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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Siwon Kim Sangdoo Yun Hwaran Lee Martin Gubri Sungroh Yoon Seong Joon Oh Department of Electrical and Computer Engineering Seoul National University NAVER AI Lab University of Luxembourg and Parameter Lab Department of Electrical and Computer Engineering Seoul National University and Interdisciplinary Program in Artificial Intelligence Seoul National University Parameter Lab and Tübingen AI Center University of Tübingen

The rapid advancement and widespread use of large language models (LLMs) have raised significant concerns regarding the potential leakage of personally identifiable information (PII). These models are often trained on vast quantities of web-collected data, which may inadvertently include sensitive personal data. This paper presents ProPILE, a novel probing tool designed to empower data subjects, or the owners of the PII, with awareness of potential PII leakage in LLM-based services. ProPILE lets data subjects formulate prompts based on their own PII to evaluate the level of privacy intrusion in LLMs. We demonstrate its application on the OPT-1.3B model trained on the publicly available Pile dataset. We show how hypothetical data subjects may assess the likelihood of their PII being included in the Pile dataset being revealed. ProPILE can also be leveraged by LLM service providers to effectively evaluate their own levels of PII leakage with more powerful prompts specifically tuned for their in-house models. This tool represents a pioneering step towards empowering the data subjects for their awareness and control over their own data on the web. The demo can be found here: https://***/research/propile

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Long short-term memory (LSTM) model-based reinforcement learning for nonlinear mass spring damper system control

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Procedia computer Science 2023年 216卷 213-220页

作者： Santo Wijaya Yaya Heryadi Yulyani Arifin Wayan Suparta Lukas Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta 55281 Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta 12930 Indonesia

The Neural Networks (NN) model which is incorporated in the control system design has been studied, and the results show better performance than the mathematical model approach. However, some studies consider that only offline NN model learning and does not use the online NN model learning directly on the control system. As a result, the controller's performance decreases due to changes in the system environment from time to time. The Reinforcement Learning (RL) method has been investigated intensively, especially Model-based RL (Mb-RL) to predict system dynamics. It has been investigated and performs well in making the system more robust to environmental changes by enabling online learning. This paper proposes online learning of local dynamics using the Mb-RL method by utilizing Long Short-Term Memory (LSTM) model. We consider Model Predictive Control (MPC) scheme as an agent of the Mb-RL method to control the regulatory trajectory objectives with a random shooting policy to search for the minimum objective function. A nonlinear Mass Spring Damper (NMSD) system with parameter-varying linear inertia is used to demonstrate the effectiveness of the proposed method. The simulation results show that the system can effectively control high-oscillating nonlinear systems with good performance.

关键词： Model-based Reinforcement Learning Random-Shooting Policy LSTM MPC Nonlinear System

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Microsphere-assisted quantitative phase microscopy:a review

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Light(Advanced Manufacturing) 2024年第1期5卷 131-150页

作者： Vahid Abbasian Tobias Pahl Lucie Hüser Sylvain Lecler Paul Montgomery Peter Lehmann Arash Darafsheh Department of Radiation Oncology Washington University School of Medicine in St.LouisSt.LouisMO 63110USA Imaging Science Program McKelvey School of EngineeringWashington University in St.LouisSt.LouisMO 63130USA Measurement Technology Group Faculty of Electrical Engineering and Computer ScienceUniversity of KasselWilhelmshöher Allee 7134121 KasselGermany ICube Research Institute INSA StrasbourgUniversitéde StrasbourgCNRS67000 StrasbourgFrance

Light microscopes are the most widely used devices in life and material sciences that allow the study of the interaction of light with matter at a resolution better than that of the naked *** microscopes translate the spatial differences in the intensity of the reflected or transmitted light from an object to pixel brightness differences in the digital ***,a phase microscope converts the spatial differences in the phase of the light from or through an object to differences in pixel *** microscopy,a phase-based approach,has found application in various *** interferometry has brought nanometric axial resolution,the lateral resolution in quantitative phase microscopy(QPM)has still remained limited by diffraction,similar to other traditional microscopy *** the resolution has been the subject of intense investigation since the invention of the microscope in the 17th *** the past decade,microsphere-assisted microscopy(MAM)has emerged as a simple and effective approach to enhance the resolution in light *** can be integrated with QPM for 3D label-free imaging with enhanced ***,we review the integration of microspheres with coherence scanning interference and digital holographic microscopies,discussing the associated open questions,challenges,and opportunities.

