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Prediction of Stock Price Using XG boost Integrated bi-Channel LSTM

Prediction of Stock Price Using XG Boost Integrated Bi-Chann...

Advances in computer science, electrical, electronics, and Communication Technologies (Ce2CT), International Conference on

作者： Amey Adinath Choudhari Rajeswaran Ayyadurai Jayant Maruti Hudar V.K. Arthi Tasriqul Islam G Jayashree Hareesh JSPM's Rajashi Shahu College of Engineering Pune India IL Health & Beauty Natural Oils Co Inc California USA DBA BHS University Switzerland DBA in Data Science and AI PGDBM B.E. Electronics. Diploma in Computer Technology Navi Mumbai India Department of Management Studies Sri Ramakrishna Engineering College Coimbatore India Harvard University Cambridge Massachusetts USA Fahad Bin Sultan University Tabuk Saudi Arabia

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Investors' biggest prob.em is determining the appropriate stock price by studying financial information, which is a difficult process due to enormous and related inefficiencies. So, among the biggest challenges for investors is identifying a stock. Currently, it's very challenging to carry out research in the forecasts for the finance industry, including the stock market, currency value, and stock price. Due to its critical importance in financial consumers' decision-making, the prediction and examination of the stock price is yet another important research sub.ect. A novel Xgboost Integrated bichannel Long/Short-Term Memory (X-bLSTM) technique is implemented in this article to provide a sophisticated and perfect framework for the share market's price prediction The share value dataset is the first thing we collect to assess the way the suggested X-bLSTM strategy performs. Due to the existence of noisy data, min-max normalization is performed to preprocess the raw dataset. The efficiency of the proposed approach is assessed using several measures, and comparative between the proposed and current methods is also provided. The studies' findings show that the recommended approach outperforms alternative techniques for share value prediction.

关键词： Training Industries electric potential Current measurement Decision making Noise measurement Stock markets Forecasting Long short term memory Currencies

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The medical aspects of eMI effect on patients implanted with pacemakers

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Materials Today: Proceedings 2021年 45卷 5243-5248页

作者： Sahil Upadhyay Ankita Upadhya Waleed Salehi Gaurav Gupta Electrical and Computer Engineering Dept Lakehead University Ontario P7B 5E1 Canada School of Mechanical and Civil Engineering Shoolini University Solan HP 173229 India Yogananda School of AI Computers and Data Science Shoolini University Solan HP 173229 India

Pacemakers and implantab.e cardioverter defibrillators are the devices utilized for invigorating heart muscles to direct its compressions and relaxations or with the end goal to control pulses of the heart. electromagnetic radiations are produced by every single electric framework including mobiles security frameworks, remote chargers. In this way, at whatever point any electric hardware is utilized electromagnetic radiations are produced and it might conversely influence the pace of driving forces in pacemaker and cardioverter defibrillator and it is conceivab.e that pacemaker-implanted patients might be presented to life-threatening circumstances due to electromagnetic interference. This paper gives contemporaneous learning on the impacts of electromagnetic waves on pacemaker and implantab.e cardioverter defibrillators on current computerized electronic instruments mainly interference with cardiac pacemakers by cellular telephones and other wireless devices, from the various experiment conducted on different types of patients with distinct medical background and age and implanted with cardiovascular pacemakers in different modes and manufactured by various manufacturers availab.e in the market the different environment of electromagnetic radiations emitted by distinct types of devices. Patients were electrocardiographically ob.erved in all the cases when the pacemaker was exposed and operated in the environment of radiation, and from the findings, we potentially found that in most of the cases electromagnetic obstruction may happen between heart pacemakers and modern devices emitting electromagnetic radiation including mainly mobile-phones, magnetic resonance imaging devices and our wireless devices, thus representing a potential general health issue.

