1,721,044 research outputs found
A systematic analysis of deep learning algorithms in high-dimensional data regimes of limited size
No full textThere is a substantial demand for deep learning methods that can work with limited, high-dimensional, and noisy datasets. Nonetheless, current research mostly neglects this area, especially in the absence of prior expert knowledge or knowledge transfer. In this work, we bridge this gap by studying the performance of deep learning methods on the true data distribution in a limited, high-dimensional, and noisy data setting. To this end, we conduct a systematic evaluation that reduces the available training data while retaining the challenging properties mentioned above. Furthermore, we extensively search the space of hyperparameters and compare state-of-the-art architectures and models built and trained from scratch to advocate for the use of multi-objective tuning strategies. Our experiments highlight the lack of performative deep learning models in current literature and investigate the impact of training hyperparameters. We analyze the complexity of the models and demonstrate the advantage of choosing models tuned under multi-objective criteria in lower data regimes to reduce the likelihood to overfit. Lastly, we demonstrate the importance of selecting a proper inductive bias given a limited-sized dataset. Given our results, we conclude that tuning models using a multi-objective criterion results in simpler yet competitive models when reducing the number of data points.S.J. gratefully acknowledges support from Fonds Wetenschappelijk Onderzoek (FWO) via FWO PhD Fellowship strategic basic research, Belgium 1SHHV24N. P.J.K.L. wishes to express gratitude for the support received from FWO via postdoctoral fellowship, Belgium 1242021N and the research council of the Vrije Universiteit Brussel (OZR-VUB via grant number OZR3863BOF). This research was supported by funding from the Flemish Government under the “Onderzoeksprogramma Artificiele Intelligentie (AI) Vlaanderen” program ¨ and through the IMAGIca project by the Interdisciplinary Research Program of the Vrije Universiteit Brussel (reference IRP8 b). Lastly, we want to thank the HPC administration and support service of Vrije Universiteit Brussel that helped tremendously during the experimental phase, Bart Bogaerts for providing us with essential feedback and guidance throughout the development of this research and finally, Bram Silue, Denis Steckelmacher, and Samuele Pollaci for proofreadin
Model-based disease mapping using primary care registry data
No full textBackground: Spatial modeling of disease risk using primary care registry data is promising for public health surveillance. However, it remains unclear to which extent challenges such as spatially disproportionate sampling and practice-specific reporting variation affect statistical inference. Methods: Using lower respiratory tract infection data from the INTEGO registry, modeled with a logistic model incorporating patient characteristics, a spatially structured random effect at municipality level, and an unstructured random effect at practice level, we conducted a case and simulation study to assess the impact of these challenges on spatial trend estimation. Results: Even with spatial imbalance and practice-specific reporting variation, the model performed well. Performance improved with increasing spatial sample balance and decreasing practice-specific variation. Conclusion: Our findings indicate that, with correction for reporting efforts, primary care registries are valuable for spatial trend estimation. The diversity of patient locations within practice populations plays an important role.Funding statement
INTEGO is funded regularly by the Flemish Government (Ministry of Health and Welfare). TN gratefully acknowledges funding by the Internal Funds KU Leuven (project number 3M190682). PJKL acknowledges support from the Research Foundation Flanders (FWO, fwo.be) (postdoctoral fellowship 1242021N) and the Research council of the Vrije Universiteit Brussel (OZR-VUB) via grant number OZR3863BOF.
Acknowledgments
We thank the VSC (Flemish Supercomputer Center), funded by the Research Foundation Flanders (FWO) and the Flemish Government, which provided the resources and services used to perform the simulations in this work
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Synthetic population data for Belgium for STRIDE
No full textThis repository contains population files with 11 million individuals for Belgium we used to explore the impact of contact tracing and household bubbles on Belgian deconfinement strategies after the COVID-19 related lockdown in 2020 (Willem et al 2021) and universal testing strategies for COVID-19 mitigation (Libin et al 2021). We created census-based synthetic populations for Belgium consisting of individuals that are part of “contact pools”, representing a household, school-class, workplace, or community. References: Willem L, Abrams S, Libin JK P, Petrof O, Coletti P, Kuylen E, Møgelmose S, Wambua J, Herzog S A, Faes C, SIMID COVID19 team, Beutels P, Hens N: The impact of contact tracing and household bubbles on deconfinement strategies for COVID-19. Nature Communications 12, 1524 (2021) (https://doi.org/10.1038/s41467-021-21747-7). Libin JK P, Willem L, Verstraeten T, Torneri A, Vanderlocht J, Hens N. Assessing the feasibility and effectiveness of household-pooled universal testing to control COVID-19 epidemics. PLoS Computational Biology 17(3): e1008688 (2021) (https://doi.org/10.1371/journal.pcbi.1008688)STRIDE: Individual-based model to simulate infectious disease transmission
The Stride acronym stands for Simulate Transmission of Infectious DisEases and the model is developed at the University of Antwerp and Hasselt University, Belgium, to study the transmission of Influenza, Measles and COVID-19
Synthetic population data for Belgium for STRIDE
No full textThis repository contains population files with 11 million individuals for Belgium we used to explore the impact of contact tracing and household bubbles on Belgian deconfinement strategies after the COVID-19 related lockdown in 2020 (Willem et al 2021) and universal testing strategies for COVID-19 mitigation (Libin et al 2021). We created census-based synthetic populations for Belgium consisting of individuals that are part of “contact pools”, representing a household, school-class, workplace, or community. References: Willem L, Abrams S, Libin JK P, Petrof O, Coletti P, Kuylen E, Møgelmose S, Wambua J, Herzog S A, Faes C, SIMID COVID19 team, Beutels P, Hens N: The impact of contact tracing and household bubbles on deconfinement strategies for COVID-19. Nature Communications 12, 1524 (2021) (https://doi.org/10.1038/s41467-021-21747-7). Libin JK P, Willem L, Verstraeten T, Torneri A, Vanderlocht J, Hens N. Assessing the feasibility and effectiveness of household-pooled universal testing to control COVID-19 epidemics. PLoS Computational Biology 17(3): e1008688 (2021) (https://doi.org/10.1371/journal.pcbi.1008688)STRIDE: Individual-based model to simulate infectious disease transmission
