Search

Your search keyword '"Hagg, A."' showing total 2,589 results
Start Over Author "Hagg, A." Publication Year Range Last 50 years
2,589 results on '"Hagg, A."'

2. On the Suitability of Representations for Quality Diversity Optimization of Shapes

Author

Scarton, Ludovico and Hagg, Alexander

Subjects
Computer Science - Neural and Evolutionary Computing
Abstract

The representation, or encoding, utilized in evolutionary algorithms has a substantial effect on their performance. Examination of the suitability of widely used representations for quality diversity optimization (QD) in robotic domains has yielded inconsistent results regarding the most appropriate encoding method. Given the domain-dependent nature of QD, additional evidence from other domains is necessary. This study compares the impact of several representations, including direct encoding, a dictionary-based representation, parametric encoding, compositional pattern producing networks, and cellular automata, on the generation of voxelized meshes in an architecture setting. The results reveal that some indirect encodings outperform direct encodings and can generate more diverse solution sets, especially when considering full phenotypic diversity. The paper introduces a multi-encoding QD approach that incorporates all evaluated representations in the same archive. Species of encodings compete on the basis of phenotypic features, leading to an approach that demonstrates similar performance to the best single-encoding QD approach. This is noteworthy, as it does not always require the contribution of the best-performing single encoding.

Published
2023

3. Efficient Quality Diversity Optimization of 3D Buildings through 2D Pre-optimization

Author

Hagg, Alexander, Kliemank, Martin L., Asteroth, Alexander, Wilde, Dominik, Bedrunka, Mario C., Foysi, Holger, and Reith, Dirk

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Machine Learning
Abstract

Quality diversity algorithms can be used to efficiently create a diverse set of solutions to inform engineers' intuition. But quality diversity is not efficient in very expensive problems, needing 100.000s of evaluations. Even with the assistance of surrogate models, quality diversity needs 100s or even 1000s of evaluations, which can make it use infeasible. In this study we try to tackle this problem by using a pre-optimization strategy on a lower-dimensional optimization problem and then map the solutions to a higher-dimensional case. For a use case to design buildings that minimize wind nuisance, we show that we can predict flow features around 3D buildings from 2D flow features around building footprints. For a diverse set of building designs, by sampling the space of 2D footprints with a quality diversity algorithm, a predictive model can be trained that is more accurate than when trained on a set of footprints that were selected with a space-filling algorithm like the Sobol sequence. Simulating only 16 buildings in 3D, a set of 1024 building designs with low predicted wind nuisance is created. We show that we can produce better machine learning models by producing training data with quality diversity instead of using common sampling techniques. The method can bootstrap generative design in a computationally expensive 3D domain and allow engineers to sweep the design space, understanding wind nuisance in early design phases., Comment: This is the final version and has been accepted for publication in Evolutionary Computation (MIT Press)

Published
2023

4. The potential of roadside verges as insect habitat: Road salt has few effects on monarch butterfly performance and migration

Author

Amanda K. Hund, Timothy S. Mitchell, M. Isabel Ramίrez, Amod Zambre, Lili Hagg, Anne Stene, Karilyn Porter, Adrian Carper, Lauren Agnew, Alexander M. Shephard, Megan E. Kobiela, Karen S. Oberhauser, Orley R. Taylor, and Emilie C. Snell‐Rood

Subjects
conservation, migration, monarch butterfly, road, salt, sodium chloride, Ecology, QH540-549.5, General. Including nature conservation, geographical distribution, QH1-199.5
Abstract

Abstract Roadside habitat has been touted as a conservation opportunity for insect pollinators, including the declining monarch butterfly. The spectacular monarch migration is under threat from the loss of habitat and the decline of their milkweed host plants. In the northern part of their range, roadsides could potentially produce millions of monarchs annually due to high densities of milkweed; however, roadside milkweed can accumulate chemicals from roads, such as sodium from road salt. Controlled lab studies have shown mixed effects of sodium on monarch development: small increases can be beneficial as sodium is an important micronutrient in brain and muscle development, but large increases can sometimes decrease survival. It is unclear how dietary sodium affects performance in ecologically relevant conditions and the migration itself. In this experiment, we raised monarchs outdoors, in migration‐inducing conditions, on milkweed sprayed with three levels of sodium chloride. We released 2464 tagged monarchs and held an additional 246 for further lab assays. While our recovery rates to the wintering grounds were low (N = 7 individuals), individuals from all three sodium chloride treatments made it to Mexico. Butterflies reared on control milkweed and low salt concentrated sodium in their tissues, while those on high salt diets excreted sodium, suggesting high salt levels were above a physiological optimum. There were no effects of treatment on wing coloration, survival, body size, immunity, or parasite prevalence. Taken together, our results suggest that monarchs are robust to levels of sodium in milkweeds found along roadsides, which is promising with respect to the toxicity of roadside plants.

Published
2024
Full Text
View/download PDF

5. Bakeng se Afrika: Digital Skeletal Repository: Advancing Biological Anthropology and Medical Research in South Africa

Author

L’Abbé, Ericka N., Alblas, Amanda, Ackermann, Jan H. P., Bam, Lunga, Beaudet, Amélie, Bothma, Pearl N., Carmichael, Angelique, Cazenave, Marine, Cunha, Eugenia, de Beer, Frikkie, de Jager, Edwin, Erasmus, Meg-Kyla, Hagg, Alieske C., Harripershad, Miksha, Hartman, Patria C., Heuzé, Yann, Hoffman, Jakobus, Japhta, Nhlanhla, Khonyane, Reabetswe, Kobedi, Ruth, Krüger, Gabriele C., Liebenberg, Maritza, Liebenberg, Leandi, Loots, Marius, Marais, Chantelle, Mbonani, Thandolwethu, Middleton, Michaela, Muller, Samantha, Nshimirimana, Robert, Oettlé, Anna C., Pieterse, Rachel, Ridel, Alison F., Sapo, Okuhle, Swanepoel, Franci, Theye, Charlotte E. G., van der Merwe, Clarisa, Vandermeulen, Dirk, Venter, Rudolph, Rea, Paul M., Series Editor, and Shapiro, Leonard, editor

Published
2024
Full Text
View/download PDF

7. Astrocyte focal adhesion kinase reduces passive stress coping by inhibiting ciliary neurotrophic factor only in female mice

Author

Cuihong Jia, W. Drew Gill, Chiharu Lovins, Russell W. Brown, and Theo Hagg

Subjects
Chronic unpredictable stress, FAK inhibitor, Passive stress coping, Progesterone, Sex dimorphism, Stress-related disorders, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, Neurology. Diseases of the nervous system, RC346-429, Neurophysiology and neuropsychology, QP351-495
Abstract

Astrocytes have been implicated in stress responses and produce ciliary neurotrophic factor (CNTF), which we have shown in the mouse medial amygdala (MeA) to promote passive stress coping response only in females. Pharmacological inhibition of focal adhesion kinase (FAK) upregulates CNTF expression. Here, we found that inducible knockout of FAK in astrocytes or systemic treatment with an FAK inhibitor increased passive coping behavior, i.e., immobility, in an acute forced swim stress test in female, but not male, mice. Strikingly, four weeks of chronic unpredictable stress (CUS) did not further increase passive coping in female astrocytic FAK knockout mice, whereas it exacerbated it in female wildtype mice and male mice of both genotypes. These data suggest that astrocyte FAK inhibition is required for chronic stress-induced passive coping in females. Indeed, CUS reduced phospho-FAK and increased CNTF in the female MeA. Progesterone treatment after ovariectomy activated amygdala FAK and alleviated ovariectomy-induced passive coping in wildtype, but not astrocytic FAK knockout females. This suggests that progesterone-mediated activation of FAK in astrocytes reduces female stress responses. Finally, astrocytic FAK knockout or FAK inhibitor treatment increased CNTF expression in the MeA of both sexes, although not in the hippocampus. As mentioned, MeA CNTF promotes stress responses only in females, which may explain the female-specific role of astrocytic FAK inhibition. Together, this study reveals a novel female-specific progesterone-astrocytic FAK pathway that counteracts CNTF-mediated stress responses and points to opportunities for developing treatments for stress-related disorders in women.

