Predicting Explicit and Implicit Attitudes towards Gay Men using the Dual Process Model of Prejudice and the Dark Tetrad

Although attitudes towards gay men are becoming increasingly inclusive, negative attitudes are still experienced by this socially marginalised group. Prejudice research often uses the DualProcess Model of Prejudice (DPM; comprised of right-wing authoritarianism and social dominance orientation) to understand negative social attitudes, and recently researchers have begun exploring the role of personality in addition to this theoretical framework. In this paper, we explored the predictive potential of the DPM (right-wing authoritarianism, social dominance orientation) and the dark tetrad model of personality (D4: narcissism, psychopathy, Machiavellianism, everyday sadism) in explaining implicit and explicit attitudes towards gaymen. The sample (N = 182; Mage = 39.15 years, SD = 10.65) was recruited using Amazon’s Mechanical Turk. Variance in explicit negative attitudes towards gay men was predicted by the ideological variables in the DPM, and further variance was predicted with the addition of the D4 traits (narcissism, psychopathy, and Machiavellianism each contributed unique variance, while sadism did not). Variance in implicit attitudes towards gay men was not predicted by any individual difference factors. The current study offers theoretical and empirical contributions to the ongoing debate surrounding the utility of the D4 in explaining antisocial cognitions

Traversing TechSex:Benefits and risks in digitally mediated sex and relationships

Background: Digital technologies play a significant role in people’s sexual and intimate lives via smart phones, cameras, dating apps and social media. Although there is a large body of research on the potential risks posed by these technologies, research on benefits and pleasures is limited. Methods: This study explored digital sexual practices, including perceptions of risks and benefits among a sample of Australian adults (n = 445). Data were collected in 2020 via an online survey. Descriptive and bivariate analyses were undertaken to identify significant relationships between demographic variables and the use of technologies in relation to perceived risks and benefits. The mean age of participants was 42 years, over half were women (58.5%) and identified as heterosexual (61.1%). Results: Findings reveal that use of digital media was common in participants’ sex lives and relationships; 60.3% of participants had viewed pornography online, 34.9% had used dating apps, and 33.9% had sent sexual or naked self-images to another person. Over one in three reported positive outcomes from this: 38.2% felt emotionally connected to their partners due to online communication; 38.0% agreed that digital technologies facilitated closer connections; however, the majority of participants were aware of potential risks associated with online sexual engagement, particularly non-consensual exposure of their sexual or naked images, with women expressing greater concern. Conclusions: Policy, legal and educational responses should be based on holistic understanding of digital sexual engagement, acknowledging the ways in which technologies can support sexual relationships while also building people’s knowledge and capacity to manage risks

A systematic literature review of the relationship between dark personality traits and antisocial online behaviours

The sub-clinical personality traits of narcissism, psychopathy, Machiavellianism, and everyday sadism (i.e., the dark triad/tetrad) are known to predict subversive behaviours. Given increases in the prevalence of social media and internet use, and the growing knowledge about the negative consequences of their use, it is important to understand how these traits relate to online behaviours. We conducted a systematic review of the evidence for these relationships and found 26 studies which reveal these traits are related to trolling, cyber-aggression, cyber-loafing, sending unsolicited explicit images, the non-consensual dissemination of ‘sexts’, cyberbullying, problematic social media usage, problematic online gaming, problematic internet use, internet-use disorder, social media addiction, intimate partner cyberstalking, technology facilitated sexual violence, and technology facilitated infidelity. The review revealed evidence that psychopathy is the trait most strongly associated with these behaviours - Machiavellianism and everyday sadism were also consistently related to these behaviours, albeit to a lesser degree. Narcissism is the trait least consistently related to antisocial online behaviours

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

International audienceLiquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on $10^3$ pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation