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arXiv 2025年

作者： Hashmi, Arqum Farooq, M. Umar Tani, Mizuki Yabana, Kazuhiro Otobe, Tomohito Ishikawa, Kenichi L. Department of Nuclear Engineering and Management Graduate School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Department of Physics Southern University of Science and Technology Guangdong Shenzhen China Kyoto619-0215 Japan Center for Computational Sciences University of Tsukuba Tsukuba305-8577 Japan

We theoretically study the ultrafast optical control of multiple valley states in two‐dimensional (2D) tin sulfide (SnS) monolayers, a member of the layered group-IV monochalcogenides, which is a promising class of materials for overcoming current challenges in valleytronics. By combining time‐dependent density functional theory with Maxwell’s equations, we simulate how both linearly and circularly polarized ultrashort laser pulses affect the electronic excitation dynamics and valley polarization in SnS. Our results reveal that the corrugated phosphorene-like crystal structure of SnS monolayers leads to the emergence of both linear and circular dichroism, allowing flexible manipulation of multi-valley excitation by simply adjusting the light polarization. Moreover, the interplay between broken inversion symmetry and spin-orbit coupling gives rise to distinct Berry curvature effects and spin-valley coupling, thereby enabling circular dichroism. Furthermore, we propose that tuning the carrier‐envelope phase of few-cycle femtosecond laser pulses can achieve sub‐cycle, ultrafast switching among multiple valley states. These findings not only deepen our understanding of valley dynamics in 2D materials but may also open new avenues for the development of valleytronic devices. Copyright © 2025, The Authors. All rights reserved.

关键词： Maxwell equations

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Topology of three-dimensional Dirac semimetals and quantum spin Hall systems without gapless edge modes

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Physical Review Research 2023年第1期5卷 L012019-L012019页

作者： Alexander C. Tyner Shouvik Sur Danilo Puggioni James M. Rondinelli Pallab Goswami Graduate Program in Applied Physics Northwestern University Evanston Illinois 60208USA Department of Physics and Astronomy Northwestern University Evanston Illinois 60208 USA Department of Materials Science and Engineering Northwestern University Illinois 60208 USA Northwestern Argonne Institute for Science and Engineering Evanston Illinois 60208 USA

The quantum spin Hall states are usually expected to possess gapless, helical edge modes. We show that the generic, n-fold-symmetric, momentum planes of three-dimensional, stable Dirac semimetals, which are orthogonal to the direction of nodal separation are examples of generalized quantum spin Hall systems, that possess quantized, spin or relative Chern numbers as bulk topological invariants, and gapped edge modes. We demonstrate these planes and the celebrated Bernevig-Zhang-Hughes model support identical quantized, non-Abelian Berry flux of magnitude 2π. Hence, they display identical quantized, topological response such as spin-charge separation and pumping of one Kramers-pair or SU(2) doublet, when probed with a magnetic flux tube. The Dirac points are identified as unit-strength, monopoles of SO(5) Berry connection, describing topological phase transitions between generalized quantum spin Hall and trivial insulators. Our work identifies precise bulk invariant and quantized response of Dirac semimetals, which are not diagnosed by nested Wilson loops and filling anomaly of corner-localized-states, and shows that many two-dimensional higher-order topological insulators can be understood as generalized quantum spin Hall systems.

关键词： Fractionalization Geometric & topological phases Spintronics Topological insulators Topological phase transition Topological phases of matter

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State of the Art Models for Fake News Detection Tasks

State of the Art Models for Fake News Detection Tasks

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2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020

作者： Antoun, Wissam Baly, Fady Achour, Rim Hussein, Amir Hajj, Hazem American University of Beirut Electrical and Computer Engineering Department Beirut Lebanon Graduate Program in Computational Science American University of Beirut Beirut Lebanon

ISBN: (数字)9781728148212

ISBN: (纸本)9781728148212

This paper presents state of the art methods for addressing three important challenges in automated fake news detection: fake news detection, domain identification, and bot identification in tweets. The proposed solutions achieved first place in a recent international competition on fake news. For fake news detection, we present two models. The winning model in the competition combines similarity between the embedding of each article's title and the embedding of the top five corresponding google search results. The new model relies on advances in Natural Language Understanding (NLU) end to end deep learning models to identify stylistic differences between legitimate and fake news articles. This second model was developed after the competition and outperforms the winning approach. For news domain detection, the winning model is a hybrid approach composed of named entity features concatenated with semantic embeddings derived from end to end models. For twitter bot detection, we propose to use the following features: duration between account creation and tweet date, presence of a tweet's link, presence of user's location, other tweet's features, and the tweets' metadata. Experiments include insights into the importance of the different features and the results indicate the superior performances of all proposed models. © 2020 IEEE.

