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Energetic Materials Frontiers 2025年

作者： Qing-guan Song Li Meng Sheng-li Jiang Lei Zhang Ze-yao Mo Institute of Applied Physics and Computational Mathematics Beijing 100088 China CAEP Software Center for High Performance Numerical Simulation Beijing 100088 China Institute of Chemical Materials China Academy of Engineering Physics Mianyang 621999 China School of Materials Science & Engineering Beijing Institute of Technology Beijing 100081 China State Key Laboratory of Explosion Science and Safety Protection Beijing Institute of Technology Beijing 100081 China

The cage-like structures provide a unique combination of strain energy for enhanced energy storage and hyperstatic constraints to stabilize the system, positioning them as promising pioneers in advancing high-energy-density materials (HEDMs). However, the shock response of cage-like HEDMs is highly complex and exhibits significant variability, posing a grand challenge in the underlying mechanisms. This study reveals that the shock response of cage-like HEDMs is regulated by the interplay between strain energy and structural symmetry, and introduces a physical model to quantitatively describe how they determine shock stability. The investigated systems include a comprehensive series of cage-like polynitrocubane HEDMs, with each carbon atom in the backbone progressively functionalized with nitro groups. Ab initio molecular dynamics (AIMD) simulations were employed to simulate dynamic and kinetic responses at shock velocities ranging from 8 km·s -1 to 11 km·s -1 , with validation provided by consistent Hugoniot curves that align with reported experimental data. Nitro groups at the carbon-based cage structure, recognized as explosion functional groups, were found to substantially increase the system's strain energy. However, an increasing number of nitro groups also intensified electrostatic repulsion among oxygen lone pairs, which weakens structural integrity and renders the material more susceptible to disassembly under shock conditions. Conversely, structural symmetry—including both the cage-like molecular conformation and spatial packing within the crystal lattice—was found to mitigate these destabilizing effects, effectively balancing the trade-off between energy storage and structural stability. Based on these findings, we propose a physical model that captures the essential factors driving shock initiation in cage-like polynitrocubane HEDMs, offering new insights to inform the design and application of novel advanced HEDMs.

关键词： shock stability cage-like high-energy-density materials strain energy structural symmetry strain tensor

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1st International Workshop on Essence in Education Training (WEET 2020) 32

1st International Workshop on Essence in Education Training ...

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32nd IEEE Conference on software Engineering Education and Training, CSEE and T 2020

作者： Castro, Luis Fernando Cooper, Kendra M. L. Duc, Anh Nguyen Pieper, Joran Striewe, Michael Faculty of Engineering University of Quindío Quindío Colombia Independent Scholar Kelowna Canada USN School of Business Department of Business and It University of South Eastern Norway Norway Faculty of Electrical Engineering and Computer Science Iacs University of Applied Sciences Stralsund Germany Paluno-The Ruhr Institute for Software Technology University of Duisburg-Essen Essen Germany

ISBN: (纸本)9781728168074

The workshop connects to the central theme 'Educating for the Future' of CSEET 2020 and thus explores opportunities to improve software Engineering education and training by using Essence. Essence, an OMG Standard, delivers essential, universal elements found in all software Engineering (SE) endeavours and a language to describe and extend these elements and their use in concrete practices to tailor SE methods to teams' needs. Its Kernel separates the stable What and Why from the more adaptable How. That way, it provides an essential thinking framework facilitating the adoption and customization of practices and methods. As such, it supports multiple ways of educating SE students as well as training practitioners for the future. The first deployments of Essence in industry and academic courses around the globe show promising results. But since it is an entirely new way to think about SE practices, methods, and their building blocks-it requires some effort and thought to dive into the standard. This workshop facilitates the adoption by sharing experiences, best practices, and lessons learned. © 2020 IEEE.

关键词： software engineering

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Taco-DDI: accurate prediction of drug-drug interaction events using graph transformer-based architecture and dynamic co-attention matrices

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Neural networks : the official journal of the International Neural Network Society 2025年 189卷 107655页

作者： Jianbo Qiao Xu Guo Junru Jin Ding Wang Kefei Li Wenjia Gao Feifei Cui Zilong Zhang Hua Shi Leyi Wei School of Software Shandong University Jinan China. School of Computer Science and Technology Hainan University Haikou 570228 China. School of Opto-electronic and Communication Engineering Xiamen University of Technology Xiamen China. School of Information Xiamen University Xiamen China Faculty of Applied Sciences Macao Polytechnic University Macao 999078 China Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR) Shandong University Jinan China. Electronic address: weileyi@***.

