We study the problem of decentralized task offloading and load-balancing in a
dense network with numerous devices and a set of edge servers. Solving this
problem optimally is complicated due to the unknown network information and
random task sizes. The shared network resources also influence the users'
decisions and resource distribution. Our solution combines the mean field
multi-agent multi-armed bandit (MAB) game with a load-balancing technique that
adjusts the servers' rewards to achieve a target population profile despite the
distributed user decision-making. Numerical results demonstrate the efficacy of
our approach and the convergence to the target load distribution.

我们研究了在一个拥有大量设备和一组边缘服务器的稠密网络中的分散任务卸载和负载平衡问题。通过将均值场多智能体多臂赌博（MAB）游戏与一种负载平衡技术相结合，调整服务器的奖励以实现目标人口分布，解决了由于未知网络信息和随机任务大小而导致的复杂优化问题。数值结果证明了我们方法的有效性和对目标负载分布的收敛。

稠密网络中移动边缘计算的分散任务卸载和负载均衡

Decentralized Task Offloading and Load-Balancing for Mobile Edge  Computing in Dense Networks

Post-training Sparsity (PTS) is a recently emerged avenue that chases
efficient network sparsity with limited data in need. Existing PTS methods,
however, undergo significant performance degradation compared with traditional
methods that retrain the sparse networks via the whole dataset, especially at
high sparsity ratios. In this paper, we attempt to reconcile this disparity by
transposing three cardinal factors that profoundly alter the performance of
conventional sparsity into the context of PTS. Our endeavors particularly
comprise (1) A base-decayed sparsity objective that promotes efficient
knowledge transferring from dense network to the sparse counterpart. (2) A
reducing-regrowing search algorithm designed to ascertain the optimal sparsity
distribution while circumventing overfitting to the small calibration set in
PTS. (3) The employment of dynamic sparse training predicated on the preceding
aspects, aimed at comprehensively optimizing the sparsity structure while
ensuring training stability. Our proposed framework, termed UniPTS, is
validated to be much superior to existing PTS methods across extensive
benchmarks. As an illustration, it amplifies the performance of POT, a recently
proposed recipe, from 3.9% to 68.6% when pruning ResNet-50 at 90% sparsity
ratio on ImageNet. We release the code of our paper at
this https URL

本文介绍了一种名为 UniPTS 的基于三个核心因素的 Post-training Sparsity 方法，通过转换传统的稀疏性能衰减问题来促进网络稀疏化的高效性，经过实验证实 UniPTS 比现有的 PTS 方法更为优越。

UniPTS: 一种用于熟练后训练稀疏性的统一框架

UniPTS: A Unified Framework for Proficient Post-Training Sparsity

We propose a new end-to-end single image dehazing method, called Densely
Connected Pyramid Dehazing Network (DCPDN), which can jointly learn the
transmission map, atmospheric light and dehazing all together. The end-to-end
learning is achieved by directly embedding the atmospheric scattering model
into the network, thereby ensuring that the proposed method strictly follows
the physics-driven scattering model for dehazing. Inspired by the dense network
that can maximize the information flow along features from different levels, we
propose a new edge-preserving densely connected encoder-decoder structure with
multi-level pyramid pooling module for estimating the transmission map. This
network is optimized using a newly introduced edge-preserving loss function. To
further incorporate the mutual structural information between the estimated
transmission map and the dehazed result, we propose a joint-discriminator based
on generative adversarial network framework to decide whether the corresponding
dehazed image and the estimated transmission map are real or fake. An ablation
study is conducted to demonstrate the effectiveness of each module evaluated at
both estimated transmission map and dehazed result. Extensive experiments
demonstrate that the proposed method achieves significant improvements over the
state-of-the-art methods. Code will be made available at:
this https URL

本文提出了一种新的称为 Densely Connected Pyramid Dehazing Network（DCPDN）的端到端单一图像去雾方法，该方法可以联合学习传输图、大气光和去雾。该论文的方法在去雾过程中采用物理学驱动的散射模型，使用新的保边损失函数进行优化，并引入基于生成性对抗网络框架的联合鉴别器以进一步合并估计的传输图和去雾结果间的相互结构信息。实验表明，该方法在去雾效果上取得了显着提高。