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Numerical analysis of a Kretschmann surface plasmon resonance sensor with silver/TiO_(2)/BaTiO_(3)/silver/graphene for refractive index sensing

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Optoelectronics Letters 2023年第10期19卷 583-586页

作者： Huda A.Zain Malathy Batumalay Hazli R.A.Rahim Sulaiman Wadi Harun Department of Electrical Engineering University of MalayaKuala Lumpur50603Malaysia Faculty of Data Science&IT INTI International UniversityNilaiNegeri Sembilan71800Malaysia Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia MelakaMelakaMalaysia

The sensitivity of a Kretschmann surface plasmon resonance(SPR)sensor was *** Kretschmann setup had multiple layers,a BK7 prism,silver,barium titanate(BaTiO_(3)),titanium dioxide(TiO_(2)),and *** BaTiO_(3)and TiO_(2)coatings were sandwiched between two silver *** sensitivity of 260°/RIU has been *** graphene layers are added to the configuration to improve sensitivity and as a bio-compatibility *** configuration can be used for biochemical sensors.

关键词： silver surface resonance

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Positive Semidefinite Matrix Supermartingales

arXiv

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arXiv 2024年

作者： Wang, Hongjian Ramdas, Aaditya Department of Statistics and Data Science Carnegie Mellon University United States Machine Learning Department Carnegie Mellon University United States

We explore the asymptotic convergence and nonasymptotic maximal inequalities of supermartingales and backward submartingales in the space of positive semidefinite matrices. These are natural matrix analogs of scalar nonnegative supermartingales and backward nonnegative submartingales, whose convergence and maximal inequalities are the theoretical foundations for a wide and ever-growing body of results in statistics, econometrics, and theoretical computer science. Our results lead to new concentration inequalities for either martingale dependent or exchangeable random symmetric matrices under a variety of tail conditions, encompassing now-standard Chernoff bounds to self-normalized heavy-tailed settings. Further, these inequalities are usually expressed in the Loewner order, are sometimes valid simultaneously for all sample sizes or at an arbitrary data-dependent stopping time, and can often be tightened via an external randomization *** Codes 60B20, 60G48, 62L10 Copyright © 2024, The Authors. All rights reserved.

关键词： Matrix algebra

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Neuroimaging Precision: Crafting a Stacked VGG19-Logistic Fusion for Breakthrough Early Detection of Alzheimer's

Neuroimaging Precision: Crafting a Stacked VGG19-Logistic Fu...

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International Conference on science Technology Engineering and Management (ICONSTEM)

作者： G. Anbu Selvi T. Karthick Department of Computer Science and Engineering SRMIST Kattankulathur Chennai India Department of Data Science and Business Systems SRMIST Kattankulathur School of Computing Chennai India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

Several medical investigations have demonstrated that Alzheimer's disease (AD) manifests itself long before the formal diagnosis of dementia. These studies have led to the identification of numerous ideal biomarkers associated with Alzheimer's symptoms. Consequently, the imperative for early diagnosis has become evident, necessitating a high-performance computational tool capable of efficiently processing vast amounts of data. Early detection of AD not only offers valuable insights into the condition's progression but also presents a significant opportunity to benefit from timely and effective treatment. This ground-breaking study pioneers an innovative approach to the early detection and diagnosis of AD by crafting a sophisticated model SVGGL, which blends the power of stacked VGG19 neural networks with the interpretability of logistic regression. Leveraging neuro-imaging data, specifically the insights provided MRI, and our model aims to discern subtle structural and functional alterations in the brains of individuals at risk for or in the early stages of AD. The combination of feature extraction from stacked VGG19 models and subsequent logistic regression classification yields a precise and efficient classifier with an outstanding accuracy of 98.24%. With such a high degree of accuracy, the model can reliably differentiate among healthy people and those with Alzheimer's, opening the door to potential early intervention and individualized treatment plans. The study not only sheds light on the intricate neuro pathological landscape of AD but also propels us toward a future where cutting-edge technology and data-driven insights, exemplified by our high-accuracy model, converge to tackle the challenges posed by this prevalent neurodegenerative disorder.

