Universidad Torcuato di Tella

Misinformation harms society by affecting citizens' beliefs and behaviour. Recent research has shown that partisanship and cognitive reflection (i.e. engaging in analytical thinking) play key roles in the acceptance of misinformation. However, the relative importance of these factors remains a topic of ongoing debate. In this registered study, we tested four hypotheses on the relationship between each factor and the belief in statements made by Argentine politicians. Participants (N = 1353) classified fact-checked political statements as true or false, completed a cognitive reflection test, and reported their voting preferences. Using Signal Detection Theory and Bayesian modeling, we found a reliable positive association between political concordance and overall belief in a statement (median = 0.663, CI95 = [0.640, 0.685]), a reliable positive association between cognitive reflection and scepticism (median = 0.039, CI95 = [0.006, 0.072]), a positive but unreliable association between cognitive reflection and truth discernment (median = 0.016, CI95 = [− 0.015, 0.046]) and a negative but unreliable association between cognitive reflection and partisan bias (median = − 0.016, CI95 = [− 0.037, 0.006]). Our results highlight the need to further investigate the relationship between cognitive reflection and partisanship in different contexts and formats. Protocol registration The stage 1 protocol for this Registered Report was accepted in principle on 22 August 2022. The protocol, as accepted by the journal, can be found at: https://doi.org/10.17605/OSF.IO/EBRGC .

During the COVID-19 pandemic, Latin American and Caribbean countries implemented stringent public health and social measures that disrupted economic and social activities. This study used an integrated model to evaluate the epidemiological, economic, and social trade-offs in Argentina, Brazil, Jamaica, and Mexico throughout 2021. Argentina and Mexico displayed a higher gross domestic product (GDP) loss and lower deaths per million compared with Brazil. The magnitude of the trade-offs differed across countries. Reducing GDP loss at the margin by 1 percent would have increased daily deaths by 0.5 per million in Argentina but only 0.3 per million in Brazil. We observed an increase in poverty rates related to the stringency of public health and social measures but no significant income-loss differences by sex. Our results indicate that the economic impact of COVID-19 was uneven across countries as a result of different pandemic trajectories, public health and social measures, and vaccination uptake, as well as socioeconomic differences and fiscal responses. Policy makers need to be informed about the trade-offs to make strategic decisions to save lives and livelihoods.

The concept of international order has undergone significant evolution, but the dominant voices of great powers have often overshadowed regional perspectives, particularly in Latin America. Still, this region has played a crucial role in shaping the contemporary discourse on the international order. Today, the term is used in various contexts within Latin America, reflecting the increasing presence of nonstate actors and the interdependent nature of societies and the planet overall. This article focuses on Latin American diplomacy and civil society's understand-ings of international order, contending that the region has developed analytical and practical approaches that converge into a normative vision centered on international justice. Both the analytical and practical approaches view the international order as a normative concept. Civil society especially perceives the international order as a practice concept, utilizing its voice and actions as instruments for change. Yet, civil society is not mono-lithic, and various groups seek different outcomes. This article reviews critically analytical and normative perspectives of the international order, providing a comprehensive understanding of Latin American viewpoints through various data collection methods.

Public sector unions are increasingly becoming the hegemonic contemporary labor actor in terms of membership and militancy in both advanced and emerging economies. However, political economy lacks a unified theoretical approach to study mobilization by state unions. The analysis of public sector union politics has been largely separated by regional (United States vs. Europe vs. Global South) and disciplinary (American politics vs. comparative politics/political sociology) divides. We contend that though both public and private workers belong to the subaltern classes, public sector union politics and mobilization have different foundations than in the private sector. Unlike private unions, state labor mobilization is essentially driven by what we call the “reverse economic cycle” (militancy increases in bad—rather than good—economic times), by the legal enforcement of bargaining rights (which in contrast to the private sector substantially varies across and within democracies), and by the likelihood of a political exchange between labor and the government. Argentine teachers between 2006 and 2019 provide an ideal laboratory to test this argument through a multilevel (i.e., national and subnational), mixed-methods strategy, which includes comparative and statistical assessments.

