregory, Weaver, Benjamin A., Zarrouk, Pauline, Zhao, Cheng, Zhou, Rongpu, and Zou, Hu
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Astrophysics - Cosmology and Nongalactic Astrophysics - Abstract
Dark Energy Spectroscopic Instrument (DESI) uses more than 2.4 million Emission Line Galaxies (ELGs) for 3D large-scale structure (LSS) analyses in its Data Release 1 (DR1). Such large statistics enable thorough research on systematic uncertainties. In this study, we focus on spectroscopic systematics of ELGs. The redshift success rate ($f_{\rm goodz}$) is the relative fraction of secure redshifts among all measurements. It depends on observing conditions, thus introduces non-cosmological variations to the LSS. We, therefore, develop the redshift failure weight ($w_{\rm zfail}$) and a per-fibre correction ($\eta_{\rm zfail}$) to mitigate these dependences. They have minor influences on the galaxy clustering. For ELGs with a secure redshift, there are two subtypes of systematics: 1) catastrophics (large) that only occur in a few samples; 2) redshift uncertainty (small) that exists for all samples. The catastrophics represent 0.26\% of the total DR1 ELGs, composed of the confusion between O\,\textsc{ii} and sky residuals, double objects, total catastrophics and others. We simulate the realistic 0.26\% catastrophics of DR1 ELGs, the hypothetical 1\% catastrophics, and the truncation of the contaminated $1.31
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- 2024
145. Forward modeling fluctuations in the DESI LRGs target sample using image simulations
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Kong, Hui, Ross, Ashley J., Honscheid, Klaus, Lang, Dustin, Porredon, Anna, de Mattia, Arnaud, Rezaie, Mehdi, Zhou, Rongpu, Schlafly, Edward, Moustakas, John, Rosado-Marin, Alberto, Aguilar, Jessica Nicole, Ahlen, Steven, Brooks, David, Chaussidon, Edmond, Claybaugh, Todd, Cole, Shaun, de la Macorra, Axel, Dey, Arjun, Dey, Biprateep, Doel, Peter, Fanning, Kevin, Forero-Romero, Jaime E., Gaztanaga, Enrique, Gontcho, Satya Gontcho A, Gutierrez, Gaston, Howlett, Cullan, Juneau, Stephanie, Kremin, Anthony, Landriau, Martin, Levi, Michael, Manera, Marc, Martini, Paul, Meisner, Aaron, Miquel, Ramon, Mueller, Eva-Maria, Myers, Adam, Newman, Jeffrey A., Nie, Jundan, Niz, Gustavo, Percival, Will, Poppett, Claire, Prada, Francisco, Rossi, Graziano, Sanchez, Eusebio, Schlegel, David, Schubnell, Michael, Seo, Hee-Jong, Sprayberry, David, Tarle, Gregory, Magana, Mariana Vargas, Weaver, Benjamin Alan, and Zou, Hu
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Astrophysics - Cosmology and Nongalactic Astrophysics - Abstract
We use the forward modeling pipeline, Obiwan, to study the imaging systematics of the Luminous Red Galaxies (LRGs) targeted by the Dark Energy Spectroscopic Instrument (DESI). We update the Obiwan pipeline, which had previously been developed to simulate the optical images used to target DESI data, to further simulate WISE images in the infrared. This addition makes it possible to simulate the DESI LRGs sample, which utilizes WISE data in the target selection. Deep DESI imaging data combined with a method to account for biases in their shapes is used to define a truth sample of potential LRG targets. We simulate a total of 15 million galaxies to obtain a simulated LRG sample (Obiwan LRGs) that predicts the variations in target density due to imaging properties. We find that the simulations predict the trends with depth observed in the data, including how they depend on the intrinsic brightness of the galaxies. We observe that faint LRGs are the main contributing power of the imaging systematics trend induced by depth. We also find significant trends in the data against Galactic extinction that are not predicted by Obiwan. These trends depend strongly on the particular map of Galactic extinction chosen to test against, implying Large-Scale Structure systematic contamination (e.g. Cosmic-Infrared Background) in the Galactic extinction maps is a likely root cause. We additionally observe that the DESI LRGs sample exhibits a complex dependency on a combination of seeing, depth, and intrinsic galaxy brightness, which is not replicated by Obiwan, suggesting discrepancies between the current simulation settings and the actual observations. The detailed findings we present should be used to guide any observational systematics mitigation treatment for the clustering of the DESI LRG sample., Comment: 46 pages, 26 figures
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- 2024
146. Euclid. I. Overview of the Euclid mission
- Author
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Euclid Collaboration, Mellier, Y., Abdurro'uf, Barroso, J. A. Acevedo, Achúcarro, A., Adamek, J., Adam, R., Addison, G. E., Aghanim, N., Aguena, M., Ajani, V., Akrami, Y., Al-Bahlawan, A., Alavi, A., Albuquerque, I. S., Alestas, G., Alguero, G., Allaoui, A., Allen, S. W., Allevato, V., Alonso-Tetilla, A. V., Altieri, B., Alvarez-Candal, A., Alvi, S., Amara, A., Amendola, L., Amiaux, J., Andika, I. T., Andreon, S., Andrews, A., Angora, G., Angulo, R. E., Annibali, F., Anselmi, A., Anselmi, S., Arcari, S., Archidiacono, M., Aricò, G., Arnaud, M., Arnouts, S., Asgari, M., Asorey, J., Atayde, L., Atek, H., Atrio-Barandela, F., Aubert, M., Aubourg, E., Auphan, T., Auricchio, N., Aussel, B., Aussel, H., Avelino, P. P., Avgoustidis, A., Avila, S., Awan, S., Azzollini, R., Baccigalupi, C., Bachelet, E., Bacon, D., Baes, M., Bagley, M. B., Bahr-Kalus, B., Balaguera-Antolinez, A., Balbinot, E., Balcells, M., Baldi, M., Baldry, I., Balestra, A., Ballardini, M., Ballester, O., Balogh, M., Bañados, E., Barbier, R., Bardelli, S., Baron, M., Barreiro, T., Barrena, R., Barriere, J. -C., Barros, B. J., Barthelemy, A., Bartolo, N., Basset, A., Battaglia, P., Battisti, A. J., Baugh, C. M., Baumont, L., Bazzanini, L., Beaulieu, J. -P., Beckmann, V., Belikov, A. N., Bel, J., Bellagamba, F., Bella, M., Bellini, E., Benabed, K., Bender, R., Benevento, G., Bennett, C. L., Benson, K., Bergamini, P., Bermejo-Climent, J. R., Bernardeau, F., Bertacca, D., Berthe, M., Berthier, J., Bethermin, M., Beutler, F., Bevillon, C., Bhargava, S., Bhatawdekar, R., Bianchi, D., Bisigello, L., Biviano, A., Blake, R. P., Blanchard, A., Blazek, J., Blot, L., Bosco, A., Bodendorf, C., Boenke, T., Böhringer, H., Boldrini, P., Bolzonella, M., Bonchi, A., Bonici, M., Bonino, D., Bonino, L., Bonvin, C., Bon, W., Booth, J. T., Borgani, S., Borlaff, A. S., Borsato, E., Bose, B., Botticella, M. T., Boucaud, A., Bouche, F., Boucher, J. S., Boutigny, D., Bouvard, T., Bouwens, R., Bouy, H., Bowler, R. A. A., Bozza, V., Bozzo, E., Branchini