[article-cris] Perustieteiden korkeakoulu / SCI

Permanent URI for this collection

Browse

Recent Submissions

Now showing 1 - 20 of 6907
  • Item
    Minors’ and guardian access to and use of a national patient portal : A retrospective comparative case study of Sweden and Finland
    (Elsevier Ireland Ltd, 2024-07) Hagström, Josefin; Hägglund, Maria; Holmroos, Mari; Lähteenmäki, Päivi; Hörhammer, Iiris; Department of Computer Science; Lecturer Kujala Sari group; Uppsala University; Social Insurance Institution of Finland; Karolinska Institutet
    Background: Approaches to implementing online record access (ORA) via patient portals for minors and guardians vary internationally, as more countries continue to develop patient-accessible electronic health records (PAEHR) systems. Evidence of ORA usage and country-specific practices to allow or block minors’ and guardians’ access to minors’ records during adolescence (i.e. access control practices) may provide a broader understanding of possible approaches and their implications for minors' confidentiality and guardian support. Aim: To describe and compare minors’ and guardian proxy users’ PAEHR usage in Sweden and Finland. Furthermore, to investigate the use of country-specific access control practices. Methods: A retrospective, observational case study was conducted. Data were collected from PAEHR administration services in Sweden and Finland and proportional use was calculated based on population statistics. Descriptive statistics were used to analyze the results. Results: In both Sweden and Finland, the proportion of adolescents accessing their PAEHR increased from younger to older age-groups reaching the proportion of 59.9 % in Sweden and 84.8 % in Finland in the age-group of 17-year-olds. The PAEHR access gap during early adolescence in Sweden may explain the lower proportion of users among those who enter adulthood. Around half of guardians in Finland accessed their minor children's records in 2022 (46.1 %), while Swedish guardian use was the highest in 2022 for newborn children (41.8 %), and decreased thereafter. Few, mainly guardians, applied for extended access in Sweden. In Finland, where a case-by-case approach to access control relies on healthcare professionals' (HCPs) consideration of a minor's maturity, 95.8 % of minors chose to disclose prescription information to their guardians. Conclusion: While age-based access control practices can hamper ORA for minors and guardians, case-by-case approach requires HCP resources and careful guidance to ensure equality between patients. Guardians primarily access minors’ records during early childhood and adolescents show willingness to share their PAEHR with parents.
  • Item
    Understanding the predictability of path flow distribution in urban road networks using an information entropy approach
    (Elsevier, 2024-06) Guo, Bao; Huang, Zhiren; Zheng, Zhihao; Zhang, Fan; Wang, Pu; Department of Computer Science; Central South University; Shenzhen Institute of Advanced Technology
    Predicting the distributions of path flow between origin-destination (OD) pairs in an urban road network is crucial for developing efficient traffic control and management strategies. Here, we use the large-scale taxi GPS trajectory data of San Francisco and Shenzhen to study the predictability of path flow distribution in urban road networks. We develop an approach to project the time-varying path flow distributions into a high-dimensional space. In the high-dimensional space, information entropy is used to measure the predictability of path flow distribution. We find that the distributions of path flow between OD pairs are in general characterized with a high predictability. In addition, we analyze the factors affecting the predictability of path flow distribution. Finally, an n-gram model incorporating high-order gram and low-order gram is proposed to predict the distribution of path flow. A relatively high prediction accuracy is achieved.
  • Item
    Solving Proof Block Problems Using Large Language Models
    (2024-03-07) Poulsen, Seth; Sarsa, Sami; Prather, James; Leinonen, Juho; Becker, Brett A.; Hellas, Arto; Denny, Paul; Reeves, Brent N.; Department of Computer Science; Lecturer Hellas Arto group; Computer Science Lecturers; Computer Science - Computing education research and educational technology (CER); Utah State University; Department of Computer Science; Abilene Christian University; University College Dublin; University of Auckland
    Large language models (LLMs) have recently taken many fields, including computer science, by storm. Most recent work on LLMs in computing education has shown that they are capable of solving most introductory programming (CS1) exercises, exam questions, Parsons problems, and several other types of exercises and questions. Some work has investigated the ability of LLMs to solve CS2 problems as well. However, it remains unclear how well LLMs fare against more advanced upper-division coursework, such as proofs in algorithms courses. After all, while known to be proficient in many programming tasks, LLMs have been shown to have more difficulties in forming mathematical proofs. In this paper, we investigate the ability of LLMs to solve mathematical proofs by using Proof Blocks, a tool previously shown to efficaciously teach proofs to students. Our results show that GPT-3.5 is almost completely unable to provide correct solutions (11.4%), while GPT-4 shows a significant increase in correctness (64.8%). However, even given this improvement, current models still struggle to correctly order lines in a proof. It remains an open question whether this is a temporary situation or if LLMs will continue to struggle to solve these types of exercises in the future.