Published
2024
Full Text
View/download PDF

9. Counter-intuitive penetration of droplets into hydrophobic gaps in theory and experiment

Author

Daniel Hagg, Alexander Eifert, Aaron Dörr, Francisco Bodziony, and Holger Marschall

Subjects
Medicine, Science
Abstract

Abstract Droplets that spontaneously penetrate a gap between two hydrophobic surfaces without any external stimulus seems counterintuitive. However, in this work we show that it can be energetically favorable for a droplet to penetrate a gap formed by two hydrophobic or in some cases even superhydrophobic surfaces. For this purpose, we derived an analytical equation to calculate the change in Helmholtz free energy of a droplet penetrating a hydrophobic gap. The derived equation solely depends on the gap width, the droplet volume and the contact angle on the gap walls, and predicts whether a droplet penetrates a hydrophobic gap or not. Additionally, numerical simulations were conducted to provide insights into the gradual change in Helmholtz free energy during the process of penetration and to validate the analytical approach. A series of experiments with a hydrophobic gap having an advancing contact angle of $$115^\circ$$ 115 ∘ , a droplet volume of about 10 $$\mu$$ μ L and different gap widths confirmed the theoretical predictions. Limits and possible deviations between the analytical solution, the simulation and the experiments are presented and discussed.

Published
2023
Full Text
View/download PDF

10. Detection of crevassed areas with minimum geometric information: Vernagtferner case study

Author

Theresa Dobler, Wilfried Hagg, and Christoph Mayer

Subjects
Applied glaciology, crevasses, glacier hazards, Environmental sciences, GE1-350, Meteorology. Climatology, QC851-999
Abstract

Crevasses pose severe risks for mountaineers and field glaciologists. Smaller cracks between 0.5 and 2 m are still dangerous, but often not visible in medium resolution satellite imagery. If they are snow covered, they are completely undetectable by optical sensors. We set out to develop an approach to detect potentially crevassed areas by a minimum of geometric data, and to make the method generally applicable to glacier regions. On Vernagtferner, we compared a reference dataset of crevasses observed in high-resolution optical imagery with the curvature of the ice surface and the spatial gradients in driving stress. Both parameters can be derived from a digital surface model and a bedrock model, derived from ice thickness measurements. The correlation patterns show that crevasses preferably form in convex areas and in areas where the driving stress rapidly increases. This corresponds with the theory of crevasse formation. Although the method still misclassifies larger parts, the approach has the potential to define probable non-crevassed areas as well as to aid the planning of safe routes.

Published
2023
Full Text
View/download PDF

13. Designing Air Flow with Surrogate-assisted Phenotypic Niching

Author

Hagg, Alexander, Wilde, Dominik, Asteroth, Alexander, and Bäck, Thomas

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Computational Engineering, Finance, and Science, Mathematics - Numerical Analysis
Abstract

In complex, expensive optimization domains we often narrowly focus on finding high performing solutions, instead of expanding our understanding of the domain itself. But what if we could quickly understand the complex behaviors that can emerge in said domains instead? We introduce surrogate-assisted phenotypic niching, a quality diversity algorithm which allows to discover a large, diverse set of behaviors by using computationally expensive phenotypic features. In this work we discover the types of air flow in a 2D fluid dynamics optimization problem. A fast GPU-based fluid dynamics solver is used in conjunction with surrogate models to accurately predict fluid characteristics from the shapes that produce the air flow. We show that these features can be modeled in a data-driven way while sampling to improve performance, rather than explicitly sampling to improve feature models. Our method can reduce the need to run an infeasibly large set of simulations while still being able to design a large diversity of air flows and the shapes that cause them. Discovering diversity of behaviors helps engineers to better understand expensive domains and their solutions.

Published
2021
Full Text
View/download PDF

14. Expressivity of Parameterized and Data-driven Representations in Quality Diversity Search

Author

Hagg, Alexander, Berns, Sebastian, Asteroth, Alexander, Colton, Simon, and Bäck, Thomas

Subjects
Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing
Abstract

We consider multi-solution optimization and generative models for the generation of diverse artifacts and the discovery of novel solutions. In cases where the domain's factors of variation are unknown or too complex to encode manually, generative models can provide a learned latent space to approximate these factors. When used as a search space, however, the range and diversity of possible outputs are limited to the expressivity and generative capabilities of the learned model. We compare the output diversity of a quality diversity evolutionary search performed in two different search spaces: 1) a predefined parameterized space and 2) the latent space of a variational autoencoder model. We find that the search on an explicit parametric encoding creates more diverse artifact sets than searching the latent space. A learned model is better at interpolating between known data points than at extrapolating or expanding towards unseen examples. We recommend using a generative model's latent space primarily to measure similarity between artifacts rather than for search and generation. Whenever a parametric encoding is obtainable, it should be preferred over a learned representation as it produces a higher diversity of solutions., Comment: For code for reproducing experiments, see https://github.com/alexander-hagg/ExpressivityGECCO2021

Published
2021
Full Text
View/download PDF

15. An Analysis of Phenotypic Diversity in Multi-Solution Optimization

Author

Hagg, Alexander, Preuss, Mike, Asteroth, Alexander, and Bäck, Thomas

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Machine Learning
Abstract

More and more, optimization methods are used to find diverse solution sets. We compare solution diversity in multi-objective optimization, multimodal optimization, and quality diversity in a simple domain. We show that multiobjective optimization does not always produce much diversity, multimodal optimization produces higher fitness solutions, and quality diversity is not sensitive to genetic neutrality and creates the most diverse set of solutions. An autoencoder is used to discover phenotypic features automatically, producing an even more diverse solution set with quality diversity. Finally, we make recommendations about when to use which approach.

