Norm Diffusion or Norm Backsliding? A Text Analysis of Anti-LGBTQ Rhetoric

How are counter-norms manufactured, and what are their components? Current literature on norm diffusion largely views the process as linearly progressive. In other words, norms progress towards greater inclusion. However, notable cases such as Russian President Vladimir Putin’s controversial use of traditional values in the UN Human Rights Council demonstrates a potential backlash to this progression. Building on our previous case study work of Russian anti-LGBTQ rhetoric and its spread to the United States, in this paper we examine the timing and content of anti-LGBTQ rhetoric using text analysis. This allows us to model the sequencing of messaging and whether certain framing becomes pervasive. Specifically, we aim to test whether anti-LGBTQ statements in Western facing Russian media precede and inspire American anti-LGBTQ narratives, or whether they follow and amplify existing anti-LGBTQ narratives

Shaming the Truth: Naming and Shaming and Transitional Justice

While it is generally recognized that “naming and shaming” carried out by transnational human rights actors can lead to an improvement in aggregate conditions, it is less clear whether this strategy influences more specific behavior. As more states are democratizing, the international community has stepped up efforts at transitional justice to promote accountability and reconciliation. What is unclear is whether this promotion has been positive or negative for the pursuit of transitional justice broadly or if the community prioritizes some mechanisms over others. In this paper, we examine the role that human rights advocacy plays in the onset of transitional justice mechanisms. Using events data to disaggregate naming and shaming across state, intergovernmental, and nongovernmental actors, we test which kinds of activities have the greatest impact on the implementation of transitional justice in post-conflict settings

Counter-Diffusion: Does Russian Propaganda Wind Up in America?

Does the norm diffusion process work in reverse? Specifically, does the success of the Russian government in building counternarratives and counternorms to reinforce its authoritarian government mean they have the ability to diminish successful human rights advocacy in the United States? This project examines whether the rhetoric used to justify anti-LGBT policies in Russia are broadcast and adopted by anti-LGBT groups in the United States. In the United States, public support for LGBT civil rights is often cited as a success story in the adoption and diffusion of human rights norms. Often, this is used as evidence of broadening norm adoption. However, this local success has not been followed by global success. Russia, for example, remains as a country that largely denies LGBT rights and criminalizes advocacy as “homopropaganda.” Rather than causing public backlash, this position is met with widespread public support in Russia. We expect the existence of a successful counternorm in one country to be adopted and weaponized against the same group in another country where human rights norms have been adopted. We examine this question by collecting public statements and stories issued by Russian state media that reference LGBT rights issues. We then compare them to statements made by American ant-LGBT groups, measuring for changes in content. We expect that, over time, American groups start to use rhetoric similar to that used by Russia

Automated recognition of sleep arousal using multimodal and personalized deep ensembles of neural networks

Background and Aim: Monitoring physiological signals during sleep can have substantial impact on detecting temporary intrusion of wakefulness, referred to as sleep arousals. To overcome the problems associated with the cubersome visual inspection of these events by experts, sleep arousal recognition algorithms have been proposed. Method: As part of the Physionet/Computing in Cardiology Challenge 2018, this study proposes a deep ensemble neural network architecture for automatic arousal recognition from multi-modal sensor signals. Separate branches of the neural network extract features from electro-encephalography, electrooculography, electromyogram, breathing patterns and oxygen saturation level; and a final fully-connected neural network combines features computed from the signal sources to estimate the probability of arousal in each region of interest. We investigate the use of shared-parameter Siamese architectures for effective feature calibration. Namely, at each forward and backward pass through the network we concatenate to the input a user-specific template signal that is processed by an identical copy of the network. Result: The proposed architecture obtains an AUPR score of 0.40 on the test set of the official phase of Physionet/CbiC Challenge 2018. A score of 0.45 is obtained by means of 10 -fold cross-validation on the training set

Thermal inertia of heavyweight traditional buildings: Experimental measurements and simulated scenarios

Abstract This paper discusses the results of an experimental campaign aimed to describe the thermal performance of a traditional building located in Catania, Southern Italy. The building was built in the early 1900s with traditional techniques and local materials, namely basalt stones, and is currently used for residential purposes. The results of the experimental campaign are exploited to calibrate a model for the dynamic simulation of the building with DesignBuilder. The calibrated model is then used to simulate how the same building would behave with a modern envelope made of a double leaf of bricks; other simulations take into account possible retrofit solutions, such as the installation of an insulating material either on the inner or the outer side of the walls, as well as the role of nighttime natural ventilation

Tuning the Interlayer Distance of Graphene Oxide as a Function of the Oxidation Degree for o-Toluidine Removal

Graphene oxide (GO) with different oxidation degrees is prepared by a modified Hummers' method varying KMnO4 amount from 0.5 to 6.0 g. X-ray powder diffraction (XRPD), micro-Raman, thermogravimetric analysis, X-ray photoeelectron spectroscopy, Boehm titrations, high-resolution transmission electron microscopy, and, finally, positron annihilation lifetime spectroscopy (PALS) are exploited to assess the properties of GO. Results show that increasing oxidant species can tune the interlayer gap between GO sheets up to a maximum value in the case of 4.0 g KMnO4 content. Moreover, these results validate the two-component-based model of GO in which, at low oxidation degree, there are unsplit/isolated graphene planes, instead at higher oxidant amounts, a five-layer sandwiched configuration occurs comprising graphene planes having functional groups decorating the edges (bwGO), hydrated oxidative debris (OD) and "empty" spaces (revealed by PALS as the distance between (bwGO + OD) two-component layers). In addition, by XRPD analysis, the total gap between two sheets is easily computed. In order to correlate these findings to pollutant removal capability, planar o-toluidine adsorption is studied. Since this molecule diffuses in an aqueous environment, the obtained adsorption percentages are compared to the thickness of the hydrated OD grafted onto bwGO. A strict connection between the pollutant removal efficacy and the variation of the hydrated interlayer distance is found

