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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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Application of fuzzy logic for predication of depth of penetration in MIG welding

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AIP Conference Proceedings 2022年第1期2615卷

作者： Sameer S. Kulkarni Jagadeesh. P. Ganjigatti 1Department of Mechanical Engineering B. L. D. E. A's Dr. P G Halakatt College of Engineering and Technology Vijyapur Karnataka. India 2Industrial Engineering and Management Siddaganga Institute of Technology Tumkur Karnataka India

In most of the mechanical industries the fabrication of product is done through welding due to its convenience, rapid availability and economically feasibility. Welding is the processes in which two similar and dissimilar materials are joined through integration of molten metal. The quality of weldbead in the process are influenced by parameters. So due to this significance it is necessary to optimize the welding parameters to obtain best quality of weldbead and its properties. So in this regard a study has been made by conducting welding on specimen considering welding parameters like angle of torch, wire feedrate, standoff distance, welding speed and welding current through design of experiment Taguchi technique by using MIG welding for material Al 6061 alloy. The results of depth of penetration of weldbead are compared with predicated values through Fuzzy logic application.

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deep learning Hamiltonians from disordered image data in quantum materials

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Physical Review B 2023年第20期107卷 205121-205121页

作者： S. Basak M. Alzate Banguero l. Burzawa F. Simmons P. Salev l. Aigouy M. M. Qazilbash I. K. Schuller d. N. Basov A. Zimmers E. W. Carlson Department of Physics and Astronomy Purdue University West Lafayette Indiana 47907 USA Purdue Quantum Science and Engineering Institute West Lafayette Indiana 47907 USA Laboratoire de Physique et d'Étude des Matériaux ESPCI Paris PSL Université CNRS Sorbonne Université 75005 Paris France Department of Computer Science Purdue University West Lafayette Indiana 47907 USA Department of Physics and Astronomy University of Denver Denver Colorado 80208 USA Department of Physics and Center for Advanced Nanoscience University of California–San Diego La Jolla California 92093 USA Department of Physics College of William and Mary Williamsburg Virginia 23187 USA Department of Physics Columbia University New York New York 10027 USA

The capabilities of image probe experiments are rapidly expanding, providing new information about quantum materials on unprecedented length- and timescales. Many such materials feature inhomogeneous electronic properties with intricate pattern formation on the observable surface. This rich spatial structure contains information about interactions, dimensionality, and disorder—a spatial encoding of the Hamiltonian driving the pattern formation. Image recognition techniques from machine learning are an excellent tool for interpreting information encoded in the spatial relationships in such images. Here, we develop a deep learning framework for using the rich information available in these spatial correlations in order to discover the underlying Hamiltonian driving the patterns. We first vet the method on a known case, scanning near-field optical microscopy on a thin film of VO2. We then apply our trained convolutional neural network architecture to new optical microscope images of a different VO2 film as it goes through the metal-insulator transition. We find that a two-dimensional Hamiltonian with both interactions and random field disorder is required to explain the intricate, fractal intertwining of metal and insulator domains during the transition. This detailed knowledge about the underlying Hamiltonian paves the way for using the model to control the pattern formation via, e.g., tailored hysteresis protocols. We also introduce a distribution-based confidence measure on the results of a multilabel classifier, which does not rely on adversarial training. In addition, we propose a machine-learning-based criterion for diagnosing a physical system's proximity to criticality.

关键词： Metal-insulator transition Pattern formation Strongly correlated systems Critical phenomena Machine learning Optical microscopy Scanning near-field optical microscopy

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Evaluation of distance Measures for Feature based Image Registration using AlexNet

arXiv

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arXiv 2019年

作者： Kavitha, K. Rao, B. Thirumala Sandhya, B. Computer Science and Engineering K L University GunturA. P. India Computer Science and Engineering MVSR Engineering College Hyderabad India

mage registration is a classic problem of computer vision with several applications across areas like defence, remote sensing, medicine etc. Feature based image registration methods traditionally used hand-crafted feature extraction algorithms, which detect key points in an image and describe them using a region around the point. Such features are matched using a threshold either on distances or ratio of distances computed between the feature descriptors. Evolution of deep learning, in particular convolution neural networks, has enabled researchers to address several problems of vision such as recognition, tracking, localization etc. Outputs of convolution layers or fully connected layers of CNN which has been trained for applications like visual recognition are proved to be effective when used as features in other applications such as retrieval. In this work, a deep CNN, AlexNet, is used in the place of handcrafted features for feature extraction in the first stage of image registration. However, there is a need to identify a suitable distance measure and a matching method for effective results. Several distance metrics have been evaluated in the framework of nearest neighbour and nearest neighbour ratio matching methods using benchmark dataset. Evaluation is done by comparing matching and registration performance using metrics computed from ground truth. Copyright © 2019, The Authors. All rights reserved.