The development of long-term scenarios to outline pathways for achieving carbon neutrality by 2050 has become a standard practice in climate change policy planning. In Argentina, a modelling process was initiated in 2019 in the agriculture, forestry and other land use (AFOLU) sector utilizing three tools: FABLE calculator, Dinamica EGO and Nature Map. In order to generate technical inputs for the modelling exercise a stakeholder dialogue was launched. A 2050 Carbon Neutrality scenario was developed, alongside several intermediate scenarios based on stakeholders´ visions of the future. The modelling results demonstrated the biophysical feasibility of achieving carbon neutrality in the Argentinean AFOLU sector by 2050. However, alignment with current sectoral priorities was identified as a challenge, leading stakeholders to propose less ambitious scenarios as more attainable targets. This experience underscored the significance of constructing multiple policy scenarios, facilitating the evaluation of diverse potential future trajectories for policymaking. These different pathways provided contrasting perspectives between political objectives, such as achieving carbon neutrality, and the practical feasibility of local implementation. Moreover, the process highlighted the vital role of integrating the private sector and environmental non-governmental organizations (NGOs) in long-term climate planning, emphasizing the need for inclusive collaboration to address climate challenges effectively.

Although normal homologous brain structures are approximately symmetrical by definition, they also have shape differences due to e.g. natural ageing. On the other hand, neurodegenerative conditions induce their own changes in this asymmetry, making them more pronounced or altering their location. Identifying when these alterations are due to a pathological deterioration is still challenging. Current clinical tools rely either on subjective evaluations, basic volume measurements or disease-specific deep learning models. This paper introduces a novel method to learn normal asymmetry patterns in homologous brain structures based on anomaly detection and representation learning. Our framework uses a Siamese architecture to map 3D segmentations of left and right hemispherical sides of a brain structure to a normal asymmetry embedding space, learned using a support vector data description objective. Being trained using healthy samples only, it can quantify deviations-from-normal-asymmetry patterns in unseen samples by measuring the distance of their embeddings to the center of the learned normal space. We demonstrate in public and in-house sets that our method can accurately characterize normal asymmetries and detect pathological alterations due to Alzheimer’s disease and hippocampal sclerosis, even though no diseased cases were accessed for training. Our source code is available at https://github.com/duiliod/DeepNORHA.

We present a data-driven generative framework for synthesizing blood vessel 3D geometry. This is a challenging task due to the complexity of vascular systems, which are highly variating in shape, size, and structure. Existing model-based methods provide some degree of control and variation in the structures produced, but fail to capture the diversity of actual anatomical data. We developed VesselVAE, a recursive variational Neural Network that fully exploits the hierarchical organization of the vessel and learns a low-dimensional manifold encoding branch connectivity along with geometry features describing the target surface. After training, the VesselVAE latent space can be sampled to generate new vessel geometries. To the best of our knowledge, this work is the first to utilize this technique for synthesizing blood vessels. We achieve similarities of synthetic and real data for radius (.97), length (.95), and tortuosity (.96). By leveraging the power of deep neural networks, we generate 3D models of blood vessels that are both accurate and diverse, which is crucial for medical and surgical training, hemodynamic simulations, and many other purposes.

