Search Results (39)

This paper investigates the concept of “populist attitudes” resulting from the emotional symbolization of the political world. While contemporary scholars link populism to individual attitudes or socio-economic shifts, we argue that understanding populism’s underlying dynamics offers a more nuanced perspective. Addressing these emotional symbolizations sheds light on how populist attitudes and structural conditions become salient and lead to a global rise. Using data from a nationally representative Italian survey, we studied the interaction between populist attitudes and three concepts often associated with populism, which we ascribed to the domain of the emotional symbolizations of the political world: alienation, powerlessness, and protest voting. Alienation reflects passive estrangement from politics, while powerlessness fuels citizens’ potential resentment, leading to protest voting as a reaction. By emphasizing the relationship between citizens and politics, this study contributes to a deeper understanding of the socio-cultural and psychological drivers of populist attitudes, disentangling the concept of “populism” from the risk of reified stances. We underscore that approaching this phenomenon within its broader dynamic can provide an innovative perspective. We call for further contextual approaches to enhance theoretical insights and inform practical strategies to address populism’s disruptive effects. Full article

While traditional self-supervised learning methods improve performance and robustness across various medical tasks, they rely on single-vector embeddings that may not capture fine-grained concepts such as anatomical structures or organs. The ability to identify such concepts and their characteristics without supervision has the potential to improve pre-training methods, and enable novel applications such as fine-grained image retrieval and concept-based outlier detection. In this paper, we introduce ConceptVAE, a novel pre-training framework that detects and disentangles fine-grained concepts from their style characteristics in a self-supervised manner. We present a suite of loss terms and model architecture primitives designed to discretise input data into a preset number of concepts along with their local style. We validate ConceptVAE both qualitatively and quantitatively, demonstrating its ability to detect fine-grained anatomical structures such as blood pools and septum walls from 2D cardiac echocardiographies. Quantitatively, ConceptVAE outperforms traditional self-supervised methods in tasks such as region-based instance retrieval, semantic segmentation, out-of-distribution detection, and object detection. Additionally, we explore the generation of in-distribution synthetic data that maintains the same concepts as the training data but with distinct styles, highlighting its potential for more calibrated data generation. Overall, our study introduces and validates a promising new pre-training technique based on concept-style disentanglement, opening multiple avenues for developing models for medical image analysis that are more interpretable and explainable than black-box approaches. Full article

Background: Many daily activities depend on visual inputs to improve motor accuracy and minimize errors. Reaching tasks present an ecological framework for examining these visuomotor interactions, but our comprehension of how different amounts of visual input affect motor outputs is still limited. The present study fills this gap, exploring how hand-related visual bias affects motor performance in a reaching task (to draw a line between two dots). Methods: Our setup allowed us to show and hide the visual feedback related to the hand position (cursor of a computer mouse), which was further disentangled from the visual input related to the task (tip of the line). Results: Data from 53 neurotypical participants indicated that, when the hand-related visual cue was visible and disentangled from the task-related visual cue, accommodating movements in response to spatial distortions were less accurate than when the visual cue was absent. Conclusions: We interpret these findings with reference to the concepts of motor affordance of visual cues, shifts between internally- and externally-oriented cognitive strategies to perform movements, and body-related reference frames. Full article

An innovative solution for real-time monitoring of reactions within confined spaces, optimized for Raman spectroscopy applications, is presented. This approach involves the utilization of a hollow-core waveguide configured as a compact flow cell, serving both as a conduit for Raman excitation and scattering and seamlessly integrating into the effluent stream of a cracking catalytic reactor. The analytical technique, encompassing device and optical design, ensures robustness, compactness, and cost-effectiveness for implementation into process facilities. Notably, the modularity of the approach empowers customization for diverse gas monitoring needs, as it readily adapts to the specific requirements of various sensing scenarios. As a proof of concept, the efficacy of a spectroscopic approach is shown by monitoring two catalytic processes: CO₂ methanation (CO₂ + 4H₂ → CH₄ + 2H₂O) and ammonia cracking (2NH₃ → N₂ + 3H₂). Leveraging chemometric data processing techniques, spectral signatures of the individual components involved in these reactions are effectively disentangled and the results are compared to mass spectrometry data. This robust methodology underscores the versatility and reliability of this monitoring system in complex chemical environments. Full article

