Data Valuation for Vertical Federated Learning: A Model-free and Privacy-preserving Method

Vertical Federated learning (VFL) is a promising paradigm for predictive analytics, empowering an organization (i.e., task party) to enhance its predictive models through collaborations with multiple data suppliers (i.e., data parties) in a decentralized and privacy-preserving way. Despite the fast-growing interest in VFL, the lack of effective and secure tools for assessing the value of data owned by data parties hinders the application of VFL in business contexts. In response, we propose FedValue, a privacy-preserving, task-specific but model-free data valuation method for VFL, which consists of a data valuation metric and a federated computation method. Specifically, we first introduce a novel data valuation metric, namely MShapley-CMI. The metric evaluates a data party's contribution to a predictive analytics task without the need of executing a machine learning model, making it well-suited for real-world applications of VFL. Next, we develop an innovative federated computation method that calculates the MShapley-CMI value for each data party in a privacy-preserving manner. Extensive experiments conducted on six public datasets validate the efficacy of FedValue for data valuation in the context of VFL. In addition, we illustrate the practical utility of FedValue with a case study involving federated movie recommendations

REVIEW ON SUB-SYNCHRONOUS OSCILLATIONS IN WIND FARMS: ANALYSIS METHOD, STUDY SYSTEM, AND DAMPING CONTROL

More and more attention on wind farm sub-synchronous oscillation (SSO) has been paid as many SSO incidents in wind farms have occurred. This paper presents an overview of recent SSO issues in wind farm from the perspective of control, including the analysis methods, the study system, and the SSO mitigation by damping control. Three major analysis methods, as well as different study systems for wind farm SSO study, are comprehensively reviewed. The adaptability and complexity of the methods and study systems are analysed, and an overall survey of recent SSO analysis is given. Among the wind farm SSO mitigation methods, the sub-synchronous damping controller (SSDC) is one of the most commonly used methods in practice. Its configuration and signal selection are introduced in this paper

Difference-based Deep Convolutional Neural Network for Simulation-to-reality UAV Fault Diagnosis

Identifying the fault in propellers is important to keep quadrotors operating safely and efficiently. The simulation-to-reality (sim-to-real) UAV fault diagnosis methods provide a cost-effective and safe approach to detect the propeller faults. However, due to the gap between simulation and reality, classifiers trained with simulated data usually underperform in real flights. In this work, a new deep neural network (DNN) model is presented to address the above issue. It uses the difference features extracted by deep convolutional neural networks (DDCNN) to reduce the sim-to-real gap. Moreover, a new domain adaptation method is presented to further bring the distribution of the real-flight data closer to that of the simulation data. The experimental results show that the proposed approach can achieve an accuracy of 97.9\% in detecting propeller faults in real flight. Feature visualization was performed to help better understand our DDCNN model.Comment: 7 pages, 8 figure

Environmental efficiency analysis of listed cement enterprises in China

© 2016 by the authors.China's cement production has been the highest worldwide for decades and contributes significant environmental pollution. Using a non-radical DEA model with slacks-based measure (SBM), this paper analyzes the environmental efficiency of China's listed cement companies. The results suggest that the average mean of the environmental efficiency for the listed cement enterprises shows a decreasing trend in 2012 and 2013. There is a significant imbalance in environmental efficiency in these firms ranging from very low to very high. Further investigation finds that enterprise size and property structure are key factors. Increasing production concentration and decreasing the share of government investment could improve the environmental efficiency. The findings also suggest that effectively monitoring pollution products can improve environmental efficiency quickly, whereas pursuit for excessive profitability without keeping the same pace in energy saving would cause a sharp drop in environmental efficiency. Based on these findings, we proposed that companies in the Chinese cement sector might consider restructuring to improve environmental efficiency. They also need to make a trade-off between profitability and environmental protection. Finally, the Chinese government should reduce ownership control and management interventions in cement companies

Ouyang Jingwu Buddhism Socialization and Sinology Education Philosophy

The integration of Buddhism and Confucianism put forward by Ouyang Jingwu is the combination of pure Buddhism and Confucianism with original consciousnessonly theory as the focus, distinguishing itself from the traditional concept of the integration of Buddhism represented by Zen and Confucianism with the basis of Neo-Confucianism in the Song and Ming Dynasty. In the meanwhile, the integration of Confucianism and Buddhism come up with by him has a distinctive feature that Confucianism serves as the Buddhism’s knowledge of complying with the needs of society. He strictly defines the content of Confucianism and Buddhism to academically discuss the blending of inner logic of Buddhism and Confucianism. He criticizes the sinicized Buddhism which serves Tiantai Sect, Huanyan Sect and Jingtu School of Buddhism as mainstream, simply the China Buddhism classics liken Mahayana, reconstructs the inner school; he criticizes the mainstream Confucianism which is based on Neo-Confucianism in Song and Ming Dynasty, and the Confucianism which eschews quietus and ontology, he also criticized the fledgling Confucianism, and reconstructs the Confucianism. And then, he advocated to take the “Three Wisdoms and three gradual steps” as the main method to blend Confucianism and Buddhism. His fusion of Confucianism and Buddhism has brought profound influence to intellectual and elite at that time, making important significance

