Low-Rank Adaptation (LoRA) is a widely used technique for fine-tuning large pre-trained or foundational models across different modalities and tasks. However, its application to time series data, particularly within foundational models, remains underexplored. This paper examines the impact of LoRA on contemporary time series foundational models: Lag-Llama, MOIRAI, and Chronos. We demonstrate LoRA’s fine-tuning potential for forecasting the vital signs of sepsis patients in intensive care units (ICUs), emphasizing the models’ adaptability to previously unseen, out-of-domain modalities. Integrating LoRA aims to enhance forecasting performance while reducing inefficiencies associated with fine-tuning large models on limited domain-specific data. Our experiments show that LoRA fine-tuning of time series foundational models (TSFMs) significantly improves forecasting over zero-shot TSFMs, achieving results comparable to state-of-the-art models trained from scratch on similar modalities. We conduct comprehensive ablation studies to demonstrate the trade-offs between the number of tunable parameters and forecasting performance and assess the impact of varying LoRA matrix ranks on model performance.

Recent advances in deep learning have made it increasingly feasible to estimate heart rate (HR) remotely in smart environments by analyzing videos. However, a notable limitation of deep learning methods is their heavy reliance on extensive sets of labeled data for effective training. To address this issue, self-supervised learning has emerged as a promising avenue. Building on this, we introduce a solution that utilizes self-supervised contrastive learning for the estimation of remote photoplethysmography (PPG) and HR monitoring, thereby reducing the dependence on labeled data and enhancing performance. We propose the use of three spatial and three temporal augmentations for training an encoder through a contrastive framework, followed by utilizing the late-intermediate embeddings of the encoder for remote PPG and HR estimation. Our experiments on two publicly available data sets showcase the improvement of our proposed approach over several related works as well as supervised learning baselines, as our results approach the state of the art. We also perform thorough experiments to showcase the effects of using different design choices, such as the video representation learning method, the augmentations used in the pretraining stage, and others. We also demonstrate the robustness of our proposed method over the supervised learning approaches on reduced amounts of labeled data.

Remote Photoplethysmography (rPPG) is the process of estimating PPG from facial videos. While this approach benefits from contactless interaction, it is reliant on videos of faces, which often constitutes an important privacy concern. Recent research has revealed that deep learning techniques are vulnerable to attacks, which can result in significant data breaches making deep rPPG estimation even more sensitive. To address this issue, we propose a data perturbation method that involves extraction of certain areas of the face with less identity-related information, followed by pixel shuffling and blurring. Our experiments on two rPPG datasets (PURE and UBFC) show that our approach reduces the accuracy of facial recognition algorithms by over 60%, with minimal impact on rPPG extraction. We also test our method on three facial recognition datasets (LFW, CALFW, and AgeDB), where our approach reduced performance by nearly 50%. Our findings demonstrate the potential of our approach as an effective privacy-preserving solution for rPPG estimation.

Deep learning approaches have shown great success in myocardium region segmentation in Cardiac MR (CMR) images. However, most of these often ignore irregularities such as protrusions, breaks in contour, etc. As a result, the common practice by clinicians is to manually correct the obtained outputs for the evaluation of myocardium condition. This paper aims to make the deep learning systems capable of handling the aforementioned irregularities and satisfy desired clinical constraints, necessary for various downstream clinical analysis. We propose a refinement model which imposes structural constraints on the outputs of the existing deep learning-based myocardium segmentation methods. The complete system is a pipeline of deep neural networks where an initial network performs myocardium segmentation as accurate as possible and the refinement network removes defects from the initial output to make it suitable for clinical decision support systems. We experiment with datasets collected from four different sources and observe consistent final segmentation outputs with improvement up to 8% in Dice Coefficient and up to 18 pixels in Hausdorff Distance due to the proposed refinement model. The proposed refinement strategy leads to qualitative and quantitative improvements in the performances of all the considered segmentation networks. Our work is an important step towards the development of a fully automatic myocardium segmentation system. It can also be generalized for other tasks where the object of interest has regular structure and the defects can be modelled statistically.

Black-box web application vulnerability scanners are automated tools that probe web applications for security vulnerabilities. In order to assess the current state of the art, we obtained access to eight leading tools and carried out a study of: (i) the class of vulnerabilities tested by these scanners, (ii) their effectiveness against target vulnerabilities, and (iii) the relevance of the target vulnerabilities to vulnerabilities found in the wild. To conduct our study we used a custom web application vulnerable to known and projected vulnerabilities, and previous versions of widely used web applications containing known vulnerabilities. Our results show the promise and effectiveness of automated tools, as a group, and also some limitations. In particular, "stored" forms of Cross Site Scripting (XSS) and SQL Injection (SQLI) vulnerabilities are not currently found by many tools. Because our goal is to assess the potential of future research, not to evaluate specific vendors, we do not report comparative data or make any recommendations about purchase of specific tools.