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Le financement public des cultes et des philosophies:quelles perspectives sous la nouvelle législature (2024‑2029) ?
No full textLa possibilité effective de prendre connaissance de conditions générales:appréciation au regard de l’obligation d’information précontractuelle imposée dans le secteur des assurances, note d'observations sous Trib. entr. Liège, div. Namur (1re ch.), 5 juin 2023, Rôle n° A/22/00403
No full textAttentats de Bruxelles du 22 mars 2016:un procès novateur en matière de qualification des infractions terroristes ?
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Révoltes, meurtres et intrigues:L’histoire des Ptolémées d’après les parois des temples égyptiens
Full text linkDecentralized Autonomous Organizations (DAOs):Catalysts for enhanced market efficiency
Full text linkThe crypto-asset market has shown variations in efficiency across assets and time, but limited research has explored the driving factors beyond liquidity. Exploiting a dataset of 122 crypto-assets, with imbalanced data, this study investigates the impact of market conditions and inherent asset characteristics on return predictability. Our findings reveal that both factors significantly influence the efficiency of crypto-assets. Notably, Decentralized Autonomous Organizations (DAOs) exhibit higher efficiency compared to non-DAO projects. This suggests that transparent decentralized decision-making models can reduce information asymmetry, leading to a more efficient market pricing. Furthermore, we show a positive relationship between market efficiency and increased liquidity and age. These insights shed light on the role of DAOs as catalysts for enhancing market efficiency and have important implications for investors and market participants in the crypto-asset market.</p
Improved detection of chaos with Lagrangian descriptors using differential algebra
No full textLagrangian descriptors (LDs) based on the arc length of orbits previously demonstrated their utility in delineating structures governing the dynamics. Recently, a chaos indicator based on the second derivatives of the LDs, referred to as ΔLD, has been introduced to distinguish regular and chaotic trajectories. Thus far, the derivatives are numerically approximated using finite differences on fine meshes of initial conditions. In this paper, we instead use the differential algebra (DA) framework as a form of automatic differentiation to introduce and compute ΔLD up to machine precision. We discuss and exemplify benefits of this framework, such as the determination of reliable thresholds to distinguish ordered from chaotic trajectories. Our extensive parametric study quantitatively assesses the accuracy and sensitivity of both the finite differences and differential arithmetic approaches by focusing on paradigmatic discrete models of Hamiltonian chaos, namely the Chirikov's standard map and coupled 4-dimensional variants. Our results demonstrate that finite difference techniques for ΔLD might lead to significant misclassification rate, up to 20% when the phase space supports thin resonant webs, due to the difficulty to determine appropriate thresholds. On the contrary, ΔLD computed through DA arithmetic leads to clear bimodal distributions which in turn lead to robust thresholds. As a consequence, the DA framework reveals as sensitive as established first order tangent map based indicators, independently of the underlying dynamical regime. Finally, the benefits of the DA framework are also highlighted for non-uniform depleted meshes of initial conditions.</p