关键词： Image registration

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HyperProbe consortium: innovate tumour neurosurgery with innovative photonic solutions 9

HyperProbe consortium: innovate tumour neurosurgery with inn...

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diffuse Optical Spectroscopy and Imaging IX 2023

作者： Giannoni, luca Marradi, Marta Marchetti, Marco degl'Innocenti, duccio Rossi Ezhov, Ivan Caredda, Charly Gautheron, Arthur Fort, Fernand Schneider, Fabien Berhouma, Moncef Bonaudo, Camilla Picart, Thiebaud lange, Frédéric Krischak, Katharina Gordebeke, Peter Alfieri, domenico Rückert, daniel Montcel, Bruno della Puppa, Alessandro Guyotat, Jacques Tachtsidis, Ilias Pavone, Francesco S. Department of Physics and Astronomy University of Florence Florence Italy European Laboratory for Non-Linear Spectroscopy Sesto Fiorentino Italy Emoled S.r.l Sesto Fiorentino Italy Klinikum Rechts der Isar Technischen Universität München Munich Germany Imperial College London London United Kingdom Univ Lyon INSA-Lyon Université Claude Bernard Lyon 1 UJM-Saint Etienne CNRS Inserm CREATIS Lyon France Service de Radiologie Centre Hospitalier Universitaire de Saint Etienne TAPE EA7423 Université de Lyon UJM Saint Etienne Lyon France Department of Neurosurgery University Hospital of Dijon Bourgogne Dijon France Neurosurgery Department of Neuroscience Psychology Pharmacology and Child Health University of Florence Azienda Ospedaliero-Universitaria Careggi Florence Italy Service de Neurochirurgie D Hospices Civils de Lyon Bron France European Institute for Biomedical Imaging Research Vienna Austria Department of Medical Physics and Biomedical Engineering University College London London United Kingdom National Institute of Optics National Research Council Sesto Fiorentino Italy

ISBN: (数字)9781510664661

ISBN: (纸本)9781510664654

Recent advancements in imaging technologies (MRI, PET, CT, among others) have significantly improved clinical localisation of lesions of the central nervous system (CNS) before surgery, making possible for neurosurgeons to plan and navigate away from functional brain locations when removing tumours, such as gliomas. However, neuronavigation in the surgical management of brain tumours remains a significant challenge, due to the inability to maintain accurate spatial information of pathological and healthy locations intraoperatively. To answer this challenge, the HyperProbe consortium have been put together, consisting of a team of engineers, physicists, data scientists and neurosurgeons, to develop an innovative, all-optical, intraoperative imaging system based on (i) hyperspectral imaging (HSI) for rapid, multiwavelength spectral acquisition, and (ii) artificial intelligence (AI) for image reconstruction, morpho-chemical characterisation and molecular fingerprint recognition. Our HyperProbe system will (1) map, monitor and quantify biomolecules of interest in cerebral physiology;(2) be handheld, cost-effective and user-friendly;(3) apply AI-based methods for the reconstruction of the hyperspectral images, the analysis of the spatio-spectral data and the development and quantification of novel biomarkers for identification of glioma and differentiation from functional brain tissue. HyperProbe will be validated and optimised with studies in optical phantoms, in vivo against gold standard modalities in neuronavigational imaging, and finally we will provide proof of principle of its performances during routine brain tumour surgery on patients. HyperProbe aims at providing functional and structural information on biomarkers of interest that is currently missing during neuro-oncological interventions. © 2023 SPIE.

关键词： Tumors

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Roadmap on deep learning for Microscopy