Published
2021
Full Text
View/download PDF

18. Prevalence of chronic cough, its risk factors and population attributable risk in the Burden of Obstructive Lung Disease (BOLD) study: a multinational cross-sectional study

Author

Hafizi, Hasan, Aliko, Anila, Bardhi, Donika, Tafa, Holta, Thanasi, Natasha, Mezini, Arian, Teferici, Alma, Todri, Dafina, Nikolla, Jolanda, Kazasi, Rezarta, Cherkaski, Hamid Hacene, Bengrait, Amira, Haddad, Tabarek, Zgaoula, Ibtissem, Ghit, Maamar, Roubhia, Abdelhamid, Boudra, Soumaya, Atoui, Feryal, Yakoubi, Randa, Benali, Rachid, Bencheikh, Abdelghani, Ait-Khaled, Nadia, Jenkins, Christine, Marks, Guy, Bird, Tessa, Espinel, Paola, Hardaker, Kate, Toelle, Brett, Studnicka, Michael, Dawes, Torkil, Lamprecht, Bernd, Schirhofer, Lea, Islam, Akramul, Ahmed, Syed Masud, Islam, Shayla, Islam, Qazi Shafayetul, Mesbah-Ul-Haque, Chowdhury, Tridib Roy, Chatterjee, Sukantha Kumar, Mia, Dulal, Chandra Das, Shyamal, Rahman, Mizanur, Islam, Nazrul, Uddin, Shahaz, Islam, Nurul, Khatun, Luiza, Parvin, Monira, Khan, Abdul Awal, Islam, Maidul, Lawin, Herve, Kpangon, Arsene, Kpossou, Karl, Agodokpessi, Gildas, Ayelo, Paul, Fayomi, Benjamin, Mbatchou, Bertrand, Ashu, Atongno Humphrey, Tan, Wan C., Wang, Wen, Zhong, NanShan, Liu, Shengming, Lu, Jiachun, Ran, Pixin, Wang, Dali, Zheng, Jin-ping, Zhou, Yumin, Jogi, Rain, Laja, Hendrik, Ulst, Katrin, Zobel, Vappu, Lill, Toomas-Julius, Adegnika, Ayola Akim, Welte, Tobias, Bodemann, Isabelle, Geldmacher, Henning, SchwedaLinow, Alexandra, Gislason, Thorarinn, Benedikdtsdottir, Bryndis, Jorundsdottir, Kristin, Lovisa Gudmundsdottir, Gudmundsdottir, Sigrun, Gudmundsson, Gunnar, Rao, Mahesh, Koul, Parvaiz A., Malik, Sajjad, Hakim, Nissar A., Khan, Umar Hafiz, Chowgule, Rohini, Shetye, Vasant, Raphael, Jonelle, Almeda, Rosel, Tawde, Mahesh, Tadvi, Rafiq, Katkar, Sunil, Kadam, Milind, Dhanawade, Rupesh, Ghurup, Umesh, Juvekar, Sanjay, Hirve, Siddhi, Sambhudas, Somnath, Chaidhary, Bharat, Tambe, Meera, Pingale, Savita, Umap, Arati, Umap, Archana, Shelar, Nitin, Devchakke, Sampada, Chaudhary, Sharda, Bondre, Suvarna, Walke, Savita, Gawhane, Ashleshsa, Sapkal, Anil, Argade, Rupali, Gaikwad, Vijay, Salvi, Sundeep, Brashier, Bill, Londhe, Jyoti, Madas, Sapna, Aquart-Stewart, Althea, Aikman, Akosua Francia, Sooronbaev, Talant M., Estebesova, Bermet M., Akmatalieva, Meerim, Usenbaeva, Saadat, Kydyrova, Jypara, Bostonova, Eliza, Sheraliev, Ulan, Marajapov, Nuridin, Toktogulova, Nurgul, Emilov, Berik, Azilova, Toktogul, Beishekeeva, Gulnara, Dononbaeva, Nasyikat, Tabyshova, Aijamal, Mortimer, Kevin, Nyapigoti, Wezzie, Mwangoka, Ernest, Kambwili, Mayamiko, Chipeta, Martha, Banda, Gloria, Mkandawire, Suzgo, Banda, Justice, Loh, Li-Cher, Rashid, Abdul, Sholehah, Siti, Benjelloun, Mohamed C., Nejjari, Chakib, Elbiaze, Mohamed, El Rhazi, Karima, Wouters, E.F.M., Wesseling, G.J., Obaseki, Daniel, Erhabor, Gregory, Awopeju, Olayemi, Adewole, Olufemi, Gulsvik, Amund, Endresen, Tina, Svendsen, Lene, Nafees, Asaad A., Irfan, Muhammad, Fatmi, Zafar, Zahidie, Aysha, Shaukat, Natasha, Iqbal, Meesha, Idolor, Luisito F., de Guia, Teresita S., Francisco, Norberto A., Roa, Camilo C., Ayuyao, Fernando G., Tady, Cecil Z., Tan, Daniel T., Banal-Yang, Sylvia, Balanag, Vincent M., Jr., Reyes, Maria Teresita N., Dantes, Renato B., Amarillo, Lourdes, Berratio, Lakan U., Fernandez, Lenora C., Garcia, Gerard S., Naval, Sullian S., Reyes, Thessa, Roa, Camilo C., Jr., Sanchez, Flordeliza, Simpao, Leander P., Nizankowska-Mogilnicka, Ewa, Frey, Jakub, Harat, Rafal, Mejza, Filip, Nastalek, Pawel, Pajak, Andrzej, Skucha, Wojciech, Szczeklik, Andrzej, Twardowska, Magda, Barbara, Cristina, Rodrigues, Fatima, Dias, Herminia, Cardoso, Joao, Almeida, João, Matos, Maria Joao, Simão, Paula, Santos, Moutinho, Ferreira, Reis, Al Ghobain, M., Alorainy, H., El-Hamad, E., Al Hajjaj, M., Hashi, A., Dela, R., Fanuncio, R., Doloriel, E., Marciano, I., Safia, L., Bateman, Eric, Jithoo, Anamika, Adams, Desiree, Barnes, Edward, Freeman, Jasper, Hayes, Anton, Hlengwa, Sipho, Johannisen, Christine, Koopman, Mariana, Louw, Innocentia, Ludick, Ina, Olckers, Alta, Ryck, Johanna, Storbeck, Janita, Gunasekera, Kirthi, Wickremasinghe, Rajitha, Elsony, Asma, Elsadig, Hana A., Osman, Nada Bakery, Noory, Bandar Salah, Mohamed, Monjda Awad, Akasha Ahmed Osman, Hasab Alrasoul, Moham ed Elhassan, Namarig, El Zain, Abdel Mu’is, Mohamaden, Marwa Mohamed, Khalifa, Suhaiba, Elhadi, Mahmoud, Hassan, Mohand, Abdelmonam, Dalia, Janson, Christer, Olafsdottir, Inga Sif, Nisser, Katarina, SpetzNystrom, Ulrike, Hagg, Gunilla, Lund, GunMarie, Seemungal, Terence, Lutchmansingh, Fallon, Conyette, Liane, Harrabi, Imed, Denguezli, Myriam, Tabka, Zouhair, Daldoul, Hager, Boukheroufa, Zaki, Chouikha, Firas, Khalifa, Wahbi Belhaj, Kocabas, Ali, Hancioglu, Attila, Hanta, Ismail, Kuleci, Sedat, Turkyilmaz, Ahmet Sinan, Umut, Sema, Unalan, Turgay, Burney, Peter G.J., Gnatiuc, Louisa, Azar, Hadia, Patel, Jaymini, Amor, Caron, Potts, James, Tumilty, Michael, McLean, Fiona, Dudhaiya, Risha, Buist, A. Sonia, McBurnie, Mary Ann, Vollmer, William M., Gillespie, Suzanne, Sullivan, Sean, Lee, Todd A., Weiss, Kevin B., Jensen, Robert L., Crapo, Robert, Enright, Paul, Mannino, David M., Cain, John, Copeland, Rebecca, Hazen, Dana, Methvin, Jennifer, Abozid, Hazim, Burney, Peter, Hartl, Sylvia, Breyer-Kohansal, Robab, Al Ghobain, Mohammed, Denguezli, Meriam, Loh, Li Cher, Paraguas, Stefanni Nonna, Franssen, Frits M.E., Mannino, David, Anand, Mahesh Padukudru, Buist, Sonia, El Sony, Asma, Breyer, Marie-Kathrin, Burghuber, Otto C., Wouters, Emiel F.M., and Amaral, Andre F.S.

