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arXiv 2022年

作者： Álvarez Ballesteros, Miguel Ángel Chada, Neil K. Jasra, Ajay Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

In this article we consider the development of an unbiased estimator for the ensemble Kalman–Bucy filter (EnKBF). The EnKBF is a continuous-time filtering methodology which can be viewed as a continuous-time analogue of the famous discrete-time ensemble Kalman filter. Our unbiased estimators will be motivated from recent work [Rhee & Glynn 2010, [31]] which introduces randomization as a means to produce unbiased and finite variance estimators. The randomization enters through both the level of discretization, and through the number of samples at each level. Our estimator will be specific to linear and Gaussian settings, where we know that the EnKBF is consistent, in the particle limit N → ∞, with the KBF. We highlight this for two particular variants of the EnKBF, i.e. the deterministic and vanilla variants, and demonstrate this on a linear Ornstein–Uhlenbeck process. We compare this with the EnKBF and the multilevel (MLEnKBF), for experiments with varying dimension size. We also provide a proof of the multilevel deterministic EnKBF, which provides a guideline for some of the unbiased methods. © 2022, CC BY-NC-SA.

关键词： Continuous time systems

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Information and motor constraints shape melodic diversity across cultures

arXiv

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arXiv 2024年

作者： McBride, John M. Kim, Nahie Nishikawa, Yuri Saadakeev, Mekhmed Pearce, Marcus Tlusty, Tsvi Center for Algorithmic and Robotized Synthesis Institute for Basic Science Ulsan44919 Korea Republic of School of Business Administration Ulsan National Institute of Science and Technology Ulsan44919 Korea Republic of Department of Molecular Life Science Tokai University School of Medicine Kanagawa Japan Department of Biomedical Engineering Ulsan National Institute of Science and Technology Ulsan44919 Korea Republic of Cognitive Science Research Group School of Electronic Engineering & Computer Science Queen Mary University of London London United Kingdom Department of Clinical Medicine Aarhus University Aarhus Denmark Center for Soft and Living Matter Institute for Basic Science Ulsan44919 Korea Republic of Departments of Physics and Chemistry Ulsan National Institute of Science and Technology Ulsan44919 Korea Republic of

The number of possible melodies is unfathomably large, yet despite this virtually unlimited potential for melodic variation, melodies from different societies can be surprisingly similar. The motor constraint hypothesis accounts for certain similarities, such as scalar motion and contour shape, but not for other major common features, such as repetition, song length, and scale size. Here we investigate the role of information constraints arising from limitations on human memory in shaping these hallmarks of melodies. We measure determinants of information rate in 62 corpora of Folk melodies spanning several continents, finding multiple trade-offs that all act to constrain the information rate across societies. By contrast, 39 corpora of Art music from Europe (including Turkey) show longer, more complex melodies, and increased complexity over time, suggesting different cultural-evolutionary selection pressures in Art and Folk music, possibly due to the use of written versus oral transmission. Our parameter-free model predicts the empirical scale degree distribution using information constraints on scalar motion, melody length, and, most importantly, information rate. This provides strong evidence that information constraints during cultural transmission of music limit the number of notes in a scale, and proposes that preference for intermediate melodic complexity is a fundamental constraint on the cultural evolution of melody. © 2024, CC BY-NC-SA.

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Air Pollution Hotspot Detection and Source Feature Analysis using Cross-Domain Urban Data 21

Air Pollution Hotspot Detection and Source Feature Analysis ...

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29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2021

作者： Zhang, Yawen Hannigan, Michael Lv, Qin Department of Computer Science University of Colorado Boulder BoulderCO United States Department of Mechanical Engineering Environmental Engineering Program University of Colorado Boulder BoulderCO United States

ISBN: (纸本)9781450386647

Air pollution is a major global environmental health threat, in particular for people who live or work near air pollution sources. Areas adjacent to pollution sources often have high ambient pollution concentrations, and those areas are commonly referred to as air pollution hotspots. In this work, we explore the use of mobile sensing data to detect pollution hotspots. We propose a two-step approach to detect hotspots from unevenly sampled mobile sensing data. To contextualize the detected hotspots and discover potential pollution source characteristics, we explore a variety of cross-domain urban data and extract features from them for hotspot inference. Evaluation results using real-world mobile sensing air quality data as well as cross-domain urban data demonstrate the effectiveness of our approach in detecting and inferring pollution hotspots. © 2021 Owner/Author.

