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Search for New Phenomena in Two-Body Invariant Mass Distributions Using Unsupervised Machine Learning for Anomaly Detection at √s = 13 Tev With the Atlas Detector
No full textSearches for new resonances are performed using an unsupervised anomaly-detection technique. Events with at least one electron or muon are selected from 140 fb−1 of pp collisions at √s ¼ 13 TeV recorded by ATLAS at the Large Hadron Collider. The approach involves training an autoencoder on data, and subsequently defining anomalous regions based on the reconstruction loss of the decoder. Studies focus on nine invariant mass spectra that contain pairs of objects consisting of one light jet or b jet and either one lepton (e; μ), photon, or second light jet or b jet in the anomalous regions. No significant deviations from the background hypotheses are observed. Limits on contributions from generic Gaussian signals with various widths of the resonance mass are obtained for nine invariant masses in the anomalous regions. © 2024 CERN, for the ATLAS Collaboration.IN2P3-CNRS; CC-IN2P3; 2014-2021; SCI/013; U.S. Department of Energy, USDOE; Alexander von Humboldt-Stiftung, AvH; Alabama Space Grant Consortium, ASGC; Brookhaven National Laboratory, BNL; CRC Health Group, CRC: 21/SCI/017; CRC Health Group, CRC; Canarie; Karlsruhe Institute of Technology, KIT; H2020 Marie Skłodowska-Curie Actions, MSCA; Multiple Sclerosis Scientific Research Foundation, MSSRF; CERN; Compute Canada; Göran Gustafssons Stiftelser; Natural Sciences and Engineering Research Council of Canada, NSERC; National Research Council Canada, NRC; Canada Foundation for Innovation, CFI; Science and Technology Facilities Council, STFC; Leverhulme Trust; European Research Council, ERC; European Cooperation in Science and Technology, COST; Australian Research Council, ARC; National Stroke Foundation, NSF; Neurosurgical Research Foundation, NRF; Helmholtz-Gemeinschaft, HGF; Minerva Foundation; Deutsche Forschungsgemeinschaft, DFG; Agence Nationale de la Recherche, ANR; Japan Society for the Promotion of Science, JSPS; Ministry of Education, Culture, Sports, Science and Technology, MEXT; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF; Danmarks Grundforskningsfond, DNRF; Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP; National Natural Science Foundation of China, NSFC; Ministerstvo Školství, Mládeže a Tělovýchovy, MŠMT; Fundação para a Ciência e a Tecnologia, FCT; Bundesministerium für Bildung und Forschung, BMBF; Chinese Academy of Sciences, CAS; Austrian Science Fund, FWF; Generalitat de Catalunya; Ministry of Science and Technology of the People's Republic of China, MOST; Agencia Nacional de Promoción Científica y Tecnológica, ANPCyT; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO; Bundesministerium für Wissenschaft, Forschung und Wirtschaft, BMWFW; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq; Nella and Leon Benoziyo Center for Neurological Diseases, Weizmann Institute of Science; Israel Science Foundation, ISF; Instituto Nazionale di Fisica Nucleare, INFN; Narodowe Centrum Nauki, NCN; Javna Agencija za Raziskovalno Dejavnost RS, ARRS; Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja, MPNTR; Ministerio de Ciencia e Innovación, MCIN; Centre National pour la Recherche Scientifique et Technique, CNRST; Staatssekretariat für Bildung, Forschung und Innovation, SBFI; British Columbia Knowledge Development Fund, BCKDF; European Regional Development Fund, ERDF; Defence Science Institute, DSI; Narodowa Agencja Wymiany Akademickiej, NAWA; Institutul de Fizică Atomică, IFA; Agencia Nacional de Investigación y Desarrollo, ANID; Royal Society of South Australia, RSSA; Irish Rugby Football Union, IRF
Accurate Transient Voltage Protection And Circuit Simulation For Analog Circuits With Memristors
No full textThis work focuses on utilizing memristors as protection components against voltage transients in analog integrated circuits. It is shown that memristors provide more precise protection compared to protection measures implemented with discrete components and take up less space. The proposed usage method, along with the designed circuits and the circuit simulations created for using memristors as protection elements, is demonstrated. To ensure protection, Transient Voltage Suppression (TVS) diodes will also be used as the initial protection component. © 2024 IEEE
STSeqGNN - Konum-Zaman Tabanli Hareket Karakterizasyonu
No full text2024 15th National Conference on Electrical and Electronics Engineering (ELECO)Konum-zaman tabanlı parmak-izi çıkarımı, güncel ve gezginlik (mobility) analizi alanında kritik kabul edilen bir konudur. TUL (Trajectory-user linking) adı altında, LBSN verisi üzerinde çalışan bir çok yapay sinir ağı modeli ortaya konmuştur. Fakat bildiğimiz kadarıyla GPS gibi sıralı konum-zaman verisi ile çalışan yapay sinir ağı üzerinde bir çalışma vücuda gelmemiştir. Bu çalışmada, sıralı konum-zaman verisi işleyebilen, STSeqGNN modelini sunuyoruz. Modelimiz, hem haritanın çizge yapısını, hem de verinin zaman boyutunu verimli ve başarılı bir şekilde işlemektedir. Modelimiz ayrıca güzergahların hareket bilgileri ve temel istatistiksel değerlerini de kullanabilmektedir. Test sonuçlarımızı k tahminde doğruluk