Gender Matters! Analyzing Global Cultural Gender Preferences for Venues Using Social Sensing

Gender differences is a phenomenon around the world actively researched by social scientists. Traditionally, the data used to support such studies is manually obtained, often through surveys with volunteers. However, due to their inherent high costs because of manual steps, such traditional methods do not quickly scale to large-size studies. We here investigate a particular aspect of gender differences: preferences for venues. To that end we explore the use of check-in data collected from Foursquare to estimate cultural gender preferences for venues in the physical world. For that, we first demonstrate that by analyzing the check-in data in various regions of the world we can find significant differences in preferences for specific venues between gender groups. Some of these significant differences reflect well-known cultural patterns. Moreover, we also gathered evidence that our methodology offers useful information about gender preference for venues in a given region in the real world. This suggests that gender and venue preferences observed may not be independent. Our results suggests that our proposed methodology could be a promising tool to support studies on gender preferences for venues at different spatial granularities around the world, being faster and cheaper than traditional methods, besides quickly capturing changes in the real world

Towards Understanding Political Interactions on Instagram

Online Social Networks (OSNs) allow personalities and companies to communicate directly with the public, bypassing filters of traditional medias. As people rely on OSNs to stay up-to-date, the political debate has moved online too. We witness the sudden explosion of harsh political debates and the dissemination of rumours in OSNs. Identifying such behaviour requires a deep understanding on how people interact via OSNs during political debates. We present a preliminary study of interactions in a popular OSN, namely Instagram. We take Italy as a case study in the period before the 2019 European Elections. We observe the activity of top Italian Instagram profiles in different categories: politics, music, sport and show. We record their posts for more than two months, tracking "likes" and comments from users. Results suggest that profiles of politicians attract markedly different interactions than other categories. People tend to comment more, with longer comments, debating for longer time, with a large number of replies, most of which are not explicitly solicited. Moreover, comments tend to come from a small group of very active users. Finally, we witness substantial differences when comparing profiles of different parties.Comment: 5 pages, 8 figure

Higher Order Asymptotics of Decaying Solutions of some Generalized Burgers Equations

We study the large-time behavior of solutions to a generalized Burgers Equation, with initial zero mass data. Our main purpose is to present a modified version of the Renormalization Group map, which is able to provide the higher order asymptotic properties of the solution to the Cauchy problem of a class of nonlinear time-evolution problems.Comment: 25 page

Performance Prediction of Cloud-Based Big Data Applications

Big data analytics have become widespread as a means to extract knowledge from large datasets. Yet, the heterogeneity and irregular- ity usually associated with big data applications often overwhelm the existing software and hardware infrastructures. In such con- text, the exibility and elasticity provided by the cloud computing paradigm o er a natural approach to cost-e ectively adapting the allocated resources to the application’s current needs. However, these same characteristics impose extra challenges to predicting the performance of cloud-based big data applications, a key step to proper management and planning. This paper explores three modeling approaches for performance prediction of cloud-based big data applications. We evaluate two queuing-based analytical models and a novel fast ad hoc simulator in various scenarios based on di erent applications and infrastructure setups. The three ap- proaches are compared in terms of prediction accuracy, nding that our best approaches can predict average application execution times with 26% relative error in the very worst case and about 7% on average

Role of SOCS3 in POMC Neurons in metabolic and cardiovascular regulation.

Suppressor of cytokine signaling 3 (SOCS3) is a negative regulator of leptin signaling. We previously showed that the chronic effects of leptin on blood pressure (BP) and glucose regulation are mediated by stimulation of pro-opiomelanocortin (POMC) neurons. In this study, we examined the importance of endogenous SOCS3 in POMC neurons in control of metabolic and cardiovascular function and potential sex differences. Male and female SOCS3flox/flox/POMC-Cre mice in which SOCS3 was selectively deleted in POMC neurons and control SOCS3flox/flox mice were studied during a control diet (CD) or high fat diet (HFD) and during chronic leptin infusion. On CD, male and female SOCS3flox/flox/POMC-Cre mice were lighter in body weight despite similar food intake compared to control mice. Male SOCS3flox/flox/POMC-Cre mice exhibited increased energy expenditure. BP and heart rate were similar in male and female SOCS3flox/flox/POMC-Cre and control mice on CD. On a HFD, male and female SOCS3flox/flox/POMC-Cre mice showed attenuated weight gain. Female SOCS3flox/flox/POMC-Cre mice exhibited greater HFD-induced elevations in baseline BP and BP responses to air jet stress test compared to control mice. Chronic leptin infusion produced similar responses in all groups for food intake, body weight, oxygen consumption, blood glucose, BP and heart rate. Thus, SOCS3 deficiency in POMC neurons influences body weight regulation in CD and HFD and differentially affects BP and energy balance in a sex specific manner, but does not amplify the dietary, glycemic or cardiovascular effects of leptin

