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Plasma–enhanced chemical vapor deposition of Co3O4 thin films as a new approach for improving oxygen evolution activity

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Catalysis Today 2025年 458卷

作者： Knozowski, Dominik Kędzierska-Sar, Aleksandra Ranecki, Robert Kuświk, Piotr Fronczak, Maciej Gierlotka, Stanislaw Arabasz, Sebastian Aligayev, Amil Jabbarli, Ulkar Dominguez–Gutierrez, Francisco Javier Gmurek, Marta Lodz University of Technology Faculty of Process and Environmental Engineering Wólczańska 213 St Lodz93–005 Poland Lodz University of Technology Faculty of Chemistry Institute of General and Ecological Chemistry Żeromskiego 116 St. Lodz90–924 Poland Institute of Molecular Physics Polish Academy of Sciences Mariana Smoluchowskiego 17 St. Poznań60–179 Poland Polish Academy of Sciences Laboratory of Nanostructures Sokolowska 29/37 St. Warsaw01–142 Poland Lukasiewicz Research Network – PORT Polish Center for Technology Development Stablowicka 147 St. Wroclaw54–066 Poland CAS Key Laboratory of High Magnetic Field and Iron Beam Physical Biology Institute of Intelligent Machines Hefei Institutes of Physical Science Chinese Academy of Science Hefei230031 China Science Island Branch of Graduate School University of Science and Technology of China Hefei230026 China NOMATEN Centre of Excellence National Centrefor Nuclear Research A. Soltana 7 St. Otwock05–400 Poland Institute of Theoretical Physics Faculty of Physics University of Warsaw Pasteura 5 St Warsaw02–093 Poland

The development of efficient and durable electrocatalysts for the oxygen evolution reaction (OER) remains a key challenge in advancing sustainable energy technologies. Among cobalt – based materials, spinel Co3O4 is a widely studied catalyst;however, its bulk form often suffers from poor electrical conductivity and limited accessibility of active sites. In this study, we demonstrate that plasma–enhanced chemical vapor deposition (PECVD), followed by thermal annealing in air, provides an effective route for vacancy engineering in CoxOy thin films inducing both oxygen and cobalt vacancies without relying on foreign dopants or ion–exchange processes. The Co3O4 films demonstrated exceptional OER performance, with the best sample achieving a low overpotential of 343 mV at 10 mA cm–2, and a high capability for Co4+ formation, a key intermediate in the OER process. Additionally, the films exhibited catalytic activity not only at the surface, but also throughout their entire volume, highlighting the significance of deposition time and film thickness in optimizing OER performance. Ab–initio calculations, including Bader charge analysis and density of states evaluations, further elucidate the role of defect chemistry: oxygen vacancies enhance *OH adsorption and drive structural reconstruction, while cobalt vacancies reduce the energy barrier for deprotonation and facilitate the Co3+ → Co4+ oxidation process. Overall, this work establishes PECVD combined with thermal treatment as a powerful and scalable strategy for tailoring defect concentrations in cobalt oxide catalysts. The approach offers valuable insights into the rational design of high–performance OER catalysts, with broad implications for electrochemical energy conversion technologies. © 2025 The Authors

关键词： Thermal Engineering

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ReconBoost: Boosting Can Achieve Modality Reconcilement 41

ReconBoost: Boosting Can Achieve Modality Reconcilement

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41st International Conference on Machine Learning, ICML 2024

作者： Hua, Cong Xu, Qianqian Bao, Shilong Yang, Zhiyong Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores a novel multi-modal alternating learning paradigm pursuing a reconciliation between the exploitation of uni-modal features and the exploration of cross-modal interactions. This is motivated by the fact that current paradigms of multi-modal learning tend to explore multi-modal features simultaneously. The resulting gradient prohibits further exploitation of the features in the weak modality, leading to modality competition, where the dominant modality overpowers the learning process. To address this issue, we study the modality-alternating learning paradigm to achieve reconcilement. Specifically, we propose a new method called ReconBoost to update a fixed modality each time. Herein, the learning objective is dynamically adjusted with a reconcilement regularization against competition with the historical models. By choosing a KL-based reconcilement, we show that the proposed method resembles Friedman's Gradient-Boosting (GB) algorithm, where the updated learner can correct errors made by others and help enhance the overall performance. The major difference with the classic GB is that we only preserve the newest model for each modality to avoid overfitting caused by ensembling strong learners. Furthermore, we propose a memory consolidation scheme and a global rectification scheme to make this strategy more effective. Experiments over six multi-modal benchmarks speak to the efficacy of the method. We release the code at https://***/huacong/ReconBoost. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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A fine-grained perspective on the robustness of global cargo ship transportation networks

