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Substrate tuned reconstructed polymerization of naphthalocyanine on Ag(110)

Chinese Physics B 2022年第1期31卷 107-111页

作者： Qi Zheng Li Huang Deliang Bao Rongting Wu Yan Li Xiao Lin Shixuan Du Hong-Jun Gao Beijing National Center for Condensed Matter Physics and Institute of Physics Chinese Academy of SciencesBeijing 100190China School of Physical Sciences University of Chinese Academy of SciencesBeijing 100049China CAS Center for Excellence in Topological Quantum Computation University of Chinese Academy of SciencesBeijing 100190China Department of Physics and Astronomy and Department of Electrical Engineering and Computer Science Vanderbilt UniversityNashvilleTN 37235USA Songshan Lake Materials Laboratory Dongguan 523808China

The linkage structures between monomers make great influence on the properties of *** synthesis of some special linkage structures can be challenging,which is often overcome by employing special reaction ***,we build dihydropentalene linkage in poly-naphthalocyanine on Ag(110)*** tunneling microscopy(STM)and non-contact atomic force microscopy(nc-AFM)measurements confirm the dihydropentalene linkage structure and a possible formation path with reconstruction steps is *** controlled experiment on Ag(100)surface shows no dihydropentalene structures formed,which indicates the grooved substrate is necessary for the *** work provides insights into the surface restricted reactions that can yield special structures in organic polymers.

关键词： on-surface polymerization reconstruction scanning tunneling microscopy noncontact atomic force microscopy

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TOWARDS FAITHFUL XAI EVALUATION VIA GENERALIZATION-LIMITED BACKDOOR WATERMARK 12

TOWARDS FAITHFUL XAI EVALUATION VIA GENERALIZATION-LIMITED B...

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12th International Conference on Learning Representations, ICLR 2024

作者： Ya, Mengxi Li, Yiming Dai, Tao Wang, Bin Jiang, Yong Xia, Shu-Tao Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China The State Key Laboratory of Blockchain and Data Security Zhejiang University China ZJU-Hangzhou Global Scientific and Technological Innovation Center China College of Computer Science and Software Engineering Shenzhen University China Guangzhou Intelligence Communications Technology Co. Ltd. China

Saliency-based representation visualization (SRV) (e.g., Grad-CAM) is one of the most classical and widely adopted explainable artificial intelligence (XAI) methods for its simplicity and efficiency. It can be used to interpret deep neural networks by locating saliency areas contributing the most to their predictions. However, it is difficult to automatically measure and evaluate the performance of SRV methods due to the lack of ground-truth salience areas of samples. In this paper, we revisit the backdoor-based SRV evaluation, which is currently the only feasible method to alleviate the previous problem. We first reveal its implementation limitations and unreliable nature due to the trigger generalization of existing backdoor watermarks. Given these findings, we propose a generalization-limited backdoor watermark (GLBW), based on which we design a more faithful XAI evaluation. Specifically, we formulate the training of watermarked DNNs as a min-max problem, where we find the 'worst' potential trigger (with the highest attack effectiveness and differences from the ground-truth trigger) via inner maximization and minimize its effects and the loss over benign and poisoned samples via outer minimization in each iteration. In particular, we design an adaptive optimization method to find desired potential triggers in each inner maximization. Extensive experiments on benchmark datasets are conducted, verifying the effectiveness of our generalization-limited watermark. Our codes are available at https://***/yamengxi/GLBW. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Deep neural networks

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The Advanced Encryption Standard with a Dynamic S-Box Using the RC4 Key Schedule 4