Drug-drug interactions (DDIs) are critical in pharmaceutical research, as adverse interactions can pose significant risks for patient treatment plans. Accurate prediction of DDI events risk levels can provide valuable guidance for designing safer and more effective medical regimens. However, existing approaches often focus on interaction networks while overlooking the inherent molecular properties of drugs. In this study, we present Taco-DDI, a novel drug representation learning framework that utilizes a graph transformer-based model combined with a dynamic co-attention mechanism. Taco-DDI leverages the transformer architecture to derive atom-level feature encodings, capturing comprehensive molecular representations. Furthermore, it employs an adaptive co-attention matrix to identify essential substructures in drug molecules solely from structural information. Our results demonstrate that Taco-DDI achieves a 6.59 % relative accuracy improvement in DDI events risk levels prediction. Additionally, interpretability analysis confirms that Taco-DDI provides meaningful insights into DDI mechanisms, highlighting its practical utility as a robust tool for identifying DDI events risk levels.

关键词： Bioinformatics Deep learning Drug discovery Drug-drug interactions Graph neural network

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Asynchronous Filtering for Takagi-Sugeno Fuzzy Neural Networks with General Transition Probabilities and Missing Measurements

Asynchronous Filtering for Takagi-Sugeno Fuzzy Neural Networ...

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IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Yaonan Shan Kun She Shouming Zhong Taotao Hu Shiqiang Hao School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Applied Mathematics University of Electronic Science and Technology of China Chengdu China MediaTek Software Design Co. Ltd. Chengdu China Si Chuan Association of Automobile Industry Chengdu China

ISBN: (数字)9781728160245

ISBN: (纸本)9781728160252

This paper focuses on the issue of asynchronous filtering for Markov jump Takagi-Sugeno (T-S) fuzzy neural networks (MJTSFNNs). We firstly consider the general transition probabilities (GTPs) including both uncertain parts and unknown ones. Then, by introducing stochastic variables, the filtering system involves not only packet loss but also quantization. After that, through Lyapunov stability theory and stochastic process knowledge, we design an asynchronous fuzzy filter and obtain a sufficient criterion to ensure the MJTSFNNs stochastically mean square stable with some performance. Finally, an example verifies the validity of our results.

关键词： Markov processes Fuzzy neural networks Lyapunov methods Takagi-Sugeno model Stochastic processes

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Investigating Bi-level optimization for learning and vision from a unified perspective: A survey and beyond

arXiv

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arXiv 2021年

作者： Liu, Risheng Gao, Jiaxin Zhang, Jin Meng, Deyu Lin, Zhouchen DUT-RU International School of Information Science & Engineering Dalian University of Technology Key Laboratory for Ubiquitous Network Service Software of Liaoning Province Dalian116024 China Department of Mathematics Southern University of Science and Technology National Center for Applied Mathematics Shenzhen China School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an Shaanxi China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Cooperative Medianet Innovation Center Shanghai Jiao Tong University Shanghai200240 China

Bi-Level Optimization (BLO) is originated from the area of economic game theory and then introduced into the optimization community. BLO is able to handle problems with a hierarchical structure, involving two levels of optimization tasks, where one task is nested inside the other. In machine learning and computer vision fields, despite the different motivations and mechanisms, a lot of complex problems, such as hyper-parameter optimization, multi-task and meta learning, neural architecture search, adversarial learning and deep reinforcement learning, actually all contain a series of closely related subproblms. In this paper, we first uniformly express these complex learning and vision problems from the perspective of BLO. Then we construct a best-response-based single-level reformulation and establish a unified algorithmic framework to understand and formulate mainstream gradient-based BLO methodologies, covering aspects ranging from fundamental automatic differentiation schemes to various accelerations, simplifications, extensions and their convergence and complexity properties. Last but not least, we discuss the potentials of our unified BLO framework for designing new algorithms and point out some promising directions for future research. © 2021, CC BY.

关键词： Deep learning

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Novel Valence Transition in Elemental Metal Europium around 80 GPa

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Physical Review Letters 2022年第1期129卷 016401-016401页

作者： Bijuan Chen Mingfeng Tian Jurong Zhang Bing Li Yuming Xiao Paul Chow Curtis Kenney-Benson Hongshan Deng Jianbo Zhang Raimundas Sereika Xia Yin Dong Wang Xinguo Hong Changqing Jin Yan Bi Hanyu Liu Haifeng Liu Jun Li Ke Jin Qiang Wu Jun Chang Yang Ding Ho-kwang Mao Center for High-Pressure Science and Technology Advanced Research Beijing 100094 China Institute of Applied Physics and Computational Mathematics Beijing 100088 China Shandong Provincial Engineering and Technical Center of Light Manipulations and Shandong Provincial Key Laboratory of Optics and Photonic Device School of Physics and Electronics Shandong Normal University Jinan 250358 China HPCAT X-ray Science Division Argonne National Laboratory Argonne Illinois 60439 USA Institute of Physics Chinese Academy of Sciences Beijing 100190 China International Center for Computational Method and Software College of Physics Jilin University Changchun 130012 China National Key Laboratory of Shock Wave and Detonation Physics Institute of Fluid Physics CAEP Mianyang 621900 China College of Physics and Information Technology Shaanxi Normal University Xi’an 710119 China

