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A Conceptual Exploration of Generative AI-Induced Cognitive Dissonance and its Emergence in University-Level Academic Writing

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arXiv 2025年

作者： Seran, Carl Errol Tan, Myles Joshua Toledo Karim, Hezerul Abdul AlDahoul, Nouar Biology Program College of Arts and Sciences University of St. La Salle Bacolod Philippines Department of Natural Sciences College of Arts and Sciences University of St. La Salle Bacolod Philippines Faculty of Education University of the Philippines Open University Los Baños Philippines Department of Electrical and Computer Engineering Herbert Wertheim College of Engineering University of Florida GainesvilleFL United States Department of Epidemiology College of Public Health and Health Professions College of Medicine University of Florida GainesvilleFL United States Department of Chemical Engineering College of Engineering and Technology University of St. La Salle Bacolod Philippines Department of Electronics Engineering College of Engineering and Technology University of St. La Salle Bacolod Philippines Yo-Vivo Corporation Bacolod Philippines Faculty of Engineering Multimedia University Persiaran Multimedia Cyberjaya Malaysia Department of Computer Science Division of Science New York University Abu Dhabi Abu Dhabi United Arab Emirates

The integration of Generative Artificial Intelligence (GenAI) intouniversity-level academic writing presents both opportunities and challenges,particularly in relation to cognitive dissonance (CD). This work explores howGenAI serves as both a trigger and amplifier of CD, as students navigateethical concerns, academic integrity, and self-efficacy in their writingpractices. By synthesizing empirical evidence and theoretical insights, weintroduce a hypothetical construct of GenAI-induced CD, illustrating thepsychological tension between AI-driven efficiency and the principles oforiginality, effort, and intellectual ownership. We further discuss strategiesto mitigate this dissonance, including reflective pedagogy, AI literacyprograms, transparency in GenAI use, and discipline-specific task *** approaches reinforce critical engagement with AI, fostering a balancedperspective that integrates technological advancements while safeguarding humancreativity and learning. Our findings contribute to ongoing discussions on AIin education, self-regulated learning, and ethical AI use, offering aconceptual framework for institutions to develop guidelines that align AIadoption with academic values. © 2025, CC BY.

关键词： Generative adversarial networks

Large-Scale Multidisciplinary Design Optimization of an eVTOL Aircraft Using Comprehensive Analysis

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Large-Scale Multidisciplinary Design Optimization of an eVTO...

AIAA SciTech Forum and Exposition, 2023

作者： Sarojini, Darshan Ruh, Marius L. Sperry, Mark Yan, Jiayao Fletcher, Andrew Joshy, Anugrah Jo Scotzniovsky, Luca Ivanov, Alexander Orndorff, Nicholas Gandarillas, Victor Asher, Isaac Chambers, Jeff Gill, Hyunjune Lee, Seongkyu Cheng, Zeyu Rodriguez, Gabriel Zhao, Shuofeng Mi, Chunting Nascenzi, Thomas Cuatt, Timothy Winter, Tyler Guibert, Alexandre Cronk, Ashley Kim, Alicia Meng, Shirley Hwang, John T. Department of Mechanical and Aerospace Engineering University of California San Diego 9500 Gilman Dr La Jolla CA92093 United States Flight Physics Aurora Flight Sciences A Boeing Company 9950 Wakeman Drive ManassasVA20110 United States Engineering Department Aurora Flight Sciences A Boeing Company 9950 Wakeman Drive ManassasVA20110 United States Department of Mechanical and Aerospace Engineering University of California Davis 1 Shields Avenue DavisCA95616 United States Department of Electrical and Computer Engineering San Diego State University 5500 Campanile Dr San Diego92182 United States Research and Development M4 Engineering Inc 4020 Long Beach Blvd # 2 Long BeachCA90807 United States Structural Engineering Department University of California San Diego 9500 Gilman Dr La Jolla CA92093 United States Materials Science and Engineering Program University of California San Diego 9500 Gilman Dr La Jolla CA92093 United States Department of NanoEngineering University of California San Diego 9500 Gilman Dr La Jolla CA92093 United States

