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1. Patients’ Measurement Priorities for Remote Measurement Technologies to Aid Chronic Health Conditions: Qualitative Analysis

Author

Simblett, Sara, Matcham, Faith, Curtis, Hannah, Greer, Ben, Polhemus, Ashley, Novák, Jan, Ferrao, Jose, Gamble, Peter, Hotopf, Matthew, Narayan, Vaibhav, and Wykes, Til

Subjects
Information technology, T58.5-58.64, Public aspects of medicine, RA1-1270
Abstract

BackgroundRemote measurement technology (RMT), including the use of mobile phone apps and wearable devices, may provide the opportunity for real-world assessment and intervention that will streamline clinical input for years to come. In order to establish the benefits of this approach, we need to operationalize what is expected in terms of a successful measurement. We focused on three clinical long-term conditions where a novel case has been made for the benefits of RMT: major depressive disorder (MDD), multiple sclerosis (MS), and epilepsy. ObjectiveThe aim of this study was to conduct a consultation exercise on the clinical end point or outcome measurement priorities for RMT studies, drawing on the experiences of people with chronic health conditions. MethodsA total of 24 participants (16/24 women, 67%), ranging from 28 to 65 years of age, with a diagnosis of one of three chronic health conditions―MDD, MS, or epilepsy―took part in six focus groups. A systematic thematic analysis was used to extract themes and subthemes of clinical end point or measurement priorities. ResultsThe views of people with MDD, epilepsy, and MS differed. Each group highlighted unique measurements of importance, relevant to their specific needs. Although there was agreement that remote measurement could be useful for tracking symptoms of illness, some symptoms were specific to the individual groups. Measuring signs of wellness was discussed more by people with MDD than by people with MS and epilepsy. However, overlap did emerge when considering contextual factors, such as life events and availability of support (MDD and epilepsy) as well as ways of coping (epilepsy and MS). ConclusionsThis is a unique study that puts patients’ views at the forefront of the design of a clinical study employing novel digital resources. In all cases, measuring symptom severity is key; people want to know when their health is getting worse. Second, symptom severity needs to be placed into context. A holistic approach that, in some cases, considers signs of wellness as well as illness, should be the aim of studies employing RMT to understand the health of people with chronic conditions.

Published
2020
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2. Human-Centered Design Strategies for Device Selection in mHealth Programs: Development of a Novel Framework and Case Study

Author

Polhemus, Ashley Marie, Novák, Jan, Ferrao, Jose, Simblett, Sara, Radaelli, Marta, Locatelli, Patrick, Matcham, Faith, Kerz, Maximilian, Weyer, Janice, Burke, Patrick, Huang, Vincy, Dockendorf, Marissa Fallon, Temesi, Gergely, Wykes, Til, Comi, Giancarlo, Myin-Germeys, Inez, Folarin, Amos, Dobson, Richard, Manyakov, Nikolay V, Narayan, Vaibhav A, and Hotopf, Matthew

Subjects
Information technology, T58.5-58.64, Public aspects of medicine, RA1-1270
Abstract

BackgroundDespite the increasing use of remote measurement technologies (RMT) such as wearables or biosensors in health care programs, challenges associated with selecting and implementing these technologies persist. Many health care programs that use RMT rely on commercially available, “off-the-shelf” devices to collect patient data. However, validation of these devices is sparse, the technology landscape is constantly changing, relative benefits between device options are often unclear, and research on patient and health care provider preferences is often lacking. ObjectiveTo address these common challenges, we propose a novel device selection framework extrapolated from human-centered design principles, which are commonly used in de novo digital health product design. We then present a case study in which we used the framework to identify, test, select, and implement off-the-shelf devices for the Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) consortium, a research program using RMT to study central nervous system disease progression. MethodsThe RADAR-CNS device selection framework describes a human-centered approach to device selection for mobile health programs. The framework guides study designers through stakeholder engagement, technology landscaping, rapid proof of concept testing, and creative problem solving to develop device selection criteria and a robust implementation strategy. It also describes a method for considering compromises when tensions between stakeholder needs occur. ResultsThe framework successfully guided device selection for the RADAR-CNS study on relapse in multiple sclerosis. In the initial stage, we engaged a multidisciplinary team of patients, health care professionals, researchers, and technologists to identify our primary device-related goals. We desired regular home-based measurements of gait, balance, fatigue, heart rate, and sleep over the course of the study. However, devices and measurement methods had to be user friendly, secure, and able to produce high quality data. In the second stage, we iteratively refined our strategy and selected devices based on technological and regulatory constraints, user feedback, and research goals. At several points, we used this method to devise compromises that addressed conflicting stakeholder needs. We then implemented a feedback mechanism into the study to gather lessons about devices to improve future versions of the RADAR-CNS program. ConclusionsThe RADAR device selection framework provides a structured yet flexible approach to device selection for health care programs and can be used to systematically approach complex decisions that require teams to consider patient experiences alongside scientific priorities and logistical, technical, or regulatory constraints.

Published
2020
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3. Waveform Modelling for the Laser Interferometer Space Antenna

Author

LISA Consortium Waveform Working Group, Afshordi, Niayesh, Akçay, Sarp, Seoane, Pau Amaro, Antonelli, Andrea, Aurrekoetxea, Josu C., Barack, Leor, Barausse, Enrico, Benkel, Robert, Bernard, Laura, Bernuzzi, Sebastiano, Berti, Emanuele, Bonetti, Matteo, Bonga, Béatrice, Bozzola, Gabriele, Brito, Richard, Buonanno, Alessandra, Cárdenas-Avendaño, Alejandro, Casals, Marc, Chernoff, David F., Chua, Alvin J. K., Clough, Katy, Colleoni, Marta, Dhesi, Mekhi, Druart, Adrien, Durkan, Leanne, Faye, Guillaume, Ferguson, Deborah, Field, Scott E., Gabella, William E., García-Bellido, Juan, Gracia-Linares, Miguel, Gerosa, Davide, Green, Stephen R., Haney, Maria, Hannam, Mark, Heffernan, Anna, Hinderer, Tanja, Helfer, Thomas, Hughes, Scott A., Husa, Sascha, Isoyama, Soichiro, Katz, Michael L., Kavanagh, Chris, Khanna, Gaurav, Kidder, Larry E., Korol, Valeriya, Küchler, Lorenzo, Laguna, Pablo, Larrouturou, François, Tiec, Alexandre Le, Leather, Benjamin, Lim, Eugene A., Lim, Hyun, Littenberg, Tyson B., Long, Oliver, Lousto, Carlos O., Lovelace, Geoffrey, Lukes-Gerakopoulos, Georgios, Lynch, Philip, Macedo, Rodrigo P., Markakis, Charalampos, Maggio, Elisa, Mandel, Ilya, Maselli, Andrea, Mathews, Josh, Mourier, Pierre, Neilsen, David, Nagar, Alessandro, Nichols, David A., Novák, Jan, Okounkova, Maria, O'Shaughnessy, Richard, Oshita, Naritaka, O'Toole, Conor, Pan, Zhen, Pani, Paolo, Pappas, George, Paschalidis, Vasileios, Pfeiffer, Harald P., Pompili, Lorenzo, Pound, Adam, Pratten, Geraint, Rüter, Hannes R., Ruiz, Milton, Sam, Zeyd, Sberna, Laura, Shapiro, Stuart L., Shoemaker, Deirdre M., Sopuerta, Carlos F., Spiers, Andrew, Sundar, Hari, Tamanini, Nicola, Thompson, Jonathan E., Toubiana, Alexandre, Tsokaros, Antonios, Upton, Samuel D., van de Meent, Maarten, Vernieri, Daniele, Wachter, Jeremy M., Warburton, Niels, Wardell, Barry, Witek, Helvi, Witzany, Vojtěch, Yang, Huan, Zilhão, Miguel, Albertini, Angelica, Arun, K. G., Bezares, Miguel, Bonilla, Alexander, Chapman-Bird, Christian, Cownden, Bradley, Cunningham, Kevin, Devitt, Chris, Dolan, Sam, Duque, Francisco, Dyson, Conor, Fryer, Chris L., Gair, Jonathan R., Giacomazzo, Bruno, Gupta, Priti, Han, Wen-Biao, Haas, Roland, Hirschmann, Eric W., Huerta, E. A., Jetzer, Philippe, Kelly, Bernard, Khalil, Mohammed, Lewis, Jack, Lloyd-Ronning, Nicole, Marsat, Sylvain, Nardini, Germano, Neef, Jakob, Ottewill, Adrian, Pantelidou, Christiana, Piovano, Gabriel Andres, Redondo-Yuste, Jaime, Sagunski, Laura, Stein, Leo C., Skoupý, Viktor, Sperhake, Ulrich, Speri, Lorenzo, Spieksma, Thomas F. M., Stevens, Chris, Trestini, David, and Vañó-Viñuales, Alex