关键词： Neuroimaging Logistic regression Accuracy Computational modeling Brain modeling Feature extraction data models

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RIGID PROTEIN-PROTEIN DOCKING VIA EQUIVARIANT ELLIPTIC-PARABOLOID INTERFACE PREDICTION 12

RIGID PROTEIN-PROTEIN DOCKING VIA EQUIVARIANT ELLIPTIC-PARAB...

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12th International Conference on Learning Representations, ICLR 2024

作者： Yu, Ziyang Huang, Wenbing Liu, Yang Department of Computer Science Tsinghua University China Tsinghua University China Gaoling School of Artificial Intelligence Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

The study of rigid protein-protein docking plays an essential role in a variety of tasks such as drug design and protein engineering. Recently, several learning-based methods have been proposed, exhibiting much faster docking speed than those computational methods. In this paper, we propose a novel learning-based method called ElliDock, which predicts an elliptic paraboloid to represent the protein-protein docking interface. To be specific, our model estimates elliptic paraboloid interfaces for the two input proteins respectively, and obtains the roto-translation transformation for docking by making two interfaces coincide. By its design, ElliDock is independently equivariant with respect to arbitrary rotations/translations of the proteins, which is an indispensable property to ensure the generalization of the docking process. Experimental evaluations show that ElliDock achieves the fastest inference time among all compared methods and is strongly competitive with current state-of-the-art learning-based models such as DiffDock-PP and Multimer particularly for antibody-antigen docking. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Proteins

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Scenario, Role, and Persona: A Scoping Review of Design Strategies for Socially Intelligent AI Agents

Scenario, Role, and Persona: A Scoping Review of Design Stra...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Niu, Ruowen Hu, Jiaxiong Peng, Siyu Cao, Caleb Chen Liu, Chengzhong Han, Sirui Guo, Yike Hong Kong Generative AI Research and Development Center Hong Kong Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong The Hong Kong University of Science and Technology Hong Kong Big Data Institute Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9798400713958

As artificial intelligence (AI) agents have often been perceived as social actors, there is a growing expectation for them to demonstrate social intelligence. Social intelligence encompasses the ability of AI agents to align their actions with human intentions and behave in socially and culturally appropriate ways. This study conducts a scoping review to analyze the design strategies employed in current AI agent designs that support the exhibition of social intelligence. Our findings reveal three design strategy themes that interdependently and collectively shape the capabilities and behaviors of AI agents: scenario, role, and persona. These findings provide a structured perspective and actionable insights for designing AI agents that effectively integrate social intelligence into human-AI interactions. © 2025 Copyright held by the owner/author(s).

关键词： Design for manufacturability

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Addressing Non-stationarity with Stochastic Trend in the Context of Limited Time Series data: An Experimental Survey

Research Square

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Research Square 2025年

作者： Batoure Bamana, Apollinaire Sokdou Bila Lamou, Yannick Fotsa-Mbogne, David Jaures Shafiee Kamalabad, Mahdi Department of Computer Engineering University Institute of Technology The University of Ngaoundere Cameroon Department of Robotic and Industrial Computing School of Chemical Engineering and Mineral Industries The University of Ngaoundere Cameroon Department of Mathematics and Computer Science Faculty of Science The University of Ngaoundere Cameroon Laboratory of Mathematics Computer Science and Applications Department of Mathematics and Computer Science National Higher School of Agro-Industrial Sciences The University of Ngaoundéré Cameroon Laboratory of Mathematics and Computer Science Department of Mathematics Statistics and Computer Science Faculty of Science The University of Bertoua Cameroon Department of Methodology and Statistics Faculty of Social and Behavioural Sciences Utrecht University Utrecht Netherlands Centre for Complex Systems Studies Utrecht University Utrecht Netherlands

