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Complementarity relations of a delayed-choice quantum eraser in a quantum circuit

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arXiv 2022年

作者： Chiou, Dah-Wei Hsu, Hsiu-Chuan Department of Electrical Engineering National Taiwan University Taipei10617 Taiwan Graduate Institute of Applied Physics National Chengchi University Taipei11605 Taiwan Department of Computer Science National Chengchi University Taipei11605 Taiwan

We propose a quantum circuit that emulates a delayed-choice quantum eraser via bipartite entanglement with the extension that the degree of entanglement between the two paired quantons is adjustable. This provides a broader setting to test complementarity relations between interference visibility and which-way distinguishability in the scenario that the which-way information is obtained through entanglement without direct contact with the quantum state for interference. The visibility-distinguishability relations are investigated from three perspectives that differ in how the which-way information is taken into consideration. These complementarity relations can be understood in terms of entropic uncertainty relations in the information-theoretic framework and the triality relation that incorporates single-particle and bipartite properties. We then perform experiments on the quantum computers provided by the IBM Quantum platform to verify the theoretical predictions. We also apply the delay gate to delay the measurement of the which-way information to affirm that the measurement can be made truly in the "delayed-choice" manner. Copyright © 2022, The Authors. All rights reserved.

关键词： Visibility

Unmasking Illusions: Understanding Human Perception of Audiovisual Deepfakes

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arXiv 2024年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei11529 Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu300044 Taiwan Department of Computer Science National Chengchi University Taipei11529 Taiwan Department of Electrical Engineering Institute of Communications Engineering National Tsing Hua University Hsinchu300044 Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan Institute of Information Science Academia Sinica Taipei11529 Taiwan

The emergence of contemporary deepfakes has attracted significant attention in machine learning research, as artificial intelligence (AI) generated synthetic media increases the incidence of misinterpretation and is difficult to distinguish from genuine content. Currently, machine learning techniques have been extensively studied for automatically detecting deepfakes. However, human perception has been less explored. Malicious deepfakes could ultimately cause public and social problems. Can we humans correctly perceive the authenticity of the content of the videos we watch? The answer is obviously uncertain;therefore, this paper aims to evaluate the human ability to discern deepfake videos through a subjective study. We present our findings by comparing human observers to five state-of-the-art deepfake detection models. To this end, we used gamification concepts to provide 110 participants (55 native English speakers and 55 non-native English speakers) with a web-based platform where they could access a series of 40 videos (20 real and 20 fake) to determine their authenticity. Each participant performed the experiment twice with the same 40 videos in different random orders. The videos are manually selected from the FakeAVCeleb dataset. We found that all AI models performed significantly better than humans when evaluated on the same 40 videos. The study also reveals that while deception is not impossible, humans tend to overestimate their detection capabilities. Our experimental results may help benchmark human versus machine performance, advance forensics analysis, and enable adaptive countermeasures. © 2024, CC BY.

关键词： Audiovisual

Vacuum Ultraviolet Light-Generating Metalens

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Vacuum Ultraviolet Light-Generating Metalens

2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO-PR 2022

作者： Tseng, Ming Lun Semmlinger, Michael Zhang, Ming Arndt, Catherine Huang, Tzu-Ting Yang, Jian Kuo, Hsin Yu Su, Vin-Cent Chen, Mu Ku Chu, Cheng Hung Cerjan, Benjamin Tsai, Din Ping Nordlander, Peter Halas, Naomi J. Institute of Electronics National Yang Ming Chiao Tung University Hsinchu300 Taiwan Research Center for Applied Sciences Academia Sinica Taipei115 Taiwan Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Laboratory for Nanophotonics Rice University HoustonTX77005 United States Applied Physics Graduate Program Rice University HoustonTX77005 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States Department of Physics National Taiwan University Taipei10617 Taiwan Department of Electrical Engineering National United University Miaoli36003 Taiwan Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong Department of Chemistry Rice University HoustonTX77005 United States

Vacuum ultraviolet (VUV) light is of great importance to many applications such as photochemistry and nanolithography. In this talk, a nonlinear metalens composed of ZnO nanoantennas for VUV generation and focusing wi...

