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Improving Conversion Kinetics of Sodium Polysulfides through Electron Spillover Effect with V/Co Dual-Atomic Site Anchoring on N-Doped MXene

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Advanced Materials 2025年第0期 e2501371页

作者： Liu, Ronghui Feng, Chao Wu, Pengfei Sun, Yuzhuo Chu, Zhaoqin Hu, Jingtian Chen, Wenxing Guo, Lingling Huang, Qing Wang, Degao Zhejiang Key Laboratory of Data-Driven High-Safety Energy Materials and Applications Advanced Interdisciplinary Science Research Center Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Zhejiang Ningbo315201 China Key Laboratory of Bio-Inspired Smart Interfacial Science and Technology of Ministry of Education School of Chemistry Beihang University Beijing100191 China College of Chemical and Biological Engineering Shandong University of Science and Technology Shandong Qingdao266590 China Energy & Catalysis Center School of Materials Science and Engineering Beijing Institute of Technology Beijing100081 China Shanghai Synchrotron Radiation Facility Shanghai Advanced Research Institute Chinese Academy of Science Shanghai201204 China

Room-temperature sodium─sulfur (RT/Na─S) batteries, with a theoretical capacity of 1672 mAh g⁻1, face challenges such as the insulating nature of sulfur and slow redox kinetics, particularly during complex liquid–solid (Na2S4→Na2S2) and solid–solid (Na2S2→Na2S) conversions. Herein, vanadium-cobalt (VCo) diatomic sites implanted in vacancy-rich N-doped MXene (VCo DACs/N-MXene) are introduced to address these issues. The N-bridged VCo diatomic pairs are demonstrated and their strong electronic interactions are also validated through experimental and theoretical analyses. The RT/Na─S battery with optimized VCo DACs/N-MXene delivers an average capacity of 1255.3 mAh g⁻1 at 0.1 C and remarkable cycling stability, with only ≈0.001% capacity decay per cycle over 1500 cycles at 1 C. DFT calculations reveal that VCo diatomic sites enhance reaction kinetics by reducing the Gibbs free energy for polysulfide conversions, notably reducing the solid–solid conversion energy barriers from 1.17/0.96 eV for V/Co SACs/N-MXene to 0.53 eV for VCo DACs/N-MXene. XANES and DFT analyses attribute this improvement to a unique electron spillover effect, facilitating efficient electron transport during charge and discharge. This work highlights the potential of optimizing electronic configurations and coordinating environments to activate bidirectional kinetics with improved capacity and longevity of RT/Na─S batteries. © 2025 Wiley-VCH GmbH.

关键词： Gibbs free energy

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Replay Without Saving: Prototype Derivation and Distribution Rebalance for Class-Incremental Semantic Segmentation

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第6期47卷 4699-4716页

作者： Jinpeng Chen Runmin Cong Yuxuan Luo Horace Ho Shing Ip Sam Kwong Department of Computer Science City University of Hong Kong Kowloon Hong Kong School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China Department of Computer Science and Centre for Innovative Applications of Internet and Multimedia Technologies City University of Hong Kong Kowloon Hong Kong School of Data Science Lingnan University Tuen Mun Hong Kong

The research of class-incremental semantic segmentation (CISS) seeks to enhance semantic segmentation methods by enabling the progressive learning of new classes while preserving knowledge of previously learned ones. A significant yet often neglected challenge in this domain is class imbalance. In CISS, each task focuses on different foreground classes, with the training set for each task exclusively comprising images that contain these currently focused classes. This results in an overrepresentation of these classes within the single-task training set, leading to a classification bias towards them. To address this issue, we propose a novel CISS method named STAR, whose core principle is to reintegrate the missing proportions of previous classes into current single-task training samples by replaying their prototypes. Moreover, we develop a prototype deviation technique that enables the deduction of past-class prototypes, integrating the recognition patterns of the classifiers and the extraction patterns of the feature extractor. With this technique, replay can be accomplished without using any storage to save prototypes. Complementing our method, we devise two loss functions to enforce cross-task feature constraints: the Old-Class Features Maintaining (OCFM) loss and the Similarity-Aware Discriminative (SAD) loss. The OCFM loss is designed to stabilize the feature space of old classes, thus preserving previously acquired knowledge without compromising the ability to learn new classes. The SAD loss aims to enhance feature distinctions between similar old and new class pairs, minimizing potential confusion. Our experiments on two public datasets, Pascal VOC 2012 and ADE20 K, demonstrate that our STAR achieves state-of-the-art performance.