关键词： Phase microscopy Digital holographic microscopy Microsphere Resolution

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ControlDreamer: Blending Geometry and Style in Text-to-3D

arXiv

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arXiv 2023年

作者： Oh, Yeongtak Choi, Jooyoung Kim, Yongsung Park, Minjun Shin, Chaehun Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Korea Republic of

Recent advancements in text-to-3D generation have significantly contributed to the automation and democratization of 3D content creation. Building upon these developments, we aim to address the limitations of current methods in blending geometries and styles in text-to-3D generation. We introduce multi-view ControlNet, a novel depth-aware multi-view diffusion model trained on generated datasets from a carefully curated text corpus. Our multi-view ControlNet is then integrated into our two-stage pipeline, ControlDreamer, enabling text-guided generation of stylized 3D models. Additionally, we present a comprehensive benchmark for 3D style editing, encompassing a broad range of subjects, including objects, animals, and characters, to further facilitate research on diverse 3D generation. Our comparative analysis reveals that this new pipeline outperforms existing text-to-3D methods as evidenced by human evaluations and CLIP score metrics. Project page: https://***. © 2023, CC BY.

关键词： 3D modeling

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Probabilistic Concept Bottleneck Models

arXiv

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arXiv 2023年

作者： Kim, Eunji Jung, Dahuin Park, Sangha Kim, Siwon Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Korea Republic of

Interpretable models are designed to make decisions in a human-interpretable manner. Representatively, Concept Bottleneck Models (CBM) follow a two-step process of concept prediction and class prediction based on the predicted concepts. CBM provides explanations with high-level concepts derived from concept predictions;thus, reliable concept predictions are important for trustworthiness. In this study, we address the ambiguity issue that can harm reliability. While the existence of a concept can often be ambiguous in the data, CBM predicts concepts deterministically without considering this ambiguity. To provide a reliable interpretation against this ambiguity, we propose Probabilistic Concept Bottleneck Models (ProbCBM). By leveraging probabilistic concept embeddings, ProbCBM models uncertainty in concept prediction and provides explanations based on the concept and its corresponding uncertainty. This uncertainty enhances the reliability of the explanations. Furthermore, as class uncertainty is derived from concept uncertainty in ProbCBM, we can explain class uncertainty by means of concept uncertainty. Code is publicly available at https://***/ejkim47/prob-cbm. © 2023, CC BY-NC-SA.

关键词： Forecasting

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A Dynamic Model for Pauses in the Synthesized Speech of Mathematical Expressions in MathML 22

A Dynamic Model for Pauses in the Synthesized Speech of Math...

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Proceedings of the 14th International Conference on Education Technology and computers

作者： Adriana Souza Diamantino Freitas Bahia Federal Institute of Education Science and Technology Brazil and Doctoral Program in Digital Media Faculty of Engineering Faculty of Engineering of University of Porto Portugal Department of Electrical and Computer Engineering Faculty of Engineering of University of Porto Portugal

ISBN: (纸本)9781450397766

Voice synthesizers still present several challenges in the speech of mathematical content, as spoken mathematics has quite peculiar rules. In the synthesized speech, pauses help blind and visually impaired students identify the limits of mathematical operators and subexpressions. However, most studies on pauses define them uniformly or use simple punctuation marks to force the synthesizer to introduce pauses in certain parts of the expression. Speech is a dynamic process and pauses in expressions also need to be dynamic to make the synthetic speech of expressions more natural, as this can help in memorizing this type of content. This work proposed a dynamic model of pauses for mathematical expressions. Collected math expressions spoken by teachers were used to create the model. These expressions were useful for identifying patterns and creating a linear regression model. Blind and visually impaired students evaluated the model. Some improvements were observed when we compared the synthesized mathematical expressions with the model and Audiomath, the parameter tool used in this study.

关键词： Accessibility Synthesized Speech Mathematics Visual Impairment

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STATISTICAL INFERENCE FOR GAUSSIAN WHITTLE-MATÉRN FIELDS ON METRIC GRAPHS

arXiv

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arXiv 2023年

作者： Bolin, David Simas, Alexandre B. Wallin, Jonas Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Saudi Arabia Department of Statistics Lund University Sweden

Whittle-Matérn fields are a recently introduced class of Gaussian processes on metric graphs, which are specified as solutions to a fractional-order stochastic differential equation. Unlike earlier covariance-based approaches for specifying Gaussian fields on metric graphs, the Whittle-Matérn fields are well-defined for any compact metric graph and can provide Gaussian processes with differentiable sample paths. We derive the main statistical properties of the model class, particularly the consistency and asymptotic normality of maximum likelihood estimators of model parameters and the necessary and sufficient conditions for asymptotic optimality properties of linear prediction based on the model with misspecified parameters. The covariance function of the Whittle-Matérn fields is generally unavailable in closed form, and they have therefore been challenging to use for statistical inference. However, we show that for specific values of the fractional exponent, when the fields have Markov properties, likelihood-based inference and spatial prediction can be performed exactly and computationally efficiently. This facilitates using the Whittle-Matérn fields in statistical applications involving big datasets without the need for any approximations. The methods are illustrated via an application to modeling of traffic data, where allowing for differentiable processes dramatically improves the *** Codes Primary 62M30, secondary 62M05, 60G60, 35R02 Copyright © 2023, The Authors. All rights reserved.

关键词： Gaussian distribution

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Uncertain Fault Diagnosis of Grid-Connected PV Systems based Improved Data-Driven Paradigms 19

Uncertain Fault Diagnosis of Grid-Connected PV Systems based...

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19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022

作者： Dhibil, Khaled Mansouri, Majdi Bouzrara, Kais Nounou, Hazem Nounou, Mohamed National Engineering School of Monastir Research Laboratory of Automation Signal Processing and Image 5019 Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha23874 Qatar Prince Sultan Univer-sity Department of Mathematics and Sciences Riyadh11586 Saudi Arabia Chemical Engineering Program Texas A&m University at Qatar Doha23874 Qatar

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

The main idea behind this work is to diagnose Grid-Connected Photovoltaic (PV) systems. The uncertainty was treated by using the interval-valued data representation. The main interventions are threefold: first, interval ensemble techniques based on the combination of several models (SVM, KNN, and tree) into one improved model are proposed in order to isolate the different PV systems operating modes using interval raw data. Then, feature extraction and selection steps are proposed to improve the fault diagnosis results. Therefore, the interval KPCA (IKPCA) method is performed in order to extract and select the important characteristics. The proposed techniques were used to diagnose the GCPV system under different operating modes. The results demonstrated the superiority of the proposed methods. © 2022 IEEE.

关键词： Support vector machines

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High Spatial Resolution Terahertz Time-Domain Imaging of Murine Pancreatic Ductal Adenocarcinoma Tissues

High Spatial Resolution Terahertz Time-Domain Imaging of Mur...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Chakraborty, Debamitra Mills, Bradley N. Cheng, Jing Komissarov, Ivan Gerber, Scott Sobolewski, Roman Materials Science Graduate Program University of Rochester RochesterNY14627-1299 United States Laboratory for Laser Energetics University of Rochester RochesterNY14623 United States Department of Surgery University of Rochester Medical Center RochesterNY14642 United States Department of Electrical and Computer Engineering University of Rochester RochesterNY14627-0231 United States

We developed a terahertz time-domain spectroscopy system to generate high-resolution 2-dimensional images of paraffin-embedded murine pancreatic ductal adenocarcinoma tissues using the refractive index and absorption ... 详细信息

ISBN: (纸本)9781957171258

关键词： Tissue

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Solid State Electrolyte to Enable Structural Supercapacitors

Solid State Electrolyte to Enable Structural Supercapacitors

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IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS)

作者： Nandu Koripally Lulu Yao Jason D. Azoulay Tse Nga Ng Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA Materials Science Engineering Program University of California San Diego La Jolla CA USA Chemisty Department Georgia Institute of Technology Atlanta GA USA

ISBN: (数字)9798350383263

ISBN: (纸本)9798350383270

Supercapacitors are known for longer cycle life and faster charging rate compared to batteries. However, the energy density of supercapacitors requires improvement to expand their application space. To raise the energy density of structural supercapacitors, this work demonstrates a low resistance and mechanically strong solid-state electrolyte.

关键词： Resistance Supercapacitors Electrolytes Sensor systems Sensors Batteries

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