Through centuries and across continents, the singular condition which brings afflicted individuals in conflict with society is what might be broadly regarded as psychopathic disorder. Within the considerable body of knowledge amassed, the investigator who has indisputably made the most substantial contribution is Robert Hare, whose conceptualization of psychopathy is measurable as a superarching dimension and with subdimensions or factors and their facets. The instrument for identifying and measuring psychopathy, the Psychopathy Checklist-Revised (PCL-R), commonly regarded as the gold standard for psychopathy research (Westen Weinberger, 2004), is widely used internationally for psychopathy research. Authors from four international domains – Germanic, Italian, Nordic and English – briefly describe the development of concepts of the psychopathic disorder, the influence of psychopathy as defined by Hare, as well as research and practical applications of PCL-R psychopathy and/or specific contributions concerning psychopathy within each of the domains. From the richness and diversity of investigative productivity within each of these domains, one can readily appreciate the impact of Hare’s work both within and between these major domains.
After the peak of the recent hype wave of interest surrounding the metaverse, virtual world applications remained in areas such as gaming, VR training, simulations, and collaboration. In this context, recordings are created which subsequently evolve into extensive collections that users may wish to access, search through, and retrieve items from. In order to facilitate searchability of metaverse recordings, it is necessary to adapt content analysis and indexing techniques to the specific characteristics of these recordings. This paper presents a reference model, the Processing Framework for Metaverse Recordings (PFMR), which details the phases of structural analysis, feature extraction, data mining, and feature fusion. The objective is to facilitate efficient retrieval of metaverse content. Our evaluation, based on a prototypical implementation, demonstrates the applicability and effectiveness of PFMR. This lays the groundwork for further integration of metaverse-specific content into Multimedia Information Retrieval systems. The evaluation of the 256 Metaverse Recording dataset shows that PFMRs' domain-specific adaptability and integratability allows effective metaverse recording information retrieval for metaverse-specific features such as avatar detection, dialog mining, and toxicity classification.
Electroencephalography (EEG) is a powerful tool for investigating neural processes underlying cognition and neuropsychiatric disorders. Yet, variability in EEG preprocessing strategies restricts reproducibility and data integration across study sites and laboratories, particularly in larger research consortia. This paper introduces the CLEAN-EEG preprocessing pipeline, designed to standardize data processing and documentation across multiple sites. The CLEAN pipeline is implemented in MATLAB using EEGLAB. It comprises three modular, script-based stages: main preprocessing (including down-sampling, filtering, line noise removal, and channel interpolation), independent component analysis preparation and decomposition with flexible options for artifact rejection or neural component extraction, and component exclusion with support for automated classification and dipole fitting. Emphasis is placed on transparency through comprehensive logging and quality-control plotting, as well as on minimizing rank reduction to preserve data suitability for advanced analyses such as source localization and connectivity modeling. By providing clear, adaptable recommendations while ensuring detailed documentation of every step, the CLEAN pipeline aims to harmonize EEG preprocessing in large-scale, multi-center studies. This open and reproducible approach facilitates high throughput analyses, supports the training of researchers, and enables the rigorous integration of neurophysiological data across study sites, study designs, and populations.
Causal discovery with latent variables is a fundamental task. Yet most existing methods rely on strong structural assumptions, such as enforcing specific indicator patterns for latents or restricting how they can interact with others. We argue that a core obstacle to a general, structural-assumption-free approach is the lack of an equivalence characterization: without knowing what can be identified, one generally cannot design methods for how to identify it. In this work, we aim to close this gap for linear non-Gaussian models. We establish the graphical criterion for when two graphs with arbitrary latent structure and cycles are distributionally equivalent, that is, they induce the same observed distribution set. Key to our approach is a new tool, edge rank constraints, which fills a missing piece in the toolbox for latent-variable causal discovery in even broader settings. We further provide a procedure to traverse the whole equivalence class and develop an algorithm to recover models from data up to such equivalence. To our knowledge, this is the first equivalence characterization with latent variables in any parametric setting without structural assumptions, and hence the first structural-assumption-free discovery method. Code and an interactive demo are available at https://equiv.cc.
The solar rebound effect (SRE) occurs when rooftop photovoltaic adoption increases household electricity consumption, driven by the perception of solar energy as a free resource. Although empirically observed, the SRE has not yet been accounted for in energy system modelling or abatement scenarios. This study integrates empirically derived SRE intensities into an open-source optimization model of the European energy system, translating behavioural effects into temporally distinct demand profiles. The results show that not only the magnitude but also the timing of the rebound determines its system impact. Additional demand leads to increases in renewable investment needs, flexibility requirements and overall system costs while inducing regressive effects, as households driving the rebound do not bear its full costs. The findings call for explicit inclusion of SRE in abatement scenarios and grid planning and highlight load-shifting incentives and energy efficiency policies as key tools to mitigate rebound effects and align demand with renewable generation.
