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37th International Cosmic Ray Conference, ICRC 2021

作者： Clay, Roger Singh, Jassimar Sushchov, Oleksandr Homola, Piotr Alvarez Castillo, David E. Beznosko, Dmitriy Budnev, Nikolai Gora, Dariusz Gupta, Alok C. Hnatyk, Bohdan Kasztelan, Marcin Kovacs, Peter Lozowski, Bartosz Medvedev, Mikhail V. Miszczyk, Justyna Mozgova, Alona Nazari, Vahab Niedzwiecki, Michael Pawlik, Maciej Rosas, Matias Rzecki, Krzyztof Smelcerz, Katarzyna Smolek, Karel Stasielak, Jaroslaw Stuglik, Slawomir Svanidze, Manana Tursunov, Arman Verbetsky, Yuri Wibig, Tadeus Zamora-Saa, Jilberto Singh, Jassimar University of Adelaide North Terrace Adelaide Australia Institute of Nuclear Physics Polish Academy of Sciences Radzikowskiego 152 Krakow31-342 Poland Joint Institute for Nuclear research Dubna141980 Russia Clayton State University MorrowGA United States Irkutsk State University Russia Manora Park Nainital263001 India Astronomical Observatory of Taras Shevchenko National University of Kyiv Kyiv04053 Ukraine National Centre for Nuclear research Andrzeja Soltana 7 Swierk Otwock05-400 Poland Institute for Particle and Nuclear Physics Wigner centre for Physics 1121 Konkoly-Thege.Miklosut Budapest29-33 Hungary Faculty of Natural Sciences University of Silesia in Katowice Bankowa 9 Katowice40-007 Poland Department of Physics and Astronomy University of Kansas LawrenceKS66045 United States Laboratory for Nuclear Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Computer Science Faculty of Computer Science and Telecommunications Crakow University of Technology Warszawaska 24 Krakow31-155 Poland ACC Cyfronet AGH-UST Krakow30-950 Poland Liceo 6 Francisco Bauza Montivideo Uruguay Institute of Experimental and Applied Physics Czech Technical Univrsity in Prague Czech Republic E. Andronikashvili Institute under Tbilisi State University Georgia United States Research Centre for Theoretical Physics and astrophysics Institute of Physics Silesian University in Opava Bezrucovo nam. 13 OpavaCZ-74601 Czech Republic University of Lodz Faculty of Physics and Applied Informatics Pomorska 149/153 Lodz90-236 Poland Universidada Andres Bello Departmento de Ciencias Fisicas Facultad de Ciences Exactas Avenida Republica 498 Santiago Chile Department of Physics University of Adelaide North Terrace Adelaide Australia

The Cosmic Ray Extremely Distributed Observatory (CREDO) pursues a global research strategy dedicated to the search for correlated cosmic rays, so-called Cosmic Ray Ensembles (CRE). Its general approach to CRE detection does not involve any a priori considerations and the search strategy encompasses both spatial and temporal correlations, on different scales. Here we search for time clustering of the cosmic ray events collected with a small sea-level air shower array at the University of Adelaide. The array consists of seven one square metre scintillators enclosing an area of 10 m x 19 m. It has a threshold energy ~0.1 PeV, and records cosmic ray showers at a rate of ~6 mHz. We have examined event times over a period of almost two years (~294k events) to determine the event time spacing distributions between individual events and the distributions of time periods which contained specific numbers of multiple events. We find that the overall time distributions are as expected for random events. The distribution which was chosen a priori for particular study was for time periods covering five events (four spacings). Overall, this fits closely with expectation but has two outliers of short ‘burst’ periods. One of these outliers contains eight events within 48 seconds. The physical characteristics of the array will be discussed together with the analysis procedure, including a fit between the observed time distributions and expectation based on randomly arriving events. © Copyright owned by the author(s).