关键词： Competition

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Predicting rice diseases using advanced technologies at different scales: present status and future perspectives

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aBIOTECH 2023年第4期4卷 359-371页

作者： Ruyue Li Sishi Chen Haruna Matsumoto Mostafa Gouda Yusufjon Gafforov Mengcen Wang Yufei Liu College of Biosystems Engineering and Food Science Zhejiang UniversityHangzhou 310058China College of Environmental and Resource Sciences Zhejiang UniversityHangzhou 310058China State Key Laboratory of Rice Biology and Ministry of Agricultural and Rural Affairs Laboratory of Molecular Biology of Crop Pathogens and InsectsZhejiang UniversityHangzhou 310058China Department of Nutrition and Food Science National Research CentreGiza 12622Egypt Central Asian Center for Development Studies New Uzbekistan UniversityTashkent 100000Uzbekistan Global Education Program for AgriScience Frontiers Graduate School of AgricultureHokkaido UniversitySapporo 060-8589Japan

The past few years have witnessed significant progress in emerging disease detection techniques foraccurately and rapidly tracking rice diseases and predicting potential solutions. In this review we focuson image processing techniques using machine learning (ML) and deep learning (DL) models related tomulti-scale rice diseases. Furthermore, we summarize applications of different detection techniques,including genomic, physiological, and biochemical approaches. In addition, we also present the state-ofthe-art in contemporary optical sensing applications of pathogen–plant interaction phenotypes. Thisreview serves as a valuable resource for researchers seeking effective solutions to address the challenges of high-throughput data and model recognition for early detection of issues affecting rice cropsthrough ML and DL models.

关键词： Artificial intelligence Rice disease Model algorithms Imaging technology Plant-pathogen interactions High-throughput data

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Application of 3D Printing and Design in Healthcare: A Focus on Undergraduate Research to Attract Students to Bioengineering and Related Disciplines

Application of 3D Printing and Design in Healthcare: A Focus...

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Frontiers in Education (FIE) Conference

作者： Cindy M. Zelada-Quiroz Brian L. Reyes-Santiago Eduardo I. Ortiz-Rivera Bio-Engineering Graduate Program University of Puerto Rico at Mayagüez Mayagüez PR Department of Computer Science and Engineering University of Puerto Rico at Mayagüez Mayagüez PR Department of Electrical and Computer Engineering University of Puerto Rico at Mayagüez Mayagüez PR

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This research-to-practice paper describes developing and analyzing state-of-the-art smart boots created by combining CAD technology and advanced 3D printing techniques to attract students in bio-engineering and related fields. The primary objective of this innovative immobilization boot is to expedite fracture recovery phases through an ergonomic design to ensure optimal patient comfort during its use. Technological solutions are crucial in aiding the rehabilitation process for fractures caused by falls, heavy lifting, or rotational trauma. However, cost and comfort-related issues persist, underscoring the need for alternative approaches. This research addresses these challenges and delves into the broader implications of fracture treatment, catalyzing future projects and investigations in bioengineering. Additionally, this study serves as an educational tool that sparks the interest of high school and engineering students, promoting multidisciplinary collaboration in innovation. By involving students in specialized courses covering 3D design, human bone anatomy, biology, and materials science, this initiative empowers them to deepen their knowledge and develop new technologies to address bone injury problems. Material analyses include evaluating the type of material depending on the fracture site, such as PLA for printing and cotton and silicone gel for the midsection between the splint and the body. This research aims to advance our understanding of the type of fracture, the methods associated with their treatment, and tissue repair processes during bone callus formation. To summarize, this multidisciplinary approach drives advancements in bio-engineering and related fields, aiming to enhance patient outcomes and inspire students to pursue further research in bio-engineering and related fields. As part of this endeavor, a list of university-level courses based on the experience of the University of Puerto Rico at Mayaguez (UPRM), such as biology, bio-materials, 3D

关键词： Technological innovation Materials science and technology Three-dimensional displays Tissue engineering Medical services Three-dimensional printing Bones Mechanical factors Manufacturing Sparks

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Cardiovascular Disease Analysis Using Correlational Analysis and Association Rules Mining for In-depth Analysis to Identify Predominant Variables

Cardiovascular Disease Analysis Using Correlational Analysis...