  • Item
    Atomic-Scale Visualization of Multiferroicity in Monolayer NiI2
    (Wiley-VCH Verlag, 2024-05-02) Amini, Mohammad; Fumega, Adolfo O.; González-Herrero, Héctor; Vaňo, Viliam; Kezilebieke, Shawulienu; Lado, Jose L.; Liljeroth, Peter; Department of Applied Physics; Atomic Scale Physics; Correlated Quantum Materials (CQM); University of Jyväskylä
    Progress in layered van der Waals materials has resulted in the discovery of ferromagnetic and ferroelectric materials down to the monolayer limit. Recently, evidence of the first purely 2D multiferroic material was reported in monolayer NiI2. However, probing multiferroicity with scattering-based and optical bulk techniques is challenging on 2D materials, and experiments on the atomic scale are needed to fully characterize the multiferroic order at the monolayer limit. Here, scanning tunneling microscopy (STM) supported by density functional theory (DFT) calculations is used to probe and characterize the multiferroic order in monolayer NiI2. It is demonstrated that the type-II multiferroic order displayed by NiI2, arising from the combination of a magnetic spin spiral order and a strong spin-orbit coupling, allows probing the multiferroic order in the STM experiments. Moreover, the magnetoelectric coupling of NiI2 is directly probed by external electric field manipulation of the multiferroic domains. The findings establish a novel point of view to analyze magnetoelectric effects at the microscopic level, paving the way toward engineering new multiferroic orders in van der Waals materials and their heterostructures.
  • Item
    Measuring End-user Developers’ Episodic Experience of a Low-code Development Platform A Preliminary Study
    (Wroclaw University of Technology, 2024) Gao, Dongmei; Fagerholm, Fabian; Department of Computer Science; Professorship Fagerholm Fabian; Computer Science Professors; Computer Science - Software and Service Engineering (SSE); Computer Science - Human-Computer Interaction and Design (HCID)
    Background: As low-code development platforms (LCDPs) are becoming a trend, understanding how end-user developers think and feel as they work with such platforms is important. Particularly, assessing experiences during episodes of use can contribute to overall experience throughout long-term use. Aim: This paper aims to understand end-user developers’ episodic experience when they are building an application on a low-code platform and to provide guidance on how such experiences can be measured. Method: We designed the Episodic Developer Experience Questionnaire for LCDPs based on prior literature and refined it through expert Delphi sessions. The instrument contains 10 individual experience items, capturing various aspects of episodic experience. We further validated it through remote online tests on an LCDP. Results: The results showed significant differences in the relationships between items describing aspects of overall experience and items describing perceptions of tool quality and task difficulty. Programming expertise also affected end-user developers’ episodic experience. Conclusion: The study illustrates the design of questionnaire-based experience assessment in the context of development and identifies the importance of separating personal experience from assessment of tasks and tools since tool quality and task difficulty do not necessarily influence experience straightforwardly.
  • Item
    Instructor Perceptions of AI Code Generation Tools - A Multi-Institutional Interview Study
    (2024-03-07) Sheard, Judy; Denny, Paul; Hellas, Arto; Leinonen, Juho; Malmi, Lauri; Simon; Department of Computer Science; Computer Science Lecturers; Computer Science - Computing education research and educational technology (CER); Lecturer Hellas Arto group; Computer Science Professors; Computer Science - Human-Computer Interaction and Design (HCID); Professorship Malmi L.; University of Auckland; Monash University Australia
    Much of the recent work investigating large language models and AI Code Generation tools in computing education has focused on assessing their capabilities for solving typical programming problems and for generating resources such as code explanations and exercises. If progress is to be made toward the inevitable lasting pedagogical change, there is a need for research that explores the instructor voice, seeking to understand how instructors with a range of experiences plan to adapt. In this paper, we report the results of an interview study involving 12 instructors from Australia, Finland and New Zealand, in which we investigate educators' current practices, concerns, and planned adaptations relating to these tools. Through this empirical study, our goal is to prompt dialogue between researchers and educators to inform new pedagogical strategies in response to the rapidly evolving landscape of AI code generation tools.