Valence transition could induce structural, insulator-metal, nonmagnetic-magnetic and superconducting transitions in rare-earth metals and compounds, while the underlying physics remains unclear due to the complex interaction of localized 4f electrons as well as their coupling with itinerant electrons. The valence transition in the elemental metal europium (Eu) still has remained as a matter of debate. Using resonant x-ray emission scattering and x-ray diffraction, we pressurize the states of 4f electrons in Eu and study its valence and structure transitions up to 160 GPa. We provide compelling evidence for a valence transition around 80 GPa, which coincides with a structural transition from a monoclinic (C2/c) to an orthorhombic phase (Pnma). We show that the valence transition occurs when the pressure-dependent energy gap between 4f and 5d electrons approaches the Coulomb interaction. Our discovery is critical for understanding the electrodynamics of Eu, including magnetism and high-pressure superconductivity.

关键词： Pressure effects Strongly correlated systems Resonant inelastic x-ray scattering X-ray diffraction

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Iterative pseudo-labeling based adaptive copy-paste supervision for semi-supervised tumor segmentation

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Knowledge-Based Systems 2025年 324卷

作者： Qiangguo Jin Hui Cui Junbo Wang Changming Sun Yimiao He Ping Xuan Linlin Wang Cong Cong Leyi Wei Ran Su School of Software Northwestern Polytechnical University Shaanxi China Yangtze River Delta Research Institute of Northwestern Polytechnical University Taicang China Department of Computer Science and Information Technology La Trobe University Melbourne Australia CSIRO Data61 Sydney Australia Department of Computer Science School of Engineering Shantou University Guangdong China Shandong Cancer Hospital and Institute Shandong First Medical University and Shandong Academy of Medical Sciences Shandong China Australian Institute of Health Innovation (AIHI) Macquarie University Australia School of Software Shandong University Shandong China Centre for Artificial Intelligence driven Drug Discovery Faculty of Applied Science Macao Polytechnic University Macao Special Administrative Region of China School of Computer Software College of Intelligence and Computing Tianjin University Tianjin China

Semi-supervised learning (SSL) has attracted considerable attention in medical image processing. The latest SSL methods use a combination of consistency regularization and pseudo-labeling to achieve remarkable success. However, most existing SSL studies focus on segmenting large organs, neglecting the challenging scenarios where there are numerous tumors or tumors of small volume. Furthermore, the extensive capabilities of data augmentation strategies, particularly in the context of both labeled and unlabeled data, have yet to be thoroughly investigated. To tackle these challenges, we introduce a straightforward yet effective approach, termed iterative pseudo-labeling based adaptive copy-paste supervision (IPA-CP), for tumor segmentation in CT scans. IPA-CP incorporates a two-way uncertainty based adaptive augmentation mechanism, aiming to inject tumor uncertainties present in the mean teacher architecture into adaptive augmentation. Additionally, IPA-CP employs an iterative pseudo-label transition strategy to generate more robust and informative pseudo labels for the unlabeled samples. Extensive experiments on both in-house and public datasets show that our framework outperforms state-of-the-art SSL methods in medical image segmentation. Ablation study results demonstrate the effectiveness of our technical contributions.

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Critical success factors for client proposal selection: A vendor perspective in offshore software maintenance outsourcing

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AIP Conference Proceedings 2023年第1期2484卷

作者： Atif Ikram b) Masita Abdul Jalil Amir Bin Ngah Ahmad Salman Khan Tahir Iqbal 1School of Informatics and Applied Mathematics Universiti Malaysia Terengganu Malaysia 2Department of Computer Science & Information Technology The University of Lahore Pakistan 3Department of Software Engineering The University of Lahore Pakistan 4Department of Computer Science Bahria University Lahore Campus Pakistan

Offshore software Maintenance Outsourcing (OSMO) is increasing by leaps and bounds. In offshore outsourcing, most of the highly-paid countries outsource software development and maintenance projects to such developing countries where skilled human resources are available at economical packages. The OSMO clients get quality-oriented services and OSMO vendors earn valuable foreign exchange if the OSMO process is executed successfully. But the existing literature advocates that all the OSMO business deals do not end successfully. The right combination of vendor and client plays a vital role. For a vendor, it is important to find a more suitable client among multiple options. The scope of the current paper is to find out such critical success factors which can help out OSMO vendor to select a more suitable OSMO client among many options. This practice will provide ease to OSMO vendors in the OSMO client selection and decision-making process to select a suitable client. The method used to conduct this study is the Literature Review (LR). The literature review found different critical success factors related to the research question. Some of these factors include the client's contract, the client's required team size, the domain of the required project, the size of the software maintenance project, etc. OSMO vendor organizations can use this information in the decision-making process to select more suitable OSMO clients for successful offshore business. This study also recommends the implementation of any suitable machine learning technique on the critical success factors (founded in the current study) in the decision-making process.