The COVID-19 pandemic underscored the significance of overcoming vaccine adoption resistance and addressing real and perceived barriers for efficient vaccination campaigns. One major problem faced by health systems around the world was that people’s preferences for a specific brand of vaccine often delayed vaccination efforts as people canceled or delayed appointments to receive their preferred brand. Therefore, in the event of another pandemic, it is important to know which factors influence preferences for specific vaccine brands. Previous literature showed that consumers choose products that are congruent with their self-concept, which includes their political affiliation. Given that the discourse around vaccine brands has been strongly politicized during the pandemic, in our work, we test whether partisanship influences preferences for COVID-19 vaccine brands. To test this, we collected survey data from Argentina (N = 432), a country with a clear bi-partisan structure and where a variety of vaccine brands were administered, both from Western and Eastern laboratories. We found that supporters of the ruling party, which had strong ties with Eastern countries such as Russia and China, perceived Eastern vaccine brands (e.g., Sputnik V) to be more effective and safer than Western ones (e.g., Pfizer) whereas the contrary was true for supporters of the opposition. Our results also showed that supporters of the opposing party were more likely to wish to hypothetically switch vaccines, to delay their appointment in case of not receiving their preferred brand, and to disapprove of their local vaccination campaign. Our results demonstrate that political party affiliation biases perceptions of both vaccine brands’ quality and vaccination campaign effectiveness. We anticipate that our results can inform public policy strategies when it comes to an efficient vaccine supply allocation, as political affiliation is a measurable and predictable consumer trait.

In “Demand Estimation Under Uncertain Consideration Sets,” Jagabathula, Mitrofanov, and Vulcano investigate statistical properties of the consider-then-choose (CTC) models, which gained recent attention in the operations literature as an alternative to the classical random utility (RUM) models. The general class of CTC models is defined by a general joint distribution over ranking lists and consideration sets. Starting from the important result that the CTC and RUM classes are equivalent in terms of explanatory power, the authors characterize conditions under which CTC models become identified. Then, they propose expectation-maximization (EM) methods to solve the related estimation problem for different subclasses of CTC models, building from the provably convergent outer-approximation algorithm. Finally, subclasses of CTC models are tested on a synthetic data set and on two real data sets: one from a grocery chain and one from a peer-to-peer (P2P) car sharing platform. The results are consistent in assessing that CTC models tend to dominate RUM models with respect to prediction accuracy when the training data are noisy (i.e., transaction records do not necessarily reflect the physical inventory records) and when there is significant asymmetry between the training data set and the testing data set. These conditions are naturally verified in P2P sharing platforms and in retailers working on long-term forecasts (e.g., semester long) or geographical aggregate forecasts (e.g., forecasts at the distribution center level).

In federal presidential democracies, discretionary transfers are often mentioned as a tool used by the national executive to build and strengthen subnational support, typically governors. Funds to local mayors, however, have been much less studied. With original data, in this study we analyze the distribution of a particular discretionary transfer (ATN) to the Argentine municipalities during two periods: 1997–2000 and 2016–2019. We show that the main driver for transfers is the mayor's political alignment. Indeed, the president is more likely to reward loyal mayors, but especially when both the latter and the President oppose the provincial governor. By this token, we highlight a nested political game, in which the President considers the loyalty of both mayors and governors combined to decide when to reward (or when not to reward) municipalities. Furthermore, we find that the Executive provides aid to smaller municipalities to circumvent the possibility of funding mayors from larger cities who may pose a threat as political rivals in the future. Since this pattern is more evident in localities with aligned mayors, we can infer that the President's strategy is aimed at preventing future challengers from within their own coalition.