The Advanced Encryption Standard with a Dynamic S-Box Using ...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Altigani, Abdelrahman Ghalib, Muhammad Rukunuddin Elshoush, Huwaida T. Elsadig, Muawia A. Alrefai, Nashat Naserelden, Shiraz Higher Colleges of Technology Computer Information Science Al Ain United Arab Emirates School of Engineering & Computing De Monfort University Dubai United Arab Emirates University of Khartoum Faculty of Mathematical Sciences and Informatics Computer Science Department Khartoum Sudan College of Computer Science and Information Technology Imam Abdulrahman bin Faisal University Dammam Saudi Arabia College of Science Imam Abdulrahman Bin Faisal University Basic and Applied Scientific Research Center Imam Abdulrahman Bin Faisal University Department of Mathematics Dammam Saudi Arabia Faculty of Science Universiti Teknologi Malaysia Department of Mathematical Sciences Johor Malaysia

ISBN: (纸本)9798350395914

Since the inception of secure communication, safeguarding data confidentiality has been paramount. With the increased reliance on technology, the need for robust security mechanisms is more critical than ever. This paper explores confidentiality in cybersecurity, focusing on symmetric encryption algorithms. While symmetric encryption, particularly the Advanced Encryption Standard (AES), is in wide use, concerns arise regarding its static nature. Therefore, the paper introduces a dynamic cipher, augmenting AES security by introducing a key-dependent S-Box. By leveraging the RC4 key schedule, the proposed cipher generates unique S-Boxes for each encryption key, significantly increasing security against non-generic attacks. Theoretical analysis demonstrates the proposed cipher is capable of resisting brute force, linear cryptanalysis, and differential cryptanalysis. Furthermore, its adaptability to existing AES implementations ensures practicality and ease of adoption. © 2024 IEEE.

关键词： Ciphertext

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Efficient Tree-based Parallel Algorithms for N-Body Simulations Using C++ Standard Parallelism 24

Efficient Tree-based Parallel Algorithms for N-Body Simulati...

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Proceedings of the SC '24 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis

作者： Thomas Lane Cassell Tom Deakin Aksel Alpay Vincent Heuveline Gonzalo Brito Gadeschi School of Computer Science University of Bristol Bristol UK Engineering Mathematics and Computing Lab Interdisciplinary Center for Scientific Computing Heidelberg University Heidelberg Germany GPU Architecture NVIDIA Munich Germany

ISBN: (纸本)9798350355543

The Barnes-Hut approximation for N-body simulations reduces the time complexity of the naive all-pairs approach from O(N2) to O(N log N) by hierarchically aggregating nearby particles into single entities using a tree data structure. This inherently irregular algorithm poses substantial challenges for performance portable implementations on multi-core CPUs and GPUs. We introduce two portable fully-parallel Barnes-Hut implementation strategies that trade-off different levels of GPU support for performance: an unbalanced concurrent octree, and a balanced bounding volume hierarchy sorted by a Hilbert spacefilling curve. We implement these algorithms in portable ISO C++ using parallel algorithms and concurrency primitives like atomics. The results demonstrate competitive performance on a range of CPUs and GPUs. Additionally, they highlight the effectiveness of the par execution policy for highly concurrent irregular algorithms, outperforming par_unseq on CPUs and GPUs with Independent Thread Scheduling.

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On the Complexity of Computing the Minimum Mean Square Error of Causal Prediction

On the Complexity of Computing the Minimum Mean Square Error...

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American Control Conference (ACC)

作者： Holger Boche Volker Pohl H. Vincent Poor School of Computation Information and Technology Technical University of Munich Munchen Germany Munich Center for Quantum Science and Technology (MCQST) Munchen Germany Department of Electrical and Computer Engineering Princeton University Princeton NJ USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This paper gives a complete characterization of the complexity of computing the minimum mean square pre-diction error for wide-sense stationary stochastic processes. It shows that if the spectral density of the stationary process is a strictly positive, computable continuous function then the minimum mean square error (MMSE) is always a computable number. It is also shown that the computation of the MMSE is a $\neq P_computation$ complete problem on the set of strictly positive, polynomial-time computable, continuous spectral densities. This means that if, as widely assumed, $FP_computation\neq\neq P_computation$ , then there exist strictly positive, polynomial-time computable continuous spectral densities for which the computation of the MMSE is not polynomial-time computable. So under the widely accepted assumptions of complexity theory, the computation of the MMSE is generally much harder than $NP_computation$ complete problems.