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Kurdish Handwritten Character Recognition using Deep Learning Techniques

arXiv

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arXiv 2022年

作者： Ahmed, Rebin M. Rashid, Tarik A. Fattah, Polla Alsadoon, Abeer Bacanin, Nebojsa Mirjalili, Seyedali Vimal, S. Chhabra, Amit IT Department Faculty of Applied Science Tishk International University Erbil Iraq Computer Science and Engineering Department University of Kurdistan Hewler Erbil Iraq Software and Informatics Engineering Salahaddin University-Erbil- Erbil Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia Sydney Australia Singidunum University Belgrade Serbia Centre for Artificial Intelligence Research and Optimisation Torrens University Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of Department of Artificial Intelligence and Data Science Ramco Institute of Technology North Venganallur Village Virudhunagar District Tamilnadu Rajapalayam626 117 India Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India

Handwriting recognition is one of the active and challenging areas of research in the field of image processing and pattern recognition. It has many applications that include: a reading aid for visual impairment, automated reading and processing for bank checks, making any handwritten document searchable, and converting them into structural text form, etc. Moreover, high accuracy rates have been recorded by handwriting recognition systems for English, Chinese Arabic, Persian, and many other languages. Yet there is no such system available for offline Kurdish handwriting recognition. In this paper, an attempt is made to design and develop a model that can recognize handwritten characters for Kurdish alphabets using deep learning techniques. Kurdish (Sorani) contains 34 characters and mainly employs an Arabic\Persian based script with modified alphabets. In this work, a Deep Convolutional Neural Network model is employed that has shown exemplary performance in handwriting recognition systems. Then, a comprehensive dataset was created for handwritten Kurdish characters, which contains more than 40 thousand images. The created dataset has been used for training the Deep Convolutional Neural Network model for classification and recognition tasks. In the proposed system, the experimental results show an acceptable recognition level. The testing results reported a 96% accuracy rate, and training accuracy reported a 97% accuracy rate. From the experimental results, it is clear that the proposed deep learning model is performing well and is comparable to the similar model of other languages' handwriting recognition systems. © 2022, CC BY.

关键词： Convolutional neural networks

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Value-Function-based Sequential Minimization for Bi-level Optimization

arXiv

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arXiv 2021年

作者： Liu, Risheng Liu, Xuan Zeng, Shangzhi Zhang, Jin Zhang, Yixuan The DUT-RU International School of Information Science & Engineering Dalian University of Technology The Key Laboratory for Ubiquitous Network Service Software of Liaoning Province Liaoning Dalian China The Pazhou Lab Guangdong Guangzhou China The Department of Mathematics and Statistics University of Victoria VictoriaBC Canada The Department of Mathematics SUSTech International Center for Mathematics Southern University of Science and Technology National Center for Applied Mathematics Shenzhen Peng Cheng Laboratory Guangdong Shenzhen China The Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong

Gradient-based Bi-Level Optimization (BLO) methods have been widely applied to handle modern learning tasks. However, most existing strategies are theoretically designed based on restrictive assumptions (e.g., convexity of the lower-level sub-problem), and computationally not applicable for high-dimensional tasks. Moreover, there are almost no gradient-based methods able to solve BLO in those challenging scenarios, such as BLO with functional constraints and pessimistic BLO. In this work, by reformulating BLO into approximated single-level problems, we provide a new algorithm, named Bi-level Value-Function-based Sequential Minimization (BVFSM), to address the above issues. Specifically, BVFSM constructs a series of value-function-based approximations, and thus avoids repeated calculations of recurrent gradient and Hessian inverse required by existing approaches, time-consuming especially for high-dimensional tasks. We also extend BVFSM to address BLO with additional functional constraints. More importantly, BVFSM can be used for the challenging pessimistic BLO, which has never been properly solved before. In theory, we prove the asymptotic convergence of BVFSM on these types of BLO, in which the restrictive lower-level convexity assumption is discarded. To our best knowledge, this is the first gradient-based algorithm that can solve different kinds of BLO (e.g., optimistic, pessimistic, and with constraints) with solid convergence guarantees. Extensive experiments verify the theoretical investigations and demonstrate our superiority on various real-world applications. © 2021, CC BY.

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