ISBN: (数字)9781624106996

ISBN: (纸本)9781624106996

This paper presents a framework under development for enabling large-scale multi-fidelity modeling and optimization of electric vertical takeoff and landing concepts (eVTOL). The key features of the framework are a geometry-centric approach to multidisciplinary design optimization (MDO), a modular functional-form representation of the disciplines involved in aircraft design, and fully automated derivative computations thereby allowing efficient gradient-based optimization. The framework is first presented in a general manner agnostic to the vehicle concept or the physics-based analyses used. The key disciplines involved in the design of eVTOL aircraft and the couplings between the disciplines are described. The framework is then applied to the design of the NASA lift-plus-cruise concept. Low-fidelity solvers of aerodynamics, propulsion, structural estimation, acoustics, powertrain, and battery are coupled together. The optimization considers 108 design variables and 16 constraints. It is shown that MDO considering geometric variables results in a design with lower gross mass than when the geometric variables are not considered. The optimization turnaround time of 30 minutes on a standard workstation demonstrates the capabilities of fully-coupled large-scale MDO using gradient-based optimization. The framework is under development and an open-source version will be released in the near future. © 2023, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Geometry

Opportunistic spectrum access: Does maximizing throughput minimize file transfer time?

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arXiv 2021年

作者： Hu, Jie Doshi, Vishwaraj Eun, Do Young The Department of Electrical and Computer Engineering The Operations Research Graduate Program North Carolina State University RaleighNC United States

The Opportunistic Spectrum Access (OSA) model has been developed for the secondary users (SUs) to exploit the stochastic dynamics of licensed channels for file transfer in an opportunistic manner. Common approaches to design channel sensing strategies for throughput-oriented applications tend to maximize the long-term throughput, with the hope that it provides reduced file transfer time as well. In this paper, we show that this is not correct in general, especially for small files. Unlike prior delay-related works that seldom consider the heterogeneous channel rate and bursty incoming packets, our work explicitly considers minimizing the file transfer time of a single file consisting of multiple packets in a set of heterogeneous channels. We formulate a mathematical framework for the static policy, and extend to dynamic policy by mapping our file transfer problem to the stochastic shortest path problem. We analyze the performance of our proposed static optimal and dynamic optimal policies over the policy that maximizes long-term throughput. We then propose a heuristic policy that takes into account the performance-complexity tradeoff and an extension to online implementation with unknown channel parameters, and also present the regret bound for our online algorithm. We also present numerical simulations that reflect our analytical results. Copyright © 2021, The Authors. All rights reserved.

关键词： Stochastic systems

Reconstructing dynamics from sparse observations with no training on target system

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arXiv 2024年

作者： Zhai, Zheng-Meng Huang, Jun-Yin Stern, Benjamin D. Lai, Ying-Cheng School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Doctor of Physical Therapy Program Tufts University School of Medicine 101 E Washington St Suite 950 PhoenixAZ85004 United States Department of Physics Arizona State University TempeAZ85287 United States

In applications, an anticipated situation is where the system of interest has never been encountered before and sparse observations can be made only once. Can the dynamics be faithfully reconstructed from the limited observations without any training data? This problem defies any known traditional methods of nonlinear time-series analysis as well as existing machine-learning methods that typically require extensive data from the target system for training. We address this challenge by developing a hybrid transformer and reservoir-computing machine-learning scheme. The key idea is that, for a complex and nonlinear target system, the training of the transformer can be conducted not using any data from the target system, but with essentially unlimited synthetic data from known chaotic systems. The trained transformer is then tested with the sparse data from the target system. The output of the transformer is further fed into a reservoir computer for predicting the long-term dynamics or the attractor of the target system. The power of the proposed hybrid machine-learning framework is demonstrated using a large number of prototypical nonlinear dynamical systems, with high reconstruction accuracy even when the available data is only 20% of that required to faithfully represent the dynamical behavior of the underlying system. The framework provides a paradigm of reconstructing complex and nonlinear dynamics in the extreme situation where training data does not exist and the observations are random and sparse. © 2024, CC BY-NC-ND.

