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Attention-based dynamic multilayer graph neural networks for loan default prediction
(2025-03-01) Zandi, Sahab; Korangi, Kamesh; Óskarsdóttir, María; Mues, Christophe; Bravo, Cristián; Department of Computer Science
Whereas traditional credit scoring tends to employ only individual borrower- or loan-level predictors, it has been acknowledged for some time that connections between borrowers may result in default risk propagating over a network. In this paper, we present a model for credit risk assessment leveraging a dynamic multilayer network built from a Graph Neural Network and a Recurrent Neural Network, each layer reflecting a different source of network connection. We test our methodology in a behavioural credit scoring context using a dataset provided by U.S. mortgage financier Freddie Mac, in which different types of connections arise from the geographical location of the borrower and their choice of mortgage provider. The proposed model considers both types of connections and the evolution of these connections over time. We enhance the model by using a custom attention mechanism that weights the different time snapshots according to their importance. After testing multiple configurations, a model with GAT, LSTM, and the attention mechanism provides the best results. Empirical results demonstrate that, when it comes to predicting probability of default for the borrowers, our proposed model brings both better results and novel insights for the analysis of the importance of connections and timestamps, compared to traditional methods.
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Time–frequency ridge characterisation of sleep stage transitions : Towards improving electroencephalogram annotations using an advanced visualisation technique
(2025-03-01) McCausland, Christopher; Biglarbeigi, Pardis; Bond, Raymond; Yadollahikhales, Golnaz; Kennedy, Alan; Islind, Anna Sigridur; Arnardóttir, Erna Sif; Finlay, Dewar; Department of Computer Science; Department of Engineering
Manual sleep stage scoring of polysomnography recordings is an expensive and time-consuming process, further complicated by inconsistent sleep stage agreement among sleep experts (clinicians and sleep technologists). Hence, development of automated sleep scoring algorithms are an emerging topic of interest. Automation typically mimics the clinical decision path by implementing a series of predefined rules, such as the American Academy of Sleep Medicine's (AASM) scoring manual. Recently, data driven methods have emerged using machine or deep learning. Both manual and automated methods of scoring have known limitations; primarily, unacceptable variation in agreement between different scorers and algorithms. Within the literature, electroencephalogram (EEG) frequency is an important feature considered by both sleep experts and automated approaches for classifying sleep stages. This study presents a novel approach to sleep stage analysis, by developing a methodology to precisely determine the temporal location of sleep stage transitions. The current gold standard fails to identify such transitional changes, which leads to poor inter-scorer reliability. Therefore, development and implementation of such methodologies is a crucial, but overlooked, step in improving the consistency of scoring within sleep studies. In this work, EEG time–frequency ridge analysis was used to characterise the dominant frequency component of EEG signals in time, at the point of sleep stage transition. An in-depth analysis of N3 → N2 and N2 → N3 transitions in the 2018 PhysioNet challenge “You Snooze, You Win” and the Wisconsin Sleep Cohort (WSC) datasets (n = 994, n = 742; approximately 13,888 h of sleep data) showed consistent time–frequency patterns at the point of transition, from one sleep stage to another. This methodology allows simple and ‘interpretable’ features to be generated in future work, to precisely identify the temporal location of sleep stage transitions with the aim of improving inter-scorer reliability.
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Between Scylla and Charybdis : Fixed-ratio avoidance response effort and unavoidable shock extinction in humans
(2025-02-04) Dymond, Simon; Xia, Weike; Zuj, Daniel V.; Quigley, Martyn; Department of Psychology
Avoidance of potential threat may become maladaptive when it is indiscriminate and resistant to change. Here, we investigated the resistance to change of high and low avoidance response effort when avoidance extinction involved unavoidable presentations of the aversive event (shock) in humans. Following fear conditioning, participants prevented upcoming shock delivery by responding on high (i.e., fixed ratio, FR-20) and low (FR-5) negative reinforcement schedules. Next, noneliminable shock was used for an avoidance extinction procedure whereby responding was followed by, rather than prevented, shock. During a subsequent standard extinction and response prevention test phase, we found that High effort (FR-20) avoidance would be more readily extinguished than Low effort (FR-5) avoidance. It was also predicted that fear, threat expectancy, and psychophysiological (skin conductance) responses would decrease on avoidable trials and increase on unavoidable trials before extinguishing to low levels. It was found that in the final extinction re-test phase when avoidance was possible, responding increased, particularly for low effort cues. Both fear and expectancy remained high. Individual differences on clinically relevant measures of trait anxiety, intolerance of uncertainty and experiential avoidance were associated with greater levels of fear and threat expectancy. Overall, unavoidable shock extinction may hold promise for further translational investigations of avoidance learning, extinction, and clinical treatment development.
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SMEs on the way to a circular economy : insights from a multi-perspective review
(2025-02) Ahmadov, Tarlan; Durst, Susanne; Gerstlberger, Wolfgang; Kraut, Elisabeth; Department of Business and Economics
The transition to a circular economy (CE) has garnered widespread attention as a solution to address economic, environmental, and social challenges. While large enterprises and policymakers have made steps in adopting CE practices, small and medium-sized enterprises (SMEs) face unique challenges due to limited resources and expertise. Understanding the multi-level perspective (MLP) is essential for SMEs to successfully transition to a CE, as it considers factors at the micro, meso, and macro levels. However, current research often focuses on single levels, necessitating a comprehensive understanding of the phenomenon through systematic research. To address this need, this study conducts a systematic literature review (SLR) using the MLP framework to analyse existing research on SMEs' transition to a CE. The study aims to identify macro-, meso-, and micro-level factors, actors, and mechanisms influencing the transition process. The SLR contributes to academic understanding by developing a conceptual model that elucidates the dynamics of the circular transition process within SMEs. Additionally, it provides practical recommendations to support SMEs in navigating the transition successfully. The adoption of the MLP framework empowers SMEs, policymakers, industry associations, and consumers to play their roles effectively in driving the CE transition. While the study acknowledges certain limitations, it opens avenues for future research and enhancement of CE practices in SMEs.
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Disease Burden Attributed to Drug use in the Nordic Countries : a Systematic Analysis for the Global Burden of Diseases, Injuries, and Risk Factors Study 2019
(2025-02) GBD 2019 Nordic Drug Use Collaborators; Department of Psychology
The Nordic countries share similarities in many social and welfare domains, but drug policies have varied over time and between countries. We wanted to compare differences in mortality and disease burden attributed to drug use over time. Using results from the Global Burden of Disease (GBD) study, we extracted age-standardized estimates of deaths, DALYs, YLLs and YLDs per 100 000 population for Denmark, Finland, Iceland, Norway, and Sweden during the years 1990 to 2019. Among males, DALY rates in 2019 were highest in Finland and lowest in Iceland. Among females, DALY rates in 2019 were highest in Iceland and lowest in Sweden. Sweden have had the highest increase in burden since 1990, from 252 DALYs to 694 among males, and from 111 to 193 among females. Norway had a peak with highest level of all countries in 2001–2004 and thereafter a strong decline. Denmark have had the most constant burden over time, 566–600 DALYs among males from 1990 to 2010 and 210–240 DALYs among females. Strict drug policies in Nordic countries have not prevented an increase in some countries, so policies need to be reviewed.

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