On network backbone extraction for modeling online collective behavior

Collective user behavior in social media applications often drives several important online and offline phenomena linked to the spread of opinions and information. Several studies have focused on the analysis of such phenomena using networks to model user interactions, represented by edges. However, only a fraction of edges contribute to the actual investigation. Even worse, the often large number of non-relevant edges may obfuscate the salient interactions, blurring the underlying structures and user communities that capture the collective behavior patterns driving the target phenomenon. To solve this issue, researchers have proposed several network backbone extraction techniques to obtain a reduced and representative version of the network that better explains the phenomenon of interest. Each technique has its specific assumptions and procedure to extract the backbone. However, the literature lacks a clear methodology to highlight such assumptions, discuss how they affect the choice of a method and offer validation strategies in scenarios where no ground truth exists. In this work, we fill this gap by proposing a principled methodology for comparing and selecting the most appropriate backbone extraction method given a phenomenon of interest. We characterize ten state-of-the-art techniques in terms of their assumptions, requirements, and other aspects that one must consider to apply them in practice. We present four steps to apply, evaluate and select the best method(s) to a given target phenomenon. We validate our approach using two case studies with different requirements: online discussions on Instagram and coordinated behavior in WhatsApp groups. We show that each method can produce very different backbones, underlying that the choice of an adequate method is of utmost importance to reveal valuable knowledge about the particular phenomenon under investigation

Towards Understanding Political Interactions on Instagram

Online Social Networks (OSNs) allow personalities and companies to communicate directly with the public, bypassing filters of traditional medias. As people rely on OSNs to stay up-to-date, the political debate has moved online too. We witness the sudden explosion of harsh political debates and the dissemination of rumours in OSNs. Identifying such behaviour requires a deep understaning on how people interact via OSNs during political debates. We present a preliminary study of interactions in a popular OSN, namely Instagram. We take Italy as a case study in the period before the 2019 European Elections. We observe the activity of top Italian Instagram profiles in different categories: politics, music, sport and show. We record their posts for more than two months, tracking "likes'' and comments from users. Results suggest that profiles of politicians attract markedly different interactions than other categories. People tend to comment more, with longer comments, debating for longer time, with a large number of replies, most of which are not explicitly solicited. Moreover, comments tend to come from a small group of very active users. Finally, we witness substantial differences when comparing profiles of different parties

Targeting immunometabolism during cardiorenal injury: roles of conventional and alternative macrophage metabolic fuels

Macrophages play critical roles in mediating and resolving tissue injury as well as tissue remodeling during cardiorenal disease. Altered immunometabolism, particularly macrophage metabolism, is a critical underlying mechanism of immune dysfunction and inflammation, particularly in individuals with underlying metabolic abnormalities. In this review, we discuss the critical roles of macrophages in cardiac and renal injury and disease. We also highlight the roles of macrophage metabolism and discuss metabolic abnormalities, such as obesity and diabetes, which may impair normal macrophage metabolism and thus predispose individuals to cardiorenal inflammation and injury. As the roles of macrophage glucose and fatty acid metabolism have been extensively discussed elsewhere, we focus on the roles of alternative fuels, such as lactate and ketones, which play underappreciated roles during cardiac and renal injury and heavily influence macrophage phenotypes

Restoration of Cardiac Function After Myocardial Infarction by Long-Term Activation of the CNS Leptin-Melanocortin System

Heart failure has a high mortality rate, and current therapies offer limited benefits. The authors demonstrate that activation of the central nervous system leptin-melanocortin pathway confers remarkable protection against progressive heart failure following severe myocardial infarction. The beneficial cardiac-protective actions of leptin require activation of brain melanocortin-4 receptors and elicit improvements in cardiac substrate oxidation, cardiomyocyte contractility, C