  • Item
    Protein function prediction through multi-view multi-label latent tensor reconstruction
    (BioMed Central, 2024-05-02) Armah-Sekum, Robert Ebo; Szedmak, Sandor; Rousu, Juho; Department of Computer Science; Professorship Rousu Juho; Computer Science Professors; Computer Science - Computational Life Sciences (CSLife); Computer Science - Artificial Intelligence and Machine Learning (AIML); Computer Science - Large-scale Computing and Data Analysis (LSCA)
    Background: In last two decades, the use of high-throughput sequencing technologies has accelerated the pace of discovery of proteins. However, due to the time and resource limitations of rigorous experimental functional characterization, the functions of a vast majority of them remain unknown. As a result, computational methods offering accurate, fast and large-scale assignment of functions to new and previously unannotated proteins are sought after. Leveraging the underlying associations between the multiplicity of features that describe proteins could reveal functional insights into the diverse roles of proteins and improve performance on the automatic function prediction task. Results: We present GO-LTR, a multi-view multi-label prediction model that relies on a high-order tensor approximation of model weights combined with non-linear activation functions. The model is capable of learning high-order relationships between multiple input views representing the proteins and predicting high-dimensional multi-label output consisting of protein functional categories. We demonstrate the competitiveness of our method on various performance measures. Experiments show that GO-LTR learns polynomial combinations between different protein features, resulting in improved performance. Additional investigations establish GO-LTR’s practical potential in assigning functions to proteins under diverse challenging scenarios: very low sequence similarity to previously observed sequences, rarely observed and highly specific terms in the gene ontology. Implementation: The code and data used for training GO-LTR is available at https://github.com/aalto-ics-kepaco/GO-LTR-prediction.
  • Item
    A Robot Jumping the Queue: Expectations About Politeness and Power During Conflicts in Everyday Human-Robot Encounters
    (2024) Babel, Franziska; Welsch, Robin; Miller, Linda; Hock, Philipp; Thellman, Sam; Ziemke, Tom; Department of Computer Science; Mueller, Florian Floyd; Kyburz, Penny; Williamson, Julie R.; Sas, Corina; Wilson, Max L.; Toups Dugas, Phoebe; Shklovski, Irina; Professorship Welsch Robin; Computer Science - Engineering Psychology (ENGPSYCH); Computer Science Professors; Computer Science - Human-Computer Interaction and Design (HCID); Linköping University; Ulm University
    Increasing encounters between people and autonomous service robots may lead to conflicts due to mismatches between human expectations and robot behaviour. This interactive online study (N = 335) investigated human-robot interactions at an elevator, focusing on the effect of communication and behavioural expectations on participants’ acceptance and compliance. Participants evaluated a humanoid delivery robot primed as either submissive or assertive. The robot either matched or violated these expectations by using a command or appeal to ask for priority and then entering either first or waiting for the next ride. The results highlight that robots are less accepted if they violate expectations by entering first or using a command. Interactions were more effective if participants expected an assertive robot which then asked politely for priority and entered first. The findings emphasize the importance of power expectations in human-robot conflicts for the robot’s evaluation and effectiveness in everyday situations.
