Perceptual losses have emerged as powerful tools for training networks to enhance Low-Dose Computed Tomography (LDCT) images, offering an alternative to traditional pixel-wise losses such as Mean Squared Error, which often lead to over-smoothed reconstructions and loss of clinically relevant details in LDCT images. The perceptual losses operate in a latent feature space defined by a pretrained encoder and aim to preserve semantic content by comparing high-level features rather than raw pixel values. However, the design of perceptual losses involves critical yet underexplored decisions, including the feature representation level, the dataset used to pretrain the encoder, and the relative importance assigned to the perceptual component during optimization. In this work, we introduce the concept of perceptual influence (a metric that quantifies the relative contribution of the perceptual loss term to the total loss) and propose a principled framework to assess the impact of the loss design choices on the model training performance. Through systematic experimentation, we show that the widely used configurations in the literature to set up a perceptual loss underperform compared to better-designed alternatives. Our findings show that better perceptual loss designs lead to significant improvements in noise reduction and structural fidelity of reconstructed CT images, without requiring any changes to the network architecture. We also provide objective guidelines, supported by statistical analysis, to inform the effective use of perceptual losses in LDCT denoising. Our source code is available at https://github.com/vngabriel/perceptual-influence.

Cryptocurrencies have gained significant attention in recent years due to their decentralized nature and potential for financial innovation. Thus, the ability to accurately predict its price has become a subject of great interest for investors, traders, and researchers. Some works in the literature show how Bitcoin's market sentiment correlates with its price fluctuations in the market. However, papers that consider the sentiment of the market associated with financial Technical Analysis indicators in order to predict Bitcoin's price are still scarce. In this paper, we present a novel approach for predicting Bitcoin price movements by combining the Fear & Greedy Index, a measure of market sentiment, Technical Analysis indicators, and the potential of Machine Learning algorithms. This work represents a preliminary study on the importance of sentiment metrics in cryptocurrency forecasting. Our initial experiments demonstrate promising results considering investment returns, surpassing the Buy & Hold baseline, and offering valuable insights about the combination of indicators of sentiment and market in a cryptocurrency prediction model.

Data analysis and machine learning are of preeminent importance in the legal domain, especially in tasks like clustering and text classification. In this study, we harnessed the power of natural language processing tools to enhance datasets meticulously curated by experts. This process significantly improved the classification workflow for legal texts using machine learning techniques. We considered the Sustainable Development Goals (SDGs) data from the United Nations 2030 Agenda as a practical case study. Data augmentation clustering-based strategy led to remarkable enhancements in the accuracy and sensitivity metrics of classification models. For certain SDGs within the 2030 Agenda, we observed performance gains of over 15%. In some cases, the example base expanded by a noteworthy factor of 5. When dealing with unclassified legal texts, data augmentation strategies centered around clustering prove to be highly effective. They provide a valuable means to expand the existing knowledge base without the need for labor-intensive manual classification efforts.

Climate change mitigation in urban mobility requires policies reconfiguring urban form to increase accessibility and facilitate low-carbon modes of transport. However, current policy research has insufficiently assessed urban form effects on car travel at three levels: (1) Causality -- Can causality be established beyond theoretical and correlation-based analyses? (2) Generalizability -- Do relationships hold across different cities and world regions? (3) Context specificity -- How do relationships vary across neighborhoods of a city? Here, we address all three gaps via causal graph discovery and explainable machine learning to detect urban form effects on intra-city car travel, based on mobility data of six cities across three continents. We find significant causal effects of urban form on trip emissions and inter-feature effects, which had been neglected in previous work. Our results demonstrate that destination accessibility matters most overall, while low density and low connectivity also sharply increase CO$_2$ emissions. These general trends are similar across cities but we find idiosyncratic effects that can lead to substantially different recommendations. In more monocentric cities, we identify spatial corridors -- about 10--50 km from the city center -- where subcenter-oriented development is more relevant than increased access to the main center. Our work demonstrates a novel application of machine learning that enables new research addressing the needs of causality, generalizability, and contextual specificity for scaling evidence-based urban climate solutions.

From a public-health perspective, the occurrence of drug-drug-interactions (DDI) from multiple drug prescriptions is a serious problem, especially in the elderly population. This is true both for individuals and the system itself since patients with complications due to DDI will likely re-enter the system at a costlier level. We conducted an 18-month study of DDI occurrence in Blumenau (Brazil; pop. 340,000) using city-wide drug dispensing data from both primary and secondary-care level. Our goal is also to identify possible risk factors in a large population, ultimately characterizing the burden of DDI for patients, doctors and the public system itself. We found 181 distinct DDI being prescribed concomitantly to almost 5% of the city population. We also discovered that women are at a 60% risk increase of DDI when compared to men, while only having a 6% co-administration risk increase. Analysis of the DDI co-occurrence network reveals which DDI pairs are most associated with the observed greater DDI risk for females, demonstrating that contraception and hormone therapy are not the main culprits of the gender disparity, which is maximized after the reproductive years. Furthermore, DDI risk increases dramatically with age, with patients age 70-79 having a 50-fold risk increase in comparison to patients aged 0-19. Interestingly, several null models demonstrate that this risk increase is not due to increased polypharmacy with age. Finally, we demonstrate that while the number of drugs and co-administrations help predict a patient's number of DDI ($R^2=.413$), they are not sufficient to flag these patients accurately, which we achieve by training classifiers with additional data (MCC=.83,F1=.72). These results demonstrate that accurate warning systems for known DDI can be devised for public and private systems alike, resulting in substantial prevention of DDI-related ADR and savings.