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Development of anti-phishing browser based on random forest and rule of extraction framework

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Cybersecurity 2018年第1期1卷 879-892页

作者： Mohith Gowda HR Adithya MV Gunesh Prasad S Vinay S B.E in Computer Science and Engineering PES College of Engineering4011Vasuda Krupa3rd CrossShankar NagarMandyaKarnataka 571401India B.E in Computer Science and Engineering PES College of Engineering19321st Main RoadHalahalli ExtNear Vinayaka Auto StandMandyaKarnataka 571401India B.E in Computer Science and Engineering PES College of Engineering20/195th crossShakthi NagarNear Shakthi Nagar ParkMysoreKarnataka 570019India Information Science and Engineering PES College of EngineeringPES Engineering College RdPES College CampusMandyaKarnataka 571401India

Phishing is a technique under Social engineering attacks which is most widely used to get user sensitive information,such as login credentials and credit and debit card information,*** is carried out by a person masquerading as an authentic *** protect web users from these attacks,various anti-phishing techniques are developed,but they fail to protect the user from these attacks in various *** this paper,we propose a novel technique to identify phishing websites effortlessly on the client side by proposing a novel browser *** this system,we use the rule of extraction framework to extract the properties or features of a website using the URL *** list consists of 30 different properties of a URL,which will later be used by the Random Forest Classification machine learning model to detect the authenticity of the website.A dataset consisting of 11,055 tuples is used to train the *** processes are carried out on the client-side with the help of a redesigned browser *** Researches have come up with machine learning frameworks to detect phishing sites,but they are not in a state to be used by individuals having no technical *** make sure that these tools are accessib.e to every individual,we have improvised and introduced detection methods into the browser architecture named as‘embedded Phishing Detection browser’(ePDb),which is a novel method to preserve the existing user experience while improving the *** newly designed browser architecture introduces a special segment to perform phishing detection operations in *** have prototyped this technique to ensure maximum security,better accuracy of 99.36% in the identification of phishing websites in realtime.

关键词： Phishing attack Machine learning Intelligent browser engine Rule of extraction algorithm browser architecture

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Developments and applications of the OPTIMADe API for materials discovery, design, and data exchange

arXiv

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arXiv 2024年

作者： evans, Matthew L. bergsma, Johan Merkys, Andrius Andersen, Casper W. Andersson, Oskar b. beltrán, Daniel blokhin, evgeny boland, Tara M. balderas, Rubén Castañeda Choudhary, Kamal Díaz, Alberto Díaz García, Rodrigo Domínguez eckert, Hagen eimre, Kristjan Montero, María elena Fuentes Krajewski, Adam M. Mortensen, Jens Jørgen Duarte, José Manuel Nápoles Pietryga, Jacob Qi, Ji de Jesús Trejo Carrillo, Felipe Vaitkus, Antanas Yu, Jusong Zettel, Adam de Castro, Pedro baptista Carlsson, Johan Cerqueira, Tiago F.T. Divilov, Simon Hajiyani, Hamidreza Hanke, Felix Jose, Kevin Oses, Corey Riebesell, Janosh Schmidt, Jonathan Winston, Donald Xie, Christen Yang, Xiaoyu bonella, Sara botti, Silvana Curtarolo, Stefano Draxl, Claudia Cobas, Luis edmundo Fuentes Hospital, Adam Liu, Zi-Kui Marques, Miguel A.L. Marzari, Nicola Morris, Andrew J. Ong, Shyue Ping Orozco, Modesto Persson, Kristin A. Thygesen, Kristian S. Wolverton, Chris Scheidgen, Markus Toher, Cormac Conduit, Gareth J. Pizzi, Giovanni Gražulis, Saulius Rignanese, Gian-Marco Armiento, Rickard Chemin des Étoiles 8 Louvain-la-Neuve1348 Belgium Matgenix SRL 185 Rue Armand Bury Gozée6534 Belgium École Polytechnique Fédérale de Lausanne Avenue de Forel 3 Lausanne1015 Switzerland Institute of Biotechnology Life Sciences Center Vilnius University Sauletekio av. 7 VilniusLT-10257 Lithuania SINTEF P.O. Box 4760 Torgarden TrondheimNO-7465 Norway Materials Design and Informatics unit Department of Physics Chemistry and Biology Linköping University Sweden Baldiri i Reixac 10-12 Barcelona08028 Spain Tilde Materials Informatics Straßmannstraße 25 Berlin10249 Germany Materials Platform for Data Science Sepapaja 6 Tallinn15551 Estonia Computational Atomic-Scale Materials Design Technical University of Denmark Kgs. Lyngby Denmark Av. Miguel de Cervantes 120 Complejo Industrial Chihuahua Chihuahua Chih. 31136 Mexico Material Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Center for Extreme Materials Duke University DurhamNC27708 United States École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Universidad Autónoma de Chihuahua Facultad de Ciencias Quimicas Chihuahua31125 Mexico Department of Materials Science and Engineering The Pennsylvania State University University ParkPA16802 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of NanoEngineering University of California 9500 Gilman Drive La Jolla San DiegoCA92093-0448 United States National Institute for Materials Science 1-2-1 Sengen Ibaraki Tsukuba305-0047 Japan Dassault Systèmes Germany GmbH Am Kabellager 11-13 Cologne51063 Germany CFisUC Department of Physics University of Coimbra Rua Larga Coimbra3004-516 Portugal Dassault Systèmes 22 Science Park CB4 0FJ United Kingdom Theory of Condensed Matter Cavendis