, E., Brando, G., Brau-Nogue, S., Brekke, P., Bremer, M. N., Brescia, M., Breton, M. -A., Brinchmann, J., Brinckmann, T., Brockley-Blatt, C., Brodwin, M., Brouard, L., Brown, M. L., Bruton, S., Bucko, J., Buddelmeijer, H., Buenadicha, G., Buitrago, F., Burger, P., Burigana, C., Busillo, V., Busonero, D., Cabanac, R., Cabayol-Garcia, L., Cagliari, M. S., Caillat, A., Caillat, L., Calabrese, M., Calabro, A., Calderone, G., Calura, F., Quevedo, B. Camacho, Camera, S., Campos, L., Canas-Herrera, G., Candini, G. P., Cantiello, M., Capobianco, V., Cappellaro, E., Cappelluti, N., Cappi, A., Caputi, K. I., Cara, C., Carbone, C., Cardone, V. F., Carella, E., Carlberg, R. G., Carle, M., Carminati, L., Caro, F., Carrasco, J. M., Carretero, J., Carrilho, P., Duque, J. Carron, Carry, B., Carvalho, A., Carvalho, C. S., Casas, R., Casas, S., Casenove, P., Casey, C. M., Cassata, P., Castander, F. J., Castelao, D., Castellano, M., Castiblanco, L., Castignani, G., Castro, T., Cavet, C., Cavuoti, S., Chabaud, P. -Y., Chambers, K. C., Charles, Y., Charlot, S., Chartab, N., Chary, R., Chaumeil, F., Cho, H., Chon, G., Ciancetta, E., Ciliegi, P., Cimatti, A., Cimino, M., Cioni, M. -R. L., Claydon, R., Cleland, C., Clément, B., Clements, D. L., Clerc, N., Clesse, S., Codis, S., Cogato, F., Colbert, J., Cole, R. E., Coles, P., Collett, T. E., Collins, R. S., Colodro-Conde, C., Colombo, C., Combes, F., Conforti, V., Congedo, G., Conseil, S., Conselice, C. J., Contarini, S., Contini, T., Conversi, L., Cooray, A. R., Copin, Y., Corasaniti, P. -S., Corcho-Caballero, P., Corcione, L., Cordes, O., Corpace, O., Correnti, M., Costanzi, M., Costille, A., Courbin, F., Mifsud, L. Courcoult, Courtois, H. M., Cousinou, M. -C., Covone, G., Cowell, T., Cragg, C., Cresci, G., Cristiani, S., Crocce, M., Cropper, M., Crouzet, P. E, Csizi, B., Cuby, J. -G., Cucchetti, E., Cucciati, O., Cuillandre, J. -C., Cunha, P. A. C., Cuozzo, V., Daddi, E., D'Addona, M., Dafonte, C., Dagoneau, N., Dalessandro, E., Dalton, G. B., D'Amico, G., Dannerbauer, H., Danto, P., Das, I., Da Silva, A., da Silva, R., Doumerg, W. d'Assignies, Daste, G., Davies, J. E., Davini, S., Dayal, P., de Boer, T., Decarli, R., De Caro, B., Degaudenzi, H., Degni, G., de Jong, J. T. A., de la Bella, L. F., de la Torre, S., Delhaise, F., Delley, D., Delucchi, G., De Lucia, G., Denniston, J., De Paolis, F., De Petris, M., Derosa, A., Desai, S., Desjacques, V., Despali, G., Desprez, G., De Vicente-Albendea, J., Deville, Y., Dias, J. D. F., Díaz-Sánchez, A., Diaz, J. J., Di Domizio, S., Diego, J. M., Di Ferdinando, D., Di Giorgio, A. M., Dimauro, P., Dinis, J., Dolag, K., Dolding, C., Dole, H., Sánchez, H. Domínguez, Doré, O., Dournac, F., Douspis, M., Dreihahn, H., Droge, B., Dryer, B., Dubath, F., Duc, P. -A., Ducret, F., Duffy, C., Dufresne, F., Duncan, C. A. J., Dupac, X., Duret, V., Durrer, R., Durret, F., Dusini, S., Ealet, A., Eggemeier, A., Eisenhardt, P. R. M., Elbaz, D., Elkhashab, M. Y., Ellien, A., Endicott, J., Enia, A., Erben, T., Vigo, J. A. Escartin, Escoffier, S., Sanz, I. Escudero, Essert, J., Ettori, S., Ezziati, M., Fabbian, G., Fabricius, M., Fang, Y., Farina, A., Farina, M., Farinelli, R., Farrens, S., Faustini, F., Feltre, A., Ferguson, A. M. N., Ferrando, P., Ferrari, A. G., Ferré-Mateu, A., Ferreira, P. G., Ferreras, I., Ferrero, I., Ferriol, S