关键词： Air quality

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Joint Source Decomposition of Concurrent EEG-FMRI Data in Epilepsy and Control Groups 22

Joint Source Decomposition of Concurrent EEG-FMRI Data in Ep...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Harding, J. Mason Pourmotabbed, Haatef Li, Yamin Rogge-Obando, Kimberly Wang, Kate Goodale, Sarah E. Wang, Shiyu Bibro, Camden Allee, Bergen Martin, Caroline Morgan, Victoria L. Englot, Dario J. Chang, Catie Departments of Electrical and Computer Engineering Vanderbilt University United States Departments of Biomedical Engineering Vanderbilt University United States Departments of Computer Science Vanderbilt University United States Neuroscience Graduate Program Vanderbilt University United States Departments of Radiology Vanderbilt University Medical Center United States Departments of Neurological Surgery Vanderbilt University Medical Center United States Vanderbilt Memory and Alzheimer's Center Vanderbilt University Medical Center United States

ISBN: (纸本)9798331520526

EEG and fMRI are complementary, noninvasive technologies for investigating human brain function. These modalities have been used to uncover large-scale functional networks and their disruptions in clinical populations. Given the high temporal resolution of EEG and high spatial resolution of fMRI, integrating these modalities can provide a more holistic understanding of brain activity. This work explores a multimodal source decomposition technique for extracting shared modes of temporal variation between fMRI BOLD signals and EEG spectral power fluctuations in the resting state. The resulting components are then compared between patients with focal epilepsy and controls, revealing multimodal network differences between groups. © 2025 IEEE.

关键词： Brain mapping

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Extending the Usability of WebTA with Unified ASTs and Errors

Extending the Usability of WebTA with Unified ASTs and Error...

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Frontiers in Education (FIE) Conference

作者： Joseph Roy Teahen Daniel T Masker Leo C. Ureel Michelle Jarvie- Eggart Jon Sticklen Laura E. Brown Dept. of Computer Science Michigan Technological University Houghton USA Dept. of Cognitive and Learning Sciences Michigan Technological University Houghton USA Department of Engineering Fundamentals Michigan Technological University Houghton USA

Expanding autocritiquers to other languages requires the difficult task of obtaining and representing antipat-terns. Under the current framework, each language requires a unique Abstract Syntax Tree (AST) and error solution such that both the resulting AST and error summaries can be searched and presented to students. Often similar antipatterns arise that have to be rewritten in each language's own context. This paper proposes the creation of a generalized AST structure for an auto-critiquer under development, that can unify common code structures across similar languages while capturing differences in the languages. This work also proposes a standard for error message representation that can capture similar errors across language specific error messages. There are two motivations to standardizing AST and error message formats. Firstly, our solution allows for a standardized User Interface (UI) and easy integration of new languages with an identical on-boarding process for both professors and students. Secondly, reuse of antipatterns will allow us to skip the time-consuming step of constructing near-identical structural and logical antipattern queries as well as unifying novice based error feedback.

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Safety Helmet Detection Using Deep Learning: Implementation and Comparative Study Using YOLOv5, YOLOv6, and YOLOv7

Safety Helmet Detection Using Deep Learning: Implementation ...

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Green Energy, Computing and Sustainable Technology (GECOST), International Conference on

作者： Nigel Dale Then Yung W. K. Wong Filbert H. Juwono Zee Ang Sim Dept. of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia Computer Science Program University of Southampton Malaysia Johor Malaysia

ISBN: (纸本)9781665486644

The safety of construction site personnel is highly dependent on the adherence of personal protective equipment (PPE) wearing. Safety helmet monitoring has become a popular topic in recent years as a result of the success in the field of image processing. Deep learning (DL) is widely used in object detection tasks due to its ability to create features based on raw data. Constant improvements in the DL models have led to numerous successful outcomes in the implementation of safety helmet detection tasks. The performance of different DL algorithms from previous studies will be assessed and studied in this review paper. The YOLOv5s (small) model, YOLOv6s (small) model, and the YOLOv7 model will be trained and evaluated in this paper.

关键词： Personal protective equipment Deep learning Head Computational modeling Object detection Feature extraction Safety

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Diabetic Retinopathy Detection: Improving Accuracy Using Multiple Transfer Learning Features from Pre-trained Deep Learning Networks

Diabetic Retinopathy Detection: Improving Accuracy Using Mul...

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Green Energy, Computing and Sustainable Technology (GECOST), International Conference on

作者： Kelvin Ka Yung Tiong W. K. Wong Filbert H. Juwono I. M. Chew Dept. of Electrical and Computer Engineering Curtin University Malaysis Miri Malaysia Computer Science Program University of Southampton Malaysia Nusajaya Malaysia

ISBN: (纸本)9781665486644

Diabetic Retinopathy (DR) is a type of complications caused by diabetes. Patients with DR may experience worsening vision, blindness, and eye pain. To effectively address this disorder, DR must be identified and classified according to its severity. Therefore, automated diagnosis of fundus lesions is of great interest for DR early detection. The development of deep learning technology has provided a strong foundation for effective implementation of the automated detection system. In particular, transfer learning techniques have greatly benefited the research community to reduce computation and reuse trained features. In this paper, the outputs from the ”average pooling” and ”fully connected” layers are used as the features to the Support Vector Machine (SVM) classifier with Error Correction Output Code (ECOC). The proposed method outperforms the fine-tuned pre-trained networks in predicting the severity classes with an accuracy of 80.1%. This means that multiple features extracted from the pre-trained networks contribute to a better recognition process.