  • Item
    Super-Droplet-Repellent Carbon-Based Printable Perovskite Solar Cells
    (Wiley-VCH Verlag, 2024) Mai, Cuc Thi Kim; Halme, Janne; Nurmi, Heikki A.; da Silva, Aldeliane M.; Lorite, Gabriela S.; Martineau, David; Narbey, Stéphanie; Mozaffari, Naeimeh; Ras, Robin H.A.; Hashmi, Syed Ghufran; Vuckovac, Maja; Department of Applied Physics; Center of Excellence in Life-Inspired Hybrid Materials, LIBER; New Energy Technologies; Soft Matter and Wetting; University of Oulu; Solaronix SA; Monash University Australia
    Despite attractive cost-effectiveness, scalability, and superior stability, carbon-based printable perovskite solar cells (CPSCs) still face moisture-induced degradation that limits their lifespan and commercial potential. Here, the moisture-preventing mechanisms of thin nanostructured super-repellent coating (advancing contact angle >167° and contact angle hysteresis 7°) integrated into CPSCs are investigated for different moisture forms (falling water droplets vs water vapor vs condensed water droplets). It is shown that unencapsulated super-repellent CPSCs have superior performance under continuous droplet impact for 12 h (rain falling experiments) compared to unencapsulated pristine (uncoated) CPSCs that degrade within seconds. Contrary to falling water droplets, where super-repellent coating serves as a shield, water vapor is found to physisorb through porous super-repellent coating (room temperature and relative humidity, RH 65% and 85%) that increase the CPSCs performance for 21% during ≈43 d similarly to pristine CPSCs. It is further shown that water condensation forms within or below the super-repellent coating (40 °C and RH 85%), followed by chemisorption and degradation of CPSCs. Because different forms of water have distinct effects on CPSC, it is suggested that future standard tests for repellent CPSCs should include rain falling and condensate formation tests. The findings will thus inspire the development of super-repellent coatings for moisture prevention.
  • Item
    WAVE : Anticipatory Movement Visualization for VR Dancing
    (2024) Laattala, Markus; Piitulainen, Roosa; Ady, Nadia; Tamariz, Monica; Hämäläinen, Perttu; Department of Computer Science; Mueller, Florian Floyd; Kyburz, Penny; Williamson, Julie R.; Sas, Corina; Wilson, Max L.; Toups Dugas, Phoebe; Shklovski, Irina; Professorship Hämäläinen Perttu; Professorship Guckelsberger Christian; Computer Science Professors; Computer Science - Visual Computing (VisualComputing); Department of Computer Science; Edinburgh Napier University; IT University of Copenhagen
    Dance games are one of the most popular game genres in Virtual Reality (VR), and active dance communities have emerged on social VR platforms such as VR Chat. However, effective instruction of dancing in VR or through other computerized means remains an unsolved human-computer interaction problem. Existing approaches either only instruct movements partially, abstracting away nuances, or require learning and memorizing symbolic notation. In contrast, we investigate how realistic, full-body movements designed by a professional choreographer can be instructed on the fly, without prior learning or memorization. Towards this end, we describe the design and evaluation of WAVE, a novel anticipatory movement visualization technique where the user joins a group of dancers performing the choreography with different time offsets, similar to spectators making waves in sports events. In our user study (N=36), the participants more accurately followed a choreography using WAVE, compared to following a single model dancer.
  • Item
    Machine learning the Kondo entanglement cloud from local measurements
    (American Physical Society, 2024-05-08) Aikebaier, Faluke; Ojanen, Teemu; Lado, Jose; Department of Applied Physics; Correlated Quantum Materials (CQM)
    A quantum coherent screening cloud around a magnetic impurity in metallic systems is the hallmark of the antiferromagnetic Kondo effect. Despite the central role of the Kondo effect in quantum materials, the structure of quantum correlations of the screening cloud has defied direct observations. In this work, we introduce a machine-learning algorithm that allows one to spatially map the entangled electronic modes in the vicinity of the impurity site from experimentally accessible data. We demonstrate that local correlators allow reconstruction of the local many-body correlation entropy in real space in a double Kondo system with overlapping entanglement clouds. Our machine-learning methodology allows bypassing the typical requirement of measuring long-range nonlocal correlators with conventional methods. We show that our machine-learning algorithm is transferable between different Kondo system sizes, and we show its robustness in the presence of noisy correlators. Our work establishes the potential machine-learning methods to map many-body entanglement from real-space measurements.