关键词： Stochastic processes Mean square error methods Complexity theory

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Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

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computers, Materials & Continua 2023年第11期77卷 2579-2594页

作者： Abdennasser Dahmani Yamina Ammi Nadjem Bailek Alban Kuriqi Nadhir Al-Ansari Salah Hanini Ilhami Colak Laith Abualigah El-Sayed M.El-kenawy Department of Mechanical Engineering Faculty of Science and TechnologyGIDD Industrial Engineering and Sustainable Development LaboratoryUniversity of RelizaneBourmadiaRelizane48000Algeria Laboratory of Biomaterials and Transport Phenomena(LBMPT) University of MedeaUrban PoleMedea26000Algeria Energies and Materials Research Laboratory Faculty of Sciences and TechnologyUniversity of TamanghassetTamanrasset10034Algeria Sustainable Development and Computer Science Laboratory Faculty of Sciences and TechnologyAhmed Draia University of AdrarAdrarAlgeria CERIS Instituto Superior TécnicoUniversidade de LisboaLisbon1049-001Portugal Civil Engineering Department University for Business and TechnologyPristina10000Kosovo Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Engineering and Architecture Faculty Nisantasi UniversityIstanbulTurkey Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains MalaysiaPulau Pinang11800Malaysia Department of Computing and Information Systems School of Engineering and TechnologySunway University MalaysiaPetaling Jaya27500Malaysia Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running *** into renewable energy sources such as solar energy is being pursued to counter *** energy is one of the most promising renewable energy sources,as it has the potential to meet the world’s energy needs *** study aims to develop and evaluate artificial intelligence(AI)models for predicting hourly global *** hyperparameters were optimized using the Broyden-FletcherGoldfarb-Shanno(BFGS)quasi-Newton training algorithm and STATISTICA *** from two stations in Algeria with different climatic zones were used to develop the *** error measurements were used to determine the accuracy of the prediction models,including the correlation coefficient,the mean absolute error,and the root mean square error(RMSE).The optimal support vector machine(SVM)model showed exceptional efficiency during the training phase,with a high correlation coefficient(R=0.99)and a low mean absolute error(MAE=26.5741 Wh/m^(2)),as well as an RMSE of 38.7045 Wh/m^(2) across all ***,this study highlights the importance of accurate prediction models in the renewable energy,which can contribute to better energy management and planning.

关键词： Renewable energy energy prediction global irradiation artificial intelligence BFGS quasi-Newton training algorithm

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Hyperbolic Neural Network Based Preselection for Expensive Multi-Objective Optimization

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IEEE Transactions on Evolutionary computation 2024年 1-1页

作者： Li, Bingdong Yang, Yanting Hong, Wenjing Yang, Peng Zhou, Aimin Shanghai Institute of AI for Education the School of Computer Science and Technology and the Shanghai Frontiers Science Center of Molecule Intelligent Syntheses East China Normal University Shanghai China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science and Engineering Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

A series of surrogate-assisted evolutionary algorithms (SAEAs) have been proposed for expensive multi-objective optimization problems (EMOPs), building cheap surrogate models to replace the expensive real function evaluations. However, the search efficiency of these SAEAs is not yet satisfactory. More efforts are needed to further exploit useful information from the real function evaluations in order to better guide the search process. Facing this challenge, this paper proposes a Hyperbolic Neural Network (HNN) based preselection operator to accelerate the optimization process based on limited evaluated solutions. First, the preselection task is modeled as a multi-label classification problem where solutions are classified into different layers (ordinal categories) through -relaxed objective aggregation. Second, in order to resemble the hierarchical structure of candidate solutions, a hyperbolic neural network is applied to tackle the multi-label classification problem. The reason for using HNN is that hyperbolic spaces more closely resemble hierarchical structures than Euclidean spaces. Moreover, to alleviate the data deficiency issue, a data augmentation strategy is employed for training the HNN. In order to evaluate its performance, the proposed HNN-based preselection operator is embedded into two surrogate-assisted evolutionary algorithms. Experimental results on two benchmark test suites and three real-world problems with up to 11 objectives and 150 decision variables involving seven state-of-the-art algorithms demonstrate the effectiveness of the proposed method. IEEE

