Explaining Image Enhancement Black-Box Methods through a Path Planning Based Algorithm

Nowadays, image-to-image translation methods, are the state of the art for the enhancement of natural images. Even if they usually show high performance in terms of accuracy, they often suffer from several limitations such as the generation of artifacts and the scalability to high resolutions. Moreover, their main drawback is the completely black-box approach that does not allow to provide the final user with any insight about the enhancement processes applied. In this paper we present a path planning algorithm which provides a step-by-step explanation of the output produced by state of the art enhancement methods, overcoming black-box limitation. This algorithm, called eXIE, uses a variant of the A* algorithm to emulate the enhancement process of another method through the application of an equivalent sequence of enhancing operators. We applied eXIE to explain the output of several state-of-the-art models trained on the Five-K dataset, obtaining sequences of enhancing operators able to produce very similar results in terms of performance and overcoming the huge limitation of poor interpretability of the best performing algorithms

Predicting Tweet Engagement with Graph Neural Networks

Social Networks represent one of the most important online sources to share content across a world-scale audience. In this context, predicting whether a post will have any impact in terms of engagement is of crucial importance to drive the profitable exploitation of these media. In the literature, several studies address this issue by leveraging direct features of the posts, typically related to the textual content and the user publishing it. In this paper, we argue that the rise of engagement is also related to another key component, which is the semantic connection among posts published by users in social media. Hence, we propose TweetGage, a Graph Neural Network solution to predict the user engagement based on a novel graph-based model that represents the relationships among posts. To validate our proposal, we focus on the Twitter platform and perform a thorough experimental campaign providing evidence of its quality.Comment: Accepted in ACM ICMR202

DUCK: Distance-based Unlearning via Centroid Kinematics

Machine Unlearning is rising as a new field, driven by the pressing necessity of ensuring privacy in modern artificial intelligence models. This technique primarily aims to eradicate any residual influence of a specific subset of data from the knowledge acquired by a neural model during its training. This work introduces a novel unlearning algorithm, denoted as Distance-based Unlearning via Centroid Kinematics (DUCK), which employs metric learning to guide the removal of samples matching the nearest incorrect centroid in the embedding space. Evaluation of the algorithm's performance is conducted across various benchmark datasets in two distinct scenarios, class removal, and homogeneous sampling removal, obtaining state-of-the-art performance. We also introduce a novel metric, called Adaptive Unlearning Score (AUS), encompassing not only the efficacy of the unlearning process in forgetting target data but also quantifying the performance loss relative to the original model. Additionally, we conducted a thorough investigation of the unlearning mechanism in DUCK, examining its impact on the organization of the feature space and employing explainable AI techniques for deeper insights

The Omega-3 fatty acid docosahexaenoic acid modulates inflammatory mediator release in human alveolar cells exposed to bronchoalveolar lavage fluid of ards patients

Background. This study investigated whether the 1 : 2 ω-3/ω-6 ratio may reduce proinflammatory response in human alveolar cells (A549) exposed to an ex vivo inflammatory stimulus (bronchoalveolar lavage fluid (BALF) of acute respiratory distress syndrome (ARDS) patients). Methods. We exposed A549 cells to the BALF collected from 12 ARDS patients. After 18 hours, fatty acids (FA) were added as docosahexaenoic acid (DHA, ω-3) and arachidonic acid (AA, ω-6) in two ratios (1 : 2 or 1 : 7). 24 hours later, in culture supernatants were evaluated cytokines (TNF-α, IL-6, IL-8, and IL-10) and prostaglandins (PGE2 and PGE3) release. The FA percentage content in A549 membrane phospholipids, content of COX-2, level of PPARγ, and NF-κB binding activity were determined. Results. The 1 : 2 DHA/AA ratio reversed the baseline predominance of ω-6 over ω-3 in the cell membranes (P < 0.001). The proinflammatory cytokine release was reduced by the 1 : 2 ratio (P < 0.01 to <0.001) but was increased by the 1 : 7 ratio (P < 0.01). The 1 : 2 ratio reduced COX-2 and PGE2 (P < 0.001) as well as NF-κB translocation into the nucleus (P < 0.01), while it increased activation of PPARγ and IL-10 release (P < 0.001). Conclusion. This study demonstrated that shifting the FA supply from ω-6 to ω-3 decreased proinflammatory mediator release in human alveolar cells exposed to BALF of ARDS patients

Subcutaneous Infusion of Fluids for Hydration or Nutrition: A Review

Subcutaneous infusion, or hypodermoclysis, is a technique whereby fluids are infused into the subcutaneous space via small-gauge needles that are typically inserted into the thighs, abdomen, back, or arms. In this review, we provide an overview of the technique, summarize findings from studies that have examined the use of subcutaneous infusion of fluids for hydration or nutrition, and describe the indications, advantages, and disadvantages of subcutaneous infusion. Taken together, the available evidence suggests that, when indicated, subcutaneous infusion can be effective for administering fluids for hydration or nutrition, with minimal complications, and has similar effectiveness and safety to the intravenous route. Of note, subcutaneous infusion offers several advantages over intravenous infusion, including ease of application, low cost, and the lack of potential serious complications, particularly infections. Subcutaneous infusion may be particularly suited for patients with mild to moderate dehydrati..