  • Item
    Navigating the Virtual Gaze: Social Anxiety's Role in VR Proxemics
    (2024) Mello, Beatriz; Welsch, Robin; Verbokkem, Marissa; Knierim, Pascal; Dechant, Martin Johannes; Department of Computer Science; Mueller, Florian Floyd; Kyburz, Penny; Williamson, Julie R.; Sas, Corina; Wilson, Max L.; Toups Dugas, Phoebe; Shklovski, Irina; Professorship Welsch Robin; Computer Science - Engineering Psychology (ENGPSYCH); Computer Science Professors; Computer Science - Human-Computer Interaction and Design (HCID); Department of Computer Science; University of Innsbruck; University College London; University of Minho
    For individuals with Social Anxiety (SA), interacting with others can be a challenging experience, a concern that extends into the virtual world. While technology has made significant strides in creating more realistic virtual human agents (VHA), the interplay of gaze and interpersonal distance when interacting with VHAs is often neglected. This paper investigates the effect of dynamic and static Gaze animations in VHAs on interpersonal distance and their relation to SA. A Bayesian analysis shows that static centered and dynamic centering gaze led participants to stand closer to VHAs than static averted and dynamic averting gaze, respectively. In the static gaze conditions, this pattern was found to be reversed in SA: participants with higher SA kept larger distances for static-centered gaze than for averted gaze VHAs. These findings update theory, elucidate how nuanced interactions with VHAs must be designed, and offer renewed guidelines for pleasant VHA interaction design.
  • Item
    Dual-Perspective Modeling of Patient Pathways: A Case Study on Kidney Cancer
    (2024) Grøndahl Larsen, Anna; Halvorsrud, Ragnhild; Berg, Rolf Eigil; Vesinurm, Märt; Department of Industrial Engineering and Management; SINTEF Digital; Oslo University Hospital
    Patient pathway has become a key concept in the organization of healthcare. However, the materialization and operationalization of pathways often focus on work processes of health personnel, clinical decision-making, and deadlines, contradicting the strong patient-oriented perspective that is inherent in their definition. In this paper, we introduce a patient-centered perspective of kidney cancer pathways, reporting on a dual-perspective strategy to map and model patient pathways. Utilizing a multi-method approach, we map and model pathways from the perspectives of both healthcare personnel and patients and investigate the feasibility of the Customer Journey Modeling Language (CJML) for modeling patient pathways. To prevent confusion, the planned pathway as seen from the hospital perspective and the actual pathway experienced by the patient are referred to as ‘pathway’ and ‘journey’, respectively. In the paper, we describe methods to engage with healthcare professionals and patients to collect the necessary information to create precise models, and we show how precise modeling of patient pathways requires the integration of several information sources. Moreover, the study underlines the value of examining pathways from a dual perspective, as the two perspectives corroborate and supplement each other, illustrating the complexity of patient journeys. Finally, the findings provide insights into the feasibility of CJML, firstly underlining that the usefulness of visual models is context-dependent, and secondly, suggesting that the methods and subsequent visualizations may be useful as organizational, instructional, and communicative tools.
  • Item
    VMS: Interactive Visualization to Support the Sensemaking and Selection of Predictive Models
    (2024-03-18) He, Chen; Raj, Vishnu; Moen, Hans; Gröhn, Tommi; Wang, Chen; Peltonen, Laura Maria; Koivusalo, Saila; Marttinen, Pekka; Jacucci, Giulio; Department of Computer Science; Professorship Marttinen P.; Professorship Kaski Samuel; Computer Science Professors; Computer Science - Artificial Intelligence and Machine Learning (AIML); University of Helsinki; Department of Computer Science; Harbin Engineering University; University of Turku; Helsinki University Central Hospital
    To compare and select machine learning models, relying on performance measures alone may not always be sufficient. This is particularly the case where different subsets, features, and predicted results may vary in importance relative to the task at hand. Explanation and visualization techniques are required to support model sensemaking and informed decision-making. However, a review shows that existing systems are mostly designed for model developers and not evaluated with target users in their effectiveness. To address this issue, this research proposes an interactive visualization, VMS (Visualization for Model Sensemaking and Selection), for users of the model to compare and select predictive models. VMS integrates performance-, instance-, and feature-level analysis to evaluate models from multiple angles. Particularly, a feature view integrating the value and contribution of hundreds of features supports model comparison on local and global scales. We exemplified VMS for comparing models predicting patients' hospital length of stay through time-series health records and evaluated the prototype with 16 participants from the medical field. Results reveal evidence that VMS supports users to rationalize models in multiple ways and enables users to select the optimal models with a small sample size. User feedback suggests future directions on incorporating domain knowledge in model training, such as for different patient groups considering different sets of features as important.
