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arXiv 2024年

作者： Benito-Altamirano, ismael Martínez-Carpena, David Lizarzaburu-Aguilar, Hanna Ventura, Carles Fàbrega, Cristian Prades, Joan Daniel MIND/IN2UB Department of Electronic and Biomedical Engineering Universitat de Barcelona Carrer de Martí i Franquès 1 Barcelona Barcelona08028 Spain Department of Mathematics and Computer Science Universitat de Barcelona Gran Via de les Corts Catalanes 585 Barcelona Barcelona08007 Spain eHealth Center Faculty of Computer Science Multimedia and Telecommunications Universitat Oberta de Catalunya Rambla del Poblenou 156 Barcelona Barcelona08018 Spain Technische Universität Braunschweig Hans-Sommer Str. 66 BraunschweigD-38106 Germany

image color consistency is the key problem in digital imaging consistency when creating datasets. Here, we propose an improved 3D thin-Plate splines (tPs3D) color correction method to be used, in conjunction with color charts (i.e. Macbeth ColorChecker) or other machine-readable patterns, to achieve image consistency by post-processing. Also, we benchmark our method against its former implementation and the alternative methods reported to date with an augmented dataset based on the Gehler’s ColorChecker dataset. Benchmark includes how corrected images resemble the ground-truth images and how fast these implementations are. Results demonstrate that the tPs3D is the best candidate to achieve image consistency. Furthermore, our smooth-tPs3D method shows equivalent results compared to the original method and reduced the 11-15% of ill-conditioned scenarios which the previous method failed to less than 1%. Moreover, we demonstrate that the smooth-tPs method is 20% faster than the original method. Finally, we discuss how different methods offer different compromises between quality, correction accuracy and computational load. © 2024, CC BY-sA.

关键词： Color image processing

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Battery Capacity of Energy-storing Quantum systems

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Physical Review Letters 2023年第3期131卷 030402-030402页

作者： Xue Yang Yan-Han Yang Mir Alimuddin Raffaele salvia shao-Ming Fei Li-Ming Zhao stefan Nimmrichter Ming-Xing Luo School of Information Science and Technology Southwest Jiaotong University Chengdu 610031 China School of Computer and Network Security Chengdu University of Technology Chengdu 610059 China Department of Physics of Complex Systems S. N. Bose National Center for Basic Sciences Kolkata 700106 India Scuola Normale Superiore I-56127 Pisa Italy School of Mathematical Sciences Capital Normal University Beijing 100048 China Max-Planck-Institute for Mathematics in the Sciences 04103 Leipzig Germany Naturwissenschaftlich-Technische Fakultät Universität Siegen Siegen 57068 Germany CAS Center for Excellence in Quantum Information and Quantum Physics Hefei 230026 China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China

the quantum battery capacity is introduced in this Letter as a figure of merit that expresses the potential of a quantum system to store and supply energy. it is defined as the difference between the highest and the lowest energy that can be reached by means of the unitary evolution of the system. this function is closely connected to the ergotropy, but it does not depend on the temporary level of energy of the system. the capacity of a quantum battery can be directly linked with the entropy of the battery state, as well as with measures of coherence and entanglement.

关键词： Quantum foundations Quantum measurements Quantum theory Quantum thermodynamics

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Leveraging Feature Extraction and Fine-tuning techniques for Enhanced Detection of Fake News Using BERt

Leveraging Feature Extraction and Fine-Tuning Techniques for...

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Advances in Computing, Communication and Networking (iCAC2N), international Conference on

作者： Madan Mohan tito Ayyalasomayajula santhosh Bussa s. sri Nandhini Kowsalya Nilamadhab Mishra shyam Prasad Arjun Mehra Dept. of Computer Science Aspen University Arizona USA Dept. Name of Organization (of Aff.) Stratford University NJ USA Dept. Name of Organization (of Aff.) Vel T.R Dr.S Avadi Chennai India Dept. of CSE VIT Bhopal University Sehore Madhya Pradesh India Dept. of EIE CVR College of Engineering Hyderabad India Dept of CSE Amity University Jaipur India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

the rise in misinformation spread through social media, exacerbated by the pandemic and global conflicts, has necessitated advancements in machine learning techniques for fake news detection. this task falls under text classification within Natural Language Processing (NLP). inspired by the exceptional performance of transformer-based models like BERt, we explored two approaches for text classification. the first approach utilized BERt for feature extraction followed by classification with conventional classifiers—an area that has seen limited research. the second approach involved fine-tuning BERt specifically for text classification. Our experiments demonstrated that various classifier models achieved impressive accuracy rates, with a peak of 97%. However, the fine-tuned BERt model outperformed all others, reaching an accuracy of 98.6% on the test dataset, positioning it on par with leading text classification models.