Published
2024
Full Text
View/download PDF

20. Predictive Criterion Validity of the Parsley Symptom Index Against the Patient-Reported Outcomes Measurement Information System-10 in a Chronic Disease Cohort: Retrospective Cohort Study

Author

Hants Williams, Sarah Steinberg, Kendall Leon, Ryan Vingum, Mengyao Hu, Robin Berzin, Heather Hagg, and Patrick Hanaway

Subjects
Medicine
Abstract

BackgroundApproximately 60% of US adults live with chronic disease, imposing a significant burden on patients and the health care system. With the rise of telehealth, patient-reported outcomes measures (PROMs) have emerged as pivotal tools for managing chronic disease. While numerous PROMs exist, few have been designed explicitly for telehealth settings. The Parsley Symptom Index (PSI) is an electronic patient-reported outcome measure (ePROM) developed specifically for telehealth environments. ObjectiveOur aim is to determine whether the PSI predicts changes in the established Patient-Reported Outcomes Measurement Information System-10 (PROMIS-10) Global Health, a 10-question short form. MethodsWe conducted a retrospective cohort study using data from 367 unique patients, amassing 1170 observations between August 30, 2017, and January 30, 2023. Patients completed the PSI and the PROMIS-10 multiple times throughout the study period. Using univariate regression models, we assess the predictive criterion validity of the PSI against PROMIS-10 scores. ResultsThis study revealed significant relationships between the PSI and PROMIS-10 physical and mental health scores through comprehensive univariate analyses, thus establishing support for the criterion validity of the PSI. These analyses highlighted the PSI’s potential as an insightful tool for understanding and predicting both mental and physical health dimensions. ConclusionsOur findings emphasize the importance of the PSI in capturing the nuanced interactions between symptomatology and health outcomes. These insights reinforce the value of the PSI in clinical contexts and support its potential as a versatile tool in both research and practice.

Published
2024
Full Text
View/download PDF

21. Prevalence of chronic cough, its risk factors and population attributable risk in the Burden of Obstructive Lung Disease (BOLD) study: a multinational cross-sectional studyResearch in context

Author

Hazim Abozid, Jaymini Patel, Peter Burney, Sylvia Hartl, Robab Breyer-Kohansal, Kevin Mortimer, Asaad A. Nafees, Mohammed Al Ghobain, Tobias Welte, Imed Harrabi, Meriam Denguezli, Li Cher Loh, Abdul Rashid, Thorarinn Gislason, Cristina Barbara, Joao Cardoso, Fatima Rodrigues, Terence Seemungal, Daniel Obaseki, Sanjay Juvekar, Stefanni Nonna Paraguas, Wan C. Tan, Frits M.E. Franssen, Filip Mejza, David Mannino, Christer Janson, Hamid Hacene Cherkaski, Mahesh Padukudru Anand, Hasan Hafizi, Sonia Buist, Parvaiz A. Koul, Asma El Sony, Marie-Kathrin Breyer, Otto C. Burghuber, Emiel F.M. Wouters, Andre F.S. Amaral, Anila Aliko, Donika Bardhi, Holta Tafa, Natasha Thanasi, Arian Mezini, Alma Teferici, Dafina Todri, Jolanda Nikolla, Rezarta Kazasi, Amira Bengrait, Tabarek Haddad, Ibtissem Zgaoula, Maamar Ghit, Abdelhamid Roubhia, Soumaya Boudra, Feryal Atoui, Randa Yakoubi, Rachid Benali, Abdelghani Bencheikh, Nadia Ait-Khaled, Christine Jenkins, Guy Marks, Tessa Bird, Paola Espinel, Kate Hardaker, Brett Toelle, Michael Studnicka, Torkil Dawes, Bernd Lamprecht, Lea Schirhofer, Akramul Islam, Syed Masud Ahmed, Shayla Islam, Qazi Shafayetul Islam, Mesbah-Ul-Haque, Tridib Roy Chowdhury, Sukantha Kumar Chatterjee, Dulal Mia, Shyamal Chandra Das, Mizanur Rahman, Nazrul Islam, Shahaz Uddin, Nurul Islam, Luiza Khatun, Monira Parvin, Abdul Awal Khan, Maidul Islam, Herve Lawin, Arsene Kpangon, Karl Kpossou, Gildas Agodokpessi, Paul Ayelo, Benjamin Fayomi, Bertrand Mbatchou, Atongno Humphrey Ashu, Wen Wang, NanShan Zhong, Shengming Liu, Jiachun Lu, Pixin Ran, Dali Wang, Jin-ping Zheng, Yumin Zhou, Rain Jogi, Hendrik Laja, Katrin Ulst, Vappu Zobel, Toomas-Julius Lill, Ayola Akim Adegnika, Isabelle Bodemann, Henning Geldmacher, Alexandra SchwedaLinow, Bryndis Benedikdtsdottir, Kristin Jorundsdottir, Lovisa Gudmundsdottir, Sigrun Gudmundsdottir, Gunnar Gudmundsson, Mahesh Rao, Sajjad Malik, Nissar A. Hakim, Umar Hafiz Khan, Rohini Chowgule, Vasant Shetye, Jonelle Raphael, Rosel Almeda, Mahesh Tawde, Rafiq Tadvi, Sunil Katkar, Milind Kadam, Rupesh Dhanawade, Umesh Ghurup, Siddhi Hirve, Somnath Sambhudas, Bharat Chaidhary, Meera Tambe, Savita Pingale, Arati Umap, Archana Umap, Nitin Shelar, Sampada Devchakke, Sharda Chaudhary, Suvarna Bondre, Savita Walke, Ashleshsa Gawhane, Anil Sapkal, Rupali Argade, Vijay Gaikwad, Sundeep Salvi, Bill Brashier, Jyoti Londhe, Sapna Madas, Althea Aquart-Stewart, Akosua Francia Aikman, Talant M. Sooronbaev, Bermet M. Estebesova, Meerim Akmatalieva, Saadat Usenbaeva, Jypara Kydyrova, Eliza Bostonova, Ulan Sheraliev, Nuridin Marajapov, Nurgul Toktogulova, Berik Emilov, Toktogul Azilova, Gulnara Beishekeeva, Nasyikat Dononbaeva, Aijamal Tabyshova, Wezzie Nyapigoti, Ernest Mwangoka, Mayamiko Kambwili, Martha Chipeta, Gloria Banda, Suzgo Mkandawire, Justice Banda, Li-Cher Loh, Siti Sholehah, Mohamed C. Benjelloun, Chakib Nejjari, Mohamed Elbiaze, Karima El Rhazi, E.F.M. Wouters, G.J. Wesseling, Gregory Erhabor, Olayemi Awopeju, Olufemi Adewole, Amund Gulsvik, Tina Endresen, Lene Svendsen, Muhammad Irfan, Zafar Fatmi, Aysha Zahidie, Natasha Shaukat, Meesha Iqbal, Luisito F. Idolor, Teresita S. de Guia, Norberto A. Francisco, Camilo C. Roa, Fernando G. Ayuyao, Cecil Z. Tady, Daniel T. Tan, Sylvia Banal-Yang, Vincent M. Balanag, Jr., Maria Teresita N. Reyes, Renato B. Dantes, Lourdes Amarillo, Lakan U. Berratio, Lenora C. Fernandez, Gerard S. Garcia, Sullian S. Naval, Thessa Reyes, Camilo C. Roa, Jr., Flordeliza Sanchez, Leander P. Simpao, Ewa Nizankowska-Mogilnicka, Jakub Frey, Rafal Harat, Pawel Nastalek, Andrzej Pajak, Wojciech Skucha, Andrzej Szczeklik, Magda Twardowska, Herminia Dias, João Almeida, Maria Joao Matos, Paula Simão, Moutinho Santos, Reis Ferreira, M. Al Ghobain, H. Alorainy, E. El-Hamad, M. Al Hajjaj, A. Hashi, R. Dela, R. Fanuncio, E. Doloriel, I. Marciano, L. Safia, Eric Bateman, Anamika Jithoo, Desiree Adams, Edward Barnes, Jasper Freeman, Anton Hayes, Sipho Hlengwa, Christine Johannisen, Mariana Koopman, Innocentia Louw, Ina Ludick, Alta Olckers, Johanna Ryck, Janita Storbeck, Kirthi Gunasekera, Rajitha Wickremasinghe, Asma Elsony, Hana A. Elsadig, Nada Bakery Osman, Bandar Salah Noory, Monjda Awad Mohamed, Hasab Alrasoul Akasha Ahmed Osman, Namarig Moham ed Elhassan, Abdel Mu’is El Zain, Marwa Mohamed Mohamaden, Suhaiba Khalifa, Mahmoud Elhadi, Mohand Hassan, Dalia Abdelmonam, Inga Sif Olafsdottir, Katarina Nisser, Ulrike SpetzNystrom, Gunilla Hagg, GunMarie Lund, Fallon Lutchmansingh, Liane Conyette, Myriam Denguezli, Zouhair Tabka, Hager Daldoul, Zaki Boukheroufa, Firas Chouikha, Wahbi Belhaj Khalifa, Ali Kocabas, Attila Hancioglu, Ismail Hanta, Sedat Kuleci, Ahmet Sinan Turkyilmaz, Sema Umut, Turgay Unalan, Peter G.J. Burney, Louisa Gnatiuc, Hadia Azar, Caron Amor, James Potts, Michael Tumilty, Fiona McLean, Risha Dudhaiya, A. Sonia Buist, Mary Ann McBurnie, William M. Vollmer, Suzanne Gillespie, Sean Sullivan, Todd A. Lee, Kevin B. Weiss, Robert L. Jensen, Robert Crapo, Paul Enright, David M. Mannino, John Cain, Rebecca Copeland, Dana Hazen, and Jennifer Methvin

