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Found 12 papers, 1 papers with code
A Package for Multi-Dimensional Monte Carlo Integration on Multi-GPUs
We have demonstrated that Tensorflow and Numba help inexperienced scientific researchers to parallelize their programs on multiple GPUs with little work.
Computational Physics
Secure and Energy-Efficient Offloading and Resource Allocation in a NOMA-Based MEC Network
In this paper, we study the task offloading and resource allocation problem in a non-orthogonal multiple access (NOMA) assisted MEC network with security and energy efficiency considerations.
Derivation of the nonlocal collision term in the relativistic Boltzmann equation for massive spin-1/2 particles from quantum field theory
We derive the Boltzmann equation and the collision kernel for massive spin-1/2 particles, using the Wigner-function formalism and employing an expansion in powers of $\hbar$.
Nuclear Theory High Energy Physics - Phenomenology
Mobility-Aware Offloading and Resource Allocation in MEC-Enabled IoT Networks
In this paper, the offloading decision and resource allocation problem is studied with mobility consideration.
User Scheduling for Federated Learning Through Over-the-Air Computation
A new machine learning (ML) technique termed as federated learning (FL) aims to preserve data at the edge devices and to only exchange ML model parameters in the learning process.
Energy-Efficient Design for IRS-Assisted MEC Networks with NOMA
Energy-efficient design is of crucial importance in wireless internet of things (IoT) networks.
When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges
The exponential growth of internet connected systems has generated numerous challenges, such as spectrum shortage issues, which require efficient spectrum sharing (SS) solutions.
Energy Efficiency and Delay Tradeoff in an MEC-Enabled Mobile IoT Network
Theoretical analysis proves that the proposed algorithm can achieve a $[O(1/V), O(V)]$ tradeoff between EE and service delay.
Adaptive Leading Cruise Control in Mixed Traffic Considering Human Behavioral Diversity
A comprehensive simulation is conducted to statistically verify the positive impacts of CAV on the holistic energy efficiency of the mixed traffic flow with uncertain and diverse human driving behaviors.
Physics-Augmented Data-EnablEd Predictive Control for Eco-driving of Mixed Traffic Considering Diverse Human Behaviors
In this paper, we employ a data-enabled predictive control (DeePC) scheme to address the eco-driving of mixed traffic flows with diverse behaviors of human drivers.
ACT-Net: Anchor-context Action Detection in Surgery Videos
In this paper, to accurately detect fine-grained actions that happen at every moment, we propose an anchor-context action detection network (ACTNet), including an anchor-context detection (ACD) module and a class conditional diffusion (CCD) module, to answer the following questions: 1) where the actions happen; 2) what actions are; 3) how confidence predictions are.
Safe Reinforcement Learning-Based Eco-Driving Control for Mixed Traffic Flows With Disturbances
This paper presents a safe learning-based eco-driving framework tailored for mixed traffic flows, which aims to optimize energy efficiency while guaranteeing safety during real-system operations.