关键词： Prototypes Training Feature extraction Semantic segmentation Continuing education Stars Semantics Vectors Transformers Image reconstruction

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Complementary knowledge Compilation Using the Hyper Extension Rule

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Chinese Journal of Electronics 2019年第2期28卷 259-265页

作者： NIU Dangdang LIU Lei LYU Shuai XU Yue College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University

We introduce the concept of Complementary formula(COMF), which is a new and non-equivalent way for knowledge compilation(KC). Based on the Hyper extension rule(HER) which is an expansion of Extension rule(ER), we design a compilation algorithm which can formula compile each Conjunctive normal form(CNF)formula to complementary Fully complementary connected diagram(c-FCCD), named as C2C(CNF formula to cFCCD). Theoretically, c-FCCD is a kind of complementary formulae of the input formulae and can support all queries and partial transformations in KC map. Experimentally,C2C is competitive with the EPCCL compilers KCER,C2E, UKCHER, DKCHER and IKCHER.

关键词： knowledge compilation Extension rule Hyper extension rule Compilation language Each pair contains complementary literals(EPCCL) theory

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Copy-Paste Detection Based on a SIFT Marked Graph Feature Vector

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Chinese Journal of Electronics 2017年第2期26卷 345-350页

作者： LYU Yingda SHEN Xuanjing CHEN Haipeng College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University

To detect copy-paste tampering,an improved SIFT(Scale invariant feature transform)-based algorithm was *** angle is defined and a maximum angle-based marked graph is *** marked graph feature vector is provided to each SIFT key point via discrete polar coordinate *** points are matched to detect the copy-paste tampering *** experimental results show that the proposed algorithm can effectively identify and detect the rotated or scaled copy-paste regions,and in comparison with the methods reported previously,it is resistant to postprocessing,such as blurring,Gaussian white noise and JPEG *** proposed algorithm performs better than the existing algorithm to dealing with scaling transformation.

关键词： Copy-paste tampering Image forensics Marked graph Maximum angle

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Model-based diagnosis of incomplete discrete-event system with rough set theory

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Science China(Information Sciences) 2017年第1期60卷 190-200页

作者： Xuena GENG Dantong OUYANG Yonggang ZHANG Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of EducationJilin University College of Computer Science and Technology Jilin University

Fault diagnosis of discrete-event system(DES) is important in the preventing of harmful events in the system. In an ideal situation, the system to be diagnosed is assumed to be complete; however, this assumption is rather restrictive. In this paper, a novel approach, which uses rough set theory as a knowledge extraction tool to deal with diagnosis problems of an incomplete model, is investigated. DESs are presented as information tables and decision tables. Based on the incomplete model and observations, an algorithm called Optimizing Incomplete Model is proposed in this paper in order to obtain the repaired model. Furthermore, a necessary and sufficient condition for a system to be diagnosable is given. In ensuring the diagnosability of a system, we also propose an algorithm to minimize the observable events and reduce the cost of sensor selection.

关键词： model-based diagnosis diagnosability discrete-event system finite state machine rough set theory

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Autonomous Domain Correlation-Based Cross-domain Network View Caching Method for SDN Distributed Controller

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Chinese Journal of Electronics 2016年第6期25卷 1127-1133页

作者： FU Tao HU Liang CHAI Sheng CHE Xilong College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computing and Knowledge Engineering of Ministry of Education Jilin University

Software-defined networks(SDN) maintain a global view of the network, thus improving the intelligence of forwarding decisions. With the expansion of the network scale, distributed controllers are used in a variety of large-scale networks in which subnetworks managed by controller instance are called autonomous domains. We analyze statistic frequency of communication across the autonomous domain. We calculate the autonomous domain correlations for controller instances using acquired statistical information. We cache network views to highly correlated controller instances. Distributed controllers are capable of considering both the average response time and overall storage. An experiment shows that our method can fully take advantage of these two performance indicators.

关键词： Software-defined networks(SDN) Distributed controller Network analyze Network view cache

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Prediction-based event-triggered identification of quantized input FIR systems with quantized output observations

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Science China(Information Sciences) 2020年第1期63卷 140-151页

作者： Jin GUO Jing-Dong DIAO School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education

This paper addresses the identification of finite impulse response(FIR) systems with both quantized and event-triggered observations. An event-triggered communication scheme for the binary-valued output quantization is introduced to save communication resources. Combining the empirical-measure-based identification technique and the weighted least-squares optimization, an algorithm is proposed to estimate the unknown parameter by full use of the received data and the not-triggered condition. Under quantized inputs, it is shown that the estimate can strongly converge to the real values and the estimator is asymptotically efficient in terms of the Cram′er-Rao lower bound. Further, the limit of the average communication rate is derived and the tradeoff between this limit and the estimation performance is discussed. Moreover, the case of multi-threshold quantized observations is considered. Numerical examples are included to illustrate the obtained main results.