Subjects
General Relativity and Quantum Cosmology, Astrophysics - High Energy Astrophysical Phenomena
Abstract

LISA, the Laser Interferometer Space Antenna, will usher in a new era in gravitational-wave astronomy. As the first anticipated space-based gravitational-wave detector, it will expand our view to the millihertz gravitational-wave sky, where a spectacular variety of interesting new sources abound: from millions of ultra-compact binaries in our Galaxy, to mergers of massive black holes at cosmological distances; from the beginnings of inspirals that will venture into the ground-based detectors' view to the death spiral of compact objects into massive black holes, and many sources in between. Central to realising LISA's discovery potential are waveform models, the theoretical and phenomenological predictions of the pattern of gravitational waves that these sources emit. This white paper is presented on behalf of the Waveform Working Group for the LISA Consortium. It provides a review of the current state of waveform models for LISA sources, and describes the significant challenges that must yet be overcome., Comment: 239 pages, 11 figures, white paper from the LISA Consortium Waveform Working Group, invited for submission to Living Reviews in Relativity, updated with comments from community

Published
2023

5. The methodology for validation of cross sections in quasi monoenergetic neutron field

Author

Matěj, Zdeněk, Košťál, Michal, Majerle, Mitja, Ansorge, Martin, Losa, Evžen, Zmeškal, Marek, Schulc, Martin, Šimon, Jan, Štefánik, Milan, Novák, Jan, Koliadko, Daniil, Cvachovec, František, Mravec, Filip, Přenosil, Václav, Zach, Václav, Czakoj, Tomáš, Rypar, Vojtěch, and Capote, Roberto

Subjects
Physics - Instrumentation and Detectors, Nuclear Experiment, Physics - Accelerator Physics
Abstract

Dosimetry cross sections are fundamental quantities essential in determination of neutron fluences in points of interest in technologies under heavy radiation load. The most common is their application to Reactor Pressure Vessel aging management, relating to correct estimation of its residual lifetime. The neutron spectrum in various reactor positions has a similar character as the fission spectrum. Due to this fact, the validation of the neutron dosimetry cross sections in reactor fields, or in a very well-known neutron field of 252Cf(s.f.) is sufficient for ensuring their validity in estimations of neutron fluxes in reactor physics. With an increasing field of applications, as in neutron dosimetry in accelerator-based fields or space applications, requests occurred on detailed validation in spectra different from fission ones. This paper presents the testing of a new methodology for the use of quasi monoenergetic neutron fields, where different sensitivity allows validations of the cross-section in different energy regions than in the fission spectrum. The exact shape of the neutron spectrum in the tested fields is determined by stilbene spectrometry and corrected to scattering by calculation, where applicable. The total flux is determined from Ni and Al flux monitors. The evaluated neutron flux in the target set of activation foils is used for calculation of theoretical reaction rate, which is compared with the experimental value determined from gamma activity. This kind of comparison can be understood as validation. It is worth noting, this methodology applied to the IRDFF-II library shows satisfactory agreement for selected reactions.

Published
2023

6. Wang tiles enable combinatorial design and robot-assisted manufacturing of modular mechanical metamaterials

Author

Doškář, Martin, Somr, Michael, Hlůžek, Radim, Havelka, Jan, Novák, Jan, and Zeman, Jan

Subjects
Condensed Matter - Materials Science, Condensed Matter - Soft Condensed Matter
Abstract

In this paper, we introduce a novel design paradigm for modular architectured materials that allows for spatially nonuniform designs from a handful of building blocks, which can be robotically assembled for efficient and scalable production. The traditional, design-limiting periodicity in material design is overcome by utilizing Wang tiles to achieve compatibility among building blocks. We illustrate our approach with the design and manufacturing of an L-shaped domain inspired by a scissor-like soft gripper, whose internal module distribution was optimized to achieve an extreme tilt of a tip of the gripper's jaw when the handle part was uniformly compressed. The geometry of individual modules was built on a 3$\times$3 grid of elliptical holes with varying semi-axes ratios and alternating orientations. We optimized the distribution of the modules within the L-shaped domain using an enumeration approach combined with a factorial search strategy. To address the challenge of seamless interface connections in modular manufacturing, we produced the final designs by casting silicone rubber into modular molds automatically assembled by a robotic arm. The predicted performance was validated experimentally using a custom-built, open-hardware test rig, Thymos, supplemented with digital image correlation measurements. Our study demonstrates the potential for enhancing the mechanical performance of architectured materials by incorporating nonuniform modular designs and efficient robot-assisted manufacturing., Comment: manuscript: 9 pages, 5 figures; supplementary: 10 pages, 10 figures

Published
2023
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7. Real-Time Neural Appearance Models

Author

Zeltner, Tizian, Rousselle, Fabrice, Weidlich, Andrea, Clarberg, Petrik, Novák, Jan, Bitterli, Benedikt, Evans, Alex, Davidovič, Tomáš, Kallweit, Simon, and Lefohn, Aaron

Subjects
Computer Science - Graphics
Abstract

We present a complete system for real-time rendering of scenes with complex appearance previously reserved for offline use. This is achieved with a combination of algorithmic and system level innovations. Our appearance model utilizes learned hierarchical textures that are interpreted using neural decoders, which produce reflectance values and importance-sampled directions. To best utilize the modeling capacity of the decoders, we equip the decoders with two graphics priors. The first prior -- transformation of directions into learned shading frames -- facilitates accurate reconstruction of mesoscale effects. The second prior -- a microfacet sampling distribution -- allows the neural decoder to perform importance sampling efficiently. The resulting appearance model supports anisotropic sampling and level-of-detail rendering, and allows baking deeply layered material graphs into a compact unified neural representation. By exposing hardware accelerated tensor operations to ray tracing shaders, we show that it is possible to inline and execute the neural decoders efficiently inside a real-time path tracer. We analyze scalability with increasing number of neural materials and propose to improve performance using code optimized for coherent and divergent execution. Our neural material shaders can be over an order of magnitude faster than non-neural layered materials. This opens up the door for using film-quality visuals in real-time applications such as games and live previews., Comment: Equal contribution by the first six authors. Order determined by a rock-paper-scissors tournament. Project page: https://research.nvidia.com/labs/rtr/neural_appearance_models/

Published
2023
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8. Inverse Global Illumination using a Neural Radiometric Prior