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Journal of Geographical sciences 2018年第7期28卷 881-899页

作者：彭澎程诗奋陈金海廖梦迪吴琳刘希亮陆锋 State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research CAS University of Chinese Academy of Sciences Navigation Aids Technology Research Center of Jimei University National & Local Joint Engineering Research Center for Marine Navigation Aids Services College of Geomatics Shandong University of Science and Technology Institute of Computing Technology CAS Fujian Collaborative Innovation Center for Big Data Applications in Governments Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application

The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current research mainly focuses on the coarse-grained, holistic cargo ship transportation network while ignoring the structural diversity of different sub-networks. In this paper, we evaluate the robustness of the global cargo ship transportation network based on the most recent Automatic Identification System（AIS） data available. First, we subdivide three typical cargo ship transportation networks（i.e., oil tanker, container ship and bulk carrier） from the original cargo ship transportation network. Then, we design statistical indices based on complex network theory and employ four attack strategies, including random attack and three intentional attacks（i.e., degree-based attack, betweenness-based attack and flux-based attack） to evaluate the robustness of the three typical cargo ship transportation networks. Finally, we compare the integrity of the remaining ports of the network when a small proportion of ports lose their function. The results show that 1） compared with the holistic cargo ship transportation network, the fine-grain-based cargo ship transportation networks can fully reflect the pattern and process of global cargo transportation; 2） different cargo ship networks behave heterogeneously in terms of their robustness, with the container network being the weakest and the bulk carrier network being the strongest; and 3） small-scale intentional attacks may have significant influence on the integrity of the container network but a minor impact on the bulk carrier and oil tanker transportation networks. These conclusions can help improve the decision support capabilities in maritime transportation planning and emergency response and facilitate the establishment of a more reliable maritime transportation ***： The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current resear

关键词： complex network fine-grained cargo ship transportation network robustness automatic identification system

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UJN-Traffic: A Benchmark dataset for Performance Evaluation of Traffic Element Classification 7th

UJN-Traffic: A Benchmark Dataset for Performance Evaluation ...

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7th International Conference on Life System Modeling and Simulation, LSMS 2021, and the 7th International Conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2021

作者： Li, Yan Han, Shi-Yuan Shen, Yuan Liu, Zhen Dong, Ji-Wen Xu, Tao University of Jinan Jinan250022 China China High-Resolution Earth Observation System Shandong Center of Data and Application University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China Land Spatial Data and Remote Sensing Technology Institute of Shandong Province Jinan250002 China

ISBN: (纸本)9789811672125

The detection of traffic element in remote sensing imagesplays an important role in the construction of traffic infrastructure, and can provide dynamic monitoring and quality evaluation for traffic construction projects. The convolutional neural network (CNN)-based method has achieved good performance in object detection in computer vision, which benefits from the popularity of large-scale datasets. However, the existing dataset can only be used for object detection, and the sample size and diversity need to be improved. These problems have brought challenges to the improvement and application of existing traffic element detection methods. This paper proposes a large-scale highresolution traffic element dataset based on multi-method detection analysis, and validates it based on the CNN-based object detection method. We screened more than 100 satellite images from high-resolution satellite images with a spatial resolution of 0.8 m. Manually labeled 5 categories and 100,000 traffic element samples. In the experiment, three CNN-based detection methods were used as the experimental baseline method. The experimental results proved effectiveness of the UJN-traffic traffic element dataset and provided a useful reference for traffic construction engineering and management. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Remote sensing

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Self-Supervised Teaching and Learning of Representations on Graphs 23

Self-Supervised Teaching and Learning of Representations on ...

