Author: Logan Hall

Authors: Qiushuo Cheng、Catherine Morgan、Arindam Sikdar、Alessandro Masullo、Alan Whone、Majid Mirmehdi Paper: https://arxiv.org/abs/2408.08182 Introduction Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by symptoms such as slowness of movement and gait dysfunction. These symptoms fluctuate throughout the day but progress slowly over the years. Current treatments focus on symptom improvement, with no available therapies to modify the disease’s course. The development of disease-modifying treatments (DMTs) is hindered by the lack of sensitive, frequent, and objective biomarkers to measure PD progression. The gold-standard clinical rating scale, MDS-UPDRS, includes subjective questionnaires and clinician ratings of scripted activities, which are limited to clinical settings and…

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Authors: Victor Verreet、Lennert De Smet、Luc De Raedt、Emanuele Sansone Paper: https://arxiv.org/abs/2408.08133 Introduction The field of neuro-symbolic (NeSy) artificial intelligence (AI) aims to combine the perceptive capabilities of neural networks with the reasoning capabilities of symbolic systems. Neural probabilistic logic systems follow this paradigm by integrating neural networks with probabilistic logic. This combination allows for better generalization, handling of uncertainty, and reduced training data requirements compared to pure neural networks. However, the main challenge lies in learning, as the learning signal for the neural network must propagate through the probabilistic logic component. Existing methods for propagating the learning signal include exact propagation…

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Authors: Aisha Khatun、Daniel G. Brown Paper: https://arxiv.org/abs/2408.07904 Introduction Fiction writing is a complex and creative process that involves crafting an engaging plot, developing coherent narratives, and employing various literary devices. With the advent of Large Language Models (LLMs), there has been a surge in their application for computational creativity, including fictional story generation. However, the suitability of LLMs for generating fiction remains questionable. This study investigates whether LLMs can maintain a consistent state of the world, which is essential for generating coherent and believable fictional narratives. Related Work Automated story generation has been a topic of interest long before the…

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Authors: Jiahao Wu、Lu Xiao、Chao Wang、Rui Peng、Kaiqiang Xiong、Ronggang Wang Paper: https://arxiv.org/abs/2408.06543 Introduction In recent years, significant progress has been made in 3D reconstruction technology, particularly with the advent of neural radiance fields (NeRF). However, reconstructing high dynamic range (HDR) radiance fields from low dynamic range (LDR) images remains a challenge. Traditional methods either rely on grids and spherical harmonics, which are memory-intensive, or use implicit multi-layer perceptrons (MLPs), which are slow and prone to overfitting. This paper introduces High Dynamic Range Gaussian Splatting (HDRGS), a novel method that leverages Gaussian Splatting for efficient and high-quality 3D HDR reconstruction. Method Preliminary HDRGS…

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Authors: Jian Xu、Delu Zeng、John Paisley Paper: https://arxiv.org/abs/2408.06699 Introduction Bayesian learning has seen significant advancements, with models like Student-t Processes (TPs) proving effective for handling heavy-tailed distributions and outlier-prone datasets. However, the computational complexity of TPs limits their scalability. Sparse Variational Student-t Processes (SVTPs) were developed to reduce computational demands while preserving the flexibility and robustness of TPs, making them suitable for large and varied datasets. Traditional gradient descent methods like Adam may not fully exploit the parameter space geometry, potentially leading to slower convergence and suboptimal performance. This paper introduces natural gradient methods from information geometry to optimize SVTPs, leveraging…

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Authors: Nicholas Gray Paper: https://arxiv.org/abs/2408.06736 Introduction In the modern world, algorithms play an increasingly significant role in our daily lives, from recommending entertainment to making critical decisions such as loan approvals and criminal sentencing. This growing reliance on algorithms brings with it numerous risks, ranging from minor irritations to severe injustices and catastrophes. Despite these risks, society continues to embrace “algorithm appreciation,” often trusting automated systems over human judgment. This paper explores the importance of incorporating risk and uncertainty into AI systems to address ethical concerns and ensure that algorithms make humane decisions. The Numbers of the Future Charles Babbage,…

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