Subjects
Chronic cough, Epidemiology, Global health, Excess risk, Medicine (General), R5-920
Abstract

Summary: Background: Chronic cough is a common respiratory symptom with an impact on daily activities and quality of life. Global prevalence data are scarce and derive mainly from European and Asian countries and studies with outcomes other than chronic cough. In this study, we aimed to estimate the prevalence of chronic cough across a large number of study sites as well as to identify its main risk factors using a standardised protocol and definition. Methods: We analysed cross-sectional data from 33,983 adults (≥40 years), recruited between Jan 2, 2003 and Dec 26, 2016, in 41 sites (34 countries) from the Burden of Obstructive Lung Disease (BOLD) study. We estimated the prevalence of chronic cough for each site accounting for sampling design. To identify risk factors, we conducted multivariable logistic regression analysis within each site and then pooled estimates using random-effects meta-analysis. We also calculated the population attributable risk (PAR) associated with each of the identifed risk factors. Findings: The prevalence of chronic cough varied from 3% in India (rural Pune) to 24% in the United States of America (Lexington,KY). Chronic cough was more common among females, both current and passive smokers, those working in a dusty job, those with a history of tuberculosis, those who were obese, those with a low level of education and those with hypertension or airflow limitation. The most influential risk factors were current smoking and working in a dusty job. Interpretation: Our findings suggested that the prevalence of chronic cough varies widely across sites in different world regions. Cigarette smoking and exposure to dust in the workplace are its major risk factors. Funding: Wellcome Trust.

Published
2024
Full Text
View/download PDF

32. Prediction of neural network performance by phenotypic modeling

Author

Hagg, Alexander, Zaefferer, Martin, Stork, Jörg, and Gaier, Adam

Subjects
Computer Science - Neural and Evolutionary Computing
Abstract

Surrogate models are used to reduce the burden of expensive-to-evaluate objective functions in optimization. By creating models which map genomes to objective values, these models can estimate the performance of unknown inputs, and so be used in place of expensive objective functions. Evolutionary techniques such as genetic programming or neuroevolution commonly alter the structure of the genome itself. A lack of consistency in the genotype is a fatal blow to data-driven modeling techniques: interpolation between points is impossible without a common input space. However, while the dimensionality of genotypes may differ across individuals, in many domains, such as controllers or classifiers, the dimensionality of the input and output remains constant. In this work we leverage this insight to embed differing neural networks into the same input space. To judge the difference between the behavior of two neural networks, we give them both the same input sequence, and examine the difference in output. This difference, the phenotypic distance, can then be used to situate these networks into a common input space, allowing us to produce surrogate models which can predict the performance of neural networks regardless of topology. In a robotic navigation task, we show that models trained using this phenotypic embedding perform as well or better as those trained on the weight values of a fixed topology neural network. We establish such phenotypic surrogate models as a promising and flexible approach which enables surrogate modeling even for representations that undergo structural changes.

Published
2019
Full Text
View/download PDF

33. Modeling User Selection in Quality Diversity

Author

Hagg, Alexander, Asteroth, Alexander, and Bäck, Thomas

Subjects
Computer Science - Neural and Evolutionary Computing
Abstract

The initial phase in real world engineering optimization and design is a process of discovery in which not all requirements can be made in advance, or are hard to formalize. Quality diversity algorithms, which produce a variety of high performing solutions, provide a unique chance to support engineers and designers in the search for what is possible and high performing. In this work we begin to answer the question how a user can interact with quality diversity and turn it into an interactive innovation aid. By modeling a user's selection it can be determined whether the optimization is drifting away from the user's preferences. The optimization is then constrained by adding a penalty to the objective function. We present an interactive quality diversity algorithm that can take into account the user's selection. The approach is evaluated in a new multimodal optimization benchmark that allows various optimization tasks to be performed. The user selection drift of the approach is compared to a state of the art alternative on both a planning and a neuroevolution control task, thereby showing its limits and possibilities.