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2023 World Wide Web Conference, WWW 2023

作者： Wan, Liangtian Fu, Zhenqiang Sun, Lu Wang, Xianpeng Xu, Gang Yan, Xiaoran Xia, Feng Key Laboratory for Ubiquitous Network Service Software of Liaoning Province School of Software Dalian University of Technology Dalian China Department of Communication Engineering Institute of Information Science Technology Dalian Maritime University Dalian China State Key Laboratory of Marine Resource Utilization in South China Sea School of Information and Communication Engineering Hainan University Haikou China State Key Laboratory of Millimeter Waves School of Information Science and Engineering Southeast University Nanjing China Research Center of Big Data Intelligence Research Institute of Artificial Intelligence Zhejiang Lab Hangzhou China School of Computing Technologies Rmit University Melbourne Australia

ISBN: (纸本)9781450394161

Recent years have witnessed significant advances in graph contrastive learning (GCL), while most GCL models use graph neural networks as encoders based on supervised learning. In this work, we propose a novel graph learning model called GraphTL, which explores self-supervised teaching and learning of representations on graphs. One critical objective of GCL is to retain original graph information. For this purpose, we design an encoder based on the idea of unsupervised dimensionality reduction of locally linear embedding (LLE). Specifically, we map one iteration of the LLE to one layer of the network. To guide the encoder to better retain the original graph information, we propose an unbalanced contrastive model consisting of two views, which are the learning view and the teaching view, respectively. Furthermore, we consider the nodes that are identical in muti-views as positive node pairs, and design the node similarity scorer so that the model can select positive samples of a target node. Extensive experiments have been conducted over multiple datasets to evaluate the performance of GraphTL in comparison with baseline models. Results demonstrate that GraphTL can reduce distances between similar nodes while preserving network topological and feature information, yielding better performance in node classification. © 2023 ACM.

关键词： Classification (of information)

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INSTANCE-SPECIFIC CANONICAL CORRELATION ANALYSIS FOR POSE ALIGNMENT

INSTANCE-SPECIFIC CANONICAL CORRELATION ANALYSIS FOR POSE AL...

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IEEE International Conference on Image Processing

作者： Deming Zhai Hong Chang Xilin Chen Wen Gao School of Computer Science and Technology Harbin Institute of Technology Harbin China 150001 Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences (CAS) Beijing China 100080

ISBN: (纸本)9781479923427

Canonical correlation analysis (CCA) based methods achieve great success for pose alignment. However, CCA has limitations as a linear and global algorithm. Although some variants have been proposed to overcome the limitations, neither of them achieves locality and nonlinearity at the same time. In this paper, we propose a novel algorithm called Instance-Specific Canonical Correlation Analysis (ISCCA), which approximates the nonlinear data by computing the instance specific projections along the smooth curve of the manifold. Based on the framework of least squares regression, CCA is extended to the instance-specific case which obtains a set of locally-linear smooth but globally-nonlinear transformations. The optimization problem is proved to be convex and could be solved efficiently by alternating optimization. And the globally optimal solutions could be achieved with theoretical guarantee. Experimental results for pose alignment demonstrate the effectiveness of our proposed method.

关键词： proposed alignment transofrmations Alignment Posing optimal solution multivariate analysis nonlinearity Manifolds Convexity

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Are Neural Ranking Models Robust?

arXiv

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arXiv 2021年

作者： Wu, Chen Zhang, Ruqing Guo, Jiafeng Fan, Yixing Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing NO. 6 Kexueyuan South Road Haidian District Beijing100190 China

Recently, we have witnessed the bloom of neural ranking models in the information retrieval (IR) field. So far, much effort has been devoted to developing effective neural ranking models that can generalize well on new data. There has been less attention paid to the robustness perspective. Unlike the effectiveness which is about the average performance of a system under normal purpose, robustness cares more about the system performance in the worst case or under malicious operations instead. When a new technique enters into the real-world application, it is critical to know not only how it works in average, but also how would it behave in abnormal situations. So we raise the question in this work: Are neural ranking models robust? To answer this question, firstly, we need to clarify what we refer to when we talk about the robustness of ranking models in IR. We show that robustness is actually a multi-dimensional concept and there are three ways to define it in IR: 1) The performance variance under the independent and identically distributed (I.I.D.) setting;2) The out-of-distribution (OOD) generalizability;and 3) The defensive ability against adversarial operations. The latter two definitions can be further specified into two different perspectives respectively, leading to 5 robustness tasks in total. Based on this taxonomy, we build corresponding benchmark datasets, design empirical experiments, and systematically analyze the robustness of several representative neural ranking models against traditional probabilistic ranking models and learning-to-rank (LTR) models. The empirical results show that there is no simple answer to our question. While neural ranking models are less robust against other IR models in most cases, some of them can still win 2 out of 5 tasks. This is the first comprehensive study on the robustness of neural ranking models. We believe the way we study the robustness as well as our findings would be beneficial to the IR community. We will also

关键词： Machine learning

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Rank Internet Service Quality Using Multiple Features: A Machine Learning Approach

Rank Internet Service Quality Using Multiple Features: A Mac...