关键词： Support vector machines Deep learning Training Retinopathy Pain Transfer learning Blindness

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Enhancing reading skills through a video game mixing action mechanics and cognitive training (vol 6, pg 545, 2022)

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NATURE HUMAN BEHAVIOUR 2022年第6期6卷 896-896页

作者： Pasqualotto, Angela Altarelli, Irene De Angeli, Antonella Menestrina, Zeno Bavelier, Daphne Venuti, Paola Department of Psychology and Cognitive Science University of Trento Trento Italy Faculte de Psychologie et Science de l’Education University of Geneva Geneva Switzerland Université de Paris LaPsyDÉ CNRS Paris France Department of Information Engineering and Computer Science University of Trento Trento Italy

In modern societies, training reading skills is fundamental since poor-reading children are at high risk of struggling both at school and in life. Reading relies not only on oral language abilities but also on several executive functions. Considering their importance for literacy, training executive functions—particularly, attentional control has been suggested as a promising way of improving reading skills. For this reason, we developed a video game-based cognitive intervention aimed at improving several facets of executive functions. This game is composed of mini-games that apply gamified versions of standard clinical exercises linked through a game environment with action video game dynamics. Here, in a study involving 151 typically reading children, we demonstrated that after this general-domain behavioural intervention reading abilities, as well as attentional and planning skills, were significantly improved. Our results showed that training attentional control can translate into better reading efficiency, maintained at a follow-up test 6 months later.

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A Portable Multiscopic Camera for Novel View and Time Synthesis in Dynamic Scenes

arXiv

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arXiv 2022年

作者： Zhang, Tianjia Lau, Yuen-Fui Chen, Qifeng The Hong Kong University of Science and Technology Hong Kong The Individualized Interdisciplinary Program The Robotics Institute Hong Kong The Department of Mathematics Hong Kong The Department of Computer Science and Engineering The Department of Electronic and Computer Engineering Hong Kong

We present a portable multiscopic camera system with a dedicated model for novel view and time synthesis in dynamic scenes. Our goal is to render high-quality images for a dynamic scene from any viewpoint at any time using our portable multiscopic camera. To achieve such novel view and time synthesis, we develop a physical multiscopic camera equipped with five cameras to train a neural radiance field (NeRF) in both time and spatial domains for dynamic scenes. Our model maps a 6D coordinate (3D spatial position, 1D temporal coordinate, and 2D viewing direction) to view-dependent and time-varying emitted radiance and volume density. Volume rendering is applied to render a photo-realistic image at a specified camera pose and time. To improve the robustness of our physical camera, we propose a camera parameter optimization module and a temporal frame interpolation module to promote information propagation across time. We conduct experiments on both real-world and synthetic datasets to evaluate our system, and the results show that our approach outperforms alternative solutions qualitatively and quantitatively. Our code and dataset are available at https://***/. Copyright © 2022, The Authors. All rights reserved.

关键词： Cameras

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Optimized Transmission Strategy for UAV-RIS 2.0 Assisted Communications Using Rate Splitting Multiple Access

Optimized Transmission Strategy for UAV-RIS 2.0 Assisted Com...

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IEEE Conference on Vehicular Technology (VTC)

作者： Aamer Mohamed Huroon Yu-Chih Huang Li-Chun Wang Department of Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Institute of Communications Engineering National Yang Ming Chiao Tung University Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University

In this paper, we study the transmission strategy of a ground-based beyond diagonal reconfigurable intelligent surface (BD-RIS), a.k.a RIS 2.0, in a network where multiple unmanned aerial vehicles (UAVs) simultaneously transmit signals to the respective groups of users. It is assumed that each group is assigned subcarriers orthogonal to those assigned to other groups and rate splitting multiple access (RSMA) is adopted within each group. A corresponding mixed integer nonlinear programming problem (MINLP) is formulated, which aims to jointly optimize 1) allocation of BD-RIS elements to groups, 2) BD-RIS phase rotations, 3) rate allocation in RSMA, and 4) precoders. To solve the problem, we propose using generalized benders decomposition (GBD) augmented with a manifold-based algorithm. GBD splits the MINLP problem into two sub-problems, namely the primal and the relaxed master problem, which are solved alternately and iteratively. In the primal problem, we apply block coordinate descent (BCD) to manage the coupling of variables effectively. Moreover, we recognize the manifold structure in the phase rotation constraint of BD-RIS, enabling the Riemannian conjugate gradient (RCG). Simulation results demonstrate the effectiveness of the proposed approach in maximizing spectral efficiency.

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