Classifying the unknown: discovering novel gravitational-wave detector glitches using similarity learning

The observation of gravitational waves from compact binary coalescences by LIGO and Virgo has begun a new era in astronomy. A critical challenge in making detections is determining whether loud transient features in the data are caused by gravitational waves or by instrumental or environmental sources. The citizen-science project \emph{Gravity Spy} has been demonstrated as an efficient infrastructure for classifying known types of noise transients (glitches) through a combination of data analysis performed by both citizen volunteers and machine learning. We present the next iteration of this project, using similarity indices to empower citizen scientists to create large data sets of unknown transients, which can then be used to facilitate supervised machine-learning characterization. This new evolution aims to alleviate a persistent challenge that plagues both citizen-science and instrumental detector work: the ability to build large samples of relatively rare events. Using two families of transient noise that appeared unexpectedly during LIGO's second observing run (O2), we demonstrate the impact that the similarity indices could have had on finding these new glitch types in the Gravity Spy program

Light Emission Properties of Thermally Evaporated CH3 NH3 PbBr3 Perovskite from Nano-to Macro-Scale: Role of Free and Localized Excitons

Over the past decade, interest about metal halide perovskites has rapidly increased, as they can find wide application in optoelectronic devices. Nevertheless, although thermal evaporation is crucial for the development and engineering of such devices based on multilayer structures, the optical properties of thermally deposited perovskite layers (spontaneous and amplified spontaneous emission) have been poorly investigated. This paper is a study from a nano-to micro-and macro-scale about the role of light-emitting species (namely free carriers and excitons) and trap states in the spontaneous emission of thermally evaporated thin layers of CH3 NH3 PbBr3 perovskite after wet air UV light trap passivation. The map of light emission from grains, carried out by SNOM at the nanoscale and by micro-PL techniques, clearly indicates that free and localized excitons (EXs) are the dominant light-emitting species, the localized excitons being the dominant ones in the presence of crystallites. These species also have a key role in the amplified spontaneous emission (ASE) process: for higher excitation densities, the relative contribution of localized EXs basically remains constant, while a clear competition between ASE and free EXs spontaneous emission is present, which suggests that ASE is due to stimulated emission from the free EXs

Impact of longitudinal social support and loneliness trajectories on mental health during the COVID-19 pandemic in France

(1) Background: Little is known about how the COVID-19 pandemic has impacted social support and loneliness over time and how this may predict subsequent mental health problems. This study aims to determine longitudinal trajectories of social support and loneliness in the French general population during the first year of the COVID-19 pandemic and study whether variations in these trajectories are associated with symptoms of depression and anxiety; (2) Methods: Analyses were based on data from 681 French participants in the international COVID-19 Mental Health Study (COMET) study, collected at four periods of time between May 2020 and April 2021. Group-based trajectory modelling (GBTM) was used to determine social support and loneliness trajectories. Associations between the identified trajectories and symptoms of depression and anxiety, measured with the Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder scale (GAD-7), were tested through multivariate linear regression models; (3) Results: Social support trajectories revealed four stable groups: ‘poor’ (17.0%), ‘moderate’ (42.4%), ‘strong’ (35.4%) and ‘very strong’ (5.1%). Loneliness trajectories also identified four groups: ‘low stable’ (17.8%), ‘low rising’ (40.2%), ‘moderate stable’ (37.6%) and ‘high rising’ (5.0%). Elevated symptoms of depression were associated with poor social support as well as all identified loneliness trajectories, while high levels of anxiety were associated with moderate stable and high rising loneliness trajectories; (4) Conclusions: High and increasing levels of loneliness are associated with increased symptoms of depression and anxiety during the pandemic. Interventions to address loneliness are essential to prevent common mental health problems during the pandemic and afterwards

Influence of magnetic micelles on assembly and deposition of porphyrin J‐aggregates

Clusters of superparamagnetic iron oxide nanoparticles (SPIONs) have been incorporated into the hydrophobic core of polyethylene glycol (PEG)‐modified phospholipid micelles. Two different PEG‐phospholipids have been selected to guarantee water solubility and provide an external corona, bearing neutral (SPIONs@PEG‐micelles) or positively charged amino groups (SPIONs@NH2‐PEG‐micelles). Under acidic conditions and with specific mixing protocols (porphyrin first, PF, or porphyrin last, PL), the water‐soluble 5,10,15,20‐tetrakis‐(4‐ sulfonatophenyl)‐porphyrin (TPPS) forms chiral J‐aggregates, and in the presence of the two different types of magnetic micelles, an increase of the aggregation rates has been generally observed. In the case of the neutral SPIONs@PEG‐micelles, PL protocol affords a stable nanosystem, whereas PF protocol is effective with the charged SPIONs@NH2‐PEG‐micelles. In both cases, chiral J‐aggregates embedded into the magnetic micelles (TPPS@SPIONs@micelles) have been characterized in solution through UV/vis absorption and circular/linear dichroism. An external magnetic field allows depositing films of the TPPS@SPIONs@micelles that retain their chiroptical properties and exhibit a high degree of alignment, which is also confirmed by atomic force microscopy