The Open databases Integration for Materials Design (OPTIMADe) application programming interface (API) empowers users with holistic access to a growing federation of databases, enhancing the accessibility and discoverability of materials and chemical data. Since the first release of the OPTIMADe specification (v1.0), the API has undergone significant development, leading to the v1.2 release, and has underpinned multiple scientific studies. In this work, we highlight the latest features of the API format, accompanying software tools, and provide an update on the implementation of OPTIMADe in contributing materials databases. We end by providing several use cases that demonstrate the utility of the OPTIMADe API in materials research that continue to drive its ongoing development. © 2024, CC bY.

关键词： electronic data interchange

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The ZTF Source Classification Project: III. A Catalog of Variab.e Sources

arXiv

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arXiv 2023年

作者： Healy, brian F. Coughlin, Michael W. Mahabal, Ashish A. Laz, Theophile Jegou du Drake, Andrew Graham, Matthew J. Hillenbrand, Lynne A. van Roestel, Jan Szkody, Paula Zielske, LeighAnna Guiga, Mohammed Hassan, Muhammad Yusuf Hughes, Jill L. Nir, Guy Parikh, Saagar Park, Sungmin Purohit, Palak Rebbapragada, Umaa Reed, Draco Warshofsky, Daniel Wold, Avery bloom, Joshua S. Masci, Frank J. Riddle, Reed Smith, Roger School of Physics and Astronomy University of Minnesota MinneapolisMN55455 United States Division of Physics Mathematics and Astronomy California Institute of Technology PasadenaCA91125 United States Center for Data Driven Discovery California Institute of Technology PasadenaCA91125 United States Department of Astronomy California Institute of Technology PasadenaCA91125 United States Anton Pannekoek Institute for Astronomy University of Amsterdam Amsterdam1090 GE Netherlands Department of Astronomy University of Washington SeattleWA98195 United States Department of Physics Pacific Lutheran University TacomaWA98447 United States Department of Computer Science and Engineering University of Minnesota MinneapolisMN55455 United States Department of Electrical Engineering Indian Institute of Technology Gandhinagar Gujarat 382055 India Department of Astronomy University of California BerkeleyCA94720 United States Lawrence Berkeley National Laboratory 1 Cyclotron Road MS 50B-4206 BerkeleyCA94720 United States Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA15213 United States Computer Vision Imaging and Graphics Lab Indian Institute of Technology Gujarat Gandhinagar382055 India School of Statistics University of Minnesota MinneapolisMN55455 United States Division of Engineering and Applied Science California Institute of Technology PasadenaCA91125 United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA91125 United States IPAC California Institute of Technology 1200 E. California Blvd. PasadenaCA91125 United States Caltech Optical Observatories California Institute of Technology PasadenaCA91125 United States

