Scalable Peaceman-Rachford Splitting Method with Proximal Terms

Along with developing of Peaceman-Rachford Splittling Method (PRSM), many batch algorithms based on it have been studied very deeply. But almost no algorithm focused on the performance of stochastic version of PRSM. In this paper, we propose a new stochastic algorithm based on PRSM, prove its convergence rate in ergodic sense, and test its performance on both artificial and real data. We show that our proposed algorithm, Stochastic Scalable PRSM (SS-PRSM), enjoys the $O(1/K)$ convergence rate, which is the same as those newest stochastic algorithms that based on ADMM but faster than general Stochastic ADMM (which is $O(1/\sqrt{K})$). Our algorithm also owns wide flexibility, outperforms many state-of-the-art stochastic algorithms coming from ADMM, and has low memory cost in large-scale splitting optimization problems

Obscuring masters : the vicissitude of Danwei and the shifting of social structures from Maoist to post-Maoist China

No abstract available.Geography and the Environmen

PIDS: Joint Point Interaction-Dimension Search for 3D Point Cloud

The interaction and dimension of points are two important axes in designing point operators to serve hierarchical 3D models. Yet, these two axes are heterogeneous and challenging to fully explore. Existing works craft point operator under a single axis and reuse the crafted operator in all parts of 3D models. This overlooks the opportunity to better combine point interactions and dimensions by exploiting varying geometry/density of 3D point clouds. In this work, we establish PIDS, a novel paradigm to jointly explore point interactions and point dimensions to serve semantic segmentation on point cloud data. We establish a large search space to jointly consider versatile point interactions and point dimensions. This supports point operators with various geometry/density considerations. The enlarged search space with heterogeneous search components calls for a better ranking of candidate models. To achieve this, we improve the search space exploration by leveraging predictor-based Neural Architecture Search (NAS), and enhance the quality of prediction by assigning unique encoding to heterogeneous search components based on their priors. We thoroughly evaluate the networks crafted by PIDS on two semantic segmentation benchmarks, showing ~1% mIOU improvement on SemanticKITTI and S3DIS over state-of-the-art 3D models.Comment: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2023: 1298-130

AO-DETR: Anti-Overlapping DETR for X-Ray Prohibited Items Detection

Prohibited item detection in X-ray images is one of the most essential and highly effective methods widely employed in various security inspection scenarios. Considering the significant overlapping phenomenon in X-ray prohibited item images, we propose an Anti-Overlapping DETR (AO-DETR) based on one of the state-of-the-art general object detectors, DINO. Specifically, to address the feature coupling issue caused by overlapping phenomena, we introduce the Category-Specific One-to-One Assignment (CSA) strategy to constrain category-specific object queries in predicting prohibited items of fixed categories, which can enhance their ability to extract features specific to prohibited items of a particular category from the overlapping foreground-background features. To address the edge blurring problem caused by overlapping phenomena, we propose the Look Forward Densely (LFD) scheme, which improves the localization accuracy of reference boxes in mid-to-high-level decoder layers and enhances the ability to locate blurry edges of the final layer. Similar to DINO, our AO-DETR provides two different versions with distinct backbones, tailored to meet diverse application requirements. Extensive experiments on the PIXray and OPIXray datasets demonstrate that the proposed method surpasses the state-of-the-art object detectors, indicating its potential applications in the field of prohibited item detection. The source code will be released at https://github.com/Limingyuan001/AO-DETR-test

Energy dissipation mechanism and damage model of marble failure under two stress paths

Marble conventional triaxial loading and unloading failure testing research is carried out to analyze the elastic strain energy and dissipated strain energy evolutionary characteristics of the marble deformation process. The study results show that the change rates of dissipated strain energy are essentially the same in compaction and elastic stages, while the change rate of dissipated strain energy in the plastic segment shows a linear increase, so that the maximum sharp point of the change rate of dissipated strain energy is the failure point. The change rate of dissipated strain energy will increase during unloading confining pressure, and a small sharp point of change rate of dissipated strain energy also appears at the unloading point. The damage variable is defined to analyze the change law of failure variable over strain. In the loading test, the damage variable growth rate is first rapid then slow as a gradual process, while in the unloading test, a sudden increase appears in the damage variable before reaching the rock peak strength. According to the deterioration law of damage and the impact of confining pressure on the elastic modulus, a rock damage constitutive model is established, which has a better fitting effect on the data in the loading and unloading failure processes