., Ferruit, P., Filleul, D., Finelli, F., Finkelstein, S. L., Finoguenov, A., Fiorini, B., Flentge, F., Focardi, P., Fonseca, J., Fontana, A., Fontanot, F., Fornari, F., Fosalba, P., Fossati, M., Fotopoulou, S., Fouchez, D., Fourmanoit, N., Frailis, M., Fraix-Burnet, D., Franceschi, E., Franco, A., Franzetti, P., Freihoefer, J., Frenk, C. . S., Frittoli, G., Frugier, P. -A., Frusciante, N., Fumagalli, A., Fumagalli, M., Fumana, M., Fu, Y., Gabarra, L., Galeotta, S., Galluccio, L., Ganga, K., Gao, H., García-Bellido, J., Garcia, K., Gardner, J. P., Garilli, B., Gaspar-Venancio, L. -M., Gasparetto, T., Gautard, V., Gavazzi, R., Gaztanaga, E., Genolet, L., Santos, R. Genova, Gentile, F., George, K., Gerbino, M., Ghaffari, Z., Giacomini, F., Gianotti, F., Gibb, G. P. S., Gillard, W., Gillis, B., Ginolfi, M., Giocoli, C., Girardi, M., Giri, S. K., Goh, L. W. K., Gómez-Alvarez, P., Gonzalez-Perez, V., Gonzalez, A. H., Gonzalez, E. J., Gonzalez, J. C., Beauchamps, S. Gouyou, Gozaliasl, G., Gracia-Carpio, J., Grandis, S., Granett, B. R., Granvik, M., Grazian, A., Gregorio, A., Grenet, C., Grillo, C., Grupp, F., Gruppioni, C., Gruppuso, A., Guerbuez, C., Guerrini, S., Guidi, M., Guillard, P., Gutierrez, C. M., Guttridge, P., Guzzo, L., Gwyn, S., Haapala, J., Haase, J., Haddow, C. R., Hailey, M., Hall, A., Hall, D., Hamaus, N., Haridasu, B. S., Harnois-Déraps, J., Harper, C., Hartley, W. G., Hasinger, G., Hassani, F., Hatch, N. A., Haugan, S. V. H., Häußler, B., Heavens, A., Heisenberg, L., Helmi, A., Helou, G., Hemmati, S., Henares, K., Herent, O., Hernández-Monteagudo, C., Heuberger, T., Hewett, P. C., Heydenreich, S., Hildebrandt, H., Hirschmann, M., Hjorth, J., Hoar, J., Hoekstra, H., Holland, A. D., Holliman, M. S., Holmes, W., Hook, I., Horeau, B., Hormuth, F., Hornstrup, A., Hosseini, S., Hu, D., Hudelot, P., Hudson, M. J., Huertas-Company, M., Huff, E. M., Hughes, A. C. N., Humphrey, A., Hunt, L. K., Huynh, D. D., Ibata, R., Ichikawa, K., Iglesias-Groth, S., Ilbert, O., Ilić, S., Ingoglia, L., Iodice, E., Israel, H., Israelsson, U. E., Izzo, L., Jablonka, P., Jackson, N., Jacobson, J., Jafariyazani, M., Jahnke, K., Jain, B., Jansen, H., Jarvis, M. J., Jasche, J., Jauzac, M., Jeffrey, N., Jhabvala, M., Jimenez-Teja, Y., Muñoz, A. Jimenez, Joachimi, B., Johansson, P. H., Joudaki, S., Jullo, E., Kajava, J. J. E., Kang, Y., Kannawadi, A., Kansal, V., Karagiannis, D., Kärcher, M., Kashlinsky, A., Kazandjian, M. V., Keck, F., Keihänen, E., Kerins, E., Kermiche, S., Khalil, A., Kiessling, A., Kiiveri, K., Kilbinger, M., Kim, J., King, R., Kirkpatrick, C. C., Kitching, T., Kluge, M., Knabenhans, M., Knapen, J. H., Knebe, A., Kneib, J. -P., Kohley, R., Koopmans, L. V. E., Koskinen, H., Koulouridis, E., Kou, R., Kovács, A., Kovačić, I., Kowalczyk, A., Koyama, K., Kraljic, K., Krause, O., Kruk, S., Kubik, B., Kuchner, U., Kuijken, K., Kümmel, M., Kunz, M., Kurki-Suonio, H., Lacasa, F., Lacey, C. G., La Franca, F., Lagarde, N., Lahav, O., Laigle, C., La Marca, A., La