The Stride acronym stands for Simulate Transmission of Infectious DisEases and the model is developed at the University of Antwerp and Hasselt University, Belgium, to study the transmission of Influenza, Measles and COVID-19
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
The impact of contact tracing and household bubbles on deconfinement strategies for COVID-19
No full textThe COVID-19 pandemic caused many governments to impose policies restricting social interactions. A controlled and persistent release of lockdown measures covers many potential strategies and is subject to extensive scenario analyses. Here, we use an individual-based model (STRIDE) to simulate interactions between 11 million inhabitants of Belgium at different levels including extended household settings, i.e., "household bubbles". The burden of COVID-19 is impacted by both the intensity and frequency of physical contacts, and therefore, household bubbles have the potential to reduce hospital admissions by 90%. In addition, we find that it is crucial to complete contact tracing 4 days after symptom onset. Assumptions on the susceptibility of children affect the impact of school reopening, though we find that business and leisure-related social mixing patterns have more impact on COVID-19 associated disease burden. An optimal deployment of the mitigation policies under study require timely compliance to physical distancing, testing and self-isolation.sponsorship: The authors are very grateful for access to the data from the Belgian Scientific Institute for Public Health, Sciensano, and from the Vaccine & Infectious Disease Institute (VaxInfectio), University of Antwerp. We thank several researchers from the SIMID COVID-19 consortium from the University of Antwerp and Hasselt University for numerous constructive discussions and meetings. L.W., S.A., P.J.K.L. and N.H. gratefully acknowledge support from the Research Foundation Flanders (FWO) (postdoctoral fellowships 1234620N and 1242021N, and RESTORE project G0G2920N). This work also received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (P.C., S.A.H. and N.H., grant number 682540-TransMID project; C.F., P.B. and N.H. grant number 101003688-EpiPose project). P.B. and N.H. acknowledge funding from the Antwerp Study Centre for Infectious Diseases (ASCID) and the Methusalem-Centre of Excellence consortium VAX-IDEA. We used computational resources and services provided by the Flemish Supercomputer Centre (VSC), funded by the FWO and the Flemish Government, with special thanks to the CalcUA-team (FB and SB). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. (Research Foundation Flanders (FWO)|1234620N, Research Foundation Flanders (FWO)|1242021N, Research Foundation Flanders (FWO)|G0G2920N, European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme|682540-TransMID, European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme|101003688-EpiPose, Antwerp Study Centre for Infectious Diseases (ASCID), Methusalem-Centre of Excellence consortium VAX-IDEA, FWO, Flemish Government)status: Publishe
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
A data-driven metapopulation model for the Belgian COVID-19 epidemic: assessing the impact of lockdown and exit strategies
No full textBACKGROUND: In response to the ongoing COVID-19 pandemic, several countries adopted measures of social distancing to a different degree. For many countries, after successfully curbing the initial wave, lockdown measures were gradually lifted. In Belgium, such relief started on May 4th with phase 1, followed by several subsequent phases over the next few weeks. METHODS: We analysed the expected impact of relaxing stringent lockdown measures taken according to the phased Belgian exit strategy. We developed a stochastic, data-informed, meta-population model that accounts for mixing and mobility of the age-structured population of Belgium. The model is calibrated to daily hospitalization data and is able to reproduce the outbreak at the national level. We consider different scenarios for relieving the lockdown, quantified in terms of relative reductions in pre-pandemic social mixing and mobility. We validate our assumptions by making comparisons with social contact data collected during and after the lockdown. RESULTS: Our model is able to successfully describe the initial wave of COVID-19 in Belgium and identifies interactions during leisure/other activities as pivotal in the exit strategy. Indeed, we find a smaller impact of school re-openings as compared to restarting leisure activities and re-openings of work places. We also assess the impact of case isolation of new (suspected) infections, and find that it allows re-establishing relatively more social interactions while still ensuring epidemic control. Scenarios predicting a second wave of hospitalizations were not observed, suggesting that the per-contact probability of infection has changed with respect to the pre-lockdown period. CONCLUSIONS: Contacts during leisure activities are found to be most influential, followed by professional contacts and school contacts, respectively, for an impending second wave of COVID-19. Regular re-assessment of social contacts in the population is therefore crucial to adjust to evolving behavioral changes that can affect epidemic diffusion.sponsorship: This work received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (PC and NH, grant number 682540 - TransMID project, PL, NH, PB grant number 101003688 - EpiPose project). SA and NH gratefully acknowledge support from the Fonds voor Wetenschappelijk Onderzoek (FWO) (RESTORE project G0G2920N). LW received funding from the Research Foundation Flanders (1234620N). PL received funding from the Research Foundation Flanders (post-doctoral grant 1242021N). The resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation - Flanders (FWO) and the Flemish Government. (European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program|682540, European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program|101003688, Fonds voor Wetenschappelijk Onderzoek (FWO)|G0G2920N, Research Foundation Flanders|1242021N, Research Foundation Flanders|1234620N, Research Foundation - Flanders (FWO), Flemish Government)status: Publishe
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