Published
2019
Full Text
View/download PDF

36. Overview of the EUROfusion Tokamak Exploitation programme in support of ITER and DEMO

Author

E. Joffrin, M. Wischmeier, M. Baruzzo, A. Hakola, A. Kappatou, D. Keeling, B. Labit, E. Tsitrone, N. Vianello, the ASDEX Upgrade Team, JET Contributors, the MAST-U Team, the TCV Team, the WEST Team, the EUROfusion Tokamak Exploitation Team:, D. Abate, J. Adamek, M. Agostini, C. Albert, F.C.P. Albert Devasagayam, S. Aleiferis, E. Alessi, J. Alhage, S. Allan, J. Allcock, M. Alonzo, G. Anastasiou, E. Andersson Sunden, C. Angioni, Y. Anquetin, L. Appel, G.M. Apruzzese, M. Ariola, C. Arnas, J.F. Artaud, W. Arter, O. Asztalos, L. Aucone, M.H. Aumeunier, F. Auriemma, J. Ayllon, E. Aymerich, A. Baciero, F. Bagnato, L. Bähner, F. Bairaktaris, P. Balázs, L. Balbinot, I. Balboa, M. Balden, A. Balestri, M. Baquero Ruiz, T. Barberis, C. Barcellona, O. Bardsley, S. Benkadda, T. Bensadon, E. Bernard, M. Bernert, H. Betar, R. Bianchetti Morales, J. Bielecki, R. Bilato, P. Bilkova, W. Bin, G. Birkenmeier, R. Bisson, P. Blanchard, A. Bleasdale, V. Bobkov, A. Boboc, A. Bock, K. Bogar, P. Bohm, T. Bolzonella, F. Bombarda, N. Bonanomi, L. Boncagni, D. Bonfiglio, R. Bonifetto, M. Bonotto, D. Borodin, I. Borodkina, T.O.S.J. Bosman, C. Bourdelle, C. Bowman, S. Brezinsek, D. Brida, F. Brochard, R. Brunet, D. Brunetti, V. Bruno, R. Buchholz, J. Buermans, H. Bufferand, P. Buratti, A. Burckhart, J. Cai, R. Calado, J. Caloud, S. Cancelli, F. Cani, B. Cannas, M. Cappelli, S. Carcangiu, A. Cardinali, S. Carli, D. Carnevale, M. Carole, M. Carpita, D. Carralero, F. Caruggi, I.S. Carvalho, I. Casiraghi, A. Casolari, F.J. Casson, C. Castaldo, A. Cathey, F. Causa, J. Cavalier, M. Cavedon, J. Cazabonne, M. Cecconello, L. Ceelen, A. Celora, J. Cerovsky, C.D. Challis, R. Chandra, A. Chankin, B. Chapman, H. Chen, M. Chernyshova, A.G. Chiariello, P. Chmielewski, A. Chomiczewska, C. Cianfarani, G. Ciraolo, J. Citrin, F. Clairet, S. Coda, R. Coelho, J.W. Coenen, I.H. Coffey, C. Colandrea, L. Colas, S. Conroy, C. Contre, N.J. Conway, L. Cordaro, Y. Corre, D. Costa, S. Costea, D. Coster, X. Courtois, C. Cowley, T. Craciunescu, G. Croci, A.M. Croitoru, K. Crombe, D.J. Cruz Zabala, G. Cseh, T. Czarski, A. Da Ros, A. Dal Molin, M. Dalla Rosa, Y. Damizia, O. D’Arcangelo, P. David, M. De Angeli, E. De la Cal, E. De La Luna, G. De Tommasi, J. Decker, R. Dejarnac, D. Del Sarto, G. Derks, C. Desgranges, P. Devynck, S. Di Genova, L.E. di Grazia, A. Di Siena, M. Dicorato, M. Diez, M. Dimitrova, T. Dittmar, L. Dittrich, J.J. Domínguez Palacios Durán, P. Donnel, D. Douai, S. Dowson, S. Doyle, M. Dreval, P. Drews, L. Dubus, R. Dumont, D. Dunai, M. Dunne, A. Durif, F. Durodie, G. Durr Legoupil Nicoud, B. Duval, R. Dux, T. Eich, A. Ekedahl, S. Elmore, G. Ericsson, J. Eriksson, B. Eriksson, F. Eriksson, S. Ertmer, A. Escarguel, B. Esposito, T. Estrada, E. Fable, M. Faitsch, N. Fakhrayi Mofrad, A. Fanni, T. Farley, M. Farník, N. Fedorczak, F. Felici, X. Feng, J. Ferreira, D. Ferreira, N. Ferron, O. Fevrier, O. Ficker, A.R. Field, A. Figueiredo, N. Fil, D. Fiorucci, M. Firdaouss, R. Fischer, M. Fitzgerald, M. Flebbe, M. Fontana, J. Fontdecaba Climent, A. Frank, E. Fransson, L. Frassinetti, D. Frigione, S. Futatani, R. Futtersack, S. Gabriellini, D. Gadariya, D. Galassi, K. Galazka, J. Galdon, S. Galeani, D. Gallart, A. Gallo, C. Galperti, M. Gambrioli, S. Garavaglia, J. Garcia, M. Garcia Munoz, J. Gardarein, L. Garzotti, J. Gaspar, R. Gatto, P. Gaudio, M. Gelfusa, J. Gerardin, S.N. Gerasimov, R. Gerru Miguelanez, G. Gervasini, Z. Ghani, F.M. Ghezzi, G. Ghillardi, L. Giannone, S. Gibson, L. Gil, A. Gillgren, E. Giovannozzi, C. Giroud, G. Giruzzi, T. Gleiter, M. Gobbin, V. Goloborodko, A. González Ganzábal, T. Goodman, V. Gopakumar, G. Gorini, T. Görler, S. Gorno, G. Granucci, D. Greenhouse, G. Grenfell, M. Griener, W. Gromelski, M. Groth, O. Grover, M. Gruca, A. Gude, C. Guillemaut, R. Guirlet, J. Gunn, T. Gyergyek, L. Hagg, J. Hall, C.J. Ham, M. Hamed, T. Happel, G. Harrer, J. Harrison, D. Harting, N.C. Hawkes, P. Heinrich, S. Henderson, P. Hennequin, R. Henriques, S. Heuraux, J. Hidalgo Salaverri, J. Hillairet, J.C. Hillesheim, A. Hjalmarsson, A. Ho, J. Hobirk, E. Hodille, M. Hölzl, M. Hoppe, J. Horacek, N. Horsten, L. Horvath, M. Houry, K. Hromasova, J. Huang, Z. Huang, A. Huber, E. Huett, P. Huynh, A. Iantchenko, M. Imrisek, P. Innocente, C. Ionita Schrittwieser, H. Isliker, P. Ivanova, I. Ivanova Stanik, M. Jablczynska, S. Jachmich, A.S. Jacobsen, P. Jacquet, A. Jansen van Vuuren, A. Jardin, H. Järleblad, A. Järvinen, F. Jaulmes, T. Jensen, I. Jepu, S. Jessica, T. Johnson, A. Juven, J. Kalis, J. Karhunen, R. Karimov, A.N. Karpushov, S. Kasilov, Y. Kazakov, P.V. Kazantzidis, W. Kernbichler, HT. Kim, D.B. King, V.G. Kiptily, A. Kirjasuo, K.K. Kirov, A. Kirschner, A. Kit, T. Kiviniemi, F. Kjær, E. Klinkby, A. Knieps, U. Knoche, M. Kochan, F. Köchl, G. Kocsis, J.T.W. Koenders, L. Kogan, Y. Kolesnichenko, Y. Kominis, M. Komm, M. Kong, B. Kool, S.B. Korsholm, D. Kos, M. Koubiti, J. Kovacic, Y. Kovtun, E. Kowalska Strzeciwilk, K. Koziol, M. Kozulia, A. Krämer