关键词： Cosmic rays

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Indian Buffet Process-Based on Nonnegative Matrix Factorization with Single Binary Component

Indian Buffet Process-Based on Nonnegative Matrix Factorizat...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xindi Ma Jie Gao Xiaoyu Liu Taiping Zhang Yuan Yan Tang School of Computer Science Chongqing University Chongqing China National Center for Applied Mathematics Chongqing Normal University Chongqing China Faculty of Science and Technology University of Macau Macau

ISBN: (纸本)9781665442084

Nonnegative matrix factorization seeks to find a basic matrix and a weight matrix to approximate the nonnegative matrix. It has proven to be a powerful low-rank decomposition technique for nonnegative multivariate data. However, its performance largely depends on the assumption of a fixed number of features. In this work, we propose a new probabilistic nonnegative matrix factorization which factorizes a nonnegative matrix into a low-rank factor matrix with {0,1} constraints and a nonnegative weight matrix. In order to automatically learn the potential binary features and feature number. A deterministic Indian buffet process variational inference is introduced to obtain the binary factor matrix. And the weight matrix is set to satisfy the exponential prior. In order to obtain the real posterior distribution of the two factor matrices, a variational Bayesian exponential Gaussian inference model is established. The comparative experiments on both the synthetic and real-world data sets show the efficacy of the proposed method.

关键词： Computational modeling Exponential distribution Conferences Benchmark testing Probabilistic logic Bayes methods Matrix decomposition

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Immersive Analysis: Enhancing Material Inspection of X-Ray Computed Tomography Datasets in Augmented Reality

arXiv

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arXiv 2024年

作者： Gall, Alexander Heim, Anja Weinberger, Patrick Fröhler, Bernhard Kastner, Johann Heinzl, Christoph Research Group Computed Tomography University of Applied Sciences Upper Austria Roseggerstraße 15 Wels4600 Austria Faculty of Computer Science and Mathematics University of Passau Innstraße 43 Passau94032 Germany Division Development Center X-ray Technology Fraunhofer Institute for Integrated Circuits IIS Flugplatzstraße 75 Fürth90768 Germany

This work introduces a novel Augmented Reality (AR) approach to visualize material data alongside real objects in order to facilitate detailed material analyses based on spatial non-destructive testing (NDT) data as generated in X-ray computed tomography (XCT) imaging. For this purpose, we introduce a framework that leverages the potential of AR devices, visualization and interaction techniques to seamlessly explore complex primary and secondary XCT data matched with real-world objects. The overall goal of the proposed analysis scheme is to enable researchers and analysts to inspect material properties and structures onsite and in-place. Coupling immersive visualization techniques with real physical objects allows for highly intuitive workflows in material analysis and inspection, which enables the identification of anomalies and accelerates informed decision making. As a result, this framework generates an immersive experience, which provides a more engaging and more natural analysis of material data. A case study on fiber-reinforced polymer datasets was used to validate the AR framework and its new workflow. Initial results revealed positive feedback from experts, in particular regarding improved understanding of spatial data and a more natural interaction with material samples, which may have significant potential when combined with conventional analysis systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Nondestructive examination

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Random hypergraphs and property B

arXiv

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arXiv 2021年

作者： Duraj, Lech Kozik, Jakub Shabanov, Dmitry Theoretical Computer Science Department Faculty of Mathematics and Computer Science Jagiellonian University Kraków Poland Moscow Institute of Physics and Technology Laboratory of Combinatorial and Geometric Structures Dolgoprudny Moscow Region Russia National Research University Higher School of Economics Faculty of Computer Science Moscow Russia

In 1964 Erd‟os proved that (formula presented)edges are sufficient to build a k-graph which is not two colorable. To this day, it is not known whether there exist such k-graphs with smaller number of edges. Erdős’ bound is consequence of the fact that a hypergraph with k2/2 vertices and (formula presented) randomly chosen edges of size k is asymptotically almost surely not two colorable. Our first main result implies that for any Ε > 0, any k-graph with (1 − Ε)M (k) randomly and uniformly chosen edges is a.a.s. two colorable. The presented proof is an adaptation of the second moment method analogous to the developments of Achlioptas and Moore from 2002 who considered the problem with fixed size of edges and number of vertices tending to infinity. In the second part of the paper we consider the problem of algorithmic coloring of random k-graphs. We show that quite simple, and somewhat greedy procedure, a.a.s. finds a proper two coloring for random k-graphs on k2/2 vertices, with at most O(k ln k · 2k ) edges. That is of the same asymptotic order as the analogue of the algorithmic barrier defined by Achlioptas and Coja-Oghlan in 2008, for the case of fixed *** Codes 05C65, 05C80, 05D40 © 2021, CC BY.