arXiv

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arXiv 2023年

作者： Volpe, Giovanni Wählby, Carolina Tian, lei Hecht, Michael Yakimovich, Artur Monakhova, Kristina Waller, laura Sbalzarini, Ivo F. Metzler, Christopher A. Xie, Mingyang Zhang, Kevin lenton, Isaac C.d. Rubinsztein-dunlop, Halina Brunner, daniel Bai, Bijie Ozcan, Aydogan Midtvedt, daniel Wang, Hao Sladoje, Nataša lindblad, Joakim Smith, Jason T. Ochoa, Marien Barroso, Margarida Intes, Xavier Qiu, Tong Yu, li-Yu You, Sixian liu, Yongtao Ziatdinov, Maxim A. Kalinin, Sergei V. Sheridan, Arlo Manor, Uri Nehme, Elias Goldenberg, Ofri Shechtman, Yoav Moberg, Henrik K. langhammer, Christoph Špačková, Barbora Helgadottir, Saga Midtvedt, Benjamin Argun, Aykut Thalheim, Tobias Cichos, Frank Bo, Stefano Hubatsch, lars Pineda, Jesus Manzo, Carlo Bachimanchi, Harshith Selander, Erik Homs-Corbera, Antoni Fränzl, Martin de Haan, Kevin Rivenson, Yair Korczak, Zofia Adiels, Caroline Beck Mijalkov, Mite Veréb, dániel Chang, Yu-Wei Pereira, Joana B. Matuszewski, damian Kylberg, Gustaf Sintorn, Ida-Maria Caicedo, Juan C. Cimini, Beth A. Bell, Muyinatu lediju A. Saraiva, Bruno M. Jacquemet, Guillaume Henriques, Ricardo Ouyang, Wei le, Trang Gómez-de-Mariscal, Estibaliz Sage, daniel Muñoz-Barrutia, Arrate lindqvist, Ebba Josefson Bergman, Johanna Department of Physics University of Gothenburg Gothenburg41296 Sweden Dept. IT and SciLifeLab Uppsala University Sweden Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Department of Biomedical Engineering Boston University BostonMA02215 United States Görlitz Germany Department of Renal Medicine Division of Medicine University College London Royal Free Hospital Campus London United Kingdom Artificial Intelligence for Life Sciences CIC Dorset United Kingdom Roche Pharma International Informatics Roche Diagnostics GmbH Mannheim Germany Department of Electrical Engineering and Computer Sciences UC Berkeley United States Technische Universität Dresden Faculty of Computer Science Dresden Germany Max Planck Institute of Molecular Cell Biology and Genetics Center for Systems Biology Dresden Dresden Germany University of Maryland College Park United States Klosterneuburg3400 Austria School of Mathematics and Physics The University of Queensland Brisbane4072 Australia Australian Research Council Centre of Excellence for Engineered Quantum Systems The University of Queensland Brisbane4072 Australia Département d’Optique P. M. Duffieux Institut FEMTO-ST Université Franche-Comté CNRS UMR 6174 Besançon France Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA United States Department of Information Technology Uppsala University Sweden Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Elephas 1 Erdman Pl. Madison53705 United States University of Wisconsin–Madison Department of Medical Physics MadisonWI53705 United States Department of Molecular and Cellular Physiology Albany Medical College AlbanyNY12208 United States Research Laboratory of Electronics Electrical Engineering and Computer Science Massachusetts Institute of Technology BostonMA United States Center for Nanophase Materials Sciences Oak R

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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detection of Nuclei Cell in Histopathological Images of Uterine Cancer: Adenocarcinoma of Endometrium

Detection of Nuclei Cell in Histopathological Images of Uter...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Shoba. R Patil Veena. I Patil Basaveshwar Engineering College (Affiliated to Visvesvaraya Technological University Bagalkot India B.L.D.E.A's V. P. Dr. P. G. Halakatti College of Engineering and Technology (Affiliated to Visvesvaraya Technological University Vijayapur India

ISBN: (数字)9781728155180

ISBN: (纸本)9781728155197

Pathologist visual inspection of Histopathological uterine tissue sample is still considered as confirmatory test for uterine cancer finding. Manual assessment depends upon the knowledge and experience of pathologist. Hence subject of concern is objective analysis. An effective way of diagnosis, grading and classification of endometrium adenocarcinoma is structural analysis of the tissue sample. The effectiveness of computer assisted diagnosis, grading and classification depends on glandular structure and detection of individual nuclei cell. Hematoxylin channel from H&E stained image is extracted using color deconvolution algorithm. Morphological operation and thresholding are carried out in this work for structural analysis of glandular region and for detection of nuclei cell. To rise the effectiveness of the system elimination of cell nuclei from stroma is done before feature extraction is also described.

关键词： Image segmentation Image color analysis Cancer deconvolution Microscopy Biopsy Morphological operations

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A study of the magnetocaloric behavior of dy-substituted YMn2O5 compounds

arXiv

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arXiv 2024年

作者： Chouaibi, H. Mansouri, S. Aitjmal, S. Balli, M. Chdil, O. Abbasi Eskandari, M. Bukhari, S.H. Fournier, P. Laboratoire de Physique Appliquée Faculté des Sciences Université de Sfax B.P. 1171 Sfax3000 Tunisia Institut National de la Recherche Scientifique Énergie Matériaux Télécommunications 1650 Boulevard Lionel-Boulet VarennesQCJ3X 1S2 Canada Institut Quantique et Regroupement québécois sur les matériaux de pointe Département de physique Université de Sherbrooke QCJ1K2R1 Canada AMEEC Team LERMA College of Engineering & Architecture International University of Rabat Parc Technopolis Rocade de Rabat-Salé 11100 Morocco Department of Physics Govt. College University Faisalabad Layyah Campus Layyah31200 Pakistan