In this paper, I demonstrate that, beyond the notion of prejudice, it is the whole Cartesian framework that Gadamer’s philosophical hermeneutics seems to reject in Truth and Method. I buttress this argument by addressing a gamut of central concepts, namely doubt, evidence, history, life, subjectivity, language, and, most importantly, method itself. In the course of the discussion, I emphasize many fine details of Gadamer’s approach to Cartesianism. I further ask whether Gadamer really succeeds in disentangling himself from a methodical demand that is fundamental for Descartes but that he also underscores in the works of other philosophers, such as Hegel and Heidegger. I suggest that Cartesianism thus appears enlightening to point out the complexities of Gadamer’s hermeneutics. Full article

Financial literacy assessments typically rely on sample surveys containing sets of questions designed to gauge respondents’ comprehension of fundamental financial concepts necessary for making informed decisions. The answers to such questions, either categorical or continuous in nature, generally include a “Do not know” option. If those who choose the “Do not know” option are not a random sample of the population but exhibit peculiar characteristics, treating these observations as either incorrect responses or as missing data may distort the results regarding the determinants of financial literacy. A noteworthy case lies in the observation from survey studies that women tend to choose the “Do not know” option more frequently than men. In similar cases, treating the “Do not know” responses as incorrect answers increases the gender gap in financial literacy while treating them as missing values reduces the gap. We propose using a model with sample selection, which enables us to disentangle the inclination to answer “Do not know” from actual responses. By applying this model to a representative sample of the UK population, we do not find any systematic gender gap in financial knowledge. The study’s novel treatment of “Do not know” responses contributes valuable insights to the broader discourse on the determinants of financial literacy and the related gender-based differences. Full article

Alain Badiou in his philosophy on ethics underscores four fields of truth procedures—love, politics, art, and science—that seek to break with the existing order or conventional flow of things. These four fields indicate both collective (politics, art, and science) as well as individual (love) instances of the subject’s relationships and actions. The individual realm of ‘love’, which is the central focus of this study, however, as a generic, complex category does not clearly explicate the significance of the associated concept, friendship. Akira Kurosawa’s filmography is illustrative as it opens up a possibility for disentangling the concept of friendship from love along with making significant contributions to the ethics of truth, particularly with respect to the “friendship event”. His films vividly capture some of the essential themes of Badiou’s philosophy of truth ethics, including “break”/“encounter”, referred to as ‘event’, “keep going”/“perseverance”, and “fidelity”. Even if the philosophers Badiou and Kurosawa do not make direct references to each other’s works, this research reveals significant parallels between cinephilosophy created through “cine-images” and the written philosophy. By analyzing Kurosawa’s films in the light of Badiou’s philosophy of truth ethics, and vice versa, this study embarks on exploring the complementarities between the works of the two. The study showcases how love and friendship as truth procedures are formed in particular contexts in Kurosawa’s filmography, and how they intersect with other truth events, particularly politics. Most importantly, this study does not view Badiou’s “truth events” such as love, friendship, and politics as mutually exclusive categories; rather, they are seen as complementary in practice. Full article

