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A model spin liquid
No full textInternational audienceIt is thought that a resonating valence bond state can form in certain correlated systems. However, this behaviour is predicted by only a few realistic models. Now it has been shown that this phase emerges in an experimentally relevant model
Monitoring and Sensing System for People's Behavior During Fall Events Based on Mobility Analysis
No full textInternational audienc
Risk-controlling Prediction with Distributionally Robust Optimization
No full textInternational audienceConformal prediction is a popular paradigm to quantify the uncertainty of a model's output on a new batch of data. Quite differently, distributionally robust optimization aims at training a model that is robust to uncertainties in the distribution of the training data. In this paper, we examine the links between the two approaches. In particular, we show that we can learn conformal prediction intervals by distributionally robust optimization on a well chosen objective. This further entails to train a model and build conformal prediction intervals all at once, using the same data
Differential regulation of the “phytoglobin-nitric oxide respiration” in Medicago truncatula roots and nodules submitted to flooding
No full textInternational audienceFlooding induces hypoxia in plant tissues, impacting various physiological and biochemical processes. This study investigates the adaptive response of the roots and nitrogen-fixing nodules of Medicago truncatula in symbiosis with Sinorhizobium meliloti under short-term hypoxia caused by flooding. Four-week-old plants were subjected to flooding for 1-4 days. Physiological parameters as well as the expression of the senescence marker gene MtCP6 remained unchanged after 4 days of flooding, indicating no senescence onset. Hypoxia was evident from the first day, as indicated by the upregulation of hypoxia marker genes (MtADH, MtPDC, MtAlaAT, MtERF73). Nitrogenfixing capacity was unaffected after 1 day but markedly decreased after 4 days, while energy state (ATP/ADP ratio) significantly decreased from 1 day and was more affected in nodules than in roots. Nitric oxide (NO) production increased in roots but decreased in nodules after prolonged flooding. Nitrate reductase (NR) activity and expression of genes associated with Phytoglobin-NO (Pgb-NO) respiration (MtNR1, MtNR2, MtPgb1.1) were upregulated, suggesting a role in maintaining energy metabolism under hypoxia, but the use of M. truncatula nr1 and nr2 mutants, impaired in nitrite production, indicated the involvement of these two genes in ATP regeneration during initial flooding response. The addition of sodium nitroprusside or tungstate revealed that Pgb-NO respiration contributes significantly to ATP regeneration in both roots and nodules under flooding. Altogether, these results highlight the importance of NR1 and Pgb1.1 in the hypoxic response of legume root systems and show that nodules are more sensitive than roots to hypoxia
Entanglement in cyclic sign invariant quantum states
No full textWe introduce and study bipartite quantum states that are invariant under the local action of the cyclic sign group. Due to symmetry, these states are sparse and can be parameterized by a triple of vectors. Their important semi-definite properties, such as positivity and positivity under partial transpose (PPT), can be simply characterized in terms of these vectors and their discrete Fourier transforms. We study in detail the entanglement properties of this family of symmetric states, showing in particular that it contains PPT entangled states. For states that are diagonal in the Dicke basis, deciding separability is equivalent to a circulant version of the complete positivity problem. We provide some geometric results for the PPT cone, showing in particular that it is polyhedral. In local dimension less than 5, we completely characterize these sets and construct entanglement witnesses; some partial results are also obtained for d = 6, 7. Finally, we initiate the study of cyclic sign covariant quantum channels, showing in particular that the PPT squared conjecture holds for some of these maps
Impact of mechanical compressive stress on pancreatic cancer progression
No full textInternational audienceContext: Mechanical compressive stress arises during pancreatic cancer progression (PDAC). In vitro, compression forces decrease PDAC cell proliferation, increase invasiveness, and induce resistance to chemotherapies. In vivo, the importance of compression is unknown. In PDAC, increased compressive stress happens simultaneously with the second wave of genetic alterations (p53 mutations/truncations) after KRAS oncogenic mutations; it is also linked with overexpression/activation of the PI3-Kinases (PI3K) pathway. We think that compression favors selective genetic backgrounds that modify the signaling environment in cells and thus cell fate. Experimental design: We generated compressive stresses to spheroids derived from PDAC cells with KRAS G12D mutation, in which p53 R172H mutation or p53 R172H;R210* truncation are induced sequentially. Further, we applied a compressive stress to KRAS G12D ±p53 R172H /p53 R172H;R210* mutated mouse allografts using a compressive device. We also used the punch method in order to evaluate the relaxation of tumors depending on their genetic background. Results: Compression decreased the spheroid growth (<30%). However, p53 R172H mutated PDAC cells developed a resistance to compression and continued to