关键词： Adversarial machine learning

Low power In Memory Computation with Reciprocal Ferromagnet/Topological Insulator Heterostructures

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arXiv 2022年

作者： Vakili, Hamed Ganguly, Samiran de Coster, George J. Neupane, Mahesh R. Ghosh, Avik W. Department of Physics University of Virginia CharlottesvilleVA22904 United States Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA22904 United States Department of Electrical and Computer Engineering Virginia Commonwealth University RichmondVA23284 United States DEVCOM Army Research Laboratory 2800 Powder Mill Rd AdelphiMD20783 United States Materials Science and Engineering Program University of California RiversideCA92521 United States

The surface state of a 3D topological insulator (3DTI) is a spin-momentum locked conductive state, whose large spin hall angle can be used for the energy-efficient spin orbit torque based switching of an overlying ferromagnet (FM). Conversely, the gated switching of the magnetization of a separate FM in or out of the TI surface plane, can turn on and off the TI surface current. By exploiting this reciprocal behaviour, we can use two FM/3DTI heterostructures to design an integrated 1-Transistor 1-magnetic tunnel junction random access memory unit (1T1MTJ RAM) for an ultra low power Processing-in-Memory (PiM) architecture. Our calculation involves combining the Fokker-Planck equation with the Non-equilibrium Green Function (NEGF) based flow of conduction electrons and Landau-Lifshitz-Gilbert (LLG) based dynamics of magnetization. Our combined approach allows us to connect device performance metrics with underlying material parameters, which can guide proposed experimental and fabrication efforts. © 2022, CC BY-NC-SA.

关键词： Magnetization

Nonsaturated large magnetoresistance and transport dynamics in a n-doped cryogenic WSe2 field-effect device

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Physical Review B 2025年第12期111卷 125307-125307页

作者： Wei-Chen Lin Ching-Chen Yeh Jia-Zhu Zou Nhat Anh Nguyen Phan Inayat Uddin Hai Yen Le Thi Kenji Watanabe Takashi Taniguchi Gil-Ho Kim Department of Engineering and System Science National Tsing Hua University Hsinchu 300 Taiwan Taiwan International Graduate Program Academia Sinica Taipei 115 Taiwan Department of Physics National Taiwan University Taipei 106 Taiwan Department of Electrical and Computer Engineering Sungkyunkwan University (SKKU) Suwon 164 Republic of Korea Sungkyunkwan Advanced Institute of Nano Technology (SAINT) Sungkyunkwan University (SKKU) Suwon 16419 Republic of Korea Research Center for Electronic and Optical Materials National Institute for Materials Science 1-1 Namiki Tsukuba 305-0044 Japan Research Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba 305-0044 Japan

Interest in the two-dimensional (2D) semiconducting transition metal dichalcogenides (TMDs) continues to intensify, driven by their suitable band gaps to supplant silicon as next-generation semiconductor materials. Among various TMDs, tungsten diselenide (WSe2) is renowned for its superior electrical properties in carrier density and mobility under ambient conditions. Despite its notable attributes, the behavior of monolayer WSe2 in the electron-doped regime under cryogenic conditions remains largely uncharted, particularly concerning its magnetotransport properties. In this study, we reveal the transport mechanisms of monolayer WSe2 from high temperatures down to the cryogenic regime. As evident by Efros–Shklovskii variable-range hopping (E-S VRH) in the cryogenic regime, strong Coulomb interactions arise between electrons. Above 8 K, an uncommon nonsaturated quadratic large magnetoresistance (MR) can be explained by the wave-function shrinkage model, which is consistent with the E-S VRH transport mechanism. Notably, the nonsaturated quadratic large MR shows a magnitude up to 1740% at 13 T. These findings underscore the potential applications for monolayer WSe2 in cryogenic field-effect devices, magnetic sensors, and memory devices and mark a significant advance in magnetotransport research.

关键词： Monolayers

Estimating Causal Effects using a Multi-task Deep Ensemble

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arXiv 2023年

作者： Jiang, Ziyang Hou, Zhuoran Liu, Yiling Ren, Yiman Li, Keyu Carlson, David Department of Civil and Environmental Engineering Duke University DurhamNC United States Department of Biostatistics and Bioinformatics Duke University School of Medicine DurhamNC United States Program in Computational Biology and Bioinformatics Duke University School of Medicine DurhamNC United States Department of Economics University of Michigan Ross School of Business Ann ArborMI United States Department of Electrical and Computer Engineering Duke University DurhamNC United States Department of Computer Science Duke University DurhamNC United States

A number of methods have been proposed for causal effect estimation, yet few have demonstrated efficacy in handling data with complex structures, such as images. To fill this gap, we propose Causal Multi-task Deep Ensemble (CMDE), a novel framework that learns both shared and group-specific information from the study population. We provide proofs demonstrating equivalency of CDME to a multi-task Gaussian process (GP) with a coregionalization kernel a priori. Compared to multi-task GP, CMDE efficiently handles high-dimensional and multi-modal covariates and provides pointwise uncertainty estimates of causal effects. We evaluate our method across various types of datasets and tasks and find that CMDE outperforms state-of-the-art methods on a majority of these tasks. © 2023, CC BY.

关键词： Data handling

Renderable Street View Map-Based Localization: Leveraging 3D Gaussian Splatting for Street-Level Positioning

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Renderable Street View Map-Based Localization: Leveraging 3D...

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Howoong Jun Hyeonwoo Yu Songhwai Oh Artificial Intelligence (IPAI) Seoul National University and Automation and Systems Research Institute (ASRI) and Sequor Robotics Inc. Seoul Korea Republic of Department of Intelligent Robotics & Mechanical Engineering Sungkyunkwan University (SKKU) Suwon-si Korea Republic of Department of Electrical and Computer Engineering (ECE) & Interdisciplinary Program in Artificial Intelligence (IPAI) Seoul National University and Automation and Systems Research Institute (ASRI) and Sequor Robotics Inc. Seoul Korea Republic of

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In this paper, we introduce a new method that first utilizes 3D Gaussian splatting in street-level localization problem. Robust localization with street-level real-world images such as street view is a major issue for autonomous vehicle, augmented reality (AR) navigation, and outdoor mobile robots. The objective is to determine the position and orientation of a query image that matches a street view database composed of RGB images. However, given the limited information available in the street view images, accurately determining the location solely based on this data presents a significant challenge. To address this challenge, we propose a novel method called renderable street view map-based localization (RSM-Loc). This approach enhances the localization process by augmenting 2D street view images into a renderable 3D map using 3D Gaussian splatting, to resolve street-level localization problems. Upon receiving a query RGB image without geometry information, the proposed method renders 2D images from a pre-made renderable map and compares image pose similarities between the rendered images and the query image. Through iterations of this process, the proposed method eventually estimates the pose of the given query image. The experimental results demonstrate that RSM-Loc outperforms the baselines with neural-field-based localization. Additionally, we conduct deep analysis on the proposed method to show that our method can serve as a new concept for the street-level localization problem.

关键词： Location awareness Visualization Matched filters Three-dimensional displays Image resolution Navigation Noise measurement Mobile robots Intelligent robots Image reconstruction

Demonstration of a polarization-agnostic geometric phase in nonlocal metasurfaces

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Demonstration of a polarization-agnostic geometric phase in ...

Conference on Lasers and Electro-Optics (CLEO)

作者： Adam C. Overvig Y. Kasahara Andrea Alù Photonics Initiative Advanced Science Research Center City University of New York New York NY USA Department of Electrical and Computer Engineering The University of Texas at Austin Austin TX USA Physics Program Graduate Center of the City University of New York New York NY USA

We theoretically propose and experimentally demonstrate nonlocal metasurfaces rationally designed using symmetry-breaking principles to manipulate the optical wavefront with a geometric phase selective to custom ellip... 详细信息

ISBN: (纸本)9781957171258

关键词： Circular polarization Constructive interference Fano resonance Polarization Polarized light Thermal emission