metriği ile göstermiş bulunmaktayız. Modelimiz farklı veri kümelerinde %99’un üzerinde başarı yakalamaktadır.Spatio-temporal finger-printing is an active research topic, which is of critical importance in mobility analysis. Various neural network models working on LBSN data have been presented within the framework of TUL (Trajectory-user linking). However, to the best of our knowledge, there are no studies on neural networks for sequential spatio-temporal data like GPS in the literature. Therefore, here we present the STSeqGNN model, which can process sequential spatiotemporal data. Our model can process the graph structure of the map and the time dimension of the data. Our model also can use movement information and basic statistical values for better finger-printing performance. We present our test results with the evaluation metric accuracy at k. Our model achieves more than 99% accuracy across different datasets
From Listing Religions To Tabulating Nationalities: Ottoman Identity Policies and Enumeration Practices
No full textTwo features are attributed to modern state censuses: measurement and classification. Yet the real contribution of the modern census is tabulation, one of the most important and yet ignored part of statistics. The literature on censuses ignores tabulation, probably because it is not possible to separate it from statistics. Tabulation is not just a geometrical drawing, rather a tool for ordering, hierarchizing, simplification and exposing of data. Its most important function is to provide comparable data, both classifications and numbers. This study demonstrates the contribution of tabulation, by comparing premodern surveys (tahrir) to modern censuses (sayim) of the Ottoman Empire. The ethno-religious identities were listed in surveys but tabulated in censuses. This study argues that the emergence and use of tabulation emanates from the change in state-subject and state-minorities relations. In other words, it originates from the politicization of population and identity. The tabulation became a tool in the hands of the modernized empire to expose social boundaries and make comparable the ethno-religious identities
A Novel One-To-One Framework for Relative Camera Pose Estimation
No full textTo address the challenge of relative camera pose estimation, many permutation-invariant neural networks have been developed to process sparse correspondences with constant latency. These networks typically utilize an n-to-n framework, where n putative correspondences from the same image pairs are placed in distinct batch instances without any specific order. This uncorrelated set-type input structure does not sufficiently facilitate the extraction of contextual information for the correspondences. In this paper, we introduce a novel one-to-one framework designed to maximize context interaction within the network. Our framework prioritizes providing specialized context for each correspondence and enhancing the interaction of context data and correspondence data through a carefully designed input structure and network architecture schema. We conducted a series of experiments using various architectures within the one-to-one framework. Our results demonstrate that one-to-one networks not only matches but often surpasses the performance of traditional n-to-n networks, highlighting the one-to-one framework's significant potential and efficacy. To ensure a fair comparison, all one-to-one and n-to-n networks were trained on Google's Tensor Processing Units (TPUs). Notably, the memory capacity of a single TPUv4 device is sufficient to train one-to-one networks presented without generating TPU pods using multiple devices. © 2013 IEEE
Gain Enhancement of a mmWave Patch Antenna Array Having Limited Number of Input Ports
No full textmmWave sensors are rapidly being used in various fields due to their low power consumption, compact size, and cost-effectiveness, particularly for applications like target detection and tracking. This study investigates the gain enhancement of a patch antenna array operating in the mmWave frequency band of 76 GHz - 81 GHz, where commercial single-chip integrated circuits are available. We analyze the S-parameters and radiation patterns of a reference antenna in detail, comparing simulation results with experimental measurements to ensure accuracy. Acknowledging the challenges posed by mmWave patch antennas, we propose a straightforward method to enhance peak gain while preserving the capabilities of the commercial patch structure. Specifically, by twinning one of the input port signals using a power divider, we increase the number of elements in the array without altering the number of input ports. Our findings suggest that this technique can increase the peak gain by 1.3 dB and narrow the beam by 4°, resulting in practical benefits such as enhanced target detection range and accuracy in radar applications while mostly preserving the functioning of the system. © 2024 IEEE.Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TUBITAK, (5230061); Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TUBITA
Numerical and Experimental Investigation of Impact Performances of Cast and Stretched Polymethyl Methacrylate Panels