ISBN: (纸本)9798350350012

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Understanding Audiovisual Deepfake Detection: Techniques, Challenges, Human Factors and Perceptual Insights

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arXiv 2024年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min The Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei11529 Taiwan The Institute of Information Systems and Applications National Tsing Hua University Hsinchu300044 Taiwan The Department of Computer Science National Chengchi University Taipei11529 Taiwan The Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Hsinchu300044 Taiwan The Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan The Institute of Information Science Academia Sinica Taipei11529 Taiwan

Deep Learning has been successfully applied in diverse fields, and its impact on deepfake detection is no exception. Deepfakes are fake yet realistic synthetic content that can be used deceitfully for political impersonation, phishing, slandering, or spreading misinformation. Despite extensive research on unimodal deepfake detection, identifying complex deepfakes through joint analysis of audio and visual streams remains relatively unexplored. To fill this gap, this survey first provides an overview of audiovisual deepfake generation techniques, applications, and their consequences, and then provides a comprehensive review of state-of-the-art methods that combine audio and visual modalities to enhance detection accuracy, summarizing and critically analyzing their strengths and limitations. Furthermore, we discuss existing open source datasets for a deeper understanding, which can contribute to the research community and provide necessary information to beginners who want to analyze deep learning-based audiovisual methods for video forensics. By bridging the gap between unimodal and multimodal approaches, this paper aims to improve the effectiveness of deepfake detection strategies and guide future research in cybersecurity and media integrity. © 2024, CC BY.

关键词： Audiovisual

Vacuum Ultraviolet Light-Generating Metalens

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Vacuum Ultraviolet Light-Generating Metalens

2022 International Conference on Optics-Photonics Design and Fabrication, ODF 2022

关键词： Nanoclay

Bayesian Parameter Inference for Partially Observed Diffusions using Multilevel Stochastic Runge-Kutta Methods

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arXiv 2023年

作者： Moral, Pierre Del Hu, Shulan Jasra, Ajay Ruzayqat, Hamza Wang, Xinyu Institut de Mathematiques de Bordeaux FR Bordeaux33405 France School of Mathematics and Statistics Wenlan School of Business China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Zhongnan University of Economics and Law CN China

We consider the problem of Bayesian estimation of static parameters associated to a partially and discretely observed diffusion process. We assume that the exact transition dynamics of the diffusion process are unavailable, even up-to an unbiased estimator and that one must time-discretize the diffusion process. In such scenarios it has been shown how one can introduce the multilevel Monte Carlo method to reduce the cost to compute posterior expected values of the parameters for a pre-specified mean square error (MSE);see [12]. These afore-mentioned methods rely on upon the Euler-Maruyama discretization scheme which is well-known in numerical analysis to have slow convergence properties. We adapt stochastic Runge-Kutta (SRK) methods for Bayesian parameter estimation of static parameters for diffusions. This can be implemented in high-dimensions of the diffusion and seemingly under-appreciated in the uncertainty quantification and statistics fields. For a class of diffusions and SRK methods, we consider the estimation of the posterior expectation of the parameters. We prove that to achieve a MSE of O(ϵ2), for ϵ > 0 given, the associated work is O(ϵ−2). Whilst the latter is achievable for the Milstein scheme, this method is often not applicable for diffusions in dimension larger than two. We also illustrate our methodology in several numerical examples. Copyright © 2023, The Authors. All rights reserved.

关键词： Diffusion

A Ready-to-Use Beam Steering Application Using a 3D-Printed System Integrated with a Dielectric Lens and Ultra-Wideband Antenna

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A Ready-to-Use Beam Steering Application Using a 3D-Printed ...

International Technical Conference o Circuits/Systems, computers and Communications (ITC-CSCC)

作者： Patchadaporn Sangpet Virote Pirajnanchai Suramate Chalermwisutkul Nonchanutt Chudpooti Deparment of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Deparment of Electronics and Telecommunication Engineering Faculty of Engineering Rajamangala University of Technology Thanyaburi Pathum Thani Thailand The Sirindhorn International Thai-German Graduate School of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Deparment of Industrial Physics and Medical Instrumentation Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350379051