Marle, O., Lamine, B., Lam, M. C., Lançon, A., Landt, H., Langer, M., Lapi, A., Larcheveque, C., Larsen, S. S., Lattanzi, M., Laudisio, F., Laugier, D., Laureijs, R., Laurent, V., Lavaux, G., Lawrenson, A., Lazanu, A., Lazeyras, T., Boulc'h, Q. Le, Brun, A. M. C. Le, Brun, V. Le, Leclercq, F., Lee, S., Graet, J. Le, Legrand, L., Leirvik, K. N., Jeune, M. Le, Lembo, M., Mignant, D. Le, Lepinzan, M. D., Lepori, F., Reun, A. Le, Leroy, G., Lesci, G. F., Lesgourgues, J., Leuzzi, L., Levi, M. E., Liaudat, T. I., Libet, G., Liebing, P., Ligori, S., Lilje, P. B., Lin, C. -C., Linde, D., Linder, E., Lindholm, V., Linke, L., Li, S. -S., Liu, S. J., Lloro, I., Lobo, F. S. N., Lodieu, N., Lombardi, M., Lombriser, L., Lonare, P., Longo, G., López-Caniego, M., Lopez, X. Lopez, Alvarez, J. Lorenzo, Loureiro, A., Loveday, J., Lusso, E., Macias-Perez, J., Maciaszek, T., Maggio, G., Magliocchetti, M., Magnard, F., Magnier, E. A., Magro, A., Mahler, G., Mainetti, G., Maino, D., Maiorano, E., Malavasi, N., Mamon, G. A., Mancini, C., Mandelbaum, R., Manera, M., Manjón-García, A., Mannucci, F., Mansutti, O., Outeiro, M. Manteiga, Maoli, R., Maraston, C., Marcin, S., Marcos-Arenal, P., Margalef-Bentabol, B., Marggraf, O., Marinucci, D., Marinucci, M., Markovic, K., Marleau, F. R., Marpaud, J., Martignac, J., Martín-Fleitas, J., Martin-Moruno, P., Martin, E. L., Martinelli, M., Martinet, N., Martin, H., Martins, C. J. A. P., Marulli, F., Massari, D., Massey, R., Masters, D. C., Matarrese, S., Matsuoka, Y., Matthew, S., Maughan, B. J., Mauri, N., Maurin, L., Maurogordato, S., McCarthy, K., McConnachie, A. W., McCracken, H. J., McDonald, I., McEwen, J. D., McPartland, C. J. R., Medinaceli, E., Mehta, V., Mei, S., Melchior, M., Melin, J. -B., Ménard, B., Mendes, J., Mendez-Abreu, J., Meneghetti, M., Mercurio, A., Merlin, E., Metcalf, R. B., Meylan, G., Migliaccio, M., Mignoli, M., Miller, L., Miluzio, M., Milvang-Jensen, B., Mimoso, J. P., Miquel, R., Miyatake, H., Mobasher, B., Mohr, J. J., Monaco, P., Monguió, M., Montoro, A., Mora, A., Dizgah, A. Moradinezhad, Moresco, M., Moretti, C., Morgante, G., Morisset, N., Moriya, T. J., Morris, P. W., Mortlock, D. J., Moscardini, L., Mota, D. F., Mottet, S., Moustakas, L. A., Moutard, T., Müller, T., Munari, E., Murphree, G., Murray, C., Murray, N., Musi, P., Nadathur, S., Nagam, B. C., Nagao, T., Naidoo, K., Nakajima, R., Nally, C., Natoli, P., Navarro-Alsina, A., Girones, D. Navarro, Neissner, C., Nersesian, A., Nesseris, S., Nguyen-Kim, H. N., Nicastro, L., Nichol, R. C., Nielbock, M., Niemi, S. -M., Nieto, S., Nilsson, K., Noller, J., Norberg, P., Nouri-Zonoz, A., Ntelis, P., Nucita, A. A., Nugent, P., Nunes, N. J., Nutma, T., Ocampo, I., Odier, J., Oesch, P. A., Oguri, M., Oliveira, D. Magalhaes, Onoue, M., Oosterbroek, T., Oppizzi, F., Ordenovic, C., Osato, K., Pacaud, F., Pace, F., Padilla, C., Paech, K., Pagano, L., Page, M. J., Palazzi, E., Paltani, S., Pamuk, S., Pandolfi, S., Paoletti, D., Paolillo, M., Papaderos, P., Pardede, K., Parimbelli, G., Parmar, A., Partmann, C., Pasian, F., Passalacqua, F., Paterson, K., Patrizii, L., Pattison, C., Paulino-Afonso, A., Paviot, R., Peacock, J. A., Pearce, F. R., Pedersen, K., Peel, A., Peletier, R. F., Ibanez, M. Pellejero, Pello, R., Penny, M. T., Percival, W. J., Perez-Garrido, A., Perotto, L., Pettorino, V., Pezzotta, A., Pezzuto, S., Philippon, A., Pierre, M., Piersanti, O., Pietroni, M., Piga, L., Pilo, L., Pires, S., Pisani, A., Pizzella, A., Pizzuti, L., Plana, C., Polenta, G., Pollack, J. E., Poncet, M., Pöntinen, M., Pool, P., Popa, L. A., Popa, V., Popp, J., Porciani, C., Porth, L., Potter, D., Poulain, M., Pourtsidou, A., Pozzetti, L., Prandoni, I., Pratt, G. W., Prezelus, S., Prieto, E., Pugno, A., Quai, S., Quilley, L., Racca, G. D., Raccanelli, A., Rácz, G., Radinović, S., Radovich, M., Ragagnin, A., Ragnit, U., Raison, F., Ramos-Chernenko, N., Ranc, C., Rasera, Y., Raylet, N., Rebolo, R., Refregier, A., Reimberg, P., Reiprich, T. H., Renk, F., Renzi, A., Retre, J., Revaz, Y., Reylé, C., Reynolds, L., Rhodes, J., Ricci, F., Ricci, M., Riccio, G., Ricken, S. O., Rissanen, S., Risso, I., Rix, H. -W., Robin, A. C., Rocca-Volmerange, B., Rocci, P. -F., Rodenhuis, M., Rodighiero, G., Monroy, M. Rodriguez, Rollins, R. P., Romanello, M., Roman, J., Romelli, E., Romero-Gomez, M., Roncarelli, M., Rosati, P., Rosset, C., Rossetti, E., Roster, W., Rottgering, H. J. A., Rozas-Fernández, A., Ruane, K., Rubino-Martin, J. A., Rudolph, A., Ruppin, F., Rusholme, B., Sacquegna, S., Sáez-Casares, I., Saga, S., Saglia, R., Sahlén, M., Saifollahi, T., Sakr, Z., Salvalaggio, J., Salvaterra, R., Salvati, L., Salvato, M., Salvignol, J. -C., Sánchez, A. G., Sanchez, E., Sanders, D. B., Sapone, D., Saponara, M., Sarpa, E., Sarron, F., Sartori, S., Sartoris, B., Sassolas, B., Sauniere, L., Sauvage, M., Sawicki, M., Scaramella, R., Scarlata, C., Scharré, L., Schaye, J., Schewtschenko, J. A., Schindler, J. -T., Schinnerer, E., Schirmer, M., Schmidt, F., Schmidt, M., Schneider, A., Schneider, M., Schneider, P., Schöneberg, N., Schrabback, T., Schultheis, M., Schulz, S., Schuster, N., Schwartz, J., Sciotti, D., Scodeggio, M., Scognamiglio, D., Scott, D., Scottez, V., Secroun, A., Sefusatti, E., Seidel, G., Seiffert, M., Sellentin, E., Selwood, M., Semboloni, E., Sereno, M., Serjeant, S., Serrano, S., Setnikar, G., Shankar, F., Sharples, R. M., Short, A., Shulevski, A., Shuntov, M., Sias, M., Sikkema, G., Silvestri, A., Simon, P., Sirignano, C., Sirri, G., Skottfelt, J., Slezak, E., Sluse, D., Smith, G. P., Smith, L. C., Smith, R. E., Smit, S. J. A., Soldano, F., Solheim, B. G. B., Sorce, J. G., Sorrenti, F., Soubrie, E., Spinoglio, L., Mancini, A. Spurio, Stadel, J., Stagnaro, L., Stanco, L., Stanford, S. A., Starck, J. -L., Stassi, P., Steinwagner, J., Stern, D., Stone, C., Strada, P., Strafella, F., Stramaccioni, D., Surace, C., Sureau, F., Suyu, S. H., Swindells, I., Szafraniec, M., Szapudi, I., Taamoli, S., Talia, M., Tallada-Crespí, P., Tanidis, K., Tao, C., Tarrío, P., Tavagnacco, D., Taylor, A. N., Taylor, J. E., Taylor, P. L., Teixeira, E. M., Tenti, M., Idiago, P. Teodoro, Teplitz, H. I., Tereno, I., Tessore, N., Testa, V., Testera, G., Tewes, M., Teyssier, R., Theret, N., Thizy, C., Thomas, P. D., Toba, Y., Toft, S., Toledo-Moreo, R., Tolstoy, E., Tommasi, E., Torbaniuk, O., Torradeflot, F., Tortora, C., Tosi, S., Tosti, S., Trifoglio, M., Troja, A., Trombetti, T., Tronconi, A., Tsedrik, M., Tsyganov, A., Tucci, M., Tutusaus, I., Uhlemann, C., Ulivi, L., Urbano, M., Vacher, L., Vaillon, L., Valageas, P., Valdes, I., Valentijn, E. A., Valenziano, L., Valieri, C., Valiviita, J., Broeck, M. Van den, Vassallo, T., Vavrek, R., Vega-Ferrero, J., Venemans, B., Venhola, A., Ventura, S., Kleijn, G. Verdoes, Vergani, D., Verma, A., Vernizzi, F., Veropalumbo, A., Verza, G., Vescovi, C., Vibert, D., Viel, M., Vielzeuf, P., Viglione, C., Viitanen, A., Villaescusa-Navarro, F., Vinciguerra, S., Visticot, F., Voggel, K., von