关键词： Multiobjective optimization

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Intralayer Phonons in Multilayer Graphene Moiré Superlattices

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Research 2022年第3期2022卷 385-395页

作者： Miao-Ling Lin Min Feng Jiang-Bin Wu Fei-Rong Ran Tao Chen Wei-Xia Luo Heng Wu Wen-Peng Han Xin Zhang Xue-Lu Liu Yang Xu Hai Li Yu-Fang Wang Ping-Heng Tan State Key Laboratory of Superlattices and Microstructures Institute of SemiconductorsChinese Academy of SciencesBeijing 100083China Center of Materials Science and Optoelectronics Engineering&CAS Center of Excellence in Topological Quantum Computation University of Chinese Academy of SciencesBeijing 100049China School of Physics Nankai UniversityTianjin 300071China Key Laboratory of Flexible Electronics and Institute of Advanced Materials Jiangsu National Synergetic Innovation Center for Advanced MaterialsNanjing Tech University30 South Puzhu RoadNanjing 211816China School of Micro-nanoelectronics State Key Laboratory of Silicon MaterialsHangzhou Global Scientific and Technological Innovation CenterZJU-UIUC Joint InstituteZhejiang UniversityHangzhou 310027China

Moirépattern in twisted multilayers(tMLs)induces many emergent phenomena by subtle variation of atomic registry to modulate quasiparticles and their interactions,such as superconductivity,moiréexcitons,and moiré*** periodic superlattice potential introduced by moirépattern also underlies patterned interlayer coupling at the interface of *** this arising patterned interfacial coupling is much weaker than in-plane atomic interactions,it is crucial in moirésystems,as captured by the renormalized interlayer phonons in twisted bilayer transitional metal ***,we determine the quantitative relationship between the lattice dynamics of intralayer out-of-plane optical(ZO)phonons and patterned interfacial coupling in multilayer graphene moirésuperlattices(MLG-MS)by the proposed perturbation model,which is previously challenging for MLGs due to their out-of-phase displacements of adjacent atoms in one atomic *** unveil that patterned interfacial coupling introduces profound modulations on Davydov components of nonfolded ZO phonon that are localized within the AB-stacked constituents,while the coupling results in layer-extended vibrations with symmetry of moirépattern for moiréZO *** work brings further degrees of freedom to engineer moiréphysics according to the modulations imprinted on the phonon frequency and wavefunction.

关键词： coupling twisted layer

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A Study and Recognition of Alzheimer's Disease Using Machine Learning 8

A Study and Recognition of Alzheimer's Disease Using Machine...

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8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Basha, S.K. Masthan Ashalatha, G. Vara Prasad, Srisailapu D Al-Hameed, Mazin R. Zearah, Sajad Ali Saikumar, K. Vignan's Institute of Management and Technology for Women ECE Department Hyderabad India VNR Vignana Jyothi Institute of Engineering and Technology Department of Computer Science-CS-DS&AI&DS Telangana Hyderabad India GITAM Deemed to Be University Computer Science and Engineering School of Technology Telangana Hyderabad India National University of Science and Technology College of Engineering Technology Department of Medical Device Industry Engineering Dhi Qar Iraq Al-Ayen University Scientific Research Center Thi-Qar Iraq Koneru Lakshmaiah Education Foundation Andhra Pradesh India