Stationarity in time series is a key property for practical data analysis, inferences, and predictions particularly in biosciences. Stationarity can be either deterministic or stochastic. If a time series data is not stationary, it can be rendered stationary through detrending or differentiation techniques. Differentiation can be performed in either an integer or fractional form. This paper investigates non-stationarity in time series data, focusing specifically on stochastic trends characterised by a unit root. It hypothesises that fractional differentiation can enhance forecast accuracy for datasets with limited volume. For experiments, 24 series corresponding to weekly Malaria and Typhoid Fever from Adamawa Region (Cameroon) are analysed and forecasted. Each series comprises 156 observations covering January 2021 to December 2023. After collecting, checking and classifying the data, four forecasting models, Auto Regressive Integrated Moving Average (ARIMA), Fractional ARIMA (ARFIMA), Long Short-Term Memory (LSTM), and the proposed Fractional Differencing and LSTM (FD-LSTM), are implemented. The models' performances are evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of determination (R2) metrics, which inform further analysis and recommendations. Results reveal that ARFIMA outperformed the ARIMA by 93% in training and 100% in testing. Similarly, FD-LSTM achieved a 100% improvement over LSTM in training and testing. These findings underscore the value of achieving stationarity through fractional differentiation, enhancing model performance and providing a robust framework for forecasting in datasets with limited observations. © 2025, CC BY.

关键词： Time series

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Advancing Alzheimer's Nodule Detection Through a Comprehensive Multi-Scene Deep Learning Framework 10

Advancing Alzheimer's Nodule Detection Through a Comprehensi...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Premkumar, C. Rajeshwari, R. Remya, R. Cholaathiraj, M.S. Reethika, A. Bharathidasan, S. Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Sathyamangalam India Cmr Institute of Technology Department of Computer Science and Engineering Bengaluru India Vel Tech Rangarajan Dr Sagunthala R&d institute of science and Technology Department of Electronics and Communication Engineering Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Sathyamangalam India Sri Ramakrishna Engineering College Department of Electronics and Communication Engineering Coimbatore India Erode Sengunthar Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (纸本)9798350384369

Determining the precise location of Alzheimer's nodules is essential for estimating the risk of brain cancer. Conventional CAD modules, including MRI, PET, and CT, struggle with feature extraction and segmentation due to time limits and complexity. This study proposes an efficient brain nodule detection technique based on the Multi-Scene Deep Learning Framework (MSDLF) that makes use of the vesselness filter. A four-channel CNN model is developed using information from two image sequences to enhance radiologists' ability to recognise four-stage nodules. This adaptable model may be used in two different classes. The outcomes demonstrate how well the MSDLF performs while processing substantial volumes of image data for brain nodule recognition, improving with accuracy 98.86% and 98.45% of sensitivity by using decreasing false positives. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Are Made and Missed Different? An analysis of Field Goal Attempts of Professional Basketball Players via Depth Based Testing Procedure

arXiv

引用

arXiv 2024年

作者： Qi, Kai Hu, Guanyu Wu, Wei Microsoft United States Department of Biostatistics and Data Science The University of Texas Health Science Center Houston United States Department of Statistics Florida State University United States

In this paper, we develop a novel depth-based testing procedure on spatial point processes to examine the difference in made and missed field goal attempts for NBA players. Specifically, our testing procedure can statistically detect the differences between made and missed field goal attempts for NBA players. We first obtain the depths of two processes under the polar coordinate system. A two-dimensional Kolmogorov–Smirnov test is then performed to test the difference between the depths of the two processes. Throughout extensive simulation studies, we show our testing procedure with good frequentist properties under both null hypothesis and alternative hypothesis. A comparison against the competing methods shows that our proposed procedure has better testing reliability and testing power. Application to the shot chart data of 191 NBA players in the 2017-2018 regular season offers interesting insights about these players’ made and missed shot patterns. © 2024, CC BY-SA.

关键词： Computational complexity

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Emotion Recognition Using Focal Network and Resnet

Emotion Recognition Using Focal Network and Resnet

引用

Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： T. Meena Sigireddy Balasai Anandh Duggireddy P Phaneendhra Kumar Department of Computer Science and Engineering Velagapudi Ramakrishna Siddartha Engineering College Department of Artificial Intelligence and Data Science Velagapudi Ramakrishna Siddartha Engineering College

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

Image emotion recognition involves finding the emotions from visual data, usually done through convolutional neural networks (CNN) or deep neural networks (DNN). The existing methodologies are often high complex or time complex and it is not giving the good considerable results and computationally expensive due to high number of parameters. In this paper we try to accomplish a better output by using focal network which stood among other transformers architecture due to the unique property of the dynamic attention Recent studies have shown the efficacy of transformer architectures in various deep learning tasks and challenged the convolution neural networks on various tasks. but, It has a tradeoff between reduced parameter complexity and the need for extensive training data. Drawing inspiration from recent publications, the study adopts a focal network for training, demonstrating its effectiveness in enhancing the model's attention and optimizing emotional feature extraction. The proposed methodology aims to do the process of image emotion recognition by using the advantages of transformers while mitigating the computational problem associated with the deep neural networks. The incorporation of a focal network addresses attention-related challenges, and it contributes to the development of more efficient emotion recognition systems. This research offers insights into the evolving landscape of image emotion recognition methodologies and underscores the potential of transformer architectures in advancing this field.

关键词： Training Emotion recognition Visualization Training data computer architecture Transformers Feature extraction

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An efficient information retrieval and tracking algorithm for multiple typesetting styles of the same paper 7

An efficient information retrieval and tracking algorithm fo...

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7th International Conference on data science and Information Technology, DSIT 2024

作者： Liu, Yingying Li, Yuan Tang, Jiyue Ji, Tong Xu, Xiaoyu Luo, Yuxin Lin, Yifeng Yang, Yuer Wu, Yubing Jinan University Library Jinan University Guangzhou China Jinan University College of Information Science and Technology Guangzhou China Nanyang Technological University College of Computing and Data Science Nanyang Ave Singapore Jinan University College of Cyber Security Guangzhou China The University of Hong Kong Department of Computer Science Hong Kong Hong Kong

ISBN: (纸本)9798350384093

Academic paper publishers like IEEE, Elsevier, and Springer often require manuscripts to be typeset according to their own templates before they are submitted. Most journals and conferences make final and binding decisions, where the decisions for the last time cannot be discussed or changed again in the future unless a new manuscript or a revised manuscript whose revision rate exceeds a certain large percentage is submitted. This makes authors have to change the typesetting of their rejected manuscripts on most occasions before transferring their manuscripts to a more suitable place. Unfortunately, when either the typesetting style or the manuscript content needs to be changed or updated, it is very inconvenient and time-consuming for the traditional typesetting method to re-format the manuscript since it is cumbersome to manage multiple versions of LaTeX folders. Although online platforms like Overleaf support multiplayer online collaboration, they cannot fundamentally solve the problem of generating several manuscripts of different typesetting styles with the same content by updating the manuscript content only once. To make it more flexible to change the typesetting styles, we aim to provide a novel typesetting method that supports multiple typesetting styles of the same manuscript at the same time. The manuscript content and the typesetting controlling parts are entirely separated. Meanwhile, the grammar detection and structure tracking of LaTeX source codes are presented in a way that corresponds to the new typesetting method, which can provide more appropriate results. Compared with the traditional method, the proposed typesetting method can efficiently generate multiple typesetting styles for the same manuscript by reducing the time complexity from O(mn) to O(m) on m updates with n typesetting templates, whereas the proposed grammar detection and structure tracking algorithm can be performed more efficiently and correctly. © 2024 IEEE.

关键词： Big data

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