Author

Hadadan, Saeed, Lin, Geng, Novák, Jan, Rousselle, Fabrice, and Zwicker, Matthias

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Graphics
Abstract

Inverse rendering methods that account for global illumination are becoming more popular, but current methods require evaluating and automatically differentiating millions of path integrals by tracing multiple light bounces, which remains expensive and prone to noise. Instead, this paper proposes a radiometric prior as a simple alternative to building complete path integrals in a traditional differentiable path tracer, while still correctly accounting for global illumination. Inspired by the Neural Radiosity technique, we use a neural network as a radiance function, and we introduce a prior consisting of the norm of the residual of the rendering equation in the inverse rendering loss. We train our radiance network and optimize scene parameters simultaneously using a loss consisting of both a photometric term between renderings and the multi-view input images, and our radiometric prior (the residual term). This residual term enforces a physical constraint on the optimization that ensures that the radiance field accounts for global illumination. We compare our method to a vanilla differentiable path tracer, and more advanced techniques such as Path Replay Backpropagation. Despite the simplicity of our approach, we can recover scene parameters with comparable and in some cases better quality, at considerably lower computation times., Comment: Homepage: https://inverse-neural-radiosity.github.io

Published
2023
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10. Advancements in risk stratification and management strategies in primary cardiovascular prevention

Author

Barkas, Fotios, Sener, Yusuf Ziya, Golforoush, Pelin Arabacilar, Kheirkhah, Azin, Rodriguez-Sanchez, Elena, Novak, Jan, Apellaniz-Ruiz, Maria, Akyea, Ralph Kwame, Bianconi, Vanessa, Ceasovschih, Alexandr, Chee, Ying Jie, Cherska, Mariia, Chora, Joana Rita, D'Oria, Mario, Demikhova, Nadiia, Kocyigit Burunkaya, Duygu, Rimbert, Antoine, Macchi, Chiara, Rathod, Krishnaraj, Roth, Lynn, Sukhorukov, Vasily, Stoica, Svetlana, Scicali, Roberto, Storozhenko, Tatyana, Uzokov, Jamol, Lupo, Maria Giovanna, van der Vorst, Emiel P.C., and Porsch, Florentina

Published
2024
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11. Hormonal responses of rice to organ-targeted cold stress

Author

Jarošová, Jana, Prerostova, Sylva, Černý, Martin, Dobrev, Petre, Gaudinova, Alena, Knirsch, Vojtech, Kobzová, Eva, Müller, Karel, Fiala, Roman, Benczúr, Kinga, Szalai, Gabriella, Novák, Jan, Brzobohatý, Břetislav, Novak, Ondrej, and Vankova, Radomira

Published
2024
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12. Heart rate variability and stroke or systemic embolism in patients with atrial fibrillation

Author

Aeschbacher, Stefanie, Bhend, Katalin, Blum, Steffen, Bonati, Leo H., Carmine, Désirée, Conen, David, Eken, Ceylan, Fischer, Urs, Girroy, Corinne, Hennings, Elisa, Krisai, Philipp, Kühne, Michael, Mäder, Nina, Meyer-Zürn, Christine, Meyre, Pascal B., Monsch, Andreas U., Mosher, Luke, Müller, Christian, Osswald, Stefan, Paladini, Rebecca E., Peter, Raffaele, Schweigler, Adrian, Sticherling, Christian, Szucs, Thomas, Völlmin, Gian, Aujesky, Drahomir, Fuhrer, Juerg, Roten, Laurent, Jung, Simon, Mattle, Heinrich, Netzer, Seraina, Adam, Luise, Aubert, Carole Elodie, Feller, Martin, Loewe, Axel, Moutzouri, Elisavet, Schneider, Claudio, Flückiger, Tanja, Groen, Cindy, Ehrsam, Lukas, Hellrigl, Sven, Nuoffer, Alexandra, Rakovic, Damiana, Schwab, Nathalie, Wenger, Rylana, Zarrabi Saffari, Tu Hanh, Rodondi, Nicolas, Reichlin, Tobias, Beynon, Christopher, Dillier, Roger, Deubelbeiss, Michèle, Eberli, Franz, Franzini, Christine, Juchli, Isabel, Liedtke, Claudia, Murugiah, Samira, Nadler, Jacqueline, Obst, Thayze, Roth, Jasmin, Schlomowitsch, Fiona, Schneider, Xiaoye, Studerus, Katrin, Tynan, Noreen, Weishaupt, Dominik, Müller, Andreas, Friedli, Corinne, Kuest, Silke, Scheuch, Karin, Hischier, Denise, Bonetti, Nicole, Grau, Alexandra, Villinger, Jonas, Laube, Eva, Baumgartner, Philipp, Filipovic, Mark, Frick, Marcel, Montrasio, Giulia, Leuenberger, Stefanie, Rutz, Franziska, Beer, Jürg-Hans, Auricchio, Angelo, Anesini, Adriana, Camporini, Cristina, Caputo, Maria Luce, Peronaci, Rebecca, Regoli, Francois, Ronchi, Martina, Conte, Giulio, Brenner, Roman, Altmann, David, Fink, Karin, Gemperle, Michaela, Ammann, Peter, Firmann, Mathieu, Foucras, Sandrine, Rime, Martine, Hayoz, Daniel, Berte, Benjamin, Bühler, Kathrin, Justi, Virgina, Kellner-Weldon, Frauke, Koch, Melanie, Mehmann, Brigitta, Meier, Sonja, Roth, Myriam, Ruckli-Kaeppeli, Andrea, Russi, Ian, Schmidt, Kai, Young, Mabelle, Kobza, Richard, Rigamonti, Elia, Cereda, Carlo, Cianfoni, Alessandro, De Perna, Maria Luisa, Frangi-Kultalahti, Jane, Mayer Melchiorre, Patrizia Assunta, Pin, Anica, Terrot, Tatiana, Vicari, Luisa, Moschovitis, Giorgio, Ehret, Georg, Gallet, Hervé, Guillermet, Elise, Lazeyras, Francois, Lovblad, Karl-Olof, Perret, Patrick, Tavel, Philippe, Teres, Cheryl, Shah, Dipen, Lauriers, Nathalie, Méan, Marie, Salzmann, Sandrine, Schläpfer, Jürg, Porretta, Alessandra Pia, Grêt, Andrea, Novak, Jan, Vitelli, Sandra, Stephan, Frank-Peter, Gallino, Augusto, Di Valentino, Marcello, Aebersold, Helena, Foster, Fabienne, Schwenkglenks, Matthias, Düring, Marco, Sinnecker, Tim, Altermatt, Anna, Amann, Michael, Huber, Petra, Hürbin, Manuel, Ruberte, Esther, Thöni, Alain, Würfel, Jens, Zuber, Vanessa, Coslovsky (Head), Michael, Neuschwander, Pia, Simon, Patrick, Wunderlin, Olivia, Schmid, Ramun, Baumann, Christian, Hämmerle, Peter, Schlageter, Vincent, Coslovsky, Michael, Coduri, Federica, Blum, Manuel R., Stauber, Annina, Beer, Jürg, and Zuern, Christine S.

