SLDLT: une technique de mise à jour de la décomposition LDLT pour l'accélération des simulations de Monte Carlo dans le cas de champs stochastiques non uniformes

National audienceSee http://hal.archives-ouvertes.fr/docs/00/59/29/54/ANNEX/r_79AK56I8.pd

Experimental evidences of a large extrinsic spin Hall effect in AuW alloy

We report an experimental study of a gold-tungsten alloy (7% at. W concentration in Au host) displaying remarkable properties for spintronics applications using both magneto-transport in lateral spin valve devices and spin-pumping with inverse spin Hall effect experiments. A very large spin Hall angle of about 10% is consistently found using both techniques with the reliable spin diffusion length of 2 nm estimated by the spin sink experiments in the lateral spin valves. With its chemical stability, high resistivity and small induced damping, this AuW alloy may find applications in the nearest future

Deep generative models for biology: represent, predict, design

Deep generative models have revolutionized the field of artificial intelligence, fundamentally changing how we generate novel objects that imitate or extrapolate from training data, and transforming how we access and consume various types of information such as texts, images, speech, and computer programs. They have the potential to radically transform other scientific disciplines, ranging from mathematical problem solving, to supporting fast and accurate simulations in high-energy physics or enabling rapid weather forecasting. In computational biology, generative models hold immense promise for improving our understanding of complex biological processes, designing new drugs and therapies, and forecasting viral evolution during pandemics, among many other applications. Biological objects pose however unique challenges due to their inherent complexity, encompassing massive spaces, multiple complementary data modalities, and a unique interplay between highly structured and relatively unstructured components. In this thesis, we develop several deep generative modeling frameworks that are motivated by key questions in computational biology. Given the interdisciplinary nature of this endeavor, we first provide a comprehensive background in generative modeling, uncertainty quantification, sequential decision making, as well as important concepts in biology and chemistry to facilitate a thorough understanding of our work. We then deep dive into the core of our contributions, which are structured around three chapters. The first chapter introduces methods for learning representations of biological sequences, laying the foundation for subsequent analyses. The second chapter illustrates how these representations can be leveraged to predict complex properties of biomolecules, focusing on three specific applications: protein fitness prediction, the effects of genetic variations on human disease risk and viral immune escape. Finally, the third chapter is dedicated to methods for designing novel biomolecules, including drug target identification, de novo molecular optimization, and protein engineering. This thesis also makes several methodological contributions to broader machine learning challenges, such as uncertainty quantification in high-dimensional spaces or efficient transformer architectures, which hold potential value in other application domains. We conclude by summarizing our key findings, highlighting shortcomings of current approaches, proposing potential avenues for future research, and discussing emerging trends within the field

Electrical spin injection and detection in Germanium using three terminal geometry

In this letter, we report on successful electrical spin injection and detection in \textit{n}-type germanium-on-insulator (GOI) using a Co/Py/Al$_{2}$O$_{3}$ spin injector and 3-terminal non-local measurements. We observe an enhanced spin accumulation signal of the order of 1 meV consistent with the sequential tunneling process via interface states in the vicinity of the Al$_{2}$O$_{3}$/Ge interface. This spin signal is further observable up to 220 K. Moreover, the presence of a strong \textit{inverted} Hanle effect points at the influence of random fields arising from interface roughness on the injected spins.Comment: 4 pages, 3 figure

Learning to Change and Changing Learning in Environmental Management: A case study of the Kaw Nature Reserve in French Guiana

Participation is a key component in socially just, successful nature conservation. Yet, participation can range from informing citizens to offering them decision-making power. Only participation that allows for an open, respectful negotiation of conservation planning and implementation opens the door to engaging, place-appropriate conservation, rather than conservation implemented by external agents with external agendas. However ecologically or socially correct these external agendas may be, collaboration by all stakeholders validates the appropriateness of conservation projects. One conservation tool is education, which typically assumes that the public lacks environmental knowledge and that information can create environmentally aware and active citizens. Often in environmental education programs, the leading organization defines the problem and goals prior to contact with the public. While education can enhance environmental literacy and open doors to environmental action, it is important to recognize the diverse knowledge and experiences of the audience so that they can contribute to successful conservation. My research was based on two connected ideas. First, collaboration among the broadest array of stakeholders requires an education model that is based on learning together, versus a one-way flow of information. Second, a useful way of beginning collaborative education is to recognize, respect and make the most of the diverse experiences, opinions and knowledge of all the stakeholders. I present a case study that focuses on the stakeholders of the Kaw Nature Reserve. This Reserve is eight years old and has been historically beset with conflict. I interviewed a diverse array of stakeholders involved with or affected by the Reserve to determine important themes regarding communication, conservation goals, and viewpoints on land use. The themes I identified can provide the groundwork to understanding the potential role of collaborative education and dialogue in this Reserve, and provide collaborative tools for participatory conservation in France and beyond. The analysis revealed four dominant themes: 1) historical, regulatory and communicative sources of conflict among the Kaw Reserve stakeholders, 2) the effect of external power relations on the Kaw village and Reserve, 3) the Atipa resource crisis, and 4) similarities among stakeholders and diversity within groups

RITA: a Study on Scaling Up Generative Protein Sequence Models

In this work we introduce RITA: a suite of autoregressive generative models for protein sequences, with up to 1.2 billion parameters, trained on over 280 million protein sequences belonging to the UniRef-100 database. Such generative models hold the promise of greatly accelerating protein design. We conduct the first systematic study of how capabilities evolve with model size for autoregressive transformers in the protein domain: we evaluate RITA models in next amino acid prediction, zero-shot fitness, and enzyme function prediction, showing benefits from increased scale. We release the RITA models openly, to the benefit of the research community