GaussianEditor: Editing 3D Gaussians Delicately with Text Instructions

Recently, impressive results have been achieved in 3D scene editing with text instructions based on a 2D diffusion model. However, current diffusion models primarily generate images by predicting noise in the latent space, and the editing is usually applied to the whole image, which makes it challenging to perform delicate, especially localized, editing for 3D scenes. Inspired by recent 3D Gaussian splatting, we propose a systematic framework, named GaussianEditor, to edit 3D scenes delicately via 3D Gaussians with text instructions. Benefiting from the explicit property of 3D Gaussians, we design a series of techniques to achieve delicate editing. Specifically, we first extract the region of interest (RoI) corresponding to the text instruction, aligning it to 3D Gaussians. The Gaussian RoI is further used to control the editing process. Our framework can achieve more delicate and precise editing of 3D scenes than previous methods while enjoying much faster training speed, i.e. within 20 minutes on a single V100 GPU, more than twice as fast as Instruct-NeRF2NeRF (45 minutes -- 2 hours).Comment: Project page: https://GaussianEditor.github.i

Enhanced capacitive deionization of saline water using N-doped rod-like porous carbon derived from dual-ligand metal-organic frameworks

Capacitive deionization (CDI) removes ions from brine, and is forward-looking technology due to its low energy consumption, low cost and prevention of secondary pollution. Removal capacity is still an issue for CDI technology. It is quite urgent to design a high-performance CDI electrode material with a reasonable porous structure, excellent conductivity and hydrophilic surface. Herein, we originally designed nitrogen-doped rod-like porous carbon derived from dual-ligand metal-organic frameworks (MOFs), in which two ligands, namely 1,4-benzenedicarbocylic acid and triethylenediamine, coordinate with zinc (Zn). 1,4-Benzenedicarbocylic acid can be used as a pore-forming agent to increase the specific surface area of the carbon material, and triethylenediamine is used as a nitrogen doping source to increase the hydrophilicity and conductivity of the carbon material. By adjusting the ratio of the two ligands, the optimal specific surface area and nitrogen doping for the carbon material is obtained, thereby achieving the highest removal capacity for capacitive deionization of brine. The obtained carbon materials possess a hierarchical porous structure with moderate nitrogen doping. The large specific surface area of the electrode materials delivers many adsorption sites for adsorption of salt ions. The hierarchically porous structure provides rapid transport channels for salt ions, and high-level N doping enhances the conductivity and hydrophilicity of the carbon materials to some extent. More importantly, the salt removal capacity of the electrodes is as high as 24.17 mg g-1 at 1.2 V in 500 mg L-1 NaCl aqueous solution. Hence, the moderate nitrogen-doping porous carbon material derived from dual-ligand MOFs is a potential electrode material for CDI application. Such results provide a new method for the preparation of high-performance electrodes to remove ions from saline water.</p

FE-Fusion-VPR: Attention-based Multi-Scale Network Architecture for Visual Place Recognition by Fusing Frames and Events

Traditional visual place recognition (VPR), usually using standard cameras, is easy to fail due to glare or high-speed motion. By contrast, event cameras have the advantages of low latency, high temporal resolution, and high dynamic range, which can deal with the above issues. Nevertheless, event cameras are prone to failure in weakly textured or motionless scenes, while standard cameras can still provide appearance information in this case. Thus, exploiting the complementarity of standard cameras and event cameras can effectively improve the performance of VPR algorithms. In the paper, we propose FE-Fusion-VPR, an attention-based multi-scale network architecture for VPR by fusing frames and events. First, the intensity frame and event volume are fed into the two-stream feature extraction network for shallow feature fusion. Next, the three-scale features are obtained through the multi-scale fusion network and aggregated into three sub-descriptors using the VLAD layer. Finally, the weight of each sub-descriptor is learned through the descriptor re-weighting network to obtain the final refined descriptor. Experimental results show that on the Brisbane-Event-VPR and DDD20 datasets, the Recall@1 of our FE-Fusion-VPR is 29.26% and 33.59% higher than Event-VPR and Ensemble-EventVPR, and is 7.00% and 14.15% higher than MultiRes-NetVLAD and NetVLAD. To our knowledge, this is the first end-to-end network that goes beyond the existing event-based and frame-based SOTA methods to fuse frame and events directly for VPR