The classification of variab.e ob.ects provides insight into a wide variety of astrophysics ranging from stellar interiors to galactic nuclei. The Zwicky Transient Facility (ZTF) provides time-series ob.ervations that record the variability of more than a billion sources. The scale of these data necessitates automated approaches to make a thorough analysis. building on previous work, this paper reports the results of the ZTF Source Classification Project (SCoPe), which trains neural network and XGboost machine-learning (ML) algorithms to perform dichotomous classification of variab.e ZTF sources using a manually constructed training set containing 170,632 light curves. We find that several classifiers achieve high precision and recall scores, suggesting the reliability of their predictions for 209,991,147 light curves across 77 ZTF fields. We also identify the most important features for XGb classification and compare the performance of the two ML algorithms, finding a pattern of higher precision among XGb classifiers. The resulting classification catalog is availab.e to the public, and the software developed for SCoPe is open-source and adaptab.e to future time-domain surveys. Copyright © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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bigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer e bouchard Lin Gu Weidong Cai Shuiwang Ji badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou erhan bas eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-bellido Jinhyun Kim edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

bigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a sub.et of the availab.e imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enab.e generalizab.e benchmarking. Together with image quality features, we pooled the data in an interactive web application that enab.es users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data sub.ets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We ob.erved that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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benchmarking bias: expanding clinical AI model card to incorporate bias reporting of social and non-social factors

arXiv

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arXiv 2023年

作者： Heming, Carolina A.M. Abdalla, Mohamed Mohanna, Shahram Ahluwalia, Monish Zhang, Linglin Trivedi, Hari Woo, MinJae Fine, benjamin Gichoya, Judy Wawira Celi, Leo Anthony Seyyed-Kalantari, Laleh University of Iowa Iowa CityIA United States Institute for Better Health Trillium Health Partners 100 Queensway West 6th floor MississaugaONL5B 1B8 Canada Kingston Health Sciences Centre Queen's University 76 Stuart St. KingstonONK7L 2V7 Canada School of Data Science and Analytics Kennesaw State University 3391 Town Point Dr NW KennesawGA30144 United States Department of Radiology and Imaging Sciences Emory University 1364 E Clifton Rd NE AtlantaGA30322 United States Vector Institute of Artificial Intelligence 661 University Ave Suite 710 TorontoONM5G 1M1 Canada Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Harvard Medical School 25 Shattuck St BostonMA02115 United States Department of Electrical Engineering and Computer Science York University 4700 Keele St TorontoONM3J 1P3 Canada Department of Medicine University of Alberta Walter C Mackenzie Health Sciences Centre St. NW Edmonton Alberta 8440 112 Canada

The model facts labels are often used to communicate artificial intelligence (AI) model performance to the end users for proper deployment and often report the overall performance averaged across all populations. However, the overall performance is not generalizab.e to different subgroups of patients. In this paper, we highlight the importance of currently unforeseen aspects, essential for AI models in healthcare, that are not included in the model fact cards. We suggest including through bias and fairness analysis outcomes in model fact labels in the health care AI model including: (a) socio-demographic fairness factors (e.g., sexes, race, socioeconomic status, etc.) and (b) Technical, environmental and biological bias Factors (e.g. disease-dependent, anatomical, or instrumental factors, etc.). The socio-demographic fairness factors have b.en explored in fairness analysis of AI models in healthcare but have not often b.en listed in model fact labels. besides, the technical, environmental, and biological bias factors have not often b.en explored in the bias analysis of AI models in healthcare and have not b.en reported in model fact cards. Here, we demonstrate disparities with respect to both sets of factors. We suggest that clinical AI model reporting cards need to be expanded to communicate such a wider range of disparities to end users for precise patient care. Analysis and communication of such a wider range of biases identify potential drivers of bias, which pave the way to debiasing. As well, it highlights the model caveats and guides the end user and policymaker on how, when, and under what circumstances the model works or fails. Copyright © 2023, The Authors. All rights reserved.

