Author: Caleb Martin

Authors: Ismaïl Baaj Paper: https://arxiv.org/abs/2408.07768 On Learning Capacities of Sugeno Integrals with Systems of Fuzzy Relational Equations Introduction In decision theory, the Sugeno integral is a widely used qualitative aggregation function. It relies on a set function called capacity, which models the importance or interaction of subsets of criteria. This integral finds applications in various fields such as uncertainty modeling, multicriteria aggregation, and game theory. Recent approaches have aimed to learn the capacity of a Sugeno integral based on training data. This paper introduces a method for learning such capacities using systems of fuzzy relational equations. Background Sugeno Integral The…

Authors: Chun Jie Chong、Chenxi Hou、Zhihao Yao、Seyed Mohammadjavad Seyed Talebi Paper: https://arxiv.org/abs/2408.07004 Introduction Large Language Models (LLMs) have become integral to various online applications, including chatbots, search engines, and translation tools. These models, trained on vast datasets, offer powerful capabilities but also raise significant privacy concerns. Users’ prompts, which may contain sensitive information, are processed and stored by cloud-based LLM providers and shared with third-party plugins. This paper introduces Casper, a browser extension designed to sanitize prompts by detecting and removing sensitive information before they are sent to LLM services. Background Online LLM Services Cloud-based LLM services like OpenAI’s ChatGPT and…

Authors: Dingyi Rong、Wenzhuo Zheng、Bozitao Zhong、Zhouhan Lin、Liang Hong、Ning Liu Paper: https://arxiv.org/abs/2408.06391 Introduction Accurately predicting enzyme functions using Enzyme Commission (EC) numbers is a significant challenge in bioinformatics. This process is crucial for understanding catalytic mechanisms, substrate specificities, and potential applications in various industries. Traditional experimental methods for determining EC numbers are time-consuming and resource-intensive, making computational methods particularly important. Traditional bioinformatics methods, such as sequence alignment-based approaches like BLASTp, rely heavily on pre-existing knowledge stored in databases. These methods struggle with novel proteins lacking close homologs and the complexity of protein evolution. Recent deep learning approaches offer promising alternatives but often…

Authors: Yubing Cao、Yongming Li、Liejun Wang、Yinfeng Yu Paper: https://arxiv.org/abs/2408.06906 Introduction Speech synthesis has seen significant advancements with the introduction of deep learning techniques, particularly Generative Adversarial Networks (GANs). This paper introduces VNet, a novel GAN-based vocoder designed to generate high-fidelity speech in real-time. The VNet model addresses the challenges of using full-band Mel spectrograms as input, which often result in over-smoothing and unnatural speech output. By incorporating a Multi-Tier Discriminator (MTD) and an asymptotically constrained adversarial loss, VNet aims to enhance the stability and quality of speech synthesis. Related Work GANs have revolutionized various domains, including speech synthesis. Traditional vocoder models,…