Objectives: Evaluate the performance of a deep learning (DL)-based model for multiple sclerosis (MS) lesion segmentation and compare it to other DL and non-DL algorithms. Methods: This ambispective, multicenter study assessed the performance of a DL-based model for MS lesion segmentation and compared it to alternative DL- and non-DL-based methods. Models were tested on internal (n = 20) and external (n = 18) datasets from Latin America, and on an external dataset from Europe (n = 49). We also examined robustness by rescanning six patients (n = 6) from our MS clinical cohort. Moreover, we studied inter-human annotator agreement and discussed our findings in light of these results. Performance and robustness were assessed using intraclass correlation coefficient (ICC), Dice coefficient (DC), and coefficient of variation (CV). Results: Inter-human ICC ranged from 0.89 to 0.95, while spatial agreement among annotators showed a median DC of 0.63. Using expert manual segmentations as ground truth, our DL model achieved a median DC of 0.73 on the internal, 0.66 on the external, and 0.70 on the challenge datasets. The performance of our DL model exceeded that of the alternative algorithms on all datasets. In the robustness experiment, our DL model also achieved higher DC (ranging from 0.82 to 0.90) and lower CV (ranging from 0.7 to 7.9%) when compared to the alternative methods. Conclusion: Our DL-based model outperformed alternative methods for brain MS lesion segmentation. The model also proved to generalize well on unseen data and has a robust performance and low processing times both on real-world and challenge-based data. Clinical relevance statement: Our DL-based model demonstrated superior performance in accurately segmenting brain MS lesions compared to alternative methods, indicating its potential for clinical application with improved accuracy, robustness, and efficiency. Key points: • Automated lesion load quantification in MS patients is valuable; however, more accurate methods are still necessary. • A novel deep learning model outperformed alternative MS lesion segmentation methods on multisite datasets. • Deep learning models are particularly suitable for MS lesion segmentation in clinical scenarios.

The discourse of political leaders often contains false information that can misguide the public. Fact-checking agencies around the world try to reduce the negative influence of politicians by verifying their words. However, these agencies face a problem of scalability and require innovative solutions to deal with their growing amount of work. While the previous studies have shown that crowdsourcing is a promising approach to fact-check news in a scalable manner, it remains unclear whether crowdsourced judgements are useful to verify the speech of politicians. This article fills that gap by studying the effect of social influence on the accuracy of collective judgements about the veracity of political speech. In this work, we performed two experiments (Study 1: N = 180; Study 2: N = 240) where participants judged the veracity of 20 politically balanced phrases. Then, they were exposed to social information from politically homogeneous or heterogeneous participants. Finally, they provided revised individual judgements. We found that only heterogeneous social influence increased the accuracy of participants compared to a control condition. Overall, our results uncover the effect of social influence on the accuracy of collective judgements about the veracity of political speech and show how interactive crowdsourcing strategies can help fact-checking agencies. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Research on the drivers of foreign policy regarding climate change negotiations has extensively delved into the effects of risks and mitigation costs, as well as the relevance of interest groups such as civil society organizations or carbon-intensive industries. However, the role of governments’ ideological orientation has been neglected in most quantitative studies. Building upon an original dataset consisting of statements at the Conference of the Parties within multilateral climate change negotiations between 2010 and 2018, this paper addresses the impact of government ideology on the negotiating position of developing countries. Despite previous academic work that assert that left-wing governments are more likely to adopt pro-environment stances, I argue that this is only the case in developed countries. Results suggest that the effect of ideology is different in the Global South, where right-wing pluralist governments are more likely to adopt ambitious positions in climate change negotiations than left-wing or populist executives.

In this paper, we propose a novel approach to address the problem of functional outlier detection. Our method leverages a low-dimensional and stable representation of functions using Reproducing Kernel Hilbert Spaces (RKHS). We define a depth measure based on density kernels that satisfy desirable properties. We also address the challenges associated with estimating the density kernel depth. Throughout a Monte Carlo simulation we assess the performance of our functional depth measure in the outlier detection task under different scenarios. To illustrate the effectiveness of our method, we showcase the proposed method in action studying outliers in mortality rate curves.

This article analyzes the responses of the government of Alberto Fernández of the Frente de Todos to the profound economic, social, and judicial challenges it faced in 2022. While the measures adopted to address the pressing economic situation and rising social conflict intensified existing disputes in the governing coalition, threatening its unity, the judicial processes and the government's response to them deepened polarization between the government and the opposition, encouraging the internal unity of each coalition. The climate of polarization reached a peak with a failed assassination attempt against the vice president. Although these challenges and the internal disputes in the Frente de Todos severely weakened the president, the incumbent coalition remained stable and the ruling party remains competitive in the uncertain 2023 elections.