Published
2024
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13. Scalar perturbations of Galileon cosmologies in the mechanical approach within the late Universe

Author

Novák, Jan

Subjects
General Relativity and Quantum Cosmology
Abstract

We investigate the Universe at the late stage of its evolution and inside the cell of uniformity 150-370 Mpc. We consider the Universe to be filled at these scales with dust like matter, a minimally coupled Galileon field and radiation. We use the mechanical approach. Therefore, the peculiar velocities of the inhomogeneities as well as the fluctuations of the other perfect fluids can be considered nonrelativistic. Such fluids are said to be coupled because they are concentrated around the inhomogeneities. We investigate the conditions under which the physical Galileon field, i.e. compatible with results of the latest gravitational wave experiments GW150914, GW151226, GW170104, GW170814, GW170817 and GW170608, can become coupled. We know that at the background level coupled scalar fields behave as a two-component perfect fluids: one which mimics a network of frustrated cosmic string and an another one which corresponds to an effective cosmological constant. We found a correction for the Galileon field, which behaves like a matter component., Comment: 8 pages

Published
2021
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14. Real-time Neural Radiance Caching for Path Tracing

Author

Müller, Thomas, Rousselle, Fabrice, Novák, Jan, and Keller, Alexander

Subjects
Computer Science - Graphics, Computer Science - Machine Learning
Abstract

We present a real-time neural radiance caching method for path-traced global illumination. Our system is designed to handle fully dynamic scenes, and makes no assumptions about the lighting, geometry, and materials. The data-driven nature of our approach sidesteps many difficulties of caching algorithms, such as locating, interpolating, and updating cache points. Since pretraining neural networks to handle novel, dynamic scenes is a formidable generalization challenge, we do away with pretraining and instead achieve generalization via adaptation, i.e. we opt for training the radiance cache while rendering. We employ self-training to provide low-noise training targets and simulate infinite-bounce transport by merely iterating few-bounce training updates. The updates and cache queries incur a mild overhead -- about 2.6ms on full HD resolution -- thanks to a streaming implementation of the neural network that fully exploits modern hardware. We demonstrate significant noise reduction at the cost of little induced bias, and report state-of-the-art, real-time performance on a number of challenging scenarios., Comment: To appear at SIGGRAPH 2021. 16 pages, 16 figures

Published
2021
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15. Decoding the historical tale: COVID-19 impact on haematological malignancy patients—EPICOVIDEHA insights from 2020 to 2022

Author

Martín-Vallejo, Francisco Javier, Zdziarski, Przemyslaw, Zarrinfer, Hossein, Wittig, Jana, Win, Sein, Wai-Man, Vivien, Víšek, Benjamín, Vinh, Donald C., Vehreschild, Maria, Varricchio, Gina, Tsirigotis, Panagiotis, Torres-Tienza, Ana, Tanase, Alina Daniela, Tafuri, Agostino, Stamouli, Maria, Sramek, Jiří, Soussain, Carole, Shirinova, Ayten, Schubert, Jörg, Schalk, Enrico, Salehi, Mohammad Reza, Saleh, Modar, Rosati, Giorgio, Roldán, Elisa, Reizine, Florian, Rêgo, Mayara, Regalado-Artamendi, Isabel, Popova, Marina, Pinto, Fernando, Philippe, Laure, Orth, Hans Martin, Ommen, Hans-Beier, Obr, Aleš, Núñez-Martín-Buitrago, Lucía, Noël, Nicolas, Neuhann, Julia, Nadali, Gianpaolo, Nacov, Julia A., Munhoz Alburquerque, Ana M., Mitra, Maria Enza, Mikulska, Malgorzata, Mellinghoff, Sibylle, Mechtel, Ben, Martín-González, Juan-Alberto, Malak, Sandra, Loureiro-Amigo, Jorge, Lorenzo De La Peña, Lisset, Liberti, Giulia, Landau, Marianne, Lacej, Ira, Kolditz, Martin, Kho, Chi Shan, Khedr, Reham Abdelaziz, Karthaus, Meinolf, Karlsson, Linda Katharina, Jiménez-Lorenzo, María-Josefa, Izuzquiza, Macarena, Hoell-Neugebauer, Baerbel, Herbrecht, Raoul, Heath, Christopher H., Guolo, Fabio, Grothe, Jan, Giordano, Antonio, Gerasymchuk, Sergey, García-Sanz, Ramón, García-Poutón, Nicole, Funke, Vaneuza Araújo Moreira, Fung, Monica, Flasshove, Charlotte, Fianchi, Luana, Essame, Jenna, Egger, Matthias, Drenou, Bernard, Dragonetti, Giulia, Desole, Maximilian, Della Pepa, Roberta, Deau Fischer, Bénédicte, De Kort, Elizabeth, De Cabo, Erik, Danion, François, Daguindau, Etienne, Cushion, Tania, Cremer, Louise, Criscuolo, Marianna, Cordini, Gregorio, Cingolani, Antonella, Ciceri, Fabio, Chowdhury, Fazle Rabbi, Chelysheva, Ekaterina, Chauchet, Adrien, Chai, Louis Yi Ann, Ceesay, M. Mansour, Busch, Elena, Brehon, Mathias, Borducchi, Davimar M.M., Booth, Stephen, Bologna, Serge, Berg Venemyr, Caroline, Bailén-Almorox, Rebeca, Antoniadou, Anastasia, Anastasopoulou, Amalia N., Altuntaş, Fevzi, Salmanton-García, Jon, Marchesi, Francesco, Farina, Francesca, Weinbergerová, Barbora, Itri, Federico, Dávila-Valls, Julio, Martín-Pérez, Sonia, Glenthøj, Andreas, Hersby, Ditte Stampe, Gomes Da Silva, Maria, Nunes Rodrigues, Raquel, López-García, Alberto, Córdoba, Raúl, Bilgin, Yavuz M., Falces-Romero, Iker, El-Ashwah, Shaimaa, Emarah, Ziad, Besson, Caroline, Kohn, Milena, Van Doesum, Jaap, Ammatuna, Emanuele, Marchetti, Monia, Labrador, Jorge, Zambrotta, Giovanni Paolo Maria, Verga, Luisa, Jaksic, Ozren, Nucci, Marcio, Piukovics, Klára, Cabirta-Touzón, Alba, Jiménez, Moraima, Arellano, Elena, Espigado, Ildefonso, Blennow, Ola, Nordlander, Anna, Meers, Stef, Vian Praet, Jens, Aiello, Tommaso Francesco, Garcia-Vidal, Carolina, Fracchiolla, Nicola S., Sciumè, Mariarita, Seval, Guldane Cengiz, Žák, Pavel, Buquicchio, Caterina, Tascini, Carlo, Gräfe, Stefanie K., Schönlein, Martin, Adžić-VUKIČEVIĆ, Tatjana, Bonuomo, Valentina, Cattaneo, Chiara, Nizamuddin, Summiya, Čerňan, Martin, Plantefeve, Gaëtan, Prin, Romane, Szotkovski, Tomas, Collins, Graham P., Dargenio, Michelina, Petzer, Verena, Wolf, Dominik, Čolović, Natasha, Prezioso, Lucia, Valković, Toni, Passamonti, Francesco, Méndez, Gustavo-Adolfo, Sili, Uluhan, Vena, Antonio, Bavastro, Martina, Limongelli, Alessandro, Duarte, Rafael F., Ledoux, Marie-Pierre, Cvetanoski, Milche, Stojanoski, Zlate, Machado, Marina, Batinić, Josip, Magliano, Gabriele, Biernat, Monika M., Pantić, Nikola, Poulsen, Christian Bjørn, Cuccaro, Annarosa, Del Principe, Maria Ilaria, Kulasekararaj, Austin, Ormazabal-Vélez, Irati, Busca, Alessandro, Demirkan, Fatih, Ijaz, Marriyam, Klimko, Nikolai, Stoma, Igor, Khostelidi, Sofya, Fernández, Noemí, Omrani, Ali S., Bergantim, Rui, De Jonge, Nick, Fouquet, Guillemette, Navrátil, Milan, Abu-Zeinah, Ghaith, Samarkos, Michail, Maertens, Johan, De Ramón, Cristina, Guidetti, Anna, Magyari, Ferenc, González-López, Tomás José, Lahmer, Tobias, Finizio, Olimpia, Ali, Natasha, Pinczés, László Imre, Lavilla-Rubira, Esperanza, Romano, Alessandra, Merelli, Maria, Delia, Mario, Calbacho, Maria, Meletiadis, Joseph, Antić, Darko, Hernández-Rivas, José-Ángel, Marques De Almeida, Joyce, Al-Khabori, Murtadha, Hoenigl, Martin, Tisi, Maria Chiara, Khanna, Nina, Barać, Aleksandra, Eisa, Noha, Di Blasi, Roberta, Liévin, Raphaël, Miranda-Castillo, Carolina, Bahr, Nathan C., Lamure, Sylvain, Papa, Mario Virgilio, Yahya, Ayel, Aujayeb, Avinash, Novák, Jan, Erben, Nurettin, Fernández-Galán, María, Ribera-Santa Susana, José-María, Rinaldi, Ikhwan, Fazzi, Rita, Piedimonte, Monica, Duléry, Rémy, Gonzaga, Yung, Soto-Silva, Andrés, Sapienza, Giuseppe, Serris, Alexandra, Drgoňa, Ľuboš, Groh, Ana, Serrano, Laura, Gavriilaki, Eleni, Tragiannidis, Athanasios, Prattes, Juergen, Coppola, Nicola, Otašević, Vladimir, Mladenović, Miloš, Mitrović, Mirjana, Mišković, Bojana, Jindra, Pavel, Zompi, Sofia, Sacchi, Maria Vittoria, Krekeler, Carolin, Shumilov, Evgenii, Infante, Maria Stefania, García-Bordallo, Daniel, Çolak, Gökçe Melis, Mayer, Jiří, Nygaard, Marietta, Hanáková, Michaela, Ráčil, Zdeněk, Quattrone, Martina, Bonanni, Matteo, Koehler, Philipp, Rahimli, Laman, Cornely, Oliver A., and Pagano, Livio

Published
2024
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16. COVID-19 infection in adult patients with hematological malignancies: a European Hematology Association Survey (EPICOVIDEHA)

Author

Pagano, Livio, Salmanton-García, Jon, Marchesi, Francesco, Busca, Alessandro, Corradini, Paolo, Hoenigl, Martin, Klimko, Nikolai, Koehler, Philipp, Pagliuca, Antonio, Passamonti, Francesco, Verga, Luisa, Víšek, Benjamin, Ilhan, Osman, Nadali, Gianpaolo, Weinbergerová, Barbora, Córdoba-Mascuñano, Raúl, Marchetti, Monia, Collins, Graham P, Farina, Francesca, Cattaneo, Chiara, Cabirta, Alba, Gomes-Silva, Maria, Itri, Federico, van Doesum, Jaap, Ledoux, Marie-Pierre, Čerňan, Martin, Jakšić, Ozren, Duarte, Rafael F, Magliano, Gabriele, Omrani, Ali S, Fracchiolla, Nicola S, Kulasekararaj, Austin, Valković, Toni, Poulsen, Christian Bjørn, Machado, Marina, Glenthøj, Andreas, Stoma, Igor, Ráčil, Zdeněk, Piukovics, Klára, Navrátil, Milan, Emarah, Ziad, Sili, Uluhan, Maertens, Johan, Blennow, Ola, Bergantim, Rui, García-Vidal, Carolina, Prezioso, Lucia, Guidetti, Anna, del Principe, Maria Ilaria, Popova, Marina, de Jonge, Nick, Ormazabal-Vélez, Irati, Fernández, Noemí, Falces-Romero, Iker, Cuccaro, Annarosa, Meers, Stef, Buquicchio, Caterina, Antić, Darko, Al-Khabori, Murtadha, García-Sanz, Ramón, Biernat, Monika M, Tisi, Maria Chiara, Sal, Ertan, Rahimli, Laman, Čolović, Natasa, Schönlein, Martin, Calbacho, Maria, Tascini, Carlo, Miranda-Castillo, Carolina, Khanna, Nina, Méndez, Gustavo-Adolfo, Petzer, Verena, Novák, Jan, Besson, Caroline, Duléry, Rémy, Lamure, Sylvain, Nucci, Marcio, Zambrotta, Giovanni, Žák, Pavel, Seval, Guldane Cengiz, Bonuomo, Valentina, Mayer, Jiří, López-García, Alberto, Sacchi, Maria Vittoria, Booth, Stephen, Ciceri, Fabio, Oberti, Margherita, Salvini, Marco, Izuzquiza, Macarena, Nunes-Rodrigues, Raquel, Ammatuna, Emanuele, Obr, Aleš, Herbrecht, Raoul, Núñez-Martín-Buitrago, Lucía, Mancini, Valentina, Shwaylia, Hawraa, Sciumè, Mariarita, Essame, Jenna, Nygaard, Marietta, and Batinić, Josip

Subjects
Prevention, Cancer, Lymphoma, Rare Diseases, Hematology, Good Health and Well Being, Adult, Aged, Aged, 80 and over, COVID-19, Europe, Female, Hematologic Neoplasms, Hospitalization, Humans, Intensive Care Units, Male, Middle Aged, Registries, Risk Factors, SARS-CoV-2, Young Adult, Pandemic, Hematological malignancies, Epidemiology, EHA, EPICOVIDEHA working group, Cardiorespiratory Medicine and Haematology, Oncology and Carcinogenesis
Abstract

BackgroundPatients with hematological malignancies (HM) are at high risk of mortality from SARS-CoV-2 disease 2019 (COVID-19). A better understanding of risk factors for adverse outcomes may improve clinical management in these patients. We therefore studied baseline characteristics of HM patients developing COVID-19 and analyzed predictors of mortality.MethodsThe survey was supported by the Scientific Working Group Infection in Hematology of the European Hematology Association (EHA). Eligible for the analysis were adult patients with HM and laboratory-confirmed COVID-19 observed between March and December 2020.ResultsThe study sample includes 3801 cases, represented by lymphoproliferative (mainly non-Hodgkin lymphoma n = 1084, myeloma n = 684 and chronic lymphoid leukemia n = 474) and myeloproliferative malignancies (mainly acute myeloid leukemia n = 497 and myelodysplastic syndromes n = 279). Severe/critical COVID-19 was observed in 63.8% of patients (n = 2425). Overall, 2778 (73.1%) of the patients were hospitalized, 689 (18.1%) of whom were admitted to intensive care units (ICUs). Overall, 1185 patients (31.2%) died. The primary cause of death was COVID-19 in 688 patients (58.1%), HM in 173 patients (14.6%), and a combination of both COVID-19 and progressing HM in 155 patients (13.1%). Highest mortality was observed in acute myeloid leukemia (199/497, 40%) and myelodysplastic syndromes (118/279, 42.3%). The mortality rate significantly decreased between the first COVID-19 wave (March-May 2020) and the second wave (October-December 2020) (581/1427, 40.7% vs. 439/1773, 24.8%, p value

Published
2021

19. Association of COVID-19 Versus COVID-19 Vaccination With Kidney Function and Disease Activity in Primary Glomerular Disease: A Report of the Cure Glomerulonephropathy Study

