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arXiv 2021年

作者： Papaspirou, Vasilis Papathanasaki, Maria Maglaras, Leandros A. Kantzavelou, Ioanna Douligeris, Christos Ferrag, Mohamed Amine Janicke, Helge Department of Informatics and Computer Engineering University of West Attica Athens Greece Department of CS and Telecommunications University of Thessaly Lamia Greece Cyber Technology Institute De Montfort University Leicester United Kingdom Department of Informatics University of Piraeus Piraeus Greece Department of Computer Science Guelma University Guelma Algeria Cyber Security CRC Edith Cowan University Perth6027 Australia

Although sufficient authentication mechanisms were enhanced by the use of two or more factors that resulted in new multi factor authentication schemes, more sophisticated and targeted attacks have shown they are also vulnerable. This research work proposes a novel two factor authentication system that incorporates honeytokens into the two factor authentication process. The current implementation collaborates with Google authenticator. The novelty and simplicity of the presented approach aims at providing additional layers of security and protection into a system and thus making it more secure through a stronger and more efficient authentication mechanism. © 2021, CC BY.

关键词： Cybersecurity

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TopoTxR: A topology-guided deep convolutional network for breast parenchyma learning on DCE-MRIs

arXiv

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arXiv 2024年

作者： Wang, Fan Zou, Zhilin Sakla, Nicole Partyka, Luke Rawal, Nil Singh, Gagandeep Zhao, Wei Ling, Haibin Huang, Chuan Prasanna, Prateek Chen, Chao Department of Computer Science State University of New York Stony BrookNY United States Department of Radiology Newark Beth Israel Medical Center NJ United States Department of Radiology Columbia University Irving Medical Center NY United States Department of Radiology and Imaging Sciences Emory University School of Medicine GA United States Department of Radiology State University of New York Stony BrookNY United States Department of Biomedical Informatics State University of New York Stony BrookNY United States Department of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA United States

Characterization of breast parenchyma in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging task owing to the complexity of underlying tissue structures. Existing quantitative approaches, like radiomics and deep learning models, lack explicit quantification of intricate and subtle parenchymal structures, including fibroglandular tissue. To address this, we propose a novel topological approach that explicitly extracts multi-scale topological structures to better approximate breast parenchymal structures, and then incorporates these structures into a deep-learningbased prediction model via an attention mechanism. Our topology-informed deep learning model, TopoTxR, leverages topology to provide enhanced insights into tissues critical for disease pathophysiology and treatment response. We empirically validate TopoTxR using the VICTRE phantom breast dataset, showing that the topological structures extracted by our model effectively approximate the breast parenchymal structures. We further demonstrate TopoTxR's efficacy in predicting response to neoadjuvant chemotherapy. Our qualitative and quantitative analyses suggest differential topological behavior of breast tissue in treatment-naive imaging, in patients who respond favorably to therapy as achieving pathological complete response (pCR) versus those who do not. In a comparative analysis with several baselines on the publicly available I-SPY 1 dataset (N=161, including 47 patients with pCR and 114 without) and the Rutgers proprietary dataset (N=120, with 69 patients achieving pCR and 51 not), TopoTxR demonstrates a notable improvement, achieving a 2.6% increase in accuracy and a 4.6% enhancement in AUC compared to the state-of-the-art method. © 2024, CC BY-NC-ND.

关键词： Chemotherapy

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Scientific discovery in the age of artificial intelligence

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NATURE 2023年第7978期621卷 E33-E33页

作者： Wang, Hanchen Fu, Tianfan Du, Yuanqi Gao, Wenhao Huang, Kexin Liu, Ziming Chandak, Payal Liu, Shengchao Van Katwyk, Peter Deac, Andreea Anandkumar, Anima Bergen, Karianne Gomes, Carla P. Ho, Shirley Kohli, Pushmeet Lasenby, Joan Leskovec, Jure Liu, Tie-Yan Manrai, Arjun Marks, Debora Ramsundar, Bharath Song, Le Sun, Jimeng Tang, Jian Velickovic, Petar Welling, Max Zhang, Linfeng Coley, Connor W. Bengio, Yoshua Zitnik, Marinka Department of Engineering University of Cambridge Cambridge UK Department of Computing and Mathematical Sciences California Institute of Technology Pasadena CA USA NVIDIA Santa Clara CA USA Department of Computational Science and Engineering Georgia Institute of Technology Atlanta GA USA Department of Computer Science Cornell University Ithaca NY USA Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Computer Science Stanford University Stanford CA USA Department of Physics Massachusetts Institute of Technology Cambridge MA USA Harvard-MIT Program in Health Sciences and Technology Cambridge MA USA Mila – Quebec AI Institute Montreal Quebec Canada Université de Montréal Montreal Quebec Canada HEC Montréal Montreal Quebec Canada CIFAR AI Chair Toronto Ontario Canada Department of Earth Environmental and Planetary Sciences Brown University Providence RI USA Data Science Institute Brown University Providence RI USA Center for Computational Astrophysics Flatiron Institute New York NY USA Department of Astrophysical Sciences Princeton University Princeton NJ USA Department of Physics Carnegie Mellon University Pittsburgh PA USA Department of Physics and Center for Data Science New York University New York NY USA Google DeepMind London UK Department of Computer Science and Technology University of Cambridge Cambridge UK Microsoft Research Beijing China Department of Biomedical Informatics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Harvard Data Science Initiative Cambridge MA USA Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Cambridge MA USA Department of Systems Biology Harvard Medical School Boston MA USA Deep Forest Sciences Palo Alto CA USA BioMap Beijing China Mo

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Shooting-projection method for a small object moving under the influence of a force 10

Shooting-projection method for a small object moving under t...

