3,737 research outputs found
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
which includes in particular the energy space H^{1/2}(\RR^2). The
main tools that enable to reach 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 . In order to take into account Dirichlet boundary conditions
which are not compatible with the Dirac Hamiltonian , we propose a
different model built on a modified Hamiltonian displaying the same energy band
diagram as near the Dirac points. The well-posedness of the system in
this case is proved in , the domain of the fractional order Dirichlet
Laplacian operator, for
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
Characterising the interaction between the COPII component SEC24C and the human serotonin transporter
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
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