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VaTeST III: Validation of 8 Potential Super-earths from TeSS data

arXiv

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arXiv 2023年

作者： Mistry, Priyashkumar Prasad, Aniket Maity, Mousam Pathak, Kamlesh Gharat, Sarvesh Lekkas, Georgios bhattarai, Surendra Kumar, Dhruv Lissauer, Jack J. Twicken, Joseph D. Soubkiou, Ab.erahmane Pozuelos, Francisco J. Jenkins, Jon Horne, Keith Giacalone, Steven barkaoui, Khalid Timmermans, Mathilde Watkins, Cristilyn N. Sefako, Ramotholo Collins, Karen A. Ciardi, David R. Clark, Catherine A. Safonov, boris S. Shporer, Avi Schlieder, Joshua e. benkhaldoun, Zouhair Stockdale, Chris Ziegler, Carl Gilbert, emily A. Jehin, emmanuël Murgas, Felipe Crossfield, Ian J.M. Paegert, Martin Lund, Michael b. Narita, Norio Schwarz, Richard P. Goeke, Robert F. Fajardo-Acosta, Sergio b. Howell, Steve b. Tan, Thiam-Guan barclay, Thomas Kawai, Yugo Department of Physics Sardar Vallabhbhai National Institute of Technology Gujarat Surat395007 India School of Physics Faculty of Science University of New South Wales Kensington SydneyNSW2052 Australia Department of Earth and Space Science Indian Institute of Space Science and Technology Thiruvananthapuram India Department of Physics Presidency University West Bengal Kolkata700073 India Centre for Machine Intelligence and Data Science Indian Institute of Technology Bombay India Department of Computer Science International Hellenic University Kavala65404 Greece Department of Physics Indian Institute of Science Education and Research Kolkata West Bengal Mohanpur741246 India Department of Physics National Institute of Technology Tripura Agartala799046 India NASA Ames Research Center Moffett FieldCA94035 United States SETI Institute Mountain ViewCA94043 United States Oukaimeden Observatory High Energy Physics and Astrophysics Laboratory Cadi Ayyad University Marrakech Morocco Astrobiology Research Unit Université de Liège 19C Allée du 6 Août Liège4000 Belgium Glorieta de la Astronomía s/n Granada18008 Spain SUPA Physics and Astronomy University of St. Andrews FifeKY16 9SS United Kingdom Department of Astronomy University of California Berkeley BerkeleyCA94720 United States Department of Earth Atmospheric and Planetary Science Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Calle Vía Láctea s/n Tenerife La Laguna38200 Spain Center for Astrophysics Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States South African Astronomical Observatory P.O. Box 9 Observatory Cape Town7935 South Africa NASA Exoplanet Science Institute IPAC California Institute of Technology PasadenaCA91125 United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Sternberg Astronomical Institute Lomonosov Moscow State University Russia Depart

NASA’s all-sky survey mission, the Transiting exoplanet Survey Satellite (TeSS), is specifically engineered to detect exoplanets that transit bright stars. Thus far, TeSS has successfully identified approximately 400 transiting exoplanets, in addition to roughly 6000 candidate exoplanets pending confirmation. In this study, we present the results of our ongoing project, the Validation of Transiting exoplanets using Statistical Tools (VaTeST). Our dedicated effort is focused on the confirmation and characterization of new exoplanets through the application of statistical validation tools. Through a combination of ground-based telescope data, high-resolution imaging, and the utilization of the statistical validation tool known as TRICeRATOPS, we have successfully discovered eight potential super-earths. These planets bear the designations: TOI-238b (1.61+0.09–0.10 R⊗), TOI-771b (1.42+0.11–0.09 R⊗), TOI-871b (1.66+0.11–0.11 R⊗), TOI-1467b (1.83+0.16–0.15 R⊗), TOI-1739b (1.69+0.10–0.08 R⊗), TOI-2068b (1.82+0.16–0.15 R⊗), TOI-4559b (1.42+0.13–0.11 R⊗), and TOI-5799b (1.62+0.19–0.13 R⊗). Among all these planets, six of them fall within the region known as’keystone planets,’ which makes them particularly interesting for study. based on the location of TOI-771b and TOI-4559b below the radius valley we characterized them as likely super-earths, though radial velocity mass measurements for these planets will provide more details about their characterization. It is noteworthy that planets within the size range investigated herein are ab.ent from our own solar system, making their study crucial for gaining insights into the evolutionary stages between earth and Neptune. © 2023, CC bY.