In this paper, we report on the magnetic and magnetocaloric features of dy-substituted YMn2O5 (Y1-xdyxMn2O5) with x =0.6, 0.8, and 1 series elaborated by sol-gel method. X-ray diffraction and Raman measurements attest well the high quality of our polycrystalline samples that crystallize in an orthorhombic structure with the Pbam space group. The Raman phonon frequencies were carried out and compared with the lattice dynamics calculations to identify the vibrational properties of all detected modes at room temperature. As expected, our magnetic study reveals that the magnetization was enhanced by the substitution of Y3+ by dy3+. The dy-substituted YMn2O5 sets the Neel transition [TN (Mn)] in the temperature range going from 40 to 45 K favoring the emergence of a transition at a very low temperature due to the long-range ordering of the dy3+ magnetic moments below 13K [TN (dy)]. dual peaks in the magnetic entropy change curve are also observed being in good agreement with magnetization data, which enlarges the range of application of these materials. On the other hand, a large magnetocaloric effect is observed close to 13 K which is mainly due the ordering of dy3+ magnetic moments. Also, the incommensurate antiferromagnetic transition of Mn magnetic moment taking place around 40 K affects slightly the entropy change. Our refrigerant capacity (RC) findings are higher compared to the average of RC for a, b, c axis of single crystal samples as HoMn2O5 and TbMn2O5, which confirms that our polycrystalline materials stand for promising magnetic refrigerant candidates that can be invested in space technology, hydrogen and helium liquefaction at cryogenic temperature. © 2024, CC0.

关键词： Magnetocaloric effects

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Recognition Of Handwritten Word Images

Recognition Of Handwritten Word Images

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Bangalore Humanitarian Technology Conference (B-HTC), IEEE

作者： Umesh d. dixit Rahul Hiraskar Raghavendra Purohit Sagar Shivanagutti B.L.D.E.A's V. P. Dr. P. G. Halakatti College of Engineering and Technology Vijayapur India

ISBN: (数字)9781728187945

ISBN: (纸本)9781728187952

Handwritten Character Recognition (HCR) is one of the fields in which a lot of research work is going on. In recent years it has taken quite a significant place in the research area. The objective of HCR technology is to transcript the handwritten scripts to digital form which is much more advantageous in storing than manual scripts. This paper presents a method for hand written word image recognition, which can be later extended for entire document. The proposed system employs Histogram of Oriented Gradients (HOG) features of the character images to train the classifier. The trained classifier is then used to recognize the segmented characters of word image to form a computer readable word. This work also compares the results using K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) classifiers. The SVM classifier is found to provide better word recognition rate of 75%.

关键词： Character recognition Image recognition Handwriting recognition Feature extraction Support vector machines Image segmentation Conferences

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direct Observation of Exceptional Points in Photonic Crystal by Cross-Polarization Imaging in Momentum Space

arXiv

引用

arXiv 2023年

作者： Nguyen, Viet Anh le, Viet Hoang Malgrey, loïc Sarelli, Eirini luu, dang-Khue Chu, Ha linh Vu, Truong Tuan Tong, Cong Quang lam, Vu dinh Seassal, Christian le-Van, Quynh Nguyen, Hai-Son College of Engineering and Computer Science VinUniversity Gia Lam district Hanoi14000 Viet Nam Univ Lyon Ecole Centrale de Lyon CNRS INSA Lyon Université Claude Bernard Lyon 1 CPE Lyon CNRS INL UMR5270 Ecully69130 France Département de Physique Ecole Normale Supérieure de Lyon 46 Allée d’Italie LyonF 69342 Cedex 07 France Institute of Materials Science VAST 18 Hoang Quoc Viet Hanoi11300 Viet Nam Graduate University of Science and Technology Vietnam Academy of Science and Technology 18 Hoang Quoc Viet Hanoi11300 Viet Nam Paris France

This study explores exceptional points (EPs) in photonic crystals (PhCs) and introduces a novel method for their single-shot observation. Exceptional points are spectral singularities found in non-Hermitian systems, such as leaky PhC slabs. However, directly observing EPs in PhC systems using regular reflectivity spectroscopy is a considerable challenge due to interference between guided resonances and background signals. In this work, we present a simple, nondestructive technique that employs crossed polarizations to directly observe EPs in momentum-resolved resonant scattering. This approach effectively suppresses the background signal, enabling exclusive probing of the guided resonances where EPs manifest. Our results demonstrate the formation of EPs in both energy-momentum mapping and isofrequency imaging. All experimental findings align seamlessly with numerical simulations and analytical models. Our approach holds great potential as a robust tool for studying non-Hermitian physics in PhC platform. © 2023, CC BY.

关键词： Photonic crystals

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