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Computer science, Information Technology and engineering (ICCoSITE), International Conference on

作者： Boby Siswanto Haryono Soeparno Nesti F. Sianipar Widodo Budiharto Computer Science Department BINUS Graduate Program- Computer Science Bina Nusantara University Jakarta Indonesia Food Technology Department Engineering Faculty Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Cardiovascular disease is one of the dangerous non-communicable disorders or diseases that has become one of the causes of death worldwide. Various studies have been conducted to prevent cardiovascular disease in the world. This study analyzed cardiovascular disease medical record data from the Kaggle public dataset by implementing correlational analysis combined with association rule mining to identify variables that are the predominant cause of cardiovascular disease. Correlational analysis can analyze the interrelationships between variables in a dataset, but not in depth. Association rule mining can identify the interrelationships of variables in the form of frequent item sets, which can be calculated for their support and confidence values. The result of this study is a combination of correlation analysis with association rule mining that can identify predominant variables to cause cardiovascular disease. Found that the variable gender=woman, height=short (<165 cm), and age=middle (45-60 years) are more likely to be affected by cardiovascular disease. The variable gender=woman with height=short indicates a 76.07% probability of developing cardiovascular disease.

关键词： Computer science Correlation Itemsets Cardiovascular diseases Data mining Information technology

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Application of level-set type topology optimization for defect topology identification analysis using hammering response data

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Materials Today: Proceedings 2023年

作者： Kurahashi, Takahiko Koike, Towa Kishida, Masayuki Murakami, Yuki Ikeda, Fujio Department of Mechanical Engineering Institute of GIGAKU Nagaoka University of Technology Niigata Nagaoka Japan Mechanical Engineering Master's Program Graduate School of Engineering Nagaoka University of Technology Niigata Nagaoka Japan Science of Technology Innovation 5-year Integrated Doctoral Program Graduate School of Engineering Nagaoka University of Technology Niigata Nagaoka Japan Department of Civil Engineering National Institute of Technology Nagaoka College Niigata Nagaoka Japan Department of Mechanical Engineering National Institute of Technology Nagaoka College Niigata Nagaoka Japan

In this study, we presented the defect identification analysis based on the level-set type topology optimization using hammering testing data. To simulate the oscillation behavior of concrete structure, the equation of motion in three dimensions is introduced as the governing equation. The performance function is defied by the time integral of the squared residual between the computed and observed displacements. The problem is to find the defect topologies so as to minimize the performance function. In the model with a small number of observation points, it was found that nonexistent defects disappeared by using the weighted sensitivity method. In addition, it was confirmed that the model with a large number of observation points did not show the same tendency as the model with a small number of observation points because the accuracy of defect identification increased as the number of observation points increased. © 2023

关键词： Defects

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Impact of CLAHE-based image enhancement for diabetic retinopathy classification through deep learning 7

Impact of CLAHE-based image enhancement for diabetic retinop...

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7th International Conference on Computer science and computational Intelligence, CSCI 2022

作者： Hayati, Mira Muchtar, Kahlil Roslidar Maulina, Novi Syamsuddin, Irfan Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Universitas Syiah Kuala Banda Aceh23111 Indonesia Faculty of Medicine Universitas Syiah Kuala Banda Aceh23111 Indonesia Department of Computer and Networking Engineering Politeknik Negeri Ujung Pandang Makassar90245 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Diabetic retinopathy (DR) is a type of diabetes mellitus that attacks the retina of the eye. DR will cause patients to experience blindness slowly. Generally, DR can be detected by using a special instrument called an ophthalmoscope to view the inside of the eyeball. However, in conditions where there is a very small difference between the normal image and the DR image, computer-based assistance is needed for maximizing image reading value. In this research, a method of image quality improvement will be carried out which will then be integrated with a classification algorithm based on deep learning. The results of image improvement using Contrast Limited Adaptive Histogram Equalization (CLAHE) shows that the average accuracy of the method on several models is very competitive, 91% for the VGG16 model, 95% for InceptionV3, and 97% for EfficientNet compared to the results original image which only has an accuracy of 87% for VGG16 model, 90% for InceptionV3 model, and 95% for EfficientNet. However, in ResNet34 better accuracy is obtained in the original image with an accuracy of 95% while in the CLAHE image the accuracy value is only 84%. The results of this comprehensive evaluation and recommendation of famous backbone networks can be useful in the computer-Aided diagnosis of diabetic retinopathy. © 2022 Elsevier B.V.. All rights reserved.

关键词： Image enhancement

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Multistate Control of Nonlinear Photocurrents in Optoferroelectrics via phase manipulation of light field

arXiv

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arXiv 2025年

作者： Kazempour, Ali Sisakht, Esmaeil Taghizadeh Okyay, Mahmut Sait Jiang, Xiao Sato, Shunsuke Park, Noejung Department of physics Payame Noor University PO BOX Tehran119395-3697 Iran Department of Physics Ulsan National Institute of Science and Technology Ulsan689-798 Korea Republic of Materials Science and Engineering Program Department of Chemical and Environmental Engineering University of California-Riverside RiversideCA92521 United States Center for Computational Sciences University of Tsukuba Tsukuba305-8577 Japan

Ultrafast optical control of ferroelectricity based on short and intense light can be utilized to achieve accurate manipulations of ferroelectric materials, which may pave a basis for future breakthrough in nonvolatile memories. Here, we demonstrate that phase manipulation of electric field in the strong field sub-cycle regime induces a nonlinear injection current, efficiently coupling with the topology of band structure and enabling dynamic reversal of both current and polarization. Our time-dependent first-principles calculations reveal that tuning the phase of linearly or circularly polarized light through time-varying chirp, or constant carrier envelop phases within sub-laser-cycle dynamics effectively breaks the time-reversal symmetry, allowing the control over current and electronic polarization reversal over multi-ferroelectric states. Our time- and momentum-resolved transverse current analysis reveal the significance of Berry curvature higher order poles in the apparent association between the odd (even) orders of Berry curvature multipoles to odd (even) pseudo-harmonics in driving polarization dynamics reversal. We suggest that these phase manipulations of short pulse waveform may lead to unprecedented accurate control of nonlinear photocurrents and polarization states, which facilitate the development of precise ultrafast opto-ferroelectric devices. © 2025, CC BY.

关键词： Ferroelectricity

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Challenges and Emerging Issues for Generative AI and Hyper Intelligence

Challenges and Emerging Issues for Generative AI and Hyper I...

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Cyber science and Technology Congress (CyberSciTech), IEEE

作者： Jianhua Ma Qun Jin Hui-Huang Hsu John Paul C. Vergara Antonio Guerrieri Claudio Miceli Ao Guo Faculty of Computer and Information Sciences Hosei University Japan Department of Human Informatics and Cognitive Sciences Waseda University Japan Department of Computer Science and Information Engineering Tamkang University Taiwan Department of Information Systems and Computer Science Ateneo de Manila University Philippines ICAR-CNR Italy Graduate Program in Systems Engineering and Computer Science Federal University of Rio de Janeiro Brazil Graduate School of Informatics Nagoya University Japan

ISBN: (数字)9798331532093

ISBN: (纸本)9798331532109

Generative Artificial Intelligence (GenAI) represents a significant milestone in the development of artificial intelligence, bringing sophisticated AI capabilities into daily life and work. As we approach the era of Hyper Intelligence (Hyper-I), a variety of critical challenges and emerging issues have come to light, ranging from computational complexity to ethical concerns. This paper explores the evolution of AI from the perspective of human learning, comparing machine and human intelligence, and identifying key considerations for the development of future AI systems. It also highlights the growing importance of regulating advanced AI models, such as Reinforcement Learning-based Long-Term Planning Agents, to ensure that Hyper-I remains under human control. Additionally, the paper discusses the computational complexity of transformer-based models, their applicability to intractable problems, and their role in cognitive building systems and resource-constrained environments through TinyML. By analyzing these pressing challenges, this work provides insights into the future of AI and the path toward responsible innovation in generative and hyper-intelligent systems.

关键词： Hyper-intelligent systems Ethics Technological innovation Generative AI computational modeling Tiny machine learning Pressing Transformers Regulation computational complexity

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