关键词： Satellites

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Illinois express Quantum Network (IeQNeT): Metropolitan-scale experimental quantum networking over deployed optical fiber

arXiv

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arXiv 2021年

作者： Chung, Joaquin Kanter, Gregory Lauk, Nikolai Valivarthi, Raju Wu, Wenji Ceballos, Russell R. Peña, Cristián Sinclair, Neil Thomas, Jordan Xie, Si Kettimuthu, Rajkumar Kumar, Prem Spentzouris, Panagiotis Spiropulu, Maria Data Science and Learning Division Argonne National Laboratory 9700 S Cass Ave. LemontIL60439 United States University of Chicago Consortium for Advanced Science and Engineering 924 E 57th St ChicagoIL60637 United States NuCrypt LLC 1840 Oak Ave EvanstonIL60201 United States Center for Photonic Communication and Computing Department of Electrical and Computer Engineering McCormick School of Engineering and Applied Science Northwestern University 2145 Sheridan Road EvanstonIL60208-3118 United States Division of Physics Mathematics and Astronomy California Institute of Technology 1200 E. California Blvd. PasadenaCA91125 United States California Institute of Technology 1200 E. California Blvd. PasadenaCA91125 United States Fermi National Accelerator Laboratory Kirk Rd and Pine St BataviaIL60510 United States Department of Chemistry Physics and Engineering Studies Chicago State University 9501 S. King Dr. ChicagoIL60628 United States Pittsburgh Quantum Institute B4 Thaw Hall 4061 O'Hara Street PittsburghPA15260 United States John A. Paulson School of Engineering and Applied Sciences Harvard University 29 Oxford St. CambridgeMA02138 United States 2205 Tech Drive Suite 1-160 EvanstonIL60208 United States Department of Physics and Astronomy Northwestern University 2145 Sheridan Road EvanstonIL60208-3112 United States

The Illinois express Quantum Network (IeQNeT) is a program to realize metro-scale quantum networking over deployed optical fiber using currently availab.e technology. IeQNeT consists of multiple sites that are geographically dispersed in the Chicago metropolitan area. each site has one or more quantum nodes (Q-nodes) representing the communication parties in a quantum network. Q-nodes generate or measure quantum signals such as entangled photons and communicate the results via standard, classical, means. The entangled photons in IeQNeT nodes are generated at multiple wavelengths, and are selectively distributed to the desired users via optical switches. Here we describe the network architecture of IeQNeT, including the Internet-inspired layered hierarchy that leverages software-defined-networking (SDN) technology to perform traditional wavelength routing and assignment between the Q-nodes. Specifically, SDN decouples the control and data planes, with the control plane being entirely classical. Issues associated with synchronization, calibration, network monitoring, and scheduling will be discussed. An important goal of IeQNeT is demonstrating the extent to which the control plane can coexist with the data plane using the same fiber lines. This goal is furthered by the use of tunab.e narrow-band optical filtering at the receivers and, at least in some cases, a wide wavelength separation between the quantum and classical channels. We envision IeQNeT to aid in developing robust and practical quantum networks by demonstrating metro-scale quantum communication tasks such as entanglement distribution and quantum-state teleportation. © 2021, CC bY.

关键词： Optical fibers

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Searching for gravitational wave optical counterparts with the Zwicky Transient Facility: summary of O4a