This paper discusses the pivotal role emotions can play in the higher prevalence of disease and mortality in Black populations in North America. There is a large body of research on the potentially harmful effect of negative emotions upon physical well-being. However, many scholars continue to interpret this link via a biological and reactive lens of emotion. By largely disentangling the embodiment of emotions from the traditional biological framework to which they are typically tied, we seek to analyze the nexus of race, emotion, and health through political, historical, and even ontological lenses. This analysis leverages Barrett’s theory of constructed emotion to elucidate the tangible impact of emotion on physical well-being and, in conjunction with Afropessimist metatheory on race, the potential contribution to understanding premature mortality among Black populations in North America. Barrett’s theory offers insight into how the persistent experience of negative emotions related to race can disrupt the delicate balance of an individual’s body-budget. The detrimental impact of White supremacy’s affective classifications and associated emotion concepts on Black populations is a stark reality, contributing significantly to daily health challenges faced by these communities in North America. Full article

The content-style duality is a fundamental element in art. These two dimensions can be easily differentiated by humans: content refers to the objects and concepts in an artwork, and style to the way it looks. Yet, we have not found a way to fully capture this duality with visual representations. While style transfer captures the visual appearance of a single artwork, it fails to generalize to larger sets. Similarly, supervised classification-based methods are impractical since the perception of style lies on a spectrum and not on categorical labels. We thus present GOYA, which captures the artistic knowledge of a cutting-edge generative model for disentangling content and style in art. Experiments show that GOYA explicitly learns to represent the two artistic dimensions (content and style) of the original artistic image, paving the way for leveraging generative models in art analysis. Full article

Topological data analysis (TDA) is a recent approach for analyzing and interpreting complex data sets based on ideas a branch of mathematics called algebraic topology. TDA has proven useful to disentangle non-trivial data structures in a broad range of data analytics problems including the study of cardiovascular signals. Here, we aim to provide an overview of the application of TDA to cardiovascular signals and its potential to enhance the understanding of cardiovascular diseases and their treatment in the form of a literature or narrative review. We first introduce the concept of TDA and its key techniques, including persistent homology, Mapper, and multidimensional scaling. We then discuss the use of TDA in analyzing various cardiovascular signals, including electrocardiography, photoplethysmography, and arterial stiffness. We also discuss the potential of TDA to improve the diagnosis and prognosis of cardiovascular diseases, as well as its limitations and challenges. Finally, we outline future directions for the use of TDA in cardiovascular signal analysis and its potential impact on clinical practice. Overall, TDA shows great promise as a powerful tool for the analysis of complex cardiovascular signals and may offer significant insights into the understanding and management of cardiovascular diseases. Full article

Research on Explainable Artificial Intelligence has recently started exploring the idea of producing explanations that, rather than being expressed in terms of low-level features, are encoded in terms of interpretable concepts learned from data. How to reliably acquire such concepts is, however, still fundamentally unclear. An agreed-upon notion of concept interpretability is missing, with the result that concepts used by both post hoc explainers and concept-based neural networks are acquired through a variety of mutually incompatible strategies. Critically, most of these neglect the human side of the problem: a representation is understandable only insofar as it can be understood by the human at the receiving end. The key challenge in human-interpretable representation learning (hrl) is how to model and operationalize this human element. In this work, we propose a mathematical framework for acquiring interpretable representations suitable for both post hoc explainers and concept-based neural networks. Our formalization of hrl builds on recent advances in causal representation learning and explicitly models a human stakeholder as an external observer. This allows us derive a principled notion of alignment between the machine’s representation and the vocabulary of concepts understood by the human. In doing so, we link alignment and interpretability through a simple and intuitive name transfer game, and clarify the relationship between alignment and a well-known property of representations, namely disentanglement. We also show that alignment is linked to the issue of undesirable correlations among concepts, also known as concept leakage, and to content-style separation, all through a general information-theoretic reformulation of these properties. Our conceptualization aims to bridge the gap between the human and algorithmic sides of interpretability and establish a stepping stone for new research on human-interpretable representations. Full article