关键词： Analytical models Accuracy social networking (online) Computational modeling text categorization transfer learning Feature extraction transformers Natural language processing Fake news

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Automatic model of sleep apnea detection using optimized weighted fusion process of hybrid convolution (1D/2D) efficient attention network from EEG signals

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Eurasip Journal on Advances in signal Processing 2025年第1期2025卷

作者： Nandakumar, R. Arunachalam, Rajesh Pugalenthi, R. Arunachalam, Karthikayen Department of Electronics and Communication Engineering K.S.R. College of Engineering Tamilnadu Tiruchengode637215 India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Thandalam Chennai602105 India Department of Computer Science and Engineering Saveetha Engineering College Thandalam Tamilnadu Chennai602105 India Department of Electronics and Communication Engineering P.T.Lee Chengalvaraya Naicker College of Engineering and Technology Oovery Kanchipuram Tamilnadu India

Background: sleep apnea (sA) is a sleep disorder characterized by breathing interruptions, and it causes significant health risks, including cardiovascular diseases, stroke, and secondary issues such as daytime accidents. the complex nature of sA necessitates accurate and timely diagnosis. the sA detection is often performed using polysomnography (PsG). However, PsG generates extensive data, making manual analysis labor-intensive and inefficient. Methods: in this work, a hybrid deep learning framework for automated sA detection combines advanced feature extraction and efficient classification techniques. initially, the required EEG signals are collected from the standard data resource. the collected signals undergo for pre-processing stage, which is further decomposed into five signals delta, theta, alpha, beta, and gamma. Further, spectrogram images are generated from these decompositions, which are fused using optimal weights to produce image-based features F1. simultaneously, the deep restricted Boltzmann machine (DRBM) features are extracted from the five decomposed signals. then, the resultant features are fused with the optimized weight and it is designated as F2. Here the weight in DRBM is optimized using the improved Eurasian oystercatcher optimizer (iEOO). Finally, the resultant two feature formats are subjected to the developed hybrid convolution (1D/2D)–based efficient attention network (HCEAN) for sA detection. Results: Extensive experimentation is conducted with the given dataset, by comparing the proposed technique with other standard approaches. the proposed model achieves an accuracy of 95.9%, a sensitivity of 95.86%, and a specificity of 95.93% that outperforms standard methods such as DNN, DiDtCN-REsLstM, and CNN-LstM. the obtained outcome confirms that the developed technique is a more effective method for detecting apnea events. © the Author(s) 2025.

关键词： Health risks

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Analysis of the secure Cloud Framework for Mobile Banking

Analysis of the Secure Cloud Framework for Mobile Banking

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2021 international Conference on technological Advancements and innovations, iCtAi 2021

作者： Joseph, susheel George Jadhav, swati subhash sharma, smita Poonia, Amarjeet shrivastava, Anurag Hemavathi, s Kristu Jyoti College of Management and Technology Dept. of Computer Applications Kottayam India D y Patil College of Engineering D y Patil Education Complex E and Tc Dept. Sector-29 Nigdi Pradhikaran Akurdi Pune India U.I.T R.G.P.V Department of Cse Bhopal India Govt. Women Engineering College Department of Information Technology Nasirabad Road Makhupura India Lakshmi Narain College of Technology Ads Science Indore India Institute Chennai Academy of Scientific Innovative Research Central Electrochemical Research Ghaziabad India

ISBN: (纸本)9781665420877

Many service-related tasks can now be completed swiftly and efficiently thanks to advancements in mobile communications. Mobile banking is one such solution that has eliminated the requirement for many ordinary transactions to be completed at a branch. Mobile phones can supplement the reach in a country like india, where the last mile is reached through brick-and-mortar banking facilities. this paper explains how a mobile cloud framework can be used for banking and customer services to improve the customer's banking experience while also assuring data security. this article focuses on cloud-based risk design for banking products to address challenges such as processing speed and storage capacity in mobile banking. the system is evaluated using an improved random forecast technique. this proposed system fulfils 99% of the accuracy. © 2021 iEEE.