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Sixth International Conference on Semantics,Knowledge and Grids(第六届语义、知识与网格国际会议 SKG 2010)

作者： Dandan Tu Shuang Wang Haiyan Yu Tao Zhu Chengchun Shu Jingwei Shi Key Laboratory of Network Science and Technology ICT Institute of Computing Technology CAS P.O.Bo NEC Labs China 11F Bldg Innovation Plaza Tsinghua Science Park Bei jing 100084 China

This paper addresses the problem of Ranking Internet service quality by taking a machine learning approach using multiple service features. Ranking helps find good services for applications that use services as building blocks. Unlike other ranking problems, the goodness of Internet service qualities is dependent upon multiple key features. The key features vary across different service categories and have unequally discriminative natures. This paper divides the ranking problem into four subtasks including categorizing services according to the service functionalities, identifying key features that determine the quality, denoising for feature measurement values and computing global ranking scores with multiple key features, which are cast into machine learning problems and solved using techniques classification, feature selection, clustering, and regression respectively. In particular, we propose in this paper an efficient dense-block based denoising method for subjective features, and a Supported Vector Regression based method for computing global ranking scores. Experimental results on both synthetic and real data show that the proposed approach can quantitatively identify the key features across service categories, discard noisy measurement values in 10 times faster, and compute the global ranking scores using multiple features with low mean squared errors for both linear and nonlinear ranking functions.

关键词： Web and internet services Machine learning Computational modeling Training data Support vector machines Clustering algorithms Web services

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A Multi-layers Neural network Model based on Characteristics of Ganglions' Receptive Fields in Retina and an Algorithm for Watchfulness-keeping

A Multi-layers Neural Network Model based on Characteristics...

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The Third Asian Conference on Vision (ACV2004)

作者：危辉陆汝钤 Department of Computer Science Lab of Algorithm for Cognitive Model Brain Science Research Center Fudan University Shanghai 200433 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing 100080 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing 100080 China

Machine vision is an active branch of Artificial Intelligence. An important problem in this area is the balance among efficiency, accuracy and huge computing. The visual system of human can keep watchfulness to the perimeter of visual field while at same time their central attention is focused to the center of visual field for fine information processing. This mechanism of computing resource assignment could ease the demand for huge and complex hardware structure. Therefore designing computer model based on biological visual

关键词： A Multi-layers Neural network Model based on Characteristics of Ganglions Receptive Fields in Retina and an Algorithm for Watchfulness-keeping Model

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A User Behavior-Based Random Distribution Scheme for Adversarial Example Generated CAPTCHA

A User Behavior-Based Random Distribution Scheme for Adversa...

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IEEE International Conference on Big data and Cloud computing (BdCloud)

作者： Wenwen Zheng Weiqi Wang Wei Ren Shiyuan Feng Shiqin Liu Yi Ren School of Computer Science China University of Geosciences Wuhan China Henan Key Laboratory of Network Cryptography Technology Zhengzhou China Key Laboratory of Network Assessment Technology CAS Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Computing Science University of East Anglia Norwich U.K

The widespread and mature application of deep learning technology on human behavior analysis leads to the possibility that the Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) may not be able to accurately determine whether or not the user is human, which makes the distributed denial-of-service (DDoS) attacks launched by deep learning-based malicious automatic computer programs become possible. The CAPTCHA generated by adversarial examples can defend against the automatic identification by the deep learning-based method, however, if the deployed CAPTCHAs are all adversarial examples solely, the adversary has the chance to collect sufficient adversarial examples, which also causes security issues. In this paper, a user behavior-based random distribution scheme for adversarial example generated CAPTCHA is proposed to tackle the above sole distribution problem. Specifically, we generated two kinds of CAPTCHAs, one is normal and the other is generated by the fast gradient signed method (FGSM), i.e., the adversarial examples. Meanwhile, user behaviors are analyzed and associated with a reasonable probability model. According to the probability selection, the normal CAPTCHAs or strong CAPTCHAs will be delivered to the users. The experimental results illustrate that our scheme has an excellent ability to distinguish computers from humans. Thus, it can protect the CAPTCHA systems from DDoS attacks.

关键词： Computers Learning systems Deep learning Analytical models Computational modeling Denial-of-service attack Security

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