Author

Ahn, Wooin, Appel, Gerald, Appelbaum, Paul, Babayev, Revekka, Chan, Brenda, D’Agati, Vivette Denise, Dogra, Samitri, Fernandez, Hilda, Gharavi, Ali, Hines, William, Husain, Syed Ali, Jain, Namrata, Kiryluk, Krzysztof, Lin, Fangming, Marasa, Maddalena, Markowitz, Glen, Rasouly, Hila Milo, Mohan, Sumit, Mongera, Nicola, Nestor, Jordan, Nickolas, Thomas, Radhakrishnan, Jai, Rao, Maya, Sanna-Cherchi, Simone, Shirazian, Shayan, Stokes, Michael Barry, Uy, Natalie, Valeri, Anthony, Vena, Natalie, Foroncewicz, Bartosz, Moszczuk, Barbara, Mucha, Krzysztof, Perkowska-Ptasińska, Agnieszka, Ghiggeri, Gian Marco, Lugani, Francesca, Ambruzs, Josephine, Liapis, Helen, Baracco, Rossana, Jain, Amrish, Isa Ashoor, Aviles, Diego, Srivastava, Tarak, Ahn, Sun-Young, Devarajan, Prasad, Erkan, Elif, Claes, Donna, Stone, Hillarey, Mason, Sherene, Gbadegesin, Rasheed, Gomez-Mendez, Liliana, Yin, Hong (Julie), Cai, Yi, Jens, Goebel, Steinke, Julia, Weaver, Donald, Lane, Jerome, Cramer, Carl, Pan, Cindy, Paloian, Neil, Sreedharan, Rajasree, Selewski, David, Twombley, Katherine, Bowers, Corinna, Dreher, Mary, Kallash, Mahmoud, Mahan, John, Sharpe, Samantha, Al-Uzri, Amira, Iragorri, Sandra, Belsha, Craig, Alge, Joseph, Braun, Michael, Gomez, A.C., Wenderfer, Scott, Vasylyeva, Tetyana, Feig, Daniel, Fuentes, Gabriel Cara, Hannah, Melisha, Nester, Carla, Chishti, Aftab, Klein, Jon, Katsoufis, Chryso, Seeherunvong, Wacharee, Wong, Craig, Mathews, Nisha, Barcia, John, Swiatecka-Urban, Agnes, Bartosh, Sharon, Hunley, Tracy, Dharnidharka, Vikas, Gaut, Joseph, Laurin, Louis-Philippe, Royal, Virginie, Achanti, Anand, Budisavljevic, Milos, Self, Sally, Ghossein, Cybele, Peleg, Yonatan, Wadhwani, Shikha, Almaani, Salem, Ayoub, Isabelle, Nadasdy, Tibor, Parikh, Samir, Rovin, Brad, Chang, Anthony, Fatima, Huma, Julian, Bruce, Novak, Jan, Renfrow, Matthew, Rizk, Dana, Chen, Dhruti, Derebail, Vimal, Falk, Ronald, Gibson, Keisha, Hogan, Susan, Jain, Koyal, Jennette, J. Charles, Mottl, Amy, Poulton, Caroline, Saha, Manish Kanti, Fogo, Agnes, Sanghani, Neil, Kidd, Jason, Muthusamy, Selvaraj, Hou, Jean, Lemley, Kevin, Mika, Warren, Russo, Pierre, Denburg, Michelle, Kogon, Amy, Meyers, Kevin, Pradhan, Madhura, O’Toole, John, Sedor, John, Sethna, Christine, Vento, Suzanne, Atta, Mohamed, Bagnasco, Serena, Neu, Alicia, Sperati, John, Adler, Sharon, Dai, Tiane, Dukkipati, Ram, Fervenza, Fernando, Sethi, Sanjeev, Kaskel, Frederick, Brathwaite, Kaye, Reidy, Kimberly, Weisstuch, Joseph, Wu, Ming, Zhdanova, Olga, Heymann, Jurgen, Kopp, Jeffrey, Waldman, Meryl, Winkler, Cheryl, Krissberg, Jill, Lafayette, Richard, Fahmeedah, Kamal, Talley, Elizabeth, Hladunewich, Michelle, Parekh, Rulan, Avila-Casado, Carmen, Cattran, Daniel, Heather, Reich, Boll, Philip, Drexler, Yelena, Fornoni, Alessia, Blazius, Brooke, Hodgin, Jeffrey, Oliverio, Andrea, Hogan, Jon, Palmer, Matthew, Abromovitz, Blaise, Mortiz, Michael, Alpers, Charles, Jefferson, J. Ashley, Brown, Elizabeth, Sambandam, Kamal, Roehm, Bethany, Graff, John, Gillespie, Brenda, Kretzler, Matthias, Nast, Cynthia, Barisoni, Laura, Guay-Woodford, Lisa M., Wang, Chia-shi, Glenn, Dorey A., Helmuth, Margaret, Smith, Abigail R., Bomback, Andrew S., Canetta, Pietro A., Coppock, Gaia M., Khalid, Myda, Tuttle, Katherine R., Bou-Matar, Raed, Greenbaum, Larry A., Robinson, Bruce M., Holzman, Lawrence B., Smoyer, William E., Rheault, Michelle N., Gipson, Debbie, and Mariani, Laura H.

Published
2024
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20. An unbiased ray-marching transmittance estimator

Author

Kettunen, Markus, d'Eon, Eugene, Pantaleoni, Jacopo, and Novak, Jan

Subjects
Computer Science - Graphics
Abstract

We present an in-depth analysis of the sources of variance in state-of-the-art unbiased volumetric transmittance estimators, and propose several new methods for improving their efficiency. These combine to produce a single estimator that is universally optimal relative to prior work, with up to several orders of magnitude lower variance at the same cost, and has zero variance for any ray with non-varying extinction. We first reduce the variance of truncated power-series estimators using a novel efficient application of U-statistics. We then greatly reduce the average expansion order of the power series and redistribute density evaluations to filter the optical depth estimates with an equidistant sampling comb. Combined with the use of an online control variate built from a sampled mean density estimate, the resulting estimator effectively performs ray marching most of the time while using rarely-sampled higher order terms to correct the bias., Comment: 20 pages

Published
2021
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21. Biological factors and self-perception of stress in relation to freeze-like response in humans

Author

Vít, Michal, Kučera, Jan, Lenárt, Peter, Novák, Jan, Zlámal, Filip, Reguli, Zdenko, Bugala, Martin, Čihounková, Jitka, Přecechtěl, Pavel, Malčík, Vojtěch, Vojtíšek, Tomáš, Kučerová, Jana Fialová, Eclerová, Veronika, Tomandlová, Marie, Šíp, Radek, Ráčková, Lucie, Grulichová, Markéta, Tomandl, Josef, and Bienertová-Vašků, Julie

Published
2023
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22. Age, successive waves, immunization, and mortality in elderly COVID-19 hematological patients: EPICOVIDEHA findings

Author

MARQUES DE ALMEIDA, Joyce, HERNÁNDEZ-RIVAS, José-Ángel, GUIDETTI, Anna, FINIZIO, Olimpia, STOJANOSKI, Zlate, CVETANOSKI, Milche, MELETIADIS, Joseph, DE JONGE, Nick, ANTIĆ, Darko, ALI, Natasha, TISI, Maria Chiara, SERRANO, Laura, PLANTEFEVE, Gaëtan, KHANNA, Nina, HOENIGL, Martin, ČERŇAN, Martin, MIRANDA-CASTILLO, Carolina, FERNÁNDEZ-GALÁN, María, SERRIS, Alexandra, ERBEN, Nurettin, DULÉRY, Rémy, AUJAYEB, Avinash, PAPA, Mario Virgilio, NOVÁK, Jan, DELIA, Mario, SAPIENZA, Giuseppe, REIZINE, Florian, OMRANI, Ali S., DI BLASI, Roberta, LAMURE, Sylvain, DRGOŇA, Ľuboš, COPPOLA, Nicola, BATINIĆ, Josip, AL-KHABORI, Murtadha, RIBERA-SANTA SUSANA, José-María, PIEDIMONTE, Monica, LOUREIRO-AMIGO, Jorge, FOUQUET, Guillemette, FAZZI, Rita, DANION, François, SCHUBERT, Jörg, HOELL-NEUGEBAUER, Baerbel, BAHR, Nathan C., YAHIA, Ayel Omar, TORRES-ATIENZA, Ana, RINALDI, Ikhwan, POPOVA, Marina, OMMEN, Hans-Beier, MITRA, Maria Enza, MIKULSKA, Malgorzata, LACEJ, Ira, KHOSTELIDI, Sofya, WIN, Sein, VINH, Donald, SALEH, Modar, PRATTES, Juergen, JINDRA, Pavel, GUOLO, Fabio, DELLA PEPA, Roberta, CHELYSHEVA, Ekaterina, ZDZIARSKI, Przemyslaw, WAI-MAN, Vivien, SOTO-SILVA, Andrés, ORTH, Hans Martin, MALAK, Sandra, LORENZO DE LA PEÑA, Lisset, KOLDITZ, Martin, Shan KHO, Chi, HEATH, Christopher H., GROH, Ana, GAVRIILAKI, Eleni, FUNG, Monica, EGGER, Matthias, DE KORT, Elizabeth, DE CABO, Erik, CUSHION, Tania, CHOWDHURY, Fazle Rabbi, CEESAY, M. Mansour, BREHON, Mathias, VARRICCHIO, Gina, TAFURI, Agostino, JIMÉNEZ-LORENZO, María-Josefa, KLIMKO, Nikolai, TSIRIGOTIS, Panagiotis, ANTONIADOU, Anastasia, VEHRESCHILD, Maria, Rossi, Giuseppe, Salmanton-García, Jon, Cattaneo, Chiara, Marchesi, Francesco, Dávila-Valls, Julio, Martín-Pérez, Sonia, Itri, Federico, López-García, Alberto, Glenthøj, Andreas, Gomes da Silva, Maria, Besson, Caroline, Marchetti, Monia, Weinbergerová, Barbora, Jaksic, Ozren, Jiménez, Moraima, Bilgin, Yavuz M., Van Doesum, Jaap, Farina, Francesca, Žák, Pavel, Verga, Luisa, Collins, Graham P., Bonuomo, Valentina, Van Praet, Jens, Nucci, Marcio, Meers, Stef, Espigado, Ildefonso, Fracchiolla, Nicola S., Valković, Toni, Poulsen, Christian Bjørn, Čolović, Natasha, Dragonetti, Giulia, Ledoux, Marie-Pierre, Tascini, Carlo, Buquicchio, Caterina, Blennow, Ola, Passamonti, Francesco, Machado, Marina, Labrador, Jorge, Duarte, Rafael F., Schönlein, Martin, Prezioso, Lucia, Falces-Romero, Iker, Kulasekararaj, Austin, Garcia-Vidal, Carolina, Fernández, Noemí, Abu-Zeinah, Ghaith, Ormazabal-Vélez, Irati, Adžić-Vukičević, Tatjana, Piukovics, Klára, Stoma, Igor, Cuccaro, Annarosa, Magliano, Gabriele, Szotkowski, Tomáš, González-López, Tomás-José, El-Ashwah, Shaimaa, Bergantim, Rui, Sili, Uluhan, Maertens, Johan, Demirkan, Fatih, De Ramón, Cristina, Petzer, Verena, Del Principe, Maria Ilaria, Navrátil, Milan, Dargenio, Michelina, Seval, Guldane Cengiz, Samarkos, Michail, Ráčil, Zdeněk, Pinczés, László Imre, Lahmer, Tobias, Busca, Alessandro, Méndez, Gustavo-Adolfo, Vena, Antonio, Biernat, Monika M., Merelli, Maria, Calbacho, Maria, Barać, Aleksandra, Bavastro, Martina, Limongelli, Alessandro, Ilhan, Osman, Wolf, Dominik, Çolak, Gökçe Melis, García-Sanz, Ramón, Emarah, Ziad, Mišković, Bojana, Gräfe, Stefanie K., Mladenović, Miloš, Aiello, Tommaso Francesco, Núñez-Martín-Buitrago, Lucía, Nordlander, Anna, Arellano, Elena, Zambrotta, Giovanni Paolo Maria, Ammatuna, Emanuele, Cabirta, Alba, Sacchi, Maria Vittoria, Nunes Rodrigues, Raquel, Hersby, Ditte Stampe, Hanakova, Michaela, Rahimli, Laman, Cordoba, Raul, Cornely, Oliver A., and Pagano, Livio

Published
2023
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23. Neural Control Variates

Author

Müller, Thomas, Rousselle, Fabrice, Novák, Jan, and Keller, Alexander

Subjects
Computer Science - Machine Learning, Computer Science - Graphics, Statistics - Machine Learning
Abstract

We propose neural control variates (NCV) for unbiased variance reduction in parametric Monte Carlo integration. So far, the core challenge of applying the method of control variates has been finding a good approximation of the integrand that is cheap to integrate. We show that a set of neural networks can face that challenge: a normalizing flow that approximates the shape of the integrand and another neural network that infers the solution of the integral equation. We also propose to leverage a neural importance sampler to estimate the difference between the original integrand and the learned control variate. To optimize the resulting parametric estimator, we derive a theoretically optimal, variance-minimizing loss function, and propose an alternative, composite loss for stable online training in practice. When applied to light transport simulation, neural control variates are capable of matching the state-of-the-art performance of other unbiased approaches, while providing means to develop more performant, practical solutions. Specifically, we show that the learned light-field approximation is of sufficient quality for high-order bounces, allowing us to omit the error correction and thereby dramatically reduce the noise at the cost of negligible visible bias., Comment: To appear at SIGGRAPH Asia 2020. Updated with better loss function, leading to better results. 19 pages, 14 figures

Published
2020

24. Combining multi-site Magnetic Resonance Imaging with machine learning predicts survival in paediatric brain tumours

Author

Grist, James T., Withey, Stephanie, Bennett, Christopher, Rose, Heather E. L., MacPherson, Lesley, Oates, Adam, Powell, Stephen, Novak, Jan, Abernethy, Laurence, Pizer, Barry, Bailey, Simon, Clifford, Steven C., Mitra, Dipayan, Arvanitis, Theodoros N., Auer, Dorothee P., Avula, Shivaram, Grundy, Richard, and Peet, Andrew C

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Background Brain tumours represent the highest cause of mortality in the paediatric oncological population. Diagnosis is commonly performed with magnetic resonance imaging and spectroscopy. Survival biomarkers are challenging to identify due to the relatively low numbers of individual tumour types, especially for rare tumour types such as atypical rhabdoid tumours. Methods 69 children with biopsy-confirmed brain tumours were recruited into this study. All participants had both perfusion and diffusion weighted imaging performed at diagnosis. Data were processed using conventional methods, and a Bayesian survival analysis performed. Unsupervised and supervised machine learning were performed with the survival features, to determine novel sub-groups related to survival. Sub-group analysis was undertaken to understand differences in imaging features, which pertain to survival. Findings Survival analysis showed that a combination of diffusion and perfusion imaging were able to determine two novel sub-groups of brain tumours with different survival characteristics (p <0.01), which were subsequently classified with high accuracy (98%) by a neural network. Further analysis of high-grade tumours showed a marked difference in survival (p=0.029) between the two clusters with high risk and low risk imaging features. Interpretation This study has developed a novel model of survival for paediatric brain tumours, with an implementation ready for integration into clinical practice. Results show that tumour perfusion plays a key role in determining survival in brain tumours and should be considered as a high priority for future imaging protocols.