ISBN: (纸本)9798350379068

The study introduces a 3D-printed lens assembly that has been rotated and equipped with an ultrawideband (UWB) antenna, designed specifically for beam-steering applications. The primary objective is to achieve beam steering by integrating the rotating 3D-printed lens assembly with a hemispherical lens and UWB antenna. The UWB antenna is constructed on an FR-4 substrate, while the hemispherical lens and the rotating 3D-printed lens assembly are composed of Acrylonitrile Butadiene Styrene (ABS). To allow the hemispherical lens to rotate to a certain angle, a collection of 3D-printed lenses that can be rotated was created. These lenses were marked with five predetermined angles: 300 degrees, 330 degrees, 0 degrees, 30 degrees, and 60 degrees. Both the modeling and experimental findings demonstrate that the UWB antenna with the 3D-printed rotatable hemispherical lens assembly is unable to guide the beam at a low frequency of 3.1 GHz. Nevertheless, at frequencies of 7.0 GHz and 10.6 GHz for intermediate and high frequencies, respectively, the beam can be directed based on the rotation of the hemispherical lens.

关键词： computers Beam steering Ultra wideband antennas Frequency measurement Dielectrics High frequency Assembly

Theoretical Modelling of Smart Meter Privacy Protection with Multi-meter Energy Routing 4

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Theoretical Modelling of Smart Meter Privacy Protection with...

4th IEEE International Conference on Smart Internet of Things, SmartIoT 2020

作者： Ergen, Cankut Gulbahar, Burhan Ozyegin University Graduate School of Engineering and Science MSc Program of Computer Science Istanbul34794 Turkey Ozyegin University Applied Research Center of Technology Products Istanbul34794 Turkey

ISBN: (纸本)9781728165141

Potential of intrusion during smart meter data collection is an important problem for household privacy in next-generation smart homes. There are various privacy protection methods such as hiding the real usage with renewable batteries (RBs) and encrypted communication. There are privacy threats due to the detection of the energy transfer through RBs while encryption-based methods result in high computational complexity. In this study, a novel privacy improvement mechanism is proposed based on the routing of energy through multiple smart meters among multiple houses in a neighborhood. The optimization of privacy based on uniform randomization is theoretically modeled and some heuristic solutions are presented. In this method, energy transfer is randomly distributed among multiple smart meters, real usage of a house is buffered with the RB and smart meter indices are randomly selected. In numerical simulations, privacy improvement is shown by utilizing the random selection of smart meter indices for randomizing the energy routing and the buffers of RBs. Furthermore, energy privacy (EP) metric validating the simulation results is defined. Energy routing with multiple smart meters provides a practical and low complexity privacy protection mechanism for next-generation smart grid architectures. © 2020 IEEE.

关键词： Smart meters

Strain-induced formation of self-assembled InGaN/GaN superlattices in nominal InGaN films grown by plasma-assisted molecular beam epitaxy

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Physical Review Materials 2021年第12期5卷 124606-124606页

作者： Kamruzzaman Khan Kai Sun Christian Wurm Kanak Datta Parag B. Deotare Elaheh Ahmadi Department of Materials Science and Engineering University of Michigan Ann Arbor Michigan 48109 USA Department of Electrical and Computer Engineering University of California Santa Barbara California 93106 USA Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor Michigan 48109 USA Applied Physics Program University of Michigan Ann Arbor Michigan 48109 USA

In this work, we report the spontaneous formation of superlattice structures in nominal InGaN films grown by plasma-assisted molecular beam epitaxy. A 700-nm-thick self-assembled In0.2Ga0.8N/GaN superlattice with excellent structural quality was achieved. Strain was studied as a possible driving force for the formation of self-assembled superlattice (SASL) structure by growth of InGaN on ZnO substrate using similar growth conditions. The SASL structures were optically characterized using photoluminescence spectroscopy. Structural characterization was conducted via transmission electron microscopy and atom probe tomography. High-resolution x-ray diffraction (XRD) and XRD reciprocal space map were utilized to determine the average composition and the degree of relaxation of InGaN films. We propose that the vertical phase separation observed in the SASL structure is caused by high-temperature growth and intensified by strain. This work provides a method for engineering strain and growth of thick InGaN films for a variety of applications including solar cells and photodetectors.

关键词： Nitrides Superlattices Molecular beam epitaxy Photoluminescence Time-resolved photoluminescence X-ray diffraction