FADE: Enabling Federated Adversarial Training on Heterogeneous Resource-Constrained Edge Devices

Federated adversarial training can effectively complement adversarial robustness into the privacy-preserving federated learning systems. However, the high demand for memory capacity and computing power makes large-scale federated adversarial training infeasible on resource-constrained edge devices. Few previous studies in federated adversarial training have tried to tackle both memory and computational constraints simultaneously. In this paper, we propose a new framework named Federated Adversarial Decoupled Learning (FADE) to enable AT on heterogeneous resource-constrained edge devices. FADE differentially decouples the entire model into small modules to fit into the resource budget of each device, and each device only needs to perform AT on a single module in each communication round. We also propose an auxiliary weight decay to alleviate objective inconsistency and achieve better accuracy-robustness balance in FADE. FADE offers theoretical guarantees for convergence and adversarial robustness, and our experimental results show that FADE can significantly reduce the consumption of memory and computing power while maintaining accuracy and robustness.Comment: Preprint versio

Effectiveness and potential mechanism of Jiawei-Xiaoyao-San for hyperthyroidism: a systematic review

Objectives: To evaluate the effectiveness and potential mechanism of traditional Chinese medicine Jiawei-Xiaoyao-San (JWXYS) as an adjunct or mono- therapy for antithyroid drugs (ATDs) in the treatment of hyperthyroidism. Methods: Eight databases and three trial registries were searched from inception until May 2023. Randomized controlled trials (RCTs) were included and meta-analysis was conducted using RevMan 5.4 and Stata 14.0. The Cochrane risk of bias (ROB) tool 1.0 and GRADE tool was used for quality appraisal. The findings from case reports using mono-JWXYS and pharmacological studies were summarized in tables. Results: Thirteen RCTs with 979 participants were included. The majority of the included studies were assessed as high risk of bias in one ROB domain. Compared with ATDs, JWXYS plus ATDs resulted in lower free triiodothyronine (FT3) (MD = -1.31 pmol/L, 95% CI [-1.85, -0.76]; low-certainty), lower free thyroxine (MD = -3.24 pmol/L, 95% CI [-5.06, -1.42]; low-certainty), higher thyroid stimulating hormone (MD = 0.42 mIU/L, 95% CI [0.26, 0.59]; low-certainty), higher effectiveness rate of traditional Chinese medicine syndrome (RR = 1.28, 95% CI [1.08, 1.52]; low-certainty), lower goiter score (MD = -0.66, 95% CI [-1.04, -0.29]; very low-certainty), lower thyrotrophin receptor antibody (SMD = -0.44, 95% CI [-0.73, -0.16]; low-certainty) and fewer adverse events (AEs) (RR = 0.34, 95% CI [0.18, 0.67]; moderate-certainty). Compared with regular dosage of ATDs, JWXYS plus half-dose ATDs resulted in fewer AEs (RR = 0.24, 95% CI [0.10, 0.59]; low-certainty). Compared with ATDs in 1 trial, JWXYS resulted in higher FT3, lower goiter score and fewer AEs. Three case reports showed that the reasons patients sought TCM-only treatment include severe AEs and multiple relapses. Three pharmacological studies demonstrated that JWXYS restored Th17/Treg balance, lowered deiodinases activity, regulated thyroid cell proliferation and apoptosis, and alleviated liver oxidative stress in mouse or rat models. Conclusion: JWXYS may enhance the effectiveness of ATDs for hyperthyroidism, particularly in relieving symptoms and reducing AEs. Mono-JWXYS is not recommended except in patients intolerant to ATDs. The findings should be interpreted with caution due to overall high risk of bias. Further pharmacological studies with more reliable models are needed