Towards Trustworthy Wi-Fi Sensing: Systematic Evaluation of Deep Learning Model Robustness to Adversarial Attacks

A new framework called DeLight-Mono has been introduced to enhance self-supervised monocular depth estimation in endoscopy by addressing the challenges posed by uneven illumination in endoscopic images. This innovative approach utilizes an illumination-reflectance-depth model and auxiliary networks to improve depth estimation accuracy, particularly in low-light conditions.

The study introduces GeeSanBhava, a comprehensive dataset of Sinhala song comments sourced from YouTube, which has been meticulously tagged using Russell's Valence-Arousal model by three independent annotators, achieving a high inter-annotator agreement of 84.96%. This dataset highlights the emotional profiles associated with different songs, emphasizing the significance of comment-based emotion mapping.

A new study introduces Upstream Probabilistic Meta-Imputation (UPMI) as a novel strategy for classifying pediatric pancreatitis, a complex inflammatory condition. This method leverages machine learning techniques to enhance diagnostic accuracy by utilizing a low-dimensional meta-feature space, addressing challenges posed by limited sample sizes and the intricacies of multimodal imaging.

A new method for monocular person localization under camera ego-motion has been developed, addressing the challenges of accurately estimating a person's 3D position from 2D images captured by a moving camera. This approach utilizes a four-point model to jointly estimate the camera's 2D attitude and the person's 3D location, significantly improving localization accuracy compared to existing methods.

Researchers have explored the distillation of large pre-trained photoplethysmography (PPG) models into smaller models suitable for real-time heart rate estimation on wearable devices like smartwatches and fitness trackers. The study evaluates four distillation strategies: hard distillation, soft distillation, decoupled knowledge distillation, and feature distillation, aiming to meet the memory and latency constraints of edge devices.

A review of Generative Model Predictive Control (MPC) in manufacturing processes highlights the challenges posed by dynamic and uncertain environments, where traditional control methods often fall short. The paper discusses how integrating machine learning can enhance MPC by better modeling nonlinear dynamics and improving state estimation.

A recent study investigates the robustness of split conformal prediction under adversarial attacks, highlighting the reliance on exchangeability and the impact of adversarial perturbations on coverage validity and prediction set size. The analysis reveals how calibration-time attack strength influences coverage guarantees during adversarial testing.