Blockchain technology has generated an influx of transaction data and complex interactions, posing significant challenges for traditional machine learning methods, which struggle to capture high-dimensional patterns in transaction networks. In this paper, we present the disentangled prototypical graph convolutional network (DP-GCN), an innovative approach to account classification in Ethereum transaction records. Our method employs a unique disentanglement mechanism that isolates relevant features, enhancing pattern recognition within the network. Additionally, we apply prototyping to disentangled representations, to classify scam nodes robustly, despite extreme class imbalances. We further employ a joint learning strategy, combining triplet loss and prototypical loss with a gamma coefficient, achieving an effective balance between the two. Experiments on real Ethereum data showcase the success of our approach, as the DP-GCN attained an F1 score improvement of 32.54%p over the previous best-performing GCN model and an area under the ROC curve (AUC) improvement of 4.28%p by incorporating our novel disentangled prototyping concept. Our research highlights the importance of advanced techniques in detecting malicious activities within large-scale real-world cryptocurrency transactions. Full article

The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) offers an unparalleled opportunity to study cortical physiology by characterizing brain electrical responses to external perturbation, called transcranial-evoked potentials (TEPs). Although these reflect cortical post-synaptic potentials, they can be contaminated by auditory evoked potentials (AEPs) due to the TMS click, which partly show a similar spatial and temporal scalp distribution. Therefore, TEPs and AEPs can be difficult to disentangle by common statistical methods, especially in conditions of suboptimal AEP suppression. In this work, we explored the ability of machine learning algorithms to distinguish TEPs recorded with masking of the TMS click, AEPs and non-masked TEPs in a sample of healthy subjects. Overall, our classifier provided reliable results at the single-subject level, even for signals where differences were not shown in previous works. Classification accuracy (CA) was lower at the group level, when different subjects were used for training and test phases, and when three stimulation conditions instead of two were compared. Lastly, CA was higher when average, rather than single-trial TEPs, were used. In conclusion, this proof-of-concept study proposes machine learning as a promising tool to separate pure TEPs from those contaminated by sensory input. Full article

The concreteness effect (CE), namely a better performance with concrete compared to abstract concepts, is a constant feature in healthy people, and it usually increases in persons with aphasia (PWA). However, a reversal of the CE has been reported in patients affected by the semantic variant of Primary Progressive Aphasia (svPPA), a neurodegenerative disease characterized by anterior temporal lobe (ATL) atrophy. The present scoping review aims at identifying the extent of evidence regarding the abstract/concrete contrast in Alzheimer’s disease (AD) and svPPA and associated brain atrophy. Five online databases were searched up to January 2023 to identify papers where both concrete and abstract concepts were investigated. Thirty-one papers were selected and showed that while in patients with AD, concrete words were better processes than abstract ones, in most svPPA patients, there was a reversal of the CE, with five studies correlating the size of this effect with ATL atrophy. Furthermore, the reversal of CE was associated with category-specific impairments (living things) and with a selective deficit of social words. Future work is needed to disentangle the role of specific portions of the ATL in concept representation. Full article

High-throughput sequencing technologies have revolutionized microbial diversity studies, shedding light on the oceans’ plankton evolution, distribution, and biological activity. Whereas marine prokaryotes have been more extensively studied and specific methods developed, the research on microbial eukaryotes (protists) is falling behind, with major groups still largely unknown regarding their ecology and function. Because of numerous anthropogenic pressures, it is increasingly important to highlight the functional roles of protists in marine ecosystems. This review outlines the practices, challenges, and opportunities of high-throughput sequencing approaches (i.e., metabarcoding, metagenomics, and metatranscriptomics) to disentangle evolutionary, ecological, and functional aspects of protists in the ocean. These multidimensional approaches allow us to move from the classic picture of microbial biogeography towards functional microbial biogeography, explicitly highlighting the role of protists therein. We provide resources for functional classification and reflect on the current and future potential. We outline aspects of detecting and describing ecosystem changes at the species, population, and community levels, advancing methodological approaches for studying taxonomic diversity towards functional and evolutionary biodiversity concepts, seeking a more complete understanding and monitoring of ocean ecosystems. Full article