Big Actions with non abelian derived subgroup

For any $p>2$ we give an example of big action $(X,G)$ with non abelian derived subgroup. It is obtained as a covering of a curve related to the Ree curve

Feedback stabilization of a 3D fluid-structure model with a boundary control

We study a system coupling the incompressible Navier-Stokes equations in a 3D parallelepiped type domain with a damped plate equation. The plate is located in a part of the upper boundary of the fluid domain. The fluid domain depends on the deformation of the plate, and therefore it depends on time. We are interested in the stabilization, with a prescribed decay rate, of such a system in a neighborhood of a stationary solution, by a Dirichlet control acting at the boundary of the fluid domain. For that, we first study the stabilizability of the corresponding linearized system and we determine a finite-dimensional feedback control able to stabilize the linearized model. A crucial step in the analysis consists in showing that this linearized system can be rewritten thanks to an analytic semigroup, the infinitesimal generator of which has a compact resolvent. A fixed-point argument is used to prove the local stabilization of the original nonlinear system. The main difficulties come from the coupling between the fluid and plate equations, and the fact that the fluid domain varies with time, giving rise to geometric nonlinearities. The results of the paper may be adapted to other more complex geometrical configurations for the same type of system. Ongoing research concerns the numerics of the control problem

Teaching Agents how to Map: Spatial Reasoning for Multi-Object Navigation

In the context of visual navigation, the capacity to map a novel environment is necessary for an agent to exploit its observation history in the considered place and efficiently reach known goals. This ability can be associated with spatial reasoning, where an agent is able to perceive spatial relationships and regularities, and discover object characteristics. Recent work introduces learnable policies parametrized by deep neural networks and trained with Reinforcement Learning (RL). In classical RL setups, the capacity to map and reason spatially is learned end-to-end, from reward alone. In this setting, we introduce supplementary supervision in the form of auxiliary tasks designed to favor the emergence of spatial perception capabilities in agents trained for a goal-reaching downstream objective. We show that learning to estimate metrics quantifying the spatial relationships between an agent at a given location and a goal to reach has a high positive impact in Multi-Object Navigation settings. Our method significantly improves the performance of different baseline agents, that either build an explicit or implicit representation of the environment, even matching the performance of incomparable oracle agents taking ground-truth maps as input. A learning-based agent from the literature trained with the proposed auxiliary losses was the winning entry to the Multi-Object Navigation Challenge, part of the CVPR 2021 Embodied AI Workshop

Inertia Groups and Jacobian Varieties

Soient k un corps algébriquement clos de caractéristique p > 0 et C/k une courbe projective, lisse, intègre de genre g > 1 munie d un p-groupe d automorphismes G tel que |G| > 2p/(p-1)g. Le couple (C,G) est appelé grosse action. Si (C,G) est une grosse action, alors |G| 0 and C/k be a projective,smooth, integral curve of genus g > 1 endowed with a p-group of automorphisms G such that |G| > 2p/(p-1)g. The pair (C,G) is called big action. If (C,G) is a big action, then |G|<=4p/(p-1)^2g^2 (*). In this thesis, one studies arithmetical repercussions of geometric properties of big actions. One studies the arithmetic of the maximal wild monodromy extension of curves over a local field K of mixed characteristic p with algebraically closed residue field, with arbitrarily high genus having for potential good reduction a big action achieving equality in (*). One studies the associated Swan conductors. Then, one gives the first examples, to our knowledge, of big actions (C,G) with non abelian derived group D(G). These curves are obtained as coverings of S-ray class fields of P1(Fq) where S is a finite non empty subset of P1(Fq). Finally, one describes a method to compute S-Hilbert class fields of supersingular abelian covers of the projective line having exponent p and one illustrates it for some Deligne-Lusztig curves.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF

Early acute microvascular kidney transplant rejection in the absence of anti-HLA antibodies is associated with preformed IgG antibodies against diverse glomerular endothelial cell antigens

International audienceBACKGROUND: Although anti-HLA antibodies (Abs) cause most antibody-mediated rejections of renal allografts, non-anti-HLA Abs have also been postulated to contribute. A better understanding of such Abs in rejection is needed.METHODS: We conducted a nationwide study to identify kidney transplant recipients without anti-HLA donor-specific Abs who experienced acute graft dysfunction within 3 months after transplantation and showed evidence of microvascular injury, called acute microvascular rejection (AMVR). We developed a crossmatch assay to assess serum reactivity to human microvascular endothelial cells, and used a combination of transcriptomic and proteomic approaches to identify non-HLA Abs.RESULTS: We identified a highly selected cohort of 38 patients with early acute AMVR. Biopsy specimens revealed intense microvascular inflammation and the presence of vasculitis (in 60.5%), interstitial hemorrhages (31.6%), or thrombotic microangiopathy (15.8%). Serum samples collected at the time of transplant showed that previously proposed anti-endothelial cell Abs-angiotensin type 1 receptor (AT1R), endothelin-1 type A and natural polyreactive Abs-did not increase significantly among patients with AMVR compared with a control group of stable kidney transplant recipients. However, 26% of the tested AMVR samples were positive for AT1R Abs when a threshold of 10 IU/ml was used. The crossmatch assay identified a common IgG response that was specifically directed against constitutively expressed antigens of microvascular glomerular cells in patients with AMVR. Transcriptomic and proteomic analyses identified new targets of non-HLA Abs, with little redundancy among individuals.CONCLUSIONS: Our findings indicate that preformed IgG Abs targeting non-HLA antigens expressed on glomerular endothelial cells are associated with early AMVR, and that cell-based assays are needed to improve risk assessments before transplant

Maximal monodromy in unequal characteristic

Let $R$ be a complete discrete valuation ring of mixed characteristic $(0,p)$ with fraction field $K$. We study stable models of $p$-cyclic covers of \Proj_K. First, we determine the monodromy extension, the monodromy group, its filtration and the Swan conductor for special covers of arbitrarily high genus with potential good reduction. In the case $p=2$ we consider hyperelliptic curves of genus $2$

Maximal monodromy in unequal characteristic

Let $R$ be a complete discrete valuation ring of mixed characteristic $(0,p)$ with fraction field $K$. We study stable models of $p$-cyclic covers of \Proj_K. First, we determine the monodromy extension, the monodromy group, its filtration and the Swan conductor for special covers of arbitrarily high genus with potential good reduction. In the case $p=2$ we consider hyperelliptic curves of genus $2$

Task-conditioned adaptation of visual features in multi-task policy learning

International audienceSuccessfully addressing a wide variety of tasks is a core ability of autonomous agents, requiring flexibly adapting the underlying decision-making strategies and, as we argue in this work, also adapting the perception modules. An analogical argument would be the human visual system, which uses top-down signals to focus attention determined by the current task. Similarly, we adapt pre-trained large vision models conditioned on specific downstream tasks in the context of multi-task policy learning. We introduce task-conditioned adapters that do not require finetuning any pre-trained weights, combined with a single policy trained with behavior cloning and capable of addressing multiple tasks. We condition the visual adapters on task embeddings, which can be selected at inference if the task is known, or alternatively inferred from a set of example demonstrations. To this end, we propose a new optimization-based estimator. We evaluate the method on a wide variety of tasks from the CortexBench benchmark and show that, compared to existing work, it can be addressed with a single policy. In particular, we demonstrate that adapting visual features is a key design choice and that the method generalizes to unseen tasks given a few demonstrations

Multi-Object Navigation with dynamically learned neural implicit representations

International audienceUnderstanding and mapping a new environment are core abilities of any autonomously navigating agent. While classical robotics usually estimates maps in a stand-alone manner with SLAM variants, which maintain a topological or metric representation, end-to-end learning of navigation keeps some form of memory in a neural network. Networks are typically imbued with inductive biases, which can range from vectorial representations to birds-eye metric tensors or topological structures. In this work, we propose to structure neural networks with two neural implicit representations, which are learned dynamically during each episode and map the content of the scene: (i) the Semantic Finder predicts the position of a previously seen queried object; (ii) the Occupancy and Exploration Implicit Representation encapsulates information about explored area and obstacles, and is queried with a novel global read mechanism which directly maps from function space to a usable embedding space. Both representations are leveraged by an agent trained with Reinforcement Learning (RL) and learned online during each episode. We evaluate the agent on Multi-Object Navigation and show the high impact of using neural implicit representations as a memory source

Teaching Agents how to Map: Spatial Reasoning for Multi-Object Navigation

International audienc