Incentivized Federated Learning and Unlearning

To protect users' right to be forgotten in federated learning, federated unlearning aims at eliminating the impact of leaving users' data on the global learned model. The current research in federated unlearning mainly concentrated on developing effective and efficient unlearning techniques. However, the issue of incentivizing valuable users to remain engaged and preventing their data from being unlearned is still under-explored, yet important to the unlearned model performance. This paper focuses on the incentive issue and develops an incentive mechanism for federated learning and unlearning. We first characterize the leaving users' impact on the global model accuracy and the required communication rounds for unlearning. Building on these results, we propose a four-stage game to capture the interaction and information updates during the learning and unlearning process. A key contribution is to summarize users' multi-dimensional private information into one-dimensional metrics to guide the incentive design. We further investigate whether allowing federated unlearning is beneficial to the server and users, compared to a scenario without unlearning. Interestingly, users usually have a larger total payoff in the scenario with higher costs, due to the server's excess incentives under information asymmetry. The numerical results demonstrate the necessity of unlearning incentives for retaining valuable leaving users, and also show that our proposed mechanisms decrease the server's cost by up to 53.91\% compared to state-of-the-art benchmarks

Three novel α-L-iduronidase mutations in 10 unrelated Chinese mucopolysaccharidosis type I families

Mucopolysaccharidosis type I (MPS I) arises from a deficiency in the α-L-iduronidase (IDUA) enzyme. Although the clinical spectrum in MPS I patients is continuous, it was possible to recognize 3 phenotypes reflecting the severity of symptoms, viz., the Hurler, Scheie and Hurler/Scheie syndromes. In this study, 10 unrelated Chinese MPS I families (nine Hurler and one Hurler/Scheie) were investigated, and 16 mutant alleles were identified. Three novel mutations in IDUA genes, one missense p.R363H (c.1088G > A) and two splice-site mutations (c.1190-1G > A and c.792+1G > T), were found. Notably, 45% (nine out of 20) and 30% (six out of 20) of the mutant alleles in the 10 families studied were c.1190-1G > A and c.792+1G > T, respectively. The novel missense mutation p.R363H was transiently expressed in CHO cells, and showed retention of 2.3% IDUA activity. Neither p.W402X nor p.Q70X associated with the Hurler phenotype, or even p.R89Q associated with the Scheie phenotype, was found in this group. Finally, it was noted that the Chinese MPS I patients proved to be characterized with a unique set of IDUA gene mutations, not only entirely different from those encountered among Europeans and Americans, but also apparently not even the same as those found in other Asian countries

Microscopic characteristics of magnetorheological fluids subjected to magnetic fields

© 2020 Elsevier B.V. With the aim of studying the microscopic characteristics of a magnetorheological fluid (MRF) in a magnetic field, the theoretical analyses of the particles dynamics in a magnetic field are presented, and a model for the particle motion is proposed. Based on these analyses, a three-dimensional numerical simulation of the microstructure of MRFs in different magnetic fields is performed. Furthermore, the microstructures of the MRFs are investigated using industrial computed tomography (CT) imaging. The numerical simulation and industrial CT results indicate that the chain structure of the same MRF becomes more apparent as the magnetic field strength increases, and in the same external magnetic field, this chain structure also becomes more apparent with an increase in the particle volume fraction. The lengths of particle chains in different magnetic fields are also captured in the industrial CT experiments. When the magnetic field strength is 12 mT, the particle chains of the MRF with a particle volume fraction of 30% reach more than 10 mm in length, which bridge the inner diameter of the container, and the dense clusters-like structure is formed, the clusters-like structure becomes denser with an increase in magnetic field. Moreover, the particle chain lengths of MRF with high particle volume fractions increase sharply with the magnetic field. The experiments demonstrated that the industrial CT is an efficient method to study the microstructures of MRFs by providing particle distributions of MRFs more clearly and intuitively

RepVGG:Making VGG-style ConvNets Great Again

We present a simple but powerful architecture of convolutional neural network, which has a VGG-like inference-time body composed of nothing but a stack of 3x3 convolution and ReLU, while the training-time model has a multi-branch topology. Such decoupling of the training-time and inference-time architecture is realized by a structural re-parameterization technique so that the model is named RepVGG. On ImageNet, RepVGG reaches over 80% top-1 accuracy, which is the first time for a plain model, to the best of our knowledge. On NVIDIA 1080Ti GPU, RepVGG models run 83% faster than ResNet-50 or 101% faster than ResNet-101 with higher accuracy and show favorable accuracy-speed trade-off compared to the state-of-the-art models like EfficientNet and RegNet. The code and trained models are available at https://github.com/megvii-model/RepVGG.Comment: CVPR 202