Flecken, A. Kreter, K. Krieger, U. Kruezi, O. Krutkin, O. Kudlacek, U. Kumar, H. Kumpulainen, M.H. Kushoro, R. Kwiatkowski, M. La Matina, M. Lacquaniti, L. Laguardia, P. Lainer, P. Lang, M. Larsen, E. Laszynska, K.D. Lawson, A. Lazaros, E. Lazzaro, M.Y.K. Lee, S. Leerink, M. Lehnen, M. Lennholm, E. Lerche, Y. Liang, A. Lier, J. Likonen, O. Linder, B. Lipschultz, A. Listopad, X. Litaudon, E. Litherland Smith, D. Liuzza, T. Loarer, P.J. Lomas, J. Lombardo, N. Lonigro, R. Lorenzini, C. Lowry, T. Luda di Cortemiglia, A. Ludvig Osipov, T. Lunt, V. Lutsenko, E. Macusova, R. Mäenpää, P. Maget, C.F. Maggi, J. Mailloux, S. Makarov, K. Malinowski, P. Manas, A. Mancini, D. Mancini, P. Mantica, M. Mantsinen, J. Manyer, M. Maraschek, G. Marceca, G. Marcer, C. Marchetto, S. Marchioni, A. Mariani, M. Marin, M. Markl, T. Markovic, D. Marocco, S. Marsden, L. Martellucci, P. Martin, C. Martin, F. Martinelli, L. Martinelli, J.R. Martin Solis, R. Martone, M. Maslov, R. Masocco, M. Mattei, G.F. Matthews, D. Matveev, E. Matveeva, M.L. Mayoral, D. Mazon, S. Mazzi, C. Mazzotta, G. McArdle, R. McDermott, K. McKay, A.G. Meigs, C. Meineri, A. Mele, V. Menkovski, S. Menmuir, A. Merle, H. Meyer, K. Mikszuta Michalik, D. Milanesio, F. Militello, A. Milocco, I.G. Miron, J. Mitchell, R. Mitteau, V. Mitterauer, J. Mlynar, V. Moiseenko, P. Molna, F. Mombelli, C. Monti, A. Montisci, J. Morales, P. Moreau, J.M. Moret, A. Moro, D. Moulton, P. Mulholland, M. Muraglia, A. Murari, A. Muraro, P. Muscente, D. Mykytchuk, F. Nabais, Y. Nakeva, F. Napoli, E. Nardon, M.F. Nave, R.D. Nem, A. Nielsen, S.K. Nielsen, M. Nocente, R. Nouailletas, S. Nowak, H. Nyström, R. Ochoukov, N. Offeddu, S. Olasz, C. Olde, F. Oliva, D. Oliveira, H.J.C. Oliver, P. Ollus, J. Ongena, F.P. Orsitto, N. Osborne, R. Otin, P. Oyola Dominguez, D.I. Palade, S. Palomba, O. Pan, N. Panadero, E. Panontin, A. Papadopoulos, P. Papagiannis, G. Papp, V.V. Parail, C. Pardanaud, J. Parisi, A. Parrott, K. Paschalidis, M. Passoni, F. Pastore, A. Patel, B. Patel, A. Pau, G. Pautasso, R. Pavlichenko, E. Pawelec, B. Pegourie, G. Pelka, E. Peluso, A. Perek, E. Perelli Cippo, C. Perez Von Thun, P. Petersson, G. Petravich, Y. Peysson, V. Piergotti, L. Pigatto, C. Piron, L. Piron, A. Pironti, F. Pisano, U. Plank, B. Ploeckl, V. Plyusnin, A. Podolnik, Y. Poels, G. Pokol, J. Poley, G. Por, M. Poradzinski, F. Porcelli, L. Porte, C. Possieri, A. Poulsen, I. Predebon, G. Pucella, M. Pueschel, P. Puglia, O. Putignano, T. Pütterich, V. Quadri, A. Quercia, M. Rabinski, L. Radovanovic, R. Ragona, H. Raj, M. Rasinski, J. Rasmussen, G. Ratta, S. Ratynskaia, R. Rayaprolu, M. Rebai, A. Redl, D. Rees, D. Refy, M. Reich, H. Reimerdes, B.C.G. Reman, O. Renders, C. Reux, D. Ricci, M. Richou, S. Rienacker, D. Rigamonti, F. Rigollet, F.G. Rimini, D. Ripamonti, N. Rispoli, N. Rivals, J.F. Rivero Rodriguez, C. Roach, G. Rocchi, S. Rode, P. Rodrigues, J. Romazanov, C.F. Romero Madrid, J. Rosato, R. Rossi, G. Rubino, J. Rueda Rueda, J. Ruiz Ruiz, P. Ryan, D. Ryan, S. Saarelma, R. Sabot, M. Salewski, A. Salmi, L. Sanchis, A. Sand, J. Santos, K. Särkimäki, M. Sassano, O. Sauter, G. Schettini, S. Schmuck, P. Schneider, N. Schoonheere, R. Schramm, R. Schrittwieser, C. Schuster, N. Schwarz, F. Sciortino, M. Scotto D’Abusco, S. Scully, A. Selce, L. Senni, M. Senstius, G. Sergienko, S.E. Sharapov, R. Sharma, A. Shaw, U. Sheikh, G. Sias, B. Sieglin, S.A. Silburn, C. Silva, A. Silva, D. Silvagni, B. Simmendefeldt Schmidt, L. Simons, J. Simpson, L. Singh, S. Sipilä, Y. Siusko, S. Smith, A. Snicker, E.R. Solano, V. Solokha, M. Sos, C. Sozzi, F. Spineanu, G. Spizzo, M. Spolaore, L. Spolladore, C. Srinivasan, A. Stagni, Z. Stancar, G. Stankunas, J. Stober, P. Strand, C.I. Stuart, F. Subba, G.Y. Sun, H.J. Sun, W. Suttrop, J. Svoboda, T. Szepesi, G. Szepesi, B. Tal, T. Tala, P. Tamain, G. Tardini, M. Tardocchi, D. Taylor, G. Telesca, A. Tenaglia, A. Terra, D. Terranova, D. Testa, C. Theiler, E. Tholerus, B. Thomas, E. Thoren, A. Thornton, A. Thrysoe, Q. TICHIT, W. Tierens, A. Titarenko, P. Tolias, E. Tomasina, M. Tomes, E. Tonello, A. Tookey, M. Toscano Jiménez, C. Tsironis, C. Tsui, A. Tykhyy, M. Ugoletti, M. Usoltseva, D.F. Valcarcel, A. Valentini, M. Valisa, M. Vallar, M. Valovic, SI. Valvis, M. van Berkel, D. Van Eester, S. Van Mulders, M. van Rossem, R. Vann, B. Vanovac, J. Varela Rodriguez, J. Varje, S. Vartanian, M. Vecsei, L. Velarde Gallardo, M. Veranda, T. Verdier, G. Verdoolaege, K. Verhaegh, L. Vermare, G. Verona Rinati, J. Vicente, E. Viezzer, L. Vignitchouk, F. Villone, B. Vincent, P. Vincenzi, M.O. Vlad, G. Vogel, I. Voitsekhovitch, I. Voldiner, P. Vondracek, N.M.T. VU, T. Vuoriheimo, C. Wade, E. Wang, T. Wauters, M. Weiland, H. Weisen, N. Wendler, D. Weston, A. Widdowson, S. Wiesen, M. Wiesenberger, T. Wijkamp, M. Willensdorfer, T. Wilson, A. Wojenski, C. Wuethrich, I. Wyss, L. Xiang, S. Xu, D. Yadykin, Y. Yakovenko, H. Yang, V. Yanovskiy, R. Yi, B. Zaar, G. Zadvitskiy, L. Zakharov, P. Zanca, D. Zarzoso, Y. Zayachuk, J. Zebrowski, M. Zerbini, P. Zestanakis, C. F. B. Zimmermann, M. Zlobinski, A. Zohar, V.K. Zotta, X. Zou, M. Zuin, M. Zurita, and I. Zychor

Subjects
JET, ASDEX Upgrade, MAST-U, TCV, WEST, Tokamak Exploitation Task Force, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

Within the 9th European Framework programme, since 2021 EUROfusion is operating five tokamaks under the auspices of a single Task Force called ‘Tokamak Exploitation’. The goal is to benefit from the complementary capabilities of each machine in a coordinated way and help in developing a scientific output scalable to future largre machines. The programme of this Task Force ensures that ASDEX Upgrade, MAST-U, TCV, WEST and JET (since 2022) work together to achieve the objectives of Missions 1 and 2 of the EUROfusion Roadmap: i) demonstrate plasma scenarios that increase the success margin of ITER and satisfy the requirements of DEMO and, ii) demonstrate an integrated approach that can handle the large power leaving ITER and DEMO plasmas. The Tokamak Exploitation task force has therefore organized experiments on these two missions with the goal to strengthen the physics and operational basis for the ITER baseline scenario and for exploiting the recent plasma exhaust enhancements in all four devices (PEX: Plasma EXhaust) for exploring the solution for handling heat and particle exhaust in ITER and develop the conceptual solutions for DEMO. The ITER Baseline scenario has been developed in a similar way in ASDEX Upgrade, TCV and JET. Key risks for ITER such as disruptions and run-aways have been also investigated in TCV, ASDEX Upgrade and JET. Experiments have explored successfully different divertor configurations (standard, super-X, snowflakes) in MAST-U and TCV and studied tungsten melting in WEST and ASDEX Upgrade. The input from the smaller devices to JET has also been proven successful to set-up novel control schemes on disruption avoidance and detachment.

Published
2024
Full Text
View/download PDF

37. Prototype Discovery using Quality-Diversity

Author

Hagg, Alexander, Asteroth, Alexander, and Bäck, Thomas

Subjects
Computer Science - Neural and Evolutionary Computing
Abstract

An iterative computer-aided ideation procedure is introduced, building on recent quality-diversity algorithms, which search for diverse as well as high-performing solutions. Dimensionality reduction is used to define a similarity space, in which solutions are clustered into classes. These classes are represented by prototypes, which are presented to the user for selection. In the next iteration, quality-diversity focuses on searching within the selected class. A quantitative analysis is performed on a 2D airfoil, and a more complex 3D side view mirror domain shows how computer-aided ideation can help to enhance engineers' intuition while allowing their design decisions to influence the design process., Comment: Parallel Problem Solving using Nature 2018

Published
2018

48. Parsley Health: Feasibility and acceptability of a large-scale holistic telehealth program for chronic disease care

Author

Hants Williams, Sarah Steinberg, Ryan Vingum, Kendall Leon, Elena Céspedes, Robin Berzin, and Heather Hagg

Subjects
chronic conditions, holistic medicine, telehealth, patient engagement, healthcare system, Medicine, Public aspects of medicine, RA1-1270, Electronic computers. Computer science, QA75.5-76.95
Abstract

BackgroundA holistic, personalized approach to medicine can be used to prevent and manage a variety of chronic diseases. However, effectively managing chronic diseases can be difficult due to barriers related to insufficient provider time, staffing, and lack of patient engagement. To address these challenges telehealth strategies are being increasingly adopted, yet few studies have explored how to evaluate the feasibility and implementation success of large-scale holistic telehealth models for chronic disease care. The aim of this study is to assess the feasibility and acceptability of a large-scale holistic telehealth program for the management of chronic diseases. Our study findings can inform the future development and assessment of chronic disease programs delivered through telehealth strategies.MethodsData was collected from participants enrolled in a Parsley Health membership from June 1, 2021 to June 1, 2022, a subscription-based holistic medicine practice designed to help people prevent or manage chronic diseases. Implementation outcome frameworks were used to understand engagement with services, participant satisfaction, and preliminary effectiveness of the program via a patient-reported symptom severity tool.ResultsData from 10,205 participants with a range of chronic diseases were included in our analysis. Participants averaged 4.8 visits with their clinical team and reported high levels of satisfaction with their care (average NPS score of 81.35%). Preliminary evidence also showed substantial reduction in patient reported symptom severity.ConclusionOur findings suggest the Parsley Health program is a feasible and acceptable large-scale holistic telehealth program for chronic disease care. Successful implementation was due, in part, to services that promoted participant engagement along with tools and interfaces that were helpful and easy to use. These findings can be used to develop future holistic-focused telehealth programs for the management and prevention of chronic diseases.

Published
2023
Full Text
View/download PDF

50. Hierarchical Surrogate Modeling for Illumination Algorithms

Author

Hagg, Alexander

Subjects
Computer Science - Neural and Evolutionary Computing
Abstract

Evolutionary illumination is a recent technique that allows producing many diverse, optimal solutions in a map of manually defined features. To support the large amount of objective function evaluations, surrogate model assistance was recently introduced. Illumination models need to represent many more, diverse optimal regions than classical surrogate models. In this PhD thesis, we propose to decompose the sample set, decreasing model complexity, by hierarchically segmenting the training set according to their coordinates in feature space. An ensemble of diverse models can then be trained to serve as a surrogate to illumination.

Published
2017

Catalog

Books, media, physical & digital resources
See catalog results