Analysis of models for quantum transport of electrons in graphene layers

We present and analyze two mathematical models for the self consistent quantum transport of electrons in a graphene layer. We treat two situations. First, when the particles can move in all the plane \RR^2, the model takes the form of a system of massless Dirac equations coupled together by a selfconsistent potential, which is the trace in the plane of the graphene of the 3D Poisson potential associated to surface densities. In this case, we prove local in time existence and uniqueness of a solution in H^s(\RR^2), for $s > 3/8$ which includes in particular the energy space H^{1/2}(\RR^2). The main tools that enable to reach $s\in (3/8,1/2)$ are the dispersive Strichartz estimates that we generalized here for mixed quantum states. Second, we consider a situation where the particles are constrained in a regular bounded domain $\Omega$. In order to take into account Dirichlet boundary conditions which are not compatible with the Dirac Hamiltonian $H_{0}$, we propose a different model built on a modified Hamiltonian displaying the same energy band diagram as $H_{0}$ near the Dirac points. The well-posedness of the system in this case is proved in $H^s_{A}$, the domain of the fractional order Dirichlet Laplacian operator, for $1/2\leq s<5/2$

Sampling-based Uncertainty Estimation for an Instance Segmentation Network

The examination of uncertainty in the predictions of machine learning (ML) models is receiving increasing attention. One uncertainty modeling technique used for this purpose is Monte-Carlo (MC)-Dropout, where repeated predictions are generated for a single input. Therefore, clustering is required to describe the resulting uncertainty, but only through efficient clustering is it possible to describe the uncertainty from the model attached to each object. This article uses Bayesian Gaussian Mixture (BGM) to solve this problem. In addition, we investigate different values for the dropout rate and other techniques, such as focal loss and calibration, which we integrate into the Mask-RCNN model to obtain the most accurate uncertainty approximation of each instance and showcase it graphically

RHIC and LHC phenomena with an unified parton transport

We discuss recent applications of the partonic pQCD based cascade model BAMPS with focus on heavy-ion phenomeneology in hard and soft momentum range. The nuclear modification factor as well as elliptic flow are calculated in BAMPS for RHIC end LHC energies. These observables are also discussed within the same framework for charm and bottom quarks. Contributing to the recent jet-quenching investigations we present first preliminary results on application of jet reconstruction algorithms in BAMPS. Finally, collective effects induced by jets are investigated: we demonstrate the development of Mach cones in ideal matter as well in the highly viscous regime

Hyperparameter Optimization for Multi-Objective Reinforcement Learning

Reinforcement learning (RL) has emerged as a powerful approach for tackling complex problems. The recent introduction of multi-objective reinforcement learning (MORL) has further expanded the scope of RL by enabling agents to make trade-offs among multiple objectives. This advancement not only has broadened the range of problems that can be tackled but also created numerous opportunities for exploration and advancement. Yet, the effectiveness of RL agents heavily relies on appropriately setting their hyperparameters. In practice, this task often proves to be challenging, leading to unsuccessful deployments of these techniques in various instances. Hence, prior research has explored hyperparameter optimization in RL to address this concern. This paper presents an initial investigation into the challenge of hyperparameter optimization specifically for MORL. We formalize the problem, highlight its distinctive challenges, and propose a systematic methodology to address it. The proposed methodology is applied to a well-known environment using a state-of-the-art MORL algorithm, and preliminary results are reported. Our findings indicate that the proposed methodology can effectively provide hyperparameter configurations that significantly enhance the performance of MORL agents. Furthermore, this study identifies various future research opportunities to further advance the field of hyperparameter optimization for MORL.Comment: Presented at the MODeM workshop https://modem2023.vub.ac.be/

PFed: Recommending Plausible Federated SPARQL Queries

International audienceFederated SPARQL queries allow to query multiple inter-linked datasets hosted by remote SPARQL endpoints. However, finding federated queries over a growing number of datasets is challenging. In this paper, we propose PFed, an approach to recommend plausible fed-erated queries based on real query logs of different datasets. The problem is not to find similar federated queries, but plausible complementary queries over different datasets. Starting with a real SPARQL query from a given log, PFed stretches the query with real queries from different logs. To prune the research space, PFed proposes semantic summary to prune the query logs. Experimental results with real logs of DBpedia and SWDF demonstrate that PFed is able to prune drastically the logs and recommend plausible federated queries

Quantum corrections to vacuum energy sequestering (with monodromy)

Field theory models of axion monodromy have been shown to exhibit vacuum energy sequestering as an emergent phenomenon for cancelling radiative corrections to the cosmological constant. We study one loop corrections to this class of models coming from virtual axions using a heat kernel expansion. We find that the structure of the original sequestering proposals is no longer preserved at low energies. Nevertheless, the cancellation of radiative corrections to the cosmological constant remains robust, even with the new structures required by quantum corrections