关键词： Method of moments

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New solutions for the generalized q-deformed sinh-Gordon equation with q-calculus

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Results in physics 2024年 60卷

作者： Ali, Khalid K. Raslan, K.R. Shehata, Ahmed S. Albalawi, Wedad Abdel-Aty, Abdel-Haleem Eleuch, Hichem Department of Mathematics Faculty of Science Al-Azhar University Cairo Nasr-City Egypt Department of Mathematics Center of Basic Science Misr University for Science and Technology Giza 12511 Egypt Department of Mathematical Sciences College of Science Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Physics College of Science University of Bisha P.O. Box 344 Bisha 61922 Saudi Arabia Department of Applied Physics and Astronomy University of Sharjah Sharjah 27272 United Arab Emirates College of Arts and Sciences Abu Dhabi University Abu Dhabi 59911 United Arab Emirates Institute for Quantum Science and Engineering Texas A&M University College Station 77843 TX United States

Our objective in this paper is to investigate the sinh-Gordon equation's generalization using a q-calculus. This equation explains systems with broken symmetries. The q-deformed sinh-Gordon equation adds noncommutativity and non-linearity, expanding on its original form. We compare two methods for solving this equation: RqDTM and VIM. RqDTM is a modified differential transform technique for q-deformed equations, whereas VIM utilizes an iterative approach. We give numerical results and compare the accuracy and efficiency of the solutions achieved using these approaches. This analysis evaluates the merits and disadvantages of each strategy for solving the q-deformed sinh-Gordon equation, offering insights into their applicability and performance. © 2024 The Author(s)

关键词： Mathematica 13.2 q-calculus q-deformed equation RqDTM VIM

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Transformative Effects of ChatGPT on Modern Education: Emerging Era of AI Chatbots

arXiv

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arXiv 2023年

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, was released to provide coherent and useful replies based on analysis of large volumes of data. In this article, leading scientists, researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research seeks to improve our knowledge of ChatGPT capabilities and its use in the education sector, identifying potential concerns and challenges. Our preliminary evaluation concludes that ChatGPT performed differently in each subject area including finance, coding and maths. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within Generative AI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023, CC0.

关键词： Machine learning

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RETRACTED ARTICLE: Intrusion detection based on machine learning in the internet of things, attacks and counter measures

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The Journal of Supercomputing 2022年第6期78卷 8890-8924页

作者： Rehman, Eid Haseeb-ud-Din, Muhammad Malik, Arif Jamal Khan, Tehmina Karmat Abbasi, Aaqif Afzaal Kadry, Seifedine Khan, Muhammad Attique Rho, Seungmin Department of Software Engineering Foundation University Islamabad Islamabad Pakistan Faculty of Applied Computing and Technology Noroff University College Kristiansand Norway Department of Computer Science HITEC University Taxila Taxila Pakistan Chung-Ang University - Seoul Campus: Chung-Ang University Seoul Korea

Globally, data security and privacy over the Internet of Things (IoT) are necessary due to its emergence in daily life. As the IoT will soon invade each part of our lives, attention to IoT security is significant. The nature of attacks is dynamic, and addressing this requires designing dynamic methods and a self-adaptable scheme to discover security attacks from malicious use of IoT equipment. The best detection mechanism against attacks from compromised IoT devices includes machine learning techniques. This study emphasizes the latest literature on attack types and uses a scheme based on machine learning for network support in IoT and intrusion detection. Therefore, the current work includes a thorough analysis of multiple intelligence methods and their deployed architectures of network intrusion detection, focusing on IoT attacks and machine learning-based intrusion detection schemes. Moreover, it explores methods based on machine learning appropriate for identifying IoT devices associated with cyber attacks.

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AlScN High Electron Mobility Transistors: Integrating High Piezoelectric, High K Dielectric, and Ferroelectric Functionality

AlScN High Electron Mobility Transistors: Integrating High P...