关键词： Digital storage

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information carried by a single particle in quantum multiple-access channels

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Physical Review A 2024年第6期109卷 062420-062420页

作者： Xinan Chen Yujie Zhang Andreas Winter Virginia O. Lorenz Eric Chitambar Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA Department of Physics University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA Institució Catalana de Recerca i Estudis Avançats (ICREA) Pg. Lluís Companys 23 08010 Barcelona Spain Grup d'Informació Quàntica Departament de Física Universitat Autònoma de Barcelona 08193 Bellaterra (Barcelona) Spain Institute for Advanced Study Technische Universität München Lichtenbergstraße 2a D-85748 Garching Germany

Nonclassical features of quantum systems have the potential to strengthen the way we currently exchange information. in this paper, we explore this enhancement on the most basic level of single particles. to be more precise, we compare how well multiparty information can be transmitted to a single receiver using just one classical or quantum particle. Our approach is based on a multiple-access communication model in which messages can be encoded into a single particle that is coherently distributed across multiple spatial modes. theoretically, we derive lower bounds on the accessible information in the quantum setting that strictly separate it from the classical scenario. this separation is found whenever there is more than one sender, and also when there is just a single sender who has a shared phase reference with the receiver. When there is only one sender, the separation is impossible without a shared phase reference due to the famous Holevo's bound. Experimentally, we present a proof-of-principle demonstration of such quantum advantage in single-particle communication by implementing a multiport interferometer with messages being encoded along the different trajectories. specifically, we consider a two-sender communication protocol built by a three-port optical interferometer. in this scenario, the rate sum achievable with a classical particle is upper bounded by one bit, while we experimentally observe a rate sum of 1.0152±0.0034 bits in the quantum setup.

关键词： Optical tests of quantum theory Quantum coherence & coherence measures Quantum communication Quantum foundations Quantum information theory Quantum optics Quantum protocols

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Multimodal E-Commerce Product Classification Using Hierarchical Fusion

arXiv

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arXiv 2022年

作者： tashu, tsegaye Misikir Fattouh, sara Kiss, Péter Horváth, tomáš Department of Data Science and Engineering Faculty of Informatics ELTE - Eötvös Loránd University Pazmány Péter sétány 1/C Budapest1117 Hungary Faculty of Science Institute of Computer Science Pavol Jozef Šafárik University Jesenná 5 Košice040 01 Slovakia College of Informatics Kombolcha Institute of Technology Wollo University Kombolcha208 Ethiopia

in this work, we present a multi-modal model for commercial product classification, that combines features extracted by multiple neural network models from textual (CamemBERt and FlauBERt) and visual data (sE-ResNeXt-50), using simple fusion techniques. the proposed method significantly outperformed the unimodal models' performance and the reported performance of similar models on our specific task. We did experiments with multiple fusing techniques and found, that the best performing technique to combine the individual embedding of the unimodal network is based on combining concatenation and averaging the feature vectors. Each modality complemented the shortcomings of the other modalities, demonstrating that increasing the number of modalities can be an effective method for improving the performance of multi-label and multimodal classification problems. © 2022, CC BY.