  • Item
    Robust Magnetoelectric Coupling in FeTiO3/Ga2O3 Non-van der Waals Heterostructures
    (American Chemical Society, 2024-03-14) Jin, Cui; Tang, Xiao; Sun, Qilong; Mu, Chenxi; Krasheninnikov, Arkady V.; Kou, Liangzhi; Department of Applied Physics; Soft Matter and Wetting; Shandong Jianzhu University; Nanjing Forestry University; Queensland University of Technology
    Magnetoelectric coupling represents a significant breakthrough for next-generation electronics, offering the ability to achieve nonvolatile magnetic control via electrical means. In this comprehensive investigation, leveraging first-principles calculations, we unveil a robust magnetoelectric coupling within multiferroic heterostructures (HSs) by ingeniously integrating a non-van der Waals (non-vdW) magnetic FeTiO3 monolayer with the ferroelectric (FE) Ga2O3. Diverging from conventional van der Waals (vdW) multiferroic HSs, the magnetic states of the FeTiO3 monolayer can be efficiently toggled between ferromagnetic (FM) and antiferromagnetic (AFM) configurations by reversing the polarization of the Ga2O3 monolayer. This intriguing phenomenon arises from polarization-dependent substantial interlayer electron transfers and the interplay between superexchange and direct-exchange magnetic couplings of the iron atoms. The carrier-mediated interfacial interactions induce crucial shifts in Fermi level positions, decisively imparting distinct electronic characteristics near the Fermi level of composite systems. These novel findings offer exciting prospects for the future of magnetoelectric technology.
  • Item
    Study of the Synergistic Immunomodulatory and Antifibrotic Effects of Dual-Loaded Budesonide and Serpine1 siRNA Lipid-Polymer Nanoparticles Targeting Macrophage Dysregulation in Tendinopathy
    (American Chemical Society, 2024-04-17) López-Cerdá, Sandra; Molinaro, Giuseppina; Tello, Rubén Pareja; Correia, Alexandra; Künig, Sarojinidevi; Steinberger, Peter; Jeltsch, Michael; Hirvonen, Jouni T.; Barreto, Goncalo; Stöckl, Johannes; Santos, Hélder A.; Department of Neuroscience and Biomedical Engineering; University of Helsinki; Medical University of Vienna
    Musculoskeletal diseases involving tissue injury comprise tendon, ligament, and muscle injury. Recently, macrophages have been identified as key players in the tendon repair process, but no therapeutic strategy involving dual drug delivery and gene delivery to macrophages has been developed for targeting the two main dysregulated aspects of macrophages in tendinopathy, i.e., inflammation and fibrosis. Herein, the anti-inflammatory and antifibrotic effects of dual-loaded budesonide and serpine1 siRNA lipid-polymer hybrid nanoparticles (LPNs) are evaluated in murine and human macrophage cells. The modulation of the gene and protein expression of factors associated with inflammation and fibrosis in tendinopathy is demonstrated by real time polymerase chain reaction and Western blot. Macrophage polarization to the M2 phenotype and a decrease in the production of pro-inflammatory cytokines are confirmed in macrophage cell lines and primary cells. The increase in the activity of a matrix metalloproteinase involved in tissue remodelling is proven, and studies evaluating the interactions of LPNs with T cells proved that dual-loaded LPNs act specifically on macrophages and do not induce any collateral effects on T cells. Overall, these dual-loaded LPNs are a promising combinatorial therapeutic strategy with immunomodulatory and antifibrotic effects in dysregulated macrophages in the context of tendinopathy.
