Author: Aubrey Price

Authors: Neha R. Gupta、Jessica Hullman、Hari Subramonyam Paper: https://arxiv.org/abs/2408.10239 Introduction Machine learning (ML) model evaluation traditionally focuses on estimating prediction errors using quantifiable metrics. However, as ML systems grow in complexity, evaluations must become multifaceted, incorporating methods like A/B testing, adversarial testing, and comprehensive audits. Ethical concerns during the ML development lifecycle, particularly during evaluation, are often overlooked. This paper presents a conceptual framework to balance information gain against potential ethical harms in ML evaluations, drawing parallels with practices in clinical trials and automotive crash testing. Related Works Ethical AI The literature on ethical AI identifies several key values: 1. Non-maleficence:…

Authors: Khushi Jaiswal、Ievgeniia Kuzminykh、Sanjay Modgil Paper: https://arxiv.org/abs/2408.10788 Introduction In today’s rapidly evolving technological landscape, Artificial Intelligence (AI) is transforming industries and creating a significant demand for skilled professionals. According to LinkedIn News (2023), roles such as Machine Learning Engineers have seen a 74% increase in demand over the past six years. Similarly, LinkedIn News (2024) identified Artificial Intelligence Engineers as one of the top 10 careers in the UK. Despite this growing demand, there is a notable skills gap between what universities teach and what industries require. This study aims to investigate this gap by comparing the skills taught in…

Authors: Yifan Wang、Di Huang、Weicai Ye、Guofeng Zhang、Wanli Ouyang、Tong He Paper: https://arxiv.org/abs/2408.10178 Introduction 3D surface reconstruction is a pivotal area of research in computer vision, with applications spanning video games, augmented reality, and virtual reality systems. The goal is to recover the underlying 3D geometry from images, typically represented as meshes. Traditional methods, such as Multi-View Stereo (MVS), have been foundational but often struggle with noise and incomplete reconstructions. Recent advancements have introduced neural surface reconstruction techniques, leveraging neural networks to represent and optimize 3D scenes. NeuRodin is a novel two-stage framework designed to address the limitations of existing Signed Distance Function…

Authors: Yusuke Ide、Yuto Nishida、Miyu Oba、Yusuke Sakai、Justin Vasselli、Hidetaka Kamigaito、Taro Watanabe Paper: https://arxiv.org/abs/2408.09639 Leveraging Large Language Models for Grammatical Acceptability Judgments Introduction The grammatical knowledge of language models (LMs) is often evaluated using benchmarks of linguistic minimal pairs, where models are presented with pairs of acceptable and unacceptable sentences and are required to judge which is acceptable. The dominant approach has been to calculate and compare the probabilities of paired sentences using LMs. However, this method has limitations, and large language models (LLMs) have not been thoroughly examined in this context. This study investigates how to make the most of LLMs’ grammatical…

Authors: Taharim Rahman Anon、Jakaria Islam Emon Paper: https://arxiv.org/abs/2408.09371 Introduction The rapid advancement in artificial intelligence (AI) has led to significant progress in image generation technologies, resulting in highly realistic synthetic images. While these advancements bring numerous benefits, they also present significant challenges in misinformation and digital forensics. Maintaining the integrity of visual media relies crucially on the ability to differentiate between AI-generated and real images. This study introduces a novel detection framework adept at robustly identifying images produced by cutting-edge generative AI models, such as DALL-E 3, MidJourney, and Stable Diffusion 3. Related Work Several studies have focused on developing…

Authors: Robert J. Moss Paper: https://arxiv.org/abs/2408.08899 Introduction Background Large Language Models (LLMs) have become integral in various applications, from educational tools to corporate assistants. Ensuring these models are aligned with ethical guidelines to avoid generating harmful or toxic content is paramount. Despite rigorous training and ethical guidelines, alignment failures can still occur, necessitating robust testing methods to uncover potential vulnerabilities. Problem Statement The challenge lies in eliciting harmful behaviors from LLMs to ensure their robustness and alignment. Traditional red-teaming approaches, which involve manually engineered prompt injections, have limitations and can be easily mitigated by developers. Automated adversarial attacks, particularly on…

Authors: Yuanjian Xu、Anxian Liu、Jianing Hao、Zhenzhuo Li、Shichang Meng、Guang Zhang Paper: https://arxiv.org/abs/2408.10111 Unveiling Financial Time Series Regularities with PLUTUS: A Deep Dive Introduction Financial time series analysis is a cornerstone of modern finance and economics, underpinning decision-making processes across various sectors. However, the inherent complexities of financial data—characterized by non-linearity, non-stationarity, heteroskedasticity, and high noise levels—pose significant challenges for traditional modeling techniques. These complexities often lead to volatile data that lack consistent patterns, making it difficult for conventional statistical methods and even advanced deep learning models to achieve high predictive accuracy. Inspired by the success of large language models (LLMs) in natural…

Authors: Rui Wang、Mengshi Qi、Yingxia Shao、Anfu Zhou、Huadong Ma Paper: https://arxiv.org/abs/2408.08488 Introduction Time series analysis is a critical task in data mining, especially in medical applications such as continuous health monitoring. Blood pressure (BP) is a vital indicator of cardiovascular health, with irregularities like hypertension and hypotension being potential markers for severe conditions such as stroke or chronic kidney disease. Traditional BP measurement methods using cuffs are uncomfortable, leading to the development of cuffless BP estimation techniques using wearable devices. These devices utilize multimodal signals like bioimpedance, millimeter-wave, and photoplethysmography (PPG) to estimate BP. However, these methods often require large amounts of…

Authors: Jin Wang、Arturo Laurenzi、Nikos Tsagarakis Paper: https://arxiv.org/abs/2408.08282 Introduction Autonomous behavior planning for humanoid robots in unstructured environments is a significant challenge in robotics. This involves enabling robots to plan and execute long-horizon tasks while perceiving and correcting deviations between task execution and high-level planning. Recent advancements in large language models (LLMs) have shown promising capabilities in planning and reasoning for robot control tasks. This paper proposes a novel framework leveraging LLMs to enable humanoid robots to autonomously plan behaviors and execute tasks based on textual instructions, while also observing and correcting failures during task execution. Related Works Achieving autonomy in…

Authors: Tiancheng Shi、Yuanchen Wei、John R. Kender Paper: https://arxiv.org/abs/2408.07791 Introduction Videos are rich sources of information, combining both visual and auditory data. Extracting and summarizing this content efficiently is crucial, especially for comparing different cultural perspectives on the same event. This paper introduces a novel Convolutional-Recurrent Variational Autoencoder (CRVAE) model that integrates image and text data to generate thematic clusters from videos. The system aims to provide a quick and insightful comparison of videos from different cultures, focusing on international news events. Related Works Convolutional Variational Autoencoder Previous research has utilized Convolutional Variational Autoencoders (CVAE) for image representation and clustering. However,…