proliferate. This mutation associated with a truncation of p53 accentuated this resistance, even bringing a proliferative advantage. A transcriptomic analysis of KRAS G12D , p53 mutated and p53 truncated spheroids under compression was performed. This analysis showed a modification in adhesion properties via plasma membrane and RTK signaling activity, mechanisms regulated by PI3K pathway. In parallel, we observed, in vivo, that the growth of KRAS G12D mutated tumors decreased by 40% under compression, whereas the size of tumors with p53 R172H;R210* truncated form was similar with or without compression. Finally, KRAS G12D tumors relaxed more easily compared to the p53 R172H and p53 R172H;R210* tumors; this was due to a greater cellular and matrix homogeneity in these tumors compared to p53 R172H and p53 R172H;R210* tumors. Conclusion: Growth under pressure can influence the progression of PDAC promoting selective genetic background and activation of oncogenic signaling pathways. These observations open the way to integrate the mechanical context in the management of patients with PDAC
Control of Humanoid Robots with Parallel Mechanisms using Differential Actuation Models
No full textSeveral recently released humanoid robots, inspired by the mechanical design of Cassie, employ actuator configurations in which the motors are displaced from the joints to reduce leg inertia. While studies accounting for the full kinematic complexity have demonstrated the benefits of thesedesigns, the associated loop-closure constraints greatly increase computational cost and limit their use in control and learning.As a result, the non-linear transmission is often approximated by a constant reduction ratio, preventing exploitation of the mechanism’s full capabilities. This paper introduces a compact analytical formulation for the two standard knee and ankle mechanisms that captures the exact non-linear transmission while remaining computationally efficient. The model is fully differentiable up to second order with a minimal formulation,enabling low-cost evaluation of dynamic derivatives for trajectory optimization and of the apparent transmission impedance for reinforcement learning. We integrate this formulation into trajectory optimization and locomotion policy learning, and compare it against simplified constant-ratio approaches. Hardware experiments demonstrate improved accuracy and robustness, showing that the proposed method provides a practical means to incorporate parallel actuation into modern control algorithms
Mécanismes physiques de HEMT GaN révelés par l'instabilité de la tension de seuil
No full textNational audienceDans les études de fiabilité des composants, différents paramètres électriques comme la tension de seuil (VTH) sont caractérisés afin de suivre la dégradation du composant. Cependant, pour les HEMT GaN, les mesures de VTH sont souvent instables en raison de mécanismes comme le piégeage des charges induits par l'historique des polarisations. Cette instabilité peut être considérée comme caractéristique de la structure du transistor et n'est pas liée au viellissement. Ce travail se concentre sur la compréhension de l'origine de l'instabilité de VTH des transistors GaN normally-off, en utilisant des mesures répétées de VTH. Les mesures successives de VTH, entrecoupées de polarisations de drain ou de grille respectant les limites de la datasheet, génèrent des dérives reproductibles de VTH, formant ainsi une signature unique du composant. À travers cette signature, l'instabilité initiale de VTH sera illustrée, où les principaux acteurs de cette instabilité sont les zones de field plates et la grille p-GaN. Deux références sont testées, et les signatures uniques obtenues révèlent les différences de structure entre les composants
Minimal Observations Inverse Reinforcement Learning for Predicting Human Box-Lifting Motions
No full textInternational audienceHeavy-load manual lifting poses a significant risk of injury, motivating the need for personalized robotic assistance. The Minimal Observations Inverse Reinforcement Learning (MO-IRL) algorithm has recently demonstrated strong capabilities in recovering underlying optimality principles from very few demonstrations of simulated robotic motions, and at a very reasonable computational cost. Building on this, the present study integrates ten biomechanically informed cost functions into a direct optimal control formulation to predict human motion during heavy-load manual box-lifting tasks. Contrary to previous literature, thanks to the computational efficiency of MO-IRL, we allow time-varying optimal weights and include a collision-avoidance constraint within the set of cost functions. This constraint represents the subject's apprehension of hitting the target table, As MO-IRL requires careful tuning of multiple hyperparameters, we employ a grid search to identify the optimal set. With this configuration, the predicted motion achieves an average accuracy of 11.5 ± 6.2deg across all joint angles, outperforming comparable methods. The inferred cost weights reveal a time-varying control strategy: initially minimizing lower-limb torques, then smoothing the motion through reduced joint accelerations and load velocity, and finally adjusting to avoid table collision. These findings show that biomechanically guided MO-IRL, coupled with direct optimal control, can accurately recover complex, constrained lifting motions while providing interpretable insights into human motor objectives, paving the way for adaptive and userspecific robotic assistance