Radiologists routinely analyze hippocampal asymmetries in magnetic resonance (MR) images as a biomarker for neurodegenerative conditions like epilepsy and Alzheimer’s Disease. However, current clinical tools rely on either subjective evaluations, basic volume measurements, or disease-specific models that fail to capture more complex differences in normal shape. In this paper, we overcome these limitations by introducing NORHA, a novel NORmal Hippocampal Asymmetry deviation index that uses machine learning novelty detection to objectively quantify it from MR scans. NORHA is based on a One-Class Support Vector Machine model learned from a set of morphological features extracted from automatically segmented hippocampi of healthy subjects. Hence, in test time, the model automatically measures how far a new unseen sample falls with respect to the feature space of normal individuals. This avoids biases produced by standard classification models, which require being trained using diseased cases and therefore learning to characterize changes produced only by the ones. We evaluated our new index in multiple clinical use cases using public and private MRI datasets comprising control individuals and subjects with different levels of dementia or epilepsy. The index reported high values for subjects with unilateral atrophies and remained low for controls or individuals with mild or severe symmetric bilateral changes. It also showed high AUC values for discriminating individuals with hippocampal sclerosis, further emphasizing its ability to characterize unilateral abnormalities. Finally, a positive correlation between NORHA and the functional cognitive test CDR-SB was observed, highlighting its promising application as a biomarker for dementia.

Circadian rhythms are entrained by external factors such as sunlight and social cues, but also depend on internal factors such as age. Adolescents exhibit late chronotypes, but worldwide school starts early in the morning leading to unhealthy sleep habits. Several studies reported that adolescents benefit from later school start times. However, the effect of later school start time on different outcomes varies between studies, and most previous literature only takes into consideration the social clock (i.e. local time of school starting time) but not the solar clock (e.g. the distance between school start time and sunrise). Thus, there is an important gap in the literature: when assessing the effect of a school start time on chronotype and sleep of adolescents at different locations and/or seasons, the solar clock might differ and, consistently, the obtained results. For example, the earliest school start time for adolescents has been suggested to be 08:30 hours, but this school start time might correspond to different solar times at different times of the year, longitudes and latitudes. Here, we describe the available literature comparing different school start times, considering important factors such as geographic position, nationality, and the local school start time and its distance to sunrise. Then, we described and contrasted the relative role of both social and solar clocks on the chronotype and sleep of adolescents. As a whole, we point and discuss a gap in literature, suggesting that both clocks are relevant when addressing the effect of school start time on adolescents' chronotype and sleep.

We study the possibility of non-simultaneous blow-up for positive solutions of a coupled system of two semilinear equations,ut=J∗u-u+uαvp\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$u_t = J*u-u+ u^\alpha v^p$$\end{document}, vt=Δv+uqvβ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$v_t =\Delta v+u^qv^\beta $$\end{document}, p,q,α,β>0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p, q, \alpha , \beta >0$$\end{document} with homogeneous Dirichlet boundary conditions and positive initial data. We also give the blow-up rates for non-simultaneous blow-up.

Schools start early in the morning all over the world, contrasting with adolescents’ late chronotype. Interestingly, lower academic performance (i.e. grades or qualifications) was associated with later chronotypes. However, it is unclear whether it is a direct effect of chronotype or because students attend school too early to perform at their best. Moreover, little is known about how this affects students’ academic success beyond their grades. To address this gap in knowledge, we studied how school timing and chronotype affect grade retention (i.e. repeat a year) in a unique sample of students randomly assigned to one of three different school timings (starting at 07:45, 12:40, or 17:20). Even when controlling for academic performance, we found that later chronotypes exhibit higher odds of grade retention only in the morning, but not in later school timings. Altogether, ensuring a better alignment between school timing and students’ biological rhythms might enhance future opportunities of adolescents.