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BiCMOS and Compound Semiconductor Integrated Circuits and technology Symposium (BCICTS)

作者： Joseph Casamento Kazuki Nomoto Thai-Son Nguyen Hyunjea Lee Chandrasekhar Savant Lei Li Austin Hickman Takuya Maeda Yu-Tsun Shao Jimy Encomendero Ved Gund Timothy Vasen Shamima Afroz Daniel Hannan David A. Muller Huili Grace Xing Debdeep Jena Department of Materials Science and Engineering Cornell University Ithaca NY USA School of Electrical and Computer Engineering Cornell University Ithaca NY USA Kavli Institute at Cornell for Nanoscale Science Cornell University Ithaca NY USA School of Applied and Engineering Physics Cornell University Ithaca New York USA Northrop Grumman Mission Systems Advanced Technology Laboratory Linthicum Maryland USA Department of Physics Cornell University Ithaca New York USA

This paper presents an overview of the promising physical properties of the aluminum scandium nitride (AlScN) ternary alloy system and its integration in radiofrequency heterostructure high electron mobility transistors (RF HEMTs). Specifically, heterostructures with AlScN demonstrate enhanced piezoelectric response and enhanced relative dielectric permittivity, as well as ferroelectric behavior. These promising physical phenomena are manifested in HEMTs that show larger on current densities compared to aluminum gallium nitride (AlGaN) HEMTs and display ferroelectric behavior. An overlook on future improvements in thin film deposition and device fabrication is discussed to fully maximize the potential devices and integrated circuits incorporating AlScN.

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Time Series Classification Using Deep Learning for HAR Based on Smart Wearable Sensors

Time Series Classification Using Deep Learning for HAR Based...

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International computer science and Engineering Conference (ICSEC)

作者： Ponnipa Jantawong Narit Hnoohom Anuchit Jitpattanakul Sakorn Mekruksavanich Department of Computer Engineering School of Information and Communication Technology University of Phayao Thailand Department of Computer Engineering Faculty of Engineering Image Information and Intelligence Laboratory Mahidol University Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (纸本)9781665462730

In the last decades, time series classification (TSC) has emerged as one of the most challenging issues in data mining, and extensive studies have been done on various methods, including algorithm-based and learning-based techniques. Sensor-based human activity recognition (HAR) is a TSC issue that has become one of the most sought-after fields among business and academia specialists because of the proliferation of smartphone technology and wearable movement sensors. Conventional approaches to feature extraction provide a significant challenge in feature selection. Deep learning is an efficient strategy in the HAR scientific field and has solved the issue of feature selection. Nevertheless, several obstacles remain to study topics, including classifier interpretation. This article integrates well-known deep learning methods, namely convolutional neural networks and RNN-based models. The new approach proved to be more effective than the existing state-of-the-art approach. We assessed our network on the multivariant time-series benchmark (UCI-HAR) and revealed that our model surpasses other models in terms of training time and overall accuracy.

关键词： Deep learning Training computer science Computational modeling Time series analysis Feature extraction Data mining

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Extraction of Corrosion Areas in Pipelines Using Mathematical Morphology Tools

SSRN

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SSRN 2024年

作者： Menyem, Leopold Kombe, Timothée Mbondjo, Richard Essomba Elisée, Ndarwe Djakba Monkam, David Technology and Applied Sciences Laboratory University of Douala Douala Cameroon Institute of Fine Arts University of Douala Nkongsamaba Cameroon National Higher Polytechnic School University of Douala P.O. Box 2701 Douala Cameroon CPAMOQ University of Douala Douala Cameroon Department of Physics Faculty of Science Douala University PO BOX 24157 Douala Cameroon

The transportation of hydrocarbons over long distances, from the extraction point to refining complexes and transport tanks, is most often done through pipelines, typically buried underground. These pipelines suffer from failures due to internal pressures, floods, cracks, and the presence of corrosion. Such pipeline failures cause numerous damages as they often lead to explosions causing irreparable problems for both humans and the environment. Thus, new technologies for identifying activities likely to damage pipelines are constantly being developed to enable real-time maintenance and ensure pipeline reliability. The challenge lies in using image processing to detect and highlight corrosion areas on pipelines. The application of empirical thresholding segmentation followed by mathematical morphology operators such as Closing and Opening, with thresholds varying between 0.18 and 0.71, has provided results that enable the implementation of maintenance policies and operational safety of the facilities. © 2024, The Authors. All rights reserved.

关键词： Mathematical morphology

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