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202 10th International Conference on engineering Mathematics and Physics, ICEMP 2021

作者： Filipov, S.M. Faragó, I. Avdzhieva, A. Department of Computer Science Faculty of Chemical and System Engineering University of Chemical Technology and Metallurgy 8 St. Kliment Ohridski Blvd. Sofia1756 Bulgaria Department of Differential Equations Institute of Mathematics Budapest University of Technology and Economics MTA-ELTE Research Group Egry József u. 1 Budapest1111 Hungary Department of Numerical Methods Faculty of Mathematics and Informatics Sofia University St. Kliment Ohridski 5 James Bourchier Blvd. Sofia1164 Bulgaria

We consider a small object in 3D moving under the influence of a force that may depend explicitly on time, on the position of the object, and on its velocity. The equations of motion of classical mechanics are assumed to hold. If the position of the object is specified at some initial and some final time, obtaining the trajectory of the object requires the solution of a two-point boundary value problem. To solve the problem various numerical technics can be applied. This paper extends the recently proposed shooting-projection method to 3D. We introduce a Lagrangian from which, applying the principle of least action, the projection trajectory is derived. Analysis of the action reveals the meaning of the projection trajectory. Using the shooting-projection method, the considered two-point boundary value problem is solved for the case of a projectile motion in the presence of air resistance and wind. © 2021 institute of Physics Publishing. All rights reserved.

关键词： Trajectories

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A survey on deep learning in medical image reconstruction

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Intelligent Medicine 2021年第3期1卷 118-127页

作者： Emmanuel Ahishakiye Martin Bastiaan Van Gijzen Julius Tumwiine Ruth Wario Johnes Obungoloch Department of Computer Science Kyambogo UniversityP.O Box 1KyambogoKampalaUganda Delft University of Technology Delft Institute of Applied MathematicsMekelweg 42628 CDthe Netherlands Department of Mathematics Mbarara University of Science and TechnologyMbarara 1410Uganda Department of Informatics University of Free StateSouth Africa Department of Biomedical Sciences and Engineering Mbarara University of Science and TechnologyMbarara 1410Uganda

Medical image reconstruction aims to acquire high-quality medical images for clinical usage at minimal cost and risk to the *** learning and its applications in medical imaging,especially in image reconstruction have received considerable attention in the literature in recent *** study reviews records obtained elec-tronically through the leading scientific databases(Magnetic Resonance Imaging journal,Google Scholar,Scopus,Science Direct,Elsevier,and from other journal publications)searched using three sets of keywords:(1)Deep learning,image reconstruction,medical imaging;(2)Medical imaging,Deep learning,Image reconstruction;(3)Open science,Open imaging data,Open *** articles reviewed revealed that deep learning-based re-construction methods improve the quality of reconstructed images qualitatively and ***,deep learning techniques are generally computationally expensive,require large amounts of training datasets,lack decent theory to explain why the algorithms work,and have issues of generalization and *** challenge of lack of enough training datasets is currently being addressed by using transfer learning techniques.

关键词： Deep learning Open science Image reconstruction Medical imaging Machine Learning

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Retraction Note: Multiclass skin lesion classification using deep learning networks optimal information fusion

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Discover Applied Sciences 2025年第1期7卷 1-1页

作者： Khan, Muhammad Attique Hamza, Ameer Shabaz, Mohammad Kadry, Seifeine Rubab, Saddaf Bilal, Muhammad Abdullah Akbar, Muhammad Naeem Kesavan, Suresh Manic Department of Computer Science HITEC University Taxila Pakistan Model Institute of Engineering and Technology Jammu India Department of Applied Data Science Noroff University College Oslo Norway Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of CS SEECS NUST Islamabad Pakistan National University of Sciences and Technology (NUST) Islamabad Pakistan Department of Electrical and Communication Engineering National University of Science and Technology Muscat Sultanate of Oman

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Power of sequential protocols in hidden quantum channel discrimination

arXiv

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arXiv 2023年

作者： Sugiura, Sho Dutt, Arkopal Munro, William J. Zeytinoǧlu, Sina Chuang, Isaac L. Physics and Informatics Laboratory Ntt Research Inc. 940 Stewart Dr. SunnyvaleCA94085 United States Laboratory for Nuclear Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Co-Design Center for Quantum Advantage Massachusetts Institute of Technology CambridgeMA02139 United States Ntt Basic Research Laboratories Research Center for Theoretical Quantum Physics 3-1 Morinosato-Wakamiya Kanagawa Atsugi243-0198 Japan Department of Physics Harvard University CambridgeMA02138 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

In many natural and engineered systems, unknown quantum channels act on a subsystem that cannot be directly controlled and measured, but is instead learned through a controllable subsystem that weakly interacts with it. We study quantum channel discrimination (QCD) under these restrictions, which we call hidden system QCD (HQCD). We find that sequential protocols achieve perfect discrimination and saturate the Heisenberg limit. In contrast, depth-1 parallel and multi-shot protocols cannot solve HQCD. This suggests that sequential protocols are superior in experimentally realistic situations. Copyright © 2023, The Authors. All rights reserved.