关键词： identification FIR systems prediction-based event-triggered communication quantized observations convergence performance

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Grid-level assessment on spatial equity in access to urban public facilities by vulnerable groups based on the multi-source data

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Habitat International 2025年 161卷

作者： Lai, Wenbo Lin, Dahu Li, Zhuowen Peng, You Zhou, Weiqiang Feng, Tao School of Architecture South China University of Technology Guangzhou China Key Laboratory of Ecology and Energy Saving Study of Dense Habitat Ministry of Education China Urban and Data Science Lab of Advanced Science and Engineering Hiroshima University Higashi Hiroshima 7398529 Japan Department of Architecture College of Design and Engineering National University of Singapore 8 Architecture Drive 117566 Singapore

In the context of promoting human-centered sustainable cities and communities, achieving spatial equity in the development of urban public facilities (UPF) plays an essential role and faces a significant challenge. Although the regional and social disparity of UPF's distribution is well documented, researchers seldom consider the spatial distribution disparities between multiple types of UPF and vulnerable groups at a fine scale. This study built a comprehensive evaluation framework with a fine-grained model, which is comprised of grid-based partitioning, multi-source data crawling, cumulative opportunity model, and spatial statistical analysis, to analyze spatial equity in Guangzhou, China. The accessibility to UPF and the social vulnerability index (SVI) are measured utilizing the cumulative opportunity measure and principal component analysis (PCA). This study, therefore, analyzed the imbalance between the distribution of UPF and vulnerable groups. Moreover, the study accurately identified spatial inequity areas using the Gini coefficient and bivariate Moran's I. The results show that numerous areas within the study area have limited opportunities to access UPF. Nevertheless, the formation of multiple urban centers during urbanization has increased the diversity of accessible types of UPF within the community life unit. In addition, due to the aging population in the old city center and the complex population structure of urban villages, the areas in the old town often become high-value clusters spatially with SVI. In particular, due to planning problems caused by urban villages, these areas are often prone to become spatial inequity areas characterized by “high SVI - low comprehensive accessibility.” This evaluation framework is user-friendly and suitable for application in other cities, helping to identify spatial inequity areas that require planning intervention and priority. © 2025

关键词： Accessibility Grid-scale Multi-source data Social vulnerability Spatial equity Urban public facilities

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knowledge Compilation Methods Based on the Clausal Relevance and Extension Rule

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Chinese Journal of Electronics 2018年第5期27卷 1037-1042页

作者： NIU Dangdang LIU Lei LYU Shuai College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering (Jilin University) Ministry of Education

We introduce the concepts of Relevancematrix(RM) and Relevance-set(RS). And we construct the association between RM and the knowledge compilation(KC) methods based on Extension rule(ER). Based on the basic parameters of RS and the relationship between RM and the KC methods based on ER, we design two efficient heuristics, called M2S(maximum sum of elements in RS and sum of literals in RS) and MNE(minimum number of maximum terms not extended by RS). Both of above heuristics intend to find the minimum set of maximum terms which cannot be extended by RS. Furthermore,we apply M2S and MNE on KCER. M2S KCER(KCER with M2S) and MNE KCER(KCER with MNE) are designed and implemented based on M2S and MNE, respectively. Experimentally, for the SAT instances with random lengths of clauses, M2S KCER and MNE KCER can improve the efficiency and quality of KCER sharply, and they are two best KC algorithms of EPCCL(each pair contains complementary literal) theory in all KC algorithms based on KCER.

关键词： Extension rule knowledge compilation Clausal relevance Heuristic strategy

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A Novel Locomotion Rule Rmbedding Long Short-Term Memory Network with Attention for Human Locomotor Intent Classification Using Multi-Sensors Signals

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Computers, Materials & Continua 2024年第6期79卷 4349-4370页

作者： Jiajie Shen Yan Wang Dongxu Zhang Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of EducationColleague of Computer Science and TechnologyJilin UniversityChangchun130012China College of Software and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchun130012China

Locomotor intent classification has become a research hotspot due to its importance to the development of assistive robotics and wearable *** work have achieved impressive performance in classifying steady locomotion ***,it remains challenging for these methods to attain high accuracy when facing transitions between steady locomotion *** to the similarities between the information of the transitions and their adjacent steady ***,most of these methods rely solely on data and overlook the objective laws between physical activities,resulting in lower accuracy,particularly when encountering complex locomotion modes such as *** address the existing deficiencies,we propose the locomotion rule embedding long short-term memory(LSTM)network with Attention(LREAL)for human locomotor intent classification,with a particular focus on transitions,using data from fewer sensors(two inertial measurement units and four goniometers).The LREAL network consists of two levels:One responsible for distinguishing between steady states and transitions,and the other for the accurate identification of locomotor *** classifier in these levels is composed of multiple-LSTM layers and an attention *** introduce real-world motion rules and apply constraints to the network,a prior knowledge was added to the network via a rule-modulating *** method was tested on the ENABL3S dataset,which contains continuous locomotion date for seven steady and twelve transitions *** results showed that the LREAL network could recognize locomotor intents with an average accuracy of 99.03%and 96.52%for the steady and transitions states,*** is worth noting that the LREAL network accuracy for transition-state recognition improved by 0.18%compared to other state-of-the-art network,while using data from fewer sensors.

关键词： Lower-limb prosthetics deep neural networks motion classification

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