  • Item
    Examining the Gateway Hypothesis and Mapping Substance Use Pathways on Social Media : A Machine Learning Approach
    (JMIR Publications Inc., 2023-04-06) Yuan, Yunhao; Kasson, Erin; Taylor, Jordan; Cavazos-Rehg, Patricia; Choudhury, Munmun De; Aledavood, Talayeh; Department of Computer Science; Lecturer Aledavood Talayeh group; Computer Science Lecturers; Computer Science - Computational Life Sciences (CSLife); Washington University St. Louis; Carnegie Mellon University; Georgia Institute of Technology
    Background: Substance misuse presents significant global public health challenges. Understanding transitions between substance types and the timing of shifts to polysubstance use is vital for targeted prevention, harm reduction, and recovery strategies. The longstanding gateway hypothesis suggests high-risk substance use is preceded by lower-risk substance use. However, the source of this correlation is hotly contested. While some claim that low-risk substance use causes subsequent, riskier substance use, most users of low-risk substances also do not escalate to higher-risk substances. Social media data holds the potential to shed light on the factors contributing to substance use transitions. Objective: By leveraging social media data, our study aims to gain a better understanding of substance use pathways. By identifying and analyzing the transitions of individuals between different risk levels of substance use, our goal is to find specific linguistic cues in individuals' social media posts that could be indicative of escalating or de-escalating patterns in substance use. Methods: We conducted a large-scale analysis using data from Reddit, collected between 2015 and 2019, consisting of over 2.29 million posts and approximately 29.37 million comments by around 1.4 million users from subreddits. This data, derived from substance use subreddits, facilitated the creation of a risk transition dataset reflecting the substance use behaviors of over 1.4 million users. We deployed deep learning and machine learning techniques, including fine-tuned BERT and RoBERTa models, to predict the escalation or de-escalation in risk levels based on initial transition phases documented in posts and comments. Additionally, we conducted an extensive linguistic analysis to analyze the language patterns associated with transitions in substance use, emphasizing the role of n-gram features in predicting future risk trajectories. Results: Our results showed promise in predicting the escalation or de-escalation in risk levels based on the historical data of Reddit users created on initial transition phases among drug-related subreddits with an accuracy of 78.48% and an F1-score of 79.20%. We highlighted the vital predictive features, such as specific substance names and tools indicative of future risk escalations. Our linguistic analysis showed terms linked with harm reduction strategies were instrumental in signaling deescalation, whereas descriptors of frequent substance use were characteristic of escalating transitions. Conclusions: This study sheds light on the complexities surrounding the gateway hypothesis of substance use through an examination of online behavior on Reddit. While certain findings validate the hypothesis, indicating a progression from lower-risk substances like marijuana to higher-risk ones, a significant number of individuals did not showcase this transition. The research underscores the potential of using machine learning in conjunction with social media analysis for predicting substance use transitions. Our results emphasize the role of linguistic features as predictors and the importance of timely interventions.
  • Item
    Modeling public opinion over time and space : Trust in state institutions in Europe, 1989-2019
    (European Survey Research Association, 2024-04-16) Kołczyńska, Marta; Bürkner, Paul Christian; Kennedy, Lauren; Vehtari, Aki; Department of Computer Science; Computer Science Professors; Computer Science - Artificial Intelligence and Machine Learning (AIML); Probabilistic Machine Learning; Professorship Vehtari Aki; Institute of Political Studies of the Polish Academy of Sciences; Monash University Australia; University of Stuttgart
    Combining public opinion data from different sources enables new cross-national and longitudinal research, but is accompanied by unique challenges related to the comparability of the source survey data. The analytic strategy we propose relies on Bayesian explanatory item response theory models to address differences in the measurement of attitudes, and poststratification that uses administrative population data to improve the quality of estimates and correct for differences in sample representativeness. Partially pooled models with data from all countries would be prohibitively slow, so we estimate separate by-country models in a way that maintains comparability of estimates across countries. We apply this strategy to data from 13 cross-national research projects from 27 European countries to estimate trajectories of political trust between 1989-2019.
  • Item
    From Disorientation to Harmony: Autoethnographic Insights into Transformative Videogame Experiences
    (2024) Väkevä, Jaakko; Mekler, Elisa; Lindqvist, Janne; Department of Computer Science; Mueller, Florian Floyd; Kyburz, Penny; Williamson, Julie R.; Sas, Corina; Wilson, Max L.; Toups Dugas, Phoebe; Shklovski, Irina; Computer Science Professors; Professorship Lindqvist Janne; Helsinki Institute for Information Technology (HIIT); Department of Computer Science; IT University of Copenhagen
    Videogames can transform the perspectives and attitudes of players. Prior discussion on this transformative potential has typically been limited to non-entertainment videogames with explicit transformational goals. However, recreational gaming appears to hold considerable potential for igniting deeply personal experiences of profound transformation in players. Towards understanding this phenomenon, we conducted an explorative autoethnographic study. For this, the first author played five narrative-driven videogames while collecting self-observational and self-reflective data of his experience during and outside gameplay. Our findings offer intimate insights into the trajectory and emotional qualities of personally meaningful and transformative videogame experiences. For example, we found that gameplay experiences that were initially perceived as bewildering or disorienting could evolve into more harmonious experiences laden with personal meaning. This shift in experience developed through different forms of subsequent re-engagement with initially discrepant game encounters.