关键词： Communication channels (information theory)

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Selecting for positive responses to knock outs in boolean networks 1

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14th International Workshop on Artificial Life and Evolutionary Computation, WIVACE 2019

作者： Villani, Marco Magrì, Salvatore Roli, Andrea Serra, Roberto Department of Physics Informatics and Mathematics University of Modena and Reggio Emilia Modena Italy European Centre for Living Technology Venice Italy Department of Computer Science and Engineering University of Bologna Cesena Italy Institute for Advanced Study University of Amsterdam Amsterdam Netherlands

ISBN: (数字)9783030450168

ISBN: (纸本)9783030450151

Random Boolean networks are a widely acknowledged model for cell dynamics. Previous studies have shown the possibility of achieving Boolean Networks (BN) with given characteristics by means of evolutionary techniques. In this work we show that it is possible to evolve BNs exhibiting more positive than negative reactions to knock-out stresses. It is also interesting to observe that in the observed runs (i) the evolutionary processes can guide the BNs toward different dynamic regimes, depending on their internal structure and that (ii) the BNs forced to evolve by maintaining a critical dynamical regime achieve better results than those that do not have this characteristic;this observation supports the idea that criticality may be beneficial to an evolving population of dynamical systems. © Springer Nature Switzerland AG 2020.

关键词： Dynamical systems

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Persistent Feature Analysis of Multimodal Brain Networks Using Generalized Fused Lasso for EMCI Identification 23rd

Persistent Feature Analysis of Multimodal Brain Networks Usi...

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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Li, Jin Bian, Chenyuan Chen, Dandan Meng, Xianglian Luo, Haoran Liang, Hong Shen, Li College of Automation Harbin Engineering University HarbinHeilongjiang China School of Computer Science and Information Engineering Changzhou Institute of Technology ChangzhouJiangsu China Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States

ISBN: (纸本)9783030597276

Early Mild Cognitive Impairment (EMCI) involves very subtle changes in brain pathological process, and thus identification of EMCI can be challenging. By jointly analyzing cross-information among different neuroimaging data, an increased interest recently emerges in multimodal fusion to better understand clinical measurements with respect to both structural and functional connectivity. In this paper, we propose a novel multimodal brain network modeling method for EMCI identification. Specifically, we employ the structural connectivity based on diffusion tensor imaging (DTI), as a constraint, to guide the regression of BOLD time series from resting state functional magnetic resonance imaging (rs-fMRI). In addition, we introduce multiscale persistent homology features to avoid the uncertainty of regularization parameter selection. An empirical study on the Alzheimers Disease Neuroimaging Initiative (ADNI) database demonstrates that the proposed method effectively improves classification performance compared with several competing approaches, and reasonably yields connectivity patterns specific to different diagnostic groups. © 2020, Springer Nature Switzerland AG.

关键词： Diffusion tensor imaging

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Evaluation of Robustness Metrics for Defense of Machine Learning Systems

Evaluation of Robustness Metrics for Defense of Machine Lear...

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International Conference on Military Communications and Information Systems (ICMCIS)

作者： J. DeMarchi R. Rijken J. Melrose B. Madahar G. Fumera F. Roli E. Ledda M. Aktaş F. Kurth P. Baggenstoss B. Pelzer L. Kanestad Collaborative Engineering Systems & Aerospace Systems Information Supremacy Royal Netherlands Aerospace Centre NLR Amsterdam NLD Cyber & Information Systems Division Defence Science and Technology Laboratory Portondown GBR Department of Electrical and Electronic Engineering University of Cagliari Cagliari ITA Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa ITA Department of Computer Control and Management Engineering Sapienza University of Rome Rome ITA Defence Systems Technologies Division ASELSAN Ankara TUR Fraunhofer Institute for Communication Information Processing and Ergonomics Bonn DEU Swedish Defence Research Agency Cyber Defence and C2 Technology Division Stockholm SWE

In this paper we explore some of the potential applications of robustness criteria for machine learning (ML) systems by way of tangible “demonstrator” scenarios. In each demonstrator, ML robustness metrics are applied to real-world scenarios with military relevance, indicating how they might be used to help detect and handle possible adversarial attacks on ML systems. We conclude by sketching promising future avenues of research in order to: (1) help establish useful verification methodologies to facilitate ML robustness compliance assessment; (2) support development of ML accountability mechanisms; and (3) reliably detect, repel, and mitigate adversarial attack.

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