ISBN: (纸本)9798350334012

Nowadays, Alzheimer's disease is one of the most severe threats to people of all ages and socioeconomic backgrounds. Their everyday activities may measure this disorder, and how they speak about it shows how concerned they are about it. The way we live today is different from how it was in the past because of our working environment and the lack of opportunities to interact with people. WHO estimates that about 10 billion individuals perish each year due to Alzheimer's disease since not been appropriately diagnosed and treated. An advanced Machine Learning-based recognition system design performs in this research work. The proposed model has been designed based on the human being EEG dataset(testing) and the Kaggle Alzheimer dataset (training). The developed intelligent system will be updated with data collection and pre-processing through the Random Forest machine model to locate our health condition and recognize daily routine changes. Only by developing a machine-based approach will we be able to discover Alzheimer's patients who previously had no idea that they had the illness and are now unable to reverse the progression of their condition. The designed application has attained a sensitivity of 93.01%, an accuracy of 91.32%, a recall of 97.28%, and a detection rate of 98.91%. The implemented application is beneficial to investigators and surgeons for easy treatment. The proposed RFO Machine learning design competes with present technology and outperforms methodology. © 2023 IEEE.

关键词： Intelligent systems

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Predictability performance enhancement for suspended sediment in rivers: Inspection of newly developed hybrid adaptive neuro-fuzzy system model

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International Journal of Sediment Research 2022年第3期37卷 383-398页

作者： Rana Muhammad Adnan Zaher Mundher Yaseen Salim Heddam Shamsuddin Shahid Aboalghasem Sadeghi-Niaraki Ozgur Kisi State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering Hohai University Department of Urban Planning Engineering Networks and Systems Institute of Architecture and Construction South Ural State University New Era and Development in Civil Engineering Research Group Scientific Research Center Al-Ayen University Faculty of Science Agronomy Department Hydraulics Division University School of Civil Engineering Faculty of Engineering Universiti Teknologi Malaysia (UTM) Geoinformation Tech.Center of Excellence Faculty of Geomatics Engineering K.N.Toosi University of Technology Department of Computer Science and Engineering Sejong University Civil Engineering Department Ilia State University

Reliable modeling of river sediments transport is important as it is a defining factor of the economic viability of dams, the durability of hydroelectric-equipment, river susceptibility to pollution, suitability for navigation, and potential for aesthetics and fish habitat. The capability of a new machine learning model, fuzzy c-means based neuro-fuzzy system calibrated using the hybrid particle swarm optimization-gravitational search algorithm（ANFIS-FCM-PSOGSA） in improving the estimation accuracy of river suspended sediment loads（SSLs） is investigated in the current study. The outcomes of the proposed method were compared with those obtained using the fuzzy c-means based neuro-fuzzy system calibrated using particle swarm optimization（ANFIS-FCM-PSO）, ANFIS-FCM, and sediment rating curve（SRC） models. Various input combinations involving lagged river flow（Q） and suspended sediment（S） values were used for model development. The effect of Q and S on the model's accuracy also was assessed by including the difference between lagged Q and S values as inputs. The model performance was assessed using the root mean square error（RMSE）, mean absolute error（MAE）, Nash-Sutcliffe Efficiency（NSE）, and coefficient of determination（R2） and several graphical comparison methods. The results showed that the proposed model enhanced the prediction performance of the ANFIS-FCM-PSO（or ANFIS-FCM） models by 8.14%（1.72%）, 14.7%（5.71%）, 12.5%（2.27%）, and 25.6%（1.86%）,in terms of the RMSE, MAE, NSE and R2, respectively. The current study established the potential of the proposed ANFIS-FCM-PSOGSA model for simulation of the cumulative sediment load. The modeling results revealed the potential effects of the river flow lags on the sediment transport quantification.

关键词： Suspended sediment load Adaptive neuro-fuzzy system Particle swarm optimization Gravitational search algorithm

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