Published
2020

25. Distinguishing between paediatric brain tumour types using multi-parametric magnetic resonance imaging and machine learning: a multi-site study

Author

Grist, James T., Withey, Stephanie, MacPherson, Lesley, Oates, Adam, Powell, Stephen, Novak, Jan, Abernethy, Laurence, Pizer, Barry, Grundy, Richard, Bailey, Simon, Mitra, Dipayan, Arvanitis, Theodoros N., Auer, Dorothee P., Avula, Shivaram, and Peet, Andrew C

Subjects
Physics - Medical Physics, Electrical Engineering and Systems Science - Image and Video Processing, Quantitative Biology - Quantitative Methods
Abstract

The imaging and subsequent accurate diagnosis of paediatric brain tumours presents a radiological challenge, with magnetic resonance imaging playing a key role in providing tumour specific imaging information. Diffusion weighted and perfusion imaging are commonly used to aid the non invasive diagnosis of paediatric brain tumours, but are usually evaluated by expert qualitative review. Quantitative studies are mainly single centre and single modality. The aim of this work was to combine multi centre diffusion and perfusion imaging, with machine learning, to develop machine learning based classifiers to discriminate between three common paediatric tumour types. The results show that diffusion and perfusion weighted imaging of both the tumour and whole brain provide significant features which differ between tumour types, and that combining these features gives the optimal machine learning classifier with greater than 80 percent predictive precision. This work represents a step forward to aid in the non invasive diagnosis of paediatric brain tumours, using advanced clinical imaging., Comment: 22 pages, 7 figures and tables

Published
2019

28. Guadecitabine vs treatment choice in newly diagnosed acute myeloid leukemia: a global phase 3 randomized study

Author

Fenaux, Pierre, Gobbi, Marco, Kropf, Patricia L., Issa, Jean-Pierre J., Roboz, Gail J., Mayer, Jiri, Krauter, Jürgen, Robak, Tadeusz, Kantarjian, Hagop, Novak, Jan, Jedrzejczak, Wieslaw. W., Thomas, Xavier, Ojeda-Uribe, Mario, Miyazaki, Yasushi, Min, Yoo Hong, Yeh, Su-Peng, Brandwein, Joseph, Gercheva-Kyuchukova, Liana, Demeter, Judit, Griffiths, Elizabeth, Yee, Karen, Döhner, Konstanze, Hao, Yong, Keer, Harold, Azab, Mohammad, and Döhner, Hartmut

Published
2023
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29. Soluble prefusion-closed HIV-envelope trimers with glycan-covered bases

Author

Olia, Adam S., Cheng, Cheng, Zhou, Tongqing, Biju, Andrea, Harris, Darcy R., Changela, Anita, Duan, Hongying, Ivleva, Vera B., Kong, Wing-Pui, Ou, Li, Rawi, Reda, Tsybovsky, Yaroslav, Van Wazer, David J., Corrigan, Angela R., Gonelli, Christopher A., Lee, Myungjin, McKee, Krisha, Narpala, Sandeep, O’Dell, Sijy, Parchment, Danealle K., Stancofski, Erik-Stephane D., Stephens, Tyler, Tan, Ivy, Teng, I-Ting, Wang, Shuishu, Wei, Qing, Yang, Yongping, Yang, Zhengrong, Zhang, Baoshan, Novak, Jan, Renfrow, Matthew B., Doria-Rose, Nicole A., Koup, Richard A., McDermott, Adrian B., Gall, Jason G., Lei, Q. Paula, Mascola, John R., and Kwong, Peter D.

Published
2023
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32. Designing modular 3D printed reinforcement of wound composite hollow beams with semidefinite programming

Author

Tyburec, Marek, Zeman, Jan, Novák, Jan, Lepš, Matěj, Plachý, Tomáš, and Poul, Robin

Subjects
Physics - Applied Physics
Abstract

Fueled by their excellent stiffness-to-weight ratio and the availability of mature manufacturing technologies, filament wound carbon fiber reinforced polymers represent ideal materials for thin-walled laminate structures. However, their strong anisotropy reduces structural resistance to wall instabilities under shear and buckling. Increasing laminate thickness degrades weight and structural efficiencies and the application of a dense internal core is often uneconomical and labor-intensive. In this contribution, we introduce a convex linear semidefinite programming formulation for truss topology optimization to design an efficient non-uniform lattice-like internal structure. The internal structure not only reduces the effect of wall instabilities, mirrored in the increase of the fundamental free-vibration eigenfrequency, but also keeps weight low, secures manufacturability using conventional three-dimensional printers, and withstands the loads induced during the production process. We showcase a fully-automatic procedure in detail for the design, prototype manufacturing, and verification of a simply-supported composite machine tool component, including validation with roving hammer tests. The results confirm that the 3D-printed optimized internal structure almost doubles the fundamental free-vibration eigenfrequency, allowing to increase working frequency of the machine tool, even though the ratio between elastic properties of the carbon composite and the ABS polymer used for 3D printing exceeds two orders of magnitude., Comment: 13 pages, 13 figures

Published
2019
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33. Level-set based design of Wang tiles for modelling complex microstructures

Author

Doškář, Martin, Zeman, Jan, Rypl, Daniel, and Novák, Jan

Subjects
Computer Science - Computational Geometry, Condensed Matter - Materials Science
Abstract

Microstructural geometry plays a critical role in the response of heterogeneous materials. Consequently, methods for generating microstructural samples are increasingly crucial to advanced numerical analyses. We extend Sonon et al.'s unified framework, developed originally for generating particulate and foam-like microstructural geometries of Periodic Unit Cells, to non-periodic microstructural representations based on the formalism of Wang tiles. This formalism has been recently proposed in order to generalize the Periodic Unit Cell approach, enabling a fast synthesis of arbitrarily large, stochastic microstructural samples from a handful of domains with predefined compatibility constraints. However, a robust procedure capable of designing complex, three-dimensional, foam-like and cellular morphologies of Wang tiles has not yet been proposed. This contribution fills the gap by significantly broadening the applicability of the tiling concept. Since the original Sonon et al.'s framework builds on a random sequential addition of particles enhanced with an implicit representation of particle boundaries by the level-set field, we first devise an analysis based on a connectivity graph of a tile set, resolving the question where a particle should be copied when it intersects a tile boundary. Next, we introduce several modifications to the original algorithm that are necessary to ensure microstructural compatibility in the generalized periodicity setting of Wang tiles. Having established a universal procedure for generating tile morphologies, we compare strictly aperiodic and stochastic sets with the same cardinality in terms of reducing the artificial periodicity in reconstructed microstructural samples. We demonstrate the superiority of the vertex-defined tile sets for two-dimensional problems and illustrate the capabilities of the algorithm with two- and three-dimensional examples., Comment: 16 pages, 16 figures. Abstract has been shortened to match the arXiv.org's limit of 1920 characters

Published
2019
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35. Racial and Ethnic Disparities in Acute Care Utilization Among Patients With Glomerular Disease

Author

Ahn, Wooin, Appel, Gerald, Appelbaum, Paul, Babayev, Revekka, Bomback, Andrew, Brown, Eric, Canetta, Pietro, Carlassara, Lucrezia, Chan, Brenda, D’Agati, Vivette Denise, Dogra, Samitri, Fernandez, Hilda, Gharavi, Ali, Hines, William, Husain, Syed Ali, Kiryluk, Krzysztof, Lin, Fangming, Marasa, Maddalena, Markowitz, Glen, Rasouly, Hila Milo, Mohan, Sumit, Mongera, Nicola, Nickolas, Thomas, Radhakrishnan, Jai, Rao, Maya, Sanna-Cherchi, Simone, Shirazian, Shayan, Stokes, Michael Barry, Uy, Natalie, Valeri, Anthony, Vena, Natalie, Foroncewicz, Bartosz, Moszczuk, Barbara, Mucha, Krzysztof, Perkowska-Ptasińska, Agnieszka, Ghiggeri, Gian Marco, Ambruzs, Josephine, Liapis, Helen, Baracco, Rossana, Jain, Amrish, Ashoor, Isa, Srivastava, Tarak, Ahn, Sun-Young, Devarajan, Prasad, Erkan, Elif, Claes, Donna, Stone, Hillarey, Mason, Sherene, Silva, Cynthia, Gomez-Mendez, Liliana, Wang, Chia-shi, Yin, Hong (Julie), Jens, Goebel, Steinke, Julia, Cramer, Carl, Pan, Cindy, Sreedharan, Rajasree, Bowers, Corinna, Dreher, Mary, Kallash, Mahmoud, Mahan, John, Sharpe, Samantha, Smoyer, William, Al-Uzri, Amira, Belsha, Craig, Braun, Michael, Gomez, A.C., Feig, Daniel, Fuentes, Gabriel Cara, