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APL Quantum 2024年第2期1卷

作者： Takla Nateeboon Chanaprom Cholsuk Tobias Vogl Sujin Suwanna Optical and Quantum Physics Laboratory Department of Physics Faculty of Science Mahidol University Bangkok 10400 Thailand Department of Computer Engineering TUM School of Computation Information and Technology Technical University of Munich 80333 Munich Germany Abbe Center of Photonics Institute of Applied Physics Friedrich Schiller University Jena 07745 Jena Germany

This article was originally published online on 29 April 2024 with an affiliation identifier missing for the second author and affiliations 2 and 3 out of order

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Secondary spin current driven efficient THz spintronic emitters

arXiv

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arXiv 2022年

作者： Agarwal, Piyush Yang, Yingshu Medwal, Rohit Asada, Hironori Fukuma, Yasuhiro Battiato, Marco Singh, Ranjan Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 21 Nanyang Link Singapore637371 Singapore Center for Disruptive Photonic Technologies The Photonics Institute Nanyang Technological University Singapore639798 Singapore Department of Physics Indian Institute of Technology Uttar Pradesh Kanpur208016 India Department of Electronic Devices and Engineering Graduate School of Science and Engineering Yamaguchi University Ube755-8611 Japan Department of Physics and Information Technology Faculty of Computer Science and System Engineering Kyushu Institute of Technology Iizuka820-8502 Japan

Femtosecond laser-induced photoexcitation of ferromagnet (FM)/heavy metal (HM) heterostructures have attracted attention by emitting broadband terahertz frequencies. The phenomenon relies on the formation of ultrafast spin current, which is largely attributed to the direct photoexcitation of the FM layer. However, we reveal that during the process, the FM layer also experiences a secondary excitation led by the hot electrons from the HM layer that travel across the FM/HM interface and transfer additional energy in the FM. Thus, the generated secondary spins enhance the total spin current formation and lead to amplified spintronic terahertz emission. The results also emphasize the significance of the secondary spin current, which even exceeds the primary spin currents when FM/HM heterostructures with thicker HM are used. An analytical model is developed to provide deeper insights into the microscopic processes within the individual layers, underlining the generalized ultrafast superdiffusive spin-transport mechanism. Copyright © 2022, The Authors. All rights reserved.

关键词： Hot electrons

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Federated Learning with LoRA Optimized DeiT and Multiscale Patch Embedding for Secure Eye Disease Recognition

arXiv

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arXiv 2025年

作者： Borno, Md. Naimur Asif Shovon, Md. Sakib Hossain Sikder, M.D. Hanif Rimi, Iffat Firozy Alahmadi, Tahani Jaser Moni, Mohammad Ali Mechatronics Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Kim Jaechul Graduate School of Artificial Intelligence Korea Advanced Institute of Science & Technology Daehak-ro Daejeon34141 Korea Republic of Department of Computer Science University of South Asia-Bangladesh Dhaka1216 Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box Riyadh84428 Saudi Arabia Faculty of Health Medicine and Behavioural Sciences The University of Queensland 308 Queen St Brisbane CityQLD4000 Australia

Recent progress in image-based medical disease detection encounters challenges such as limited annotated data sets, inadequate spatial feature analysis, data security issues, and inefficient training frameworks. This study introduces a data-efficient image transformer (DeIT)-based approach that overcomes these challenges by utilizing multiscale patch embedding for better feature extraction and stratified weighted random sampling to address class imbalance. The model also incorporates a LoRA-enhanced transformer encoder, a distillation framework, and federated learning for decentralized training, improving both efficiency and data security. Consequently, it achieves state-of-the-art performance, with the highest AUC, F1 score, precision, minimal loss, and Top-5 accuracy. Additionally, Grad-CAM++ visualizations improve interpretability by highlighting critical pathological regions, enhancing the model’s clinical relevance. These results highlight the potential of this approach to advance AI-powered medical imaging and disease detection. Copyright © 2025, The Authors. All rights reserved.

关键词： Image coding

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Sentiment Analysis of the Indonesian Police Mobile Brigade Corps Based on Twitter Posts Using the SVM and NB Methods

Sentiment Analysis of the Indonesian Police Mobile Brigade C...

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International Conference on Electronics Representation and Algorithm 2019, ICERA 2019

作者： Pratama, Bryan Saputra, Dedi Dwi Novianti, Deny Purnamasari, Endah Putri Kuntoro, Antonius Yadi Hermanto Gata, Windu Wardhani, Nia K Sfenrianto, Sfenrianto Budilaksono, Sularso Master of Computer Science-Postgraduate Program STMIK Nusa Mandiri Jakarta Indonesia Graduate School Master's Degree Computer Science STMIK Nusa Mandiri Jakarta Indonesia Master Faculty of Computer Science-Postgraduate Program Universitas Mercubuana Jakarta Indonesia Information Systems Management Department BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Graduate School Master's Degree Computer Science Universitas Persada Indonesia YAI. Jakarta Indonesia

The Brimob Corps is a special police force, just like the special military detachments held by the TNI such as Paskhas and so on. At present brigade corps police national is busy being discussed in the real world and cyberspace, especially on social media twitter. Many opinions about the brigade corps police national so there are positive and negative opinions. Social media twitter is now one places to spread information about brigade corps police national. This study cases uses text mining techniques with support vector machine (SVM) method which aims to classify public sentiments towards brigade corps police national on twitter. The dataset used is tweet in Indonesian with keyword "Brimob" with a total dataset of 1000 tweets. Text mining, transform, tokenize, stemming, and classification, etc. techniques are useful for building classification and analysis of sentiment. RapidMiner and Gataframework are also used to help create sentiment analysis to measure classification values. Accuracy values obtained with support vector machine (SVM) approach 86,96%, with precision values of 86,96%, and recall values of 86,96%. © Published under licence by IOP Publishing Ltd.

关键词： Law enforcement

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Shortest Path Queries for Indoor Venues with Temporal Variations

Shortest Path Queries for Indoor Venues with Temporal Variat...

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International Conference on Data Engineering

作者： Tiantian Liu Zijin Feng Huan Li Hua Lu Muhammad Aamir Cheema Hong Cheng Jianliang Xu Department of Computer Science Aalborg University Denmark Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Faculty of Information Technology Monash University Australia Department of Computer Science Hong Kong Baptist University Hong Kong

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

Indoor shortest path query (ISPQ) is of fundamental importance for indoor location-based services (LBS). However, existing ISPQs ignore indoor temporal variations, e.g., the open and close times associated with entities like doors and rooms. In this paper, we define a new type of query called Indoor Temporal-variation aware Shortest Path Query (ITSPQ). It returns the valid shortest path based on the up-to-date indoor topology at the query time. A set of techniques is designed to answer ITSPQ efficiently. We design a graph structure (IT-Graph) that captures indoor temporal variations. To process ITSPQ using IT-Graph, we design two algorithms that check a door's accessibility synchronously and asynchronously, respectively. We experimentally evaluate the proposed techniques using synthetic data. The results show that our methods are efficient.

关键词： Topology Partitioning algorithms computer science Navigation Buildings Hospitals Legged locomotion

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An Efficient Bio-inspired Bees Colony for Breast Cancer Prediction 2nd

An Efficient Bio-inspired Bees Colony for Breast Cancer Pred...

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2nd International Conference on Advanced Data and information Engineering, DaEng 2015

作者： Shah, Habib Chiroma, Haruna Herawan, Tutut Ghazali, Rozaida Tairan, Nasser College and Department of Computer Science King Khalid University Abha Saudi Arabia Department of Information Systems University of Malaya Lembah Pantai Kuala Lumpur50603 Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Batu PahatJohor86400 Malaysia

ISBN: (纸本)9789811317972

Breast cancer has increased mortality rate, one out of eight women have these diseases. The breast cancer is viewed as the second most common type of cancer and it is a big threat to women health and survival. Accurate prediction is a challenging problem with a significant experimental interest. One of the popular methods to predict breast cancer is using the bio-inspired computing. Bio-inspired computing approaches are global optimization algorithms motivated by the natural behaviors of swarms such as ants, birds, fishes and bees. Artificial Bee Colony Algorithm (ABC) is a well-known bio-inspired algorithm, which is robust, easy to implement and has few setting parameters. However, one of ABC disadvantages is that of slow convergence due to poor exploration and exploitation processes. In this paper, a hybrid search strategy namely: Global Guided Artificial Bee Colony (GGABC) algorithm proposed for Recurrent Neural Network training. The proposed GGABC employs a new hybrid population based on a metaheuristic approach to circumvent the deficiency of the standard ABC. The GGABC algorithm was applied to predict the patient’s status of breast cancer. The approach was simulated by the foraging behavior of global best and guided honey bees. The simulation comparative analysis suggested that the GGABC algorithm was found to converge to the optimal solution faster than the ABC, Guided ABC and Global ABC with an improved accuracy. The results of this research can provide critical information to health authorities to effectively manage the risk factors of the breast cancer in an early stage. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Forecasting

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AB1556 MACHINE LEARNING APPROACH FOR CLASSIFICATION OF ARTHRITIS ACTIVITY STATE, USING DATA FROM A SINGLE ACCELEROMETER

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Annals of the Rheumatic Diseases 2023年 82卷 2012-2012页

作者： P. Mielnik A. Myhre Hjelle A. Traseth K. Tokarz B. Pollen M. Fojcik Helse Forde Section for Rheumatology； Department for Neurology Rheumatology and Physical Medicine Forde Norway Institute of Informatics Silesian University of Technology Department of Graphics Computer Vision and Digital Systems Gliwice Poland Western Norway University of Applied Sciences Faculty of Engineering and Science Forde Norway

Background Machine learning (ML) potential is not fully exploited in diagnostics and follow up of autoimmune inflammatory rheumatic disease (AIIRD). It is despite the broader use of ML in, e.g. imaging diagnostics. The specific tools for AIIRD are lacking. Objectives This is an interim analysis of data from the first checkpoint of the proof-of-concept study on using accelerometer (ACC) data in follow up of patients with AIIRD. The main goal of the study was to investigate the value of single ACC data in the classification of arthritis activity status. Methods Subjects with AIIRD are enrolled in the study when they start of new treatment due to the disease activity. Several comorbidities, such as severe neurological and cardiological disorders, as wells as impaired mobility, are part of the protocol exclusion criteria. Volunteers without AIIRD who fulfil other inclusion/exclusion criteria are included as controls. The study was approved by local competent authorities and informed consent was given by all participants. We used data collected up till the first study checkpoint. This analysis covers nine patients with AIIRD (5 rheumatoid and four psoriasis arthritis) and 13 controls. Five patients had 3 study visits, 1 had 2 visits and 3 only one. Controls have only one visit per protocol. We analysed ACC data from 3 minutes of clapping using a home-brewed device based on Arduino nano 33 BLE with 6-axis MTU by Nordic Semiconductor. Data was divided into 6-second chunks that were found optimal in our prior study. We conducted binary classification between any/ no arthritis in any of the upper extremities. We used accuracy and area under curve (AUC) as an efficacy function derived from the receiver operating characteristic curve (ROC). We extracted 54 features from 3 ACC axis signal. The features encompassed, among others, Fourier's components, autoregression coefficients, median absolute deviation (MAD), variance, Fourier's entropy, etc. We built linear discriminant anal

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Infrared upconversion imaging in nonlinear metasurfaces

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Advanced Photonics 2021年第3期3卷 83-92页

作者： Rocio Camacho-Morales Davide Rocco Lei Xu Valerio Flavio Gili Nikolay Dimitrov Lyubomir Stoyanov Zhonghua Ma Andrei Komar Mykhaylo Lysevych Fouad Karouta Alexander Dreischuh Hark Hoe Tan Giuseppe Leo Costantino De Angelis Chennupati Jagadish Andrey E.Miroshnichenko Mohsen Rahmani Dragomir N.Neshev The Australian National University Research School of PhysicsARC Centre of Excellence for Transformative Meta-Optical SystemsDepartment of Electronic Materials EngineeringCanberraACTAustralia University of Brescia Department of Information EngineeringBresciaItaly University of New South Wales School of Engineering and Information TechnologyCanberraACTAustralia Nottingham Trent University School of Science and TechnologyAdvanced Optics and Photonics LaboratoryDepartment of EngineeringNottinghamUnited Kingdom UniversitéParis Diderot Matériaux et Phénomènes QuantiquesParisFrance Friedrich Schiller University Jena Institute of Applied PhysicsAbbe Center of PhotonicsJenaGermany Sofia University Department of Quantum ElectronicsFaculty of PhysicsSofiaBulgaria

Infrared imaging is a crucial technique in a multitude of applications,including night vision,autonomous vehicle navigation,optical tomography,and food quality *** infrared imaging technologies,however,require the use of materials such as narrow bandgap semiconductors,which are sensitive to thermal noise and often require cryogenic *** demonstrate a compact all-optical alternative to perform infrared imaging in a metasurface composed of GaAs semiconductor nanoantennas,using a nonlinear wave-mixing *** experimentally show the upconversion of short-wave infrared wavelengths via the coherent parametric process of sum-frequency *** this process,an infrared image of a target is mixed inside the metasurface with a strong pump beam,translating the image from the infrared to the visible in a nanoscale ultrathin imaging *** results open up new opportunities for the development of compact infrared imaging devices with applications in infrared vision and life sciences.

关键词： metasurfaces nonlinear optical processes infrared photonics imaging.

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Organic crystal structure prediction via coupled generative adversarial networks and graph convolutional networks

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The Innovation 2024年第2期5卷

作者： Zhuyifan Ye Nannan Wang Jiantao Zhou Defang Ouyang State Key Laboratory of Quality Research in Chinese Medicine Institute of Chinese Medical Sciences University of Macau Macau 999078 China Faculty of Applied Sciences Macao Polytechnic University Macao 999078 China State Key Laboratory of Internet of Things for Smart City University of Macau Macau 999078 China Department of Computer and Information Science Faculty of Science and Technology University of Macau Macau 999078 China Department of Public Health and Medicinal Administration Faculty of Health Sciences University of Macau Macau 999078 China

Organic crystal structures exert a profound impact on the physicochemical properties and biological effects of organic compounds. Quantum mechanics (QM)–based crystal structure predictions (CSPs) have somewhat alleviated the dilemma that experimental crystal structure investigations struggle to conduct complete polymorphism studies, but the high computing cost poses a challenge to its widespread application. The present study aims to construct DeepCSP, a feasible pure machine learning framework for minute-scale rapid organic CSP. Initially, based on 177,746 data entries from the Cambridge Crystal Structure Database, a generative adversarial network was built to conditionally generate trial crystal structures under selected feature constraints for the given molecule. Simultaneously, a graph convolutional attention network was used to predict the density of stable crystal structures for the input molecule. Subsequently, the distances between the predicted density and the definition-based calculated density would be considered to be the crystal structure screening and ranking basis, and finally, the density-based crystal structure ranking would be output. Two such distinct algorithms, performing the generation and ranking functionalities, respectively, collectively constitute the DeepCSP, which has demonstrated compelling performance in marketed drug validations, achieving an accuracy rate exceeding 80% and a hit rate surpassing 85%. Inspiringly, the computing speed of the pure machine learning methodology demonstrates the potential of artificial intelligence in advancing CSP research.

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Ab initio informed 20Ne(p, pα)16O reaction elucidates the emergence of alpha clustering from chiral potentials

arXiv

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arXiv 2024年

作者： Sargsyan, G.H. Yoshida, Kazuki Ogata, Kazuyuki Launey, K.D. Escher, J.E. Langr, D. Dytrych, T. Facility for Rare Isotope Beams Michigan State University East LansingMI48824 United States Advanced Science Research Center Japan Atomic Energy Agency Ibaraki Tokai319-1195 Japan Department of Physics Kyushu University Fukuoka819-0395 Japan Osaka University Ibaraki Osaka567-0047 Japan Department of Physics and Astronomy Louisiana State University Baton RougeLA70803 United States Lawrence Livermore National Laboratory LivermoreCA94550 United States Department of Computer Systems Faculty of Information Technology Czech Technical University in Prague Prague16000 Czech Republic Nuclear Physics Institute The Czech Academy of Sciences Řež250 68 Czech Republic

We report on the first ab initio informed α knock-out reaction in the intermediate-mass region, with the aim to probe the underlying chiral potential and its impact on the emergence of alpha clustering in this mass region. The theoretical predictions of the α+16O clustering in the 20Ne ground state, based on the ab initio symmetry-adapted no-core shell model with continuum, yield a triple differential cross section for 20Ne(p, pα)16O that is in a remarkable agreement with the data. This allows us to examine predictions of surface and in-medium α-cluster features from a chiral potential and to compare these to the successful antisymmetrized molecular dynamics approach. © 2024, CC BY.

关键词： Molecular dynamics

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