  • Item
    Euclid preparation XXXVII. Galaxy colour selections with Euclid and ground photometry for cluster weak-lensing analyses
    (EDP Sciences, 2024-04-01) Lesci, G. F.; Sereno, M.; Radovich, M.; Castignani, G.; Bisigello, L.; Marulli, F.; Moscardini, L.; Baumont, L.; Covone, G.; Farrens, S.; Giocoli, C.; Ingoglia, L.; Miranda La Hera, S.; Vannier, M.; Biviano, A.; Maurogordato, S.; Aghanim, N.; Amara, A.; Andreon, S.; Auricchio, N.; Baldi, M.; Bardelli, S.; Bender, R.; Bodendorf, C.; Bonino, D.; Branchini, E.; Brescia, M.; Brinchmann, J.; Camera, S.; Capobianco, V.; Carbone, C.; Carretero, J.; Casas, S.; Castander, F. J.; Castellano, M.; Cavuoti, S.; Cimatti, A.; Congedo, G.; Conselice, C. J.; Conversi, L.; Copin, Y.; Corcione, L.; Courbin, F.; Courtois, H. M.; Niemi, S. M.; Schneider, P.; Starck, J. L.; Wang, Y.; Gozaliasl, G.; Sánchez, A. G.; , Euclid Collaboration; Department of Computer Science; Universitá di Bologna; Istituto di Astrofisica Spaziale e Fisica Cosmica di Bologna; INAF - Osservatorio Astronomico di Padova; Université Paris-Saclay; University of Naples Federico II; Niels Bohr Institute; Université Côte d'Azur; Osservatorio Astronomico di Trieste; University of Portsmouth; Osservatorio Astronomico di Brera; Max Planck Institute for Extraterrestrial Physics; National Institute for Astrophysics (INAF); University of Genoa; Universidade do Porto; Istituto Nazionale di Astrofisica (INAF); Institute for High Energy Physics; RWTH Aachen University; CSIC - Institute of Space Sciences; Osservatorio Astronomico di Roma; Osservatorio Astronomico di Capodimonte; University of Edinburgh; University of Manchester; Urbanización Villafranca Del Castillo; Université Claude Bernard Lyon 1; Swiss Federal Institute of Technology Lausanne; Institut national de physique nucléaire et de physique des particules; European Space Research and Technology Centre; University of Bonn; California Institute of Technology
    Aims. We derived galaxy colour selections from Euclid and ground-based photometry, aiming to accurately define background galaxy samples in cluster weak-lensing analyses. These selections have been implemented in the Euclid data analysis pipelines for galaxy clusters. Methods. Given any set of photometric bands, we developed a method for the calibration of optimal galaxy colour selections that maximises the selection completeness, given a threshold on purity. Such colour selections are expressed as a function of the lens redshift. Results. We calibrated galaxy selections using simulated ground-based griz and Euclid YEJEHE photometry. Both selections produce a purity higher than 97%. The griz selection completeness ranges from 30% to 84% in the lens redshift range zl ∈ [0.2, 0.8]. With the full grizYEJEHE selection, the completeness improves by up to 25 percentage points, and the zl range extends up to zl = 1.5. The calibrated colour selections are stable to changes in the sample limiting magnitudes and redshift, and the selection based on griz bands provides excellent results on real external datasets. Furthermore, the calibrated selections provide stable results using alternative photometric aperture definitions obtained from different ground-based telescopes. The griz selection is also purer at high redshift and more complete at low redshift compared to colour selections found in the literature. We find excellent agreement in terms of purity and completeness between the analysis of an independent, simulated Euclid galaxy catalogue and our calibration sample, except for galaxies at high redshifts, for which we obtain up to 50 percentage points higher completeness. The combination of colour and photo-z selections applied to simulated Euclid data yields up to 95% completeness, while the purity decreases down to 92% at high zl. We show that the calibrated colour selections provide robust results even when observations from a single band are missing from the ground-based data. Finally, we show that colour selections do not disrupt the shear calibration for stage III surveys. The first Euclid data releases will provide further insights into the impact of background selections on the shear calibration.