No full textIn this study, numerical and experimental analysis of impact performances of cast polymethyl methacrylate (CPMMA) and stretched polymethyl methacrylate (SPMMA) panels were conducted. First, the material characterization tests (tensile and compression tests as well as split Hopkinson pressure bar tests) of CPMMA and SPMMA materials were performed to determine the Johnson-Cook material model parameters to be used in the finite element models. Next, impact tests were performed for CPMMA and SPMMA panels. The velocity of the impactor before and after penetration was measured and the deformation patterns are recorded. Then, finite element models were generated using LS-DYNA to simulate the impact tests. Finally, the impact performances of the CPMMA and SPMMA panels were compared. It is found that CPMMA has better impact performance than SPMMA for the impact velocities investigated in this study.This study was supported by the Scientific and Technological Research Council of Turkiye (TUBITAK) project no. 20AG027 and 20AG008 under program no. 20AG001.Scientific and Technological Research Council of Turkiye (TUBITAK) [20AG027, 20AG008, 20AG001
Introduction To the Special Issue Near-Field Signal Processing: Algorithms, Implementations and Applications
No full textObservation of WZγ Production in pp Collisions at √s=13 TeV With the Atlas Detector
No full textThis Letter reports the observation of Formula Presented production and a measurement of its cross section using Formula Presented of proton-proton collision data recorded at a center-of-mass energy of 13 TeV by the ATLAS detector at the Large Hadron Collider. The Formula Presented production cross section, with both the Formula Presented and Formula Presented bosons decaying leptonically, Formula Presented (Formula Presented, Formula Presented), is measured in a fiducial phase-space region defined such that the leptons and the photon have high transverse momentum and the photon is isolated. The cross section is found to be Formula Presented. The corresponding standard model predicted cross section calculated at next-to-leading order in perturbative quantum chromodynamics and at leading order in the electroweak coupling constant is Formula Presented. The observed significance of the Formula Presented signal is Formula Presented, compared with an expected significance of Formula Presented. © 2024 CERN, for the ATLAS Collaboration.BSF-NSF; Agence Nationale de la Recherche, ANR; Australian Research Council, ARC; La Caixa Banking Foundation; Centre National pour la Recherche Scientifique et Technique, CNRST; Fundação para a Ciência e a Tecnologia, FCT; National Stroke Foundation, NSF; Narodowe Centrum Nauki, NCN; GenT Programs Generalitat Valenciana, Spain; CEA-DRF; ; H2020 Marie Skłodowska-Curie Actions, MSCA; Japan Society for the Promotion of Science, JSPS; INFN-CNAF; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO; Ministerio de Ciencia e Innovación, MICINN; Israel Science Foundation, ISF; Wallenberg Foundation; Leverhulme Trust; PROMETEO; Staatssekretariat für Bildung, Forschung und Innovation, SBFI; Javna Agencija za Raziskovalno Dejavnost RS, ARRS; ASGC (Taiwan), RAL; Generalitat de Catalunya; Instituto Nazionale di Fisica Nucleare, INFN; Bundesministerium für Wissenschaft, Forschung und Wirtschaft, BMWFW; Austrian Science Fund, FWF; Narodowa Agencja Wymiany Akademickiej, NAWA; Agencia Nacional de Investigación y Desarrollo, ANID; Bundesministerium für Bildung und Forschung, BMBF; Canada Foundation for Innovation, CFI; Helmholtz-Gemeinschaft, HGF; Danmarks Grundforskningsfond, DNRF; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq; Karlsruhe Institute of Technology, KIT; Canarie; GridKA; Göran Gustafssons Stiftelser; MIZŠ; Deutsche Forschungsgemeinschaft, DFG; Neurosurgical Research Foundation, NRF; Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja, MPNTR; U.S. Department of Energy, USDOE; European Cooperation in Science and Technology, COST; EU-ESF; RGC; Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP; Institutul de Fizică Atomică, IFA; Ministry of Science and Technology of the People's Republic of China, MOST; Natural Sciences and Engineering Research Council of Canada, NSERC; Nella and Leon Benoziyo Center for Neurological Diseases, Weizmann Institute of Science; Irish Rugby Football Union, IRFU; Cantons of Bern and Geneva; Chinese Academy of Sciences, CAS; Defence Science Institute, DSI; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF; MNE; Agencia Nacional de Promoción Científica y Tecnológica, ANPCyT; Minerva Foundation; European Research Council, ERC; Royal Society of South Australia, RSSA; European Regional Development Fund, ERDF; CERN; Ministerstvo Školství, Mládeže a Tělovýchovy, MŠMT; National Research Council Canada, NRC; Brookhaven National Laboratory, BNL; Alexander von Humboldt-Stiftung, AvH; Multiple Sclerosis Scientific Research Foundation, MSSRF; British Columbia Knowledge Development Fund, BCKDF; Ministry of Education, Culture, Sports, Science and Technology, MEXT; National Natural Science Foundation of China, NSFC; DNSRC, (IN2P3-CNRS); Science and Technology Facilities Council, STFC, (ST/X005984/1); Science and Technology Facilities Council, STFC; NDGF, (CC-IN2P3); Norwegian Financial Mechanism, (2014-2021); Compute Canada, (21/SCI/017); Compute Canada; UNCE, (SCI/013