Author: Grayson Foster

Authors: Hojat Asgariandehkordi、Sobhan Goudarzi、Mostafa Sharifzadeh、Adrian Basarab、Hassan Rivaz Paper: https://arxiv.org/abs/2408.10987 Introduction Ultrasound imaging is a cornerstone in modern medical diagnostics due to its non-invasive nature and cost-effectiveness. It provides real-time visualization of internal structures, aiding in the detection and diagnosis of various medical conditions. However, the presence of noise in ultrasound images can significantly impair their interpretability and diagnostic accuracy. This noise can stem from various sources, including electronic noise and acoustic artifacts, which collectively degrade image quality. To address these challenges, robust post-processing techniques have been developed to enhance ultrasound image quality. Traditional methods such as image filtering, speckle reduction…

Authors: Qiao Li、Cong Wu、Jing Chen、Zijun Zhang、Kun He、Ruiying Du、Xinxin Wang、Qingchuang Zhao、Yang Liu Paper: https://arxiv.org/abs/2408.10647 Introduction Deep neural networks (DNNs) have become integral to various critical applications, such as identity authentication and autonomous driving. However, their vulnerability to adversarial attacks—where minor perturbations in input data can lead to significant prediction errors—poses a substantial risk. Traditional defense mechanisms often require detailed model information, raising privacy concerns. Existing black-box defense methods, which do not require such information, fail to provide a universal defense against diverse adversarial attacks. This study introduces DUCD, a universal black-box defense method that preserves data privacy while offering robust defense…

Authors: Moumita Bhattacharya、Vito Ostuni、Sudarshan Lamkhede Paper: https://arxiv.org/abs/2408.10394 Introduction In the realm of digital services, search and recommendation systems are pivotal for enhancing user experience. Traditionally, these systems are developed independently, leading to increased complexity in maintenance and technical debt. This paper introduces a unified deep learning model, UniCoRn (Unified Contextual Recommender), designed to efficiently handle both search and recommendation tasks. By consolidating these models, the authors aim to reduce overhead and improve the reliability and effectiveness of machine learning systems. Related Work The complexity of maintaining separate models for search and recommendation tasks has been well-documented. Previous studies [4, 6]…

Authors: Litian Huang、Xinguo Yu、Feng Xiong、Bin He、Shengbing Tang、Jiawen Fu Paper: https://arxiv.org/abs/2408.10592 Introduction Algebra Problems with Geometry Diagrams (APGDs) present a unique challenge in educational contexts, requiring solvers to interpret both textual descriptions and geometric diagrams. This task necessitates a combined understanding of algebraic and geometric principles. The complexity arises from the need to apply geometric theorems and manage implicit information within diagrams, which are not always explicitly stated. Addressing these challenges is crucial for developing intelligent educational tools and advancing automated reasoning systems. Existing techniques for solving APGDs are divided into two primary categories: neural methods and symbolic methods. Neural methods…

Authors: Jiandong Jin、Xiao Wang、Qian Zhu、Haiyang Wang、Chenglong Li Paper: https://arxiv.org/abs/2408.09720 Introduction Pedestrian Attribute Recognition (PAR) is a critical task in the fields of Computer Vision (CV) and Artificial Intelligence (AI). It involves mapping pedestrian images to semantic labels such as gender, hairstyle, and clothing using deep neural networks. Despite significant advancements, current PAR models still face challenges in complex real-world scenarios due to factors like low illumination, motion blur, and complex backgrounds. Moreover, existing datasets have reached performance saturation, and there has been a lack of new large-scale datasets in recent years. To address these issues, this study introduces a new…

Authors: Rameez Qureshi、Naïm Es-Sebbani、Luis Galárraga、Yvette Graham、Miguel Couceiro、Zied Bouraoui Paper: https://arxiv.org/abs/2408.09489 Mitigating Language Model Stereotypes with REFINE-LM: A Deep Dive Introduction The advent of large language models (LLMs) has revolutionized natural language processing (NLP), enabling applications such as chatbots and virtual assistants to perform tasks with unprecedented accuracy and fluency. However, these models often inherit and propagate biases present in their training data, leading to ethical concerns and potential societal harm. This blog post delves into a novel approach called REFINE-LM, which aims to mitigate these biases using reinforcement learning (RL). Related Work Bias Detection in NLP Models Detecting bias in…

Authors: Dong Li、Chen Zhao、Minglai Shao、Wenjun Wang Paper: https://arxiv.org/abs/2408.09312 Introduction Machine learning models often assume that training and test data are independently and identically distributed (i.i.d.). However, this assumption does not hold in many real-world scenarios, leading to poor model performance when there is a distribution shift between training and test domains. Addressing these distribution shifts and ensuring model generalization to unseen but related test domains is the primary goal of domain generalization (DG). In addition to generalization, fairness in machine learning has become a critical concern. Fairness implies the absence of bias or favoritism towards any individual or group based…

Authors: Clinton Enwerem、Erfaun Noorani、John S. Baras、Brian M. Sadler Paper: https://arxiv.org/abs/2408.08668 Introduction In the realm of high-risk industries such as autonomous delivery and supply chain management, Stochastic Shortest-Path (SSP) problems are prevalent. These problems require robust planning algorithms to ensure successful task completion while mitigating hazardous outcomes. Traditional chance-constrained incremental sampling techniques for solving SSP problems tend to be overly conservative and often do not account for the likelihood of undesirable tail events. This paper introduces a risk-aware approach inspired by the Rapidly-Exploring Random Trees (RRT*) planning algorithm, which selects nodes along path segments with minimal Conditional Value-at-Risk (CVaR). This approach…

Authors: Robert Geirhos、Priyank Jaini、Austin Stone、Sourabh Medapati、Xi Yi、George Toderici、Abhijit Ogale、Jonathon Shlens Paper: https://arxiv.org/abs/2408.08172 Towards Flexible Perception with Visual Memory: A Detailed Exploration Introduction In the realm of machine learning, the traditional workflow involves a series of steps: data collection, preprocessing, model selection, training, evaluation, and deployment. However, the static nature of deep learning models poses significant challenges in adapting to the ever-changing real world. This paper introduces a novel approach by integrating the representational power of deep neural networks with the flexibility of a database, creating a visual memory system. This system aims to address the limitations of static knowledge representation…

Authors: Steve Yuwono、Dorothea Schwung、Andreas Schwung Paper: https://arxiv.org/abs/2408.06397 Introduction In modern manufacturing systems, the integration of Artificial Intelligence (AI), Internet of Things (IoT), and Cyber-Physical Systems (CPS) technologies has revolutionized operational efficiency by enabling functions like fault tolerance, self-optimization, and anomaly detection. These advancements have led to the development of modular production units controlled by decentralized systems, necessitating distributed optimization methodologies to dynamically adjust to fluctuating demands. This paper introduces a novel game structure called Distributed Stackelberg Strategies in State-Based Potential Games (DS2-SbPG) to address these challenges. Literature Review Multi-Objective Optimizations Multi-objective optimization involves optimizing multiple conflicting objectives simultaneously. This field…