arXiv

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arXiv 2024年

作者： Ahumada, Tomás Anand, Shreya Coughlin, Michael W. Gupta, Vaidehi Kasliwal, Mansi M. Karambelkar, Viraj R. Stein, Robert D. Waratkar, Gaurav Swain, Vishwajeet Jegou du Laz, Theophile Anumarlapudi, Akash Andreoni, Igor bulla, Mattia Srinivasaragavan, Gokul P. Toivonen, Andrew Wold, Avery bellm, eric C. Cenko, S. bradley Kaplan, David L. Sollerman, Jesper bhalerao, Varun Perley, Daniel Salgundi, Anirudh Suresh, Aswin Hinds, K.-Ryan Reusch, Simeon Necker, Jannis Cook, David O. Pletskova, Natalya Singer, Leo P. banerjee, Smaranika barna, Tyler Copperwheat, Christopher M. Healy, brian Kiendrebeogo, R. Weizmann Kumar, Harsh Kumar, Ravi Pezzella, Marianna Sagués-Carracedo, Ana Sravan, Niharika bloom, Joshua S. Chen, Tracy X. Graham, Matthew Helou, George Laher, Russ R. Mahabal, Ashish A. Purdum, Josiah Anupama, G.C. barway, Sudhanshu basu, Judhajeet Raman, Dhananjay Roychowdhury, Tamojeet Division of Physics Mathematics and Astronomy California Institute of Technology PasadenaCA91125 United States School of Physics and Astronomy University of Minnesota MinneapolisMN55455 United States Department of Physics Indian Institute of Technology West Bengal Kharagpur721302 India Department of Physics Indian Institute of Technology Bombay Powai 400 076 India Department of Physics University of Wisconsin-Milwaukee P.O. Box 413 MilwaukeeWI53201 United States Joint Space-Science Institute University of Maryland College ParkMD20742 United States Department of Astronomy University of Maryland College ParkMD20742 United States Astrophysics Science Division NASA Goddard Space Flight Center MC 661 GreenbeltMD20771 United States Department of Physics and Earth Science University of Ferrara via Saragat 1 FerraraI-44122 Italy INFN Sezione di Ferrara via Saragat 1 FerraraI-44122 Italy INAF Osservatorio Astronomico d’Abruzzo via Mentore Maggini snc Teramo64100 Italy IPAC California Institute of Technology 1200 E. California Blvd PasadenaCA91125 United States DIRAC Institute Department of Astronomy University of Washington 3910 15th Avenue NE SeattleWA98195 United States The Oskar Klein Centre Department of Astronomy Albanova Stockholm University StockholmSE-106 91 Sweden Astrophysics Research Institute Liverpool John Moores University IC2 Liverpool Science Park 146 Brownlow Hill LiverpoolL3 5RF United Kingdom Deutsches Elektronen-Synchrotron DESY Platanenallee 6 Zeuthen15738 Germany Department of Physics Drexel University PhiladelphiaPA19104 United States Astrophysics Research Institute IC2 Liverpool Science Park 146 Brownlow Hill LiverpoolL3 5RF United Kingdom Laboratoire de Physique et de Chimie de l’Environnement Université Joseph KI-ZERBO Ouagadougou Burkina Faso Artemis Observatoire de la Côte d’Azur Université Côte d’Azur Boulevard de l’Observatoire NiceF-06304 France Department of Physics Indian Institute o

During the first half of the fourth ob.erving run (O4a) of the International Gravitational Wave Network (IGWN), the Zwicky Transient Facility (ZTF) conducted a systematic search for kilonova (KN) counterparts to binary neutron star (bNS) and neutron star–black hole (NSbH) merger candidates. Here, we present a comprehensive study of the five high-significance (False Alarm Rate less than 1 per year) bNS and NSbH candidates in O4a. Our follow-up campaigns relied on both target-of-opportunity ob.ervations (ToO) and re-weighting of the nominal survey schedule to maximize coverage. We describe the toolkit we have b.en developing, Fritz, an instance of SkyPortal, instrumental in coordinating and managing our telescope scheduling, candidate vetting, and follow-up ob.ervations through a user-friendly interface. ZTF covered a total of 2841 deg2 within the skymaps of the high-significance GW events, reaching a median depth of g ≈ 20.2 mag. We circulated 15 candidates, but found no viab.e KN counterpart to any of the GW events. based on the ZTF non-detections of the high-significance events in O4a, we used a bayesian approach, nimbus, to quantify the posterior probability of KN model parameters that are consistent with our non-detections. Our analysis favors KNe with initial absolute magnitude fainter than -16 mag. The joint posterior probability of a GW170817-like KN associated with all our O4a follow-ups was 64%. Additionally, we use a survey simulation software, simsurvey, to determine that our combined filtered efficiency to detect a GW170817-like KN is 36%, when considering the 5 confirmed astrophysical events in O3 (1 bNS and 4 NSbH events), along with our O4a follow-ups. Following Kasliwal et al. (2020), we derived joint constraints on the underlying KN luminosity function based on our O3 and O4a follow-ups, determining that no more than 76% of KNe fading at 1 mag day-1 can peak at a magnitude brighter than -17.5 mag. Copyright © 2024, The Authors. All rights reserved.

关键词： Gravity waves

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