Wietersheim-Kramsta, M., Vriend, W. J., Wachter, S., Walmsley, M., Walth, G., Walton, D. M., Walton, N. A., Wander, M., Wang, L., Wang, Y., Weaver, J. R., Weller, J., Wetzstein, M., Whalen, D. J., Whittam, I. H., Widmer, A., Wiesmann, M., Wilde, J., Williams, O. R., Winther, H. -A., Wittje, A., Wong, J. H. W., Wright, A. H., Yankelevich, V., Yeung, H. W., Yoon, M., Youles, S., Yung, L. Y. A., Zacchei, A., Zalesky, L., Zamorani, G., Vitorelli, A. Zamorano, Marc, M. Zanoni, Zennaro, M., Zerbi, F. M., Zinchenko, I. A., Zoubian, J., Zucca, E., and Zumalacarregui, M.
- Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics ,Astrophysics - Astrophysics of Galaxies ,Astrophysics - Instrumentation and Methods for Astrophysics - Abstract
The current standard model of cosmology successfully describes a variety of measurements, but the nature of its main ingredients, dark matter and dark energy, remains unknown. Euclid is a medium-class mission in the Cosmic Vision 2015-2025 programme of the European Space Agency (ESA) that will provide high-resolution optical imaging, as well as near-infrared imaging and spectroscopy, over about 14,000 deg^2 of extragalactic sky. In addition to accurate weak lensing and clustering measurements that probe structure formation over half of the age of the Universe, its primary probes for cosmology, these exquisite data will enable a wide range of science. This paper provides a high-level overview of the mission, summarising the survey characteristics, the various data-processing steps, and data products. We also highlight the main science objectives and expected performance., Comment: Accepted for publication in the A&A special issue`Euclid on Sky'
- Published
- 2024
147. Class-Conditional self-reward mechanism for improved Text-to-Image models
- Author
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Ghazouali, Safouane El, Gucciardi, Arnaud, and Michelucci, Umberto
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence - Abstract
Self-rewarding have emerged recently as a powerful tool in the field of Natural Language Processing (NLP), allowing language models to generate high-quality relevant responses by providing their own rewards during training. This innovative technique addresses the limitations of other methods that rely on human preferences. In this paper, we build upon the concept of self-rewarding models and introduce its vision equivalent for Text-to-Image generative AI models. This approach works by fine-tuning diffusion model on a self-generated self-judged dataset, making the fine-tuning more automated and with better data quality. The proposed mechanism makes use of other pre-trained models such as vocabulary based-object detection, image captioning and is conditioned by the a set of object for which the user might need to improve generated data quality. The approach has been implemented, fine-tuned and evaluated on stable diffusion and has led to a performance that has been evaluated to be at least 60\% better than existing commercial and research Text-to-image models. Additionally, the built self-rewarding mechanism allowed a fully automated generation of images, while increasing the visual quality of the generated images and also more efficient following of prompt instructions. The code used in this work is freely available on https://github.com/safouaneelg/SRT2I.
- Published
- 2024
148. Weak solutions for a singular beam equation
- Author
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Atlasiuk, Olena, Heibig, Arnaud, and Petrov, Adrien
- Subjects
Mathematics - Analysis of PDEs ,35B45, 35Q74, 74H20, 74K10, 74M15. 35B45, 35Q74, 74H20, 74K10, 74M15. 35B45, 35Q74, 74H20, 74K10, 74M15 - Abstract
This paper deals with a dynamic Gao beam of infinite length subjected to a moving concentrated Dirac mass. Under appropriate regularity assumptions on the initial data, the problem possesses a weak solution which is obtained as the limit of a sequence of solutions of regularized problems.
- Published
- 2024
149. Chiral bosonic quantum spin liquid in the integer-spin Heisenberg-Kitaev model
- Author
-
Ralko, Arnaud and Merino, Jaime
- Subjects
Condensed Matter - Strongly Correlated Electrons - Abstract
Motivated by the possibility of finding a bosonic quantum spin liquid in the integer spin-$S$ Heisenberg-Kitaev model on the honeycomb lattice, we derive a Schwinger boson mean field theory involving both singlet and triplet pairing channels which includes hopping and pairing operators on equal footing. The mixed construction introduced here is justified by the good comparison with exact diagonalization energies of the $S \leq 3/2$ Heisenberg-Kitaev model and the perfect match with the Luttinger-Tisza semiclassical energies obtained at large-$S$. We find various competing gapped quantum spin liquids close to the Kitaev point. A comparison of their spin excitation spectrum with the dynamical structure factor obtained from exact diagonalizations allows us to identify the physical spin liquid $ans\"atz$ of the model. In particular, we identify a chiral quantum spin liquid state whose spin excitation spectrum follows closely the exact diagonalization data and survives up to large spin $S \lesssim 2$. We propose this state as a promising quantum spin liquid candidate for the integer spin-$S$ antiferromagnetic Kitaev model which may be realized in $S=1$ Kitaev materials A$_3$Ni$_2$XO$_6$ and KNiAsO$_4$., Comment: 13 pages, 11 figures
- Published
- 2024
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150. Cohomologie de syst\`emes locaux $p$-adiques sur les rev\^etements du demi-plan de Drinfeld
- Author
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Vanhaecke, Arnaud
- Subjects
Mathematics - Number Theory ,11E95, 11F80, 11F85, 14F30, 22E35 - Abstract
Colmez, Dospinescu and Niziol have shown that the only $p$-adic representations of $\rm{Gal}(\bar{\mathbb{Q}}_p/\mathbb{Q}_p)$ appearing in the $p$-adic \'etale cohomology of the coverings of Drinfeld's half-plane are the $2$-dimensional cuspidal representations (i.e. potentially semi-stable, whose associated Weil-Deligne representation is irreducible) with Hodge-Tate weights $0$ and $1$ and their multiplicities are given by the $p$-adic Langlands correspondence. We generalise this result to arbitrary weights, by considering the $p$-adic \'etale cohomology with coefficients in the symmetric powers of the universal local system on Drinfeld's tower. A novelty is the appearance of potentially semistable $2$-dimensional non-cristabelian representations, with expected multiplicity. The key point is that the local systems we consider turn out to be particularly simple: they are "isotrivial opers" on a curve. We develop a recipe to compute the pro\'etale cohomology of such a local system using the Hyodo-Kato cohomology of the curve and the de Rham complex of the flat filtered bundle associated to the local system., Comment: In French
- Published
- 2024
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