关键词： Electronic commerce

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Multimodal Feature Extraction for Memes sentiment Classification

arXiv

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arXiv 2022年

作者： Ouaari, sofiane tashu, tsegaye Misikir Horváth, tomáš Department of Data Science and Engineering Faculty of Informatics ELTE - Eötvös Loránd University Pazmány Péter sétány 1/C Budapest1117 Hungary Institute of Computer Science Faculty of Science Pavol Jozef Šafárik University Jesenná 5 Košice040 01 Slovakia College of Informatics Kombolcha Institute of Technology Wollo University Kombolcha208 Ethiopia

in this study, we propose feature extraction for multimodal meme classification using Deep Learning approaches. A meme is usually a photo or video with text shared by the young generation on social media platforms that expresses a culturally relevant idea. since they are an efficient way to express emotions and feelings, a good classifier that can classify the sentiment behind the meme is important. to make the learning process more efficient, reduce the likelihood of overfitting, and improve the generalizability of the model, one needs a good approach for joint feature extraction from all modalities. in this work, we proposed to use different multimodal neural network approaches for multimodal feature extraction and use the extracted features to train a classifier to identify the sentiment in a meme. © 2022, CC BY.

关键词： computer vision

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Position: Bayesian deep learning is needed in the age of large-scale Ai 24

Position: Bayesian deep learning is needed in the age of lar...

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Proceedings of the 41st international Conference on Machine Learning

作者： theodore Papamarkou Maria skoularidou Konstantina Palla Laurence Aitchison Julyan Arbel David Dunson Maurizio Filippone Vincent Fortuin Philipp Hennig José Miguel Hernández-Lobato Aliaksandr Hubin Alexander immer theofanis Karaletsos Mohammad Emtiyaz Khan Agustinus Kristiadi Yingzhen Li stephan Mandt Christopher Nemeth Michael A. Osborne tim G. J. Rudner David Rügamer Yee Whye teh Max Welling Andrew Gordon Wilson Ruqi Zhang Department of Mathematics The University of Manchester Manchester UK Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge Spotify London UK Computational Neuroscience Unit University of Bristol Bristol UK Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany and Department of Computer Science Technical University of Munich Munich Germany and Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge UK Department of Mathematics University of Oslo Oslo Norway and Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative California Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London UK Department of Computer Science UC Irvine Irvine Department of Mathematics and Statistics Lancaster University Lancaster UK Department of Engineering Science University of Oxford Oxford UK Center for Data Science New York University New York Munich Center for Machine Learning Munich Germany and Department of Statistics LMU Munich Munich Germany DeepMind London UK and Department of Statistics University of Oxford Oxford UK Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences and Center for Data Science Computer Science Department New York University New York Department of Computer Science Purdue University West Lafayette

in the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. this paper posits that BDL can elevate the capabilities of deep learning. it revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential.

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Fully integrated si-Rich silicon Nitride Wavelength Converter Based on Bragg scattering intermodal Four-Wave Mixing 49

Fully Integrated Si-Rich Silicon Nitride Wavelength Converte...

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49th European Conference on Optical Communications, ECOC 2023

作者： Vitali, Valerio Dominguez Bucio, thalia Liu, Hao Manuel Luque Gonzalez, Jose Jurado-Romero, Francisco Ortega-Monux, Alejandro Churchill, Glenn C. Gates, James Hillier, James Kalfagiannis, Nikolaos Melati, Daniele schmid, Jens H. Cristiani, ilaria Cheben, Pavel Wanguemert-Perez, J. Gonzalo inigo Molina-Fernandez Gardes, Frederic Lacava, Cosimo Petropoulos, Periklis Optoelectronics Research Centre University of Southampton SouthamptonSO17 1BJ United Kingdom Electrical Computer and Biomedical Engineering Department University of Pavia Pavia27100 Italy Universidad de Malaga CEI Andalucia TECH E.T.S.I. Telecomunicacion Malaga29010 Spain School of Science and Technology Nottingham Trent University NottinghamNG11 8NS United Kingdom Centre de Nanosciences et de Nanotechnologies Universit e Paris-Saclay CNRS Palaiseau91120 France Advanced Electronics and Photonics Research Center National Research Council Canada 1200 Montreal Road OttawaONK1A 0R6 Canada

ISBN: (纸本)9781839539954

We report the demonstration of an integrated silicon-rich silicon nitride wavelength converter based on Bragg scattering intermodal four-wave mixing process. the system integrates mode conversion, multiplexing and de-multiplexing functionalities on chip. Broadband signal conversion with a 3dB bandwidth exceeding 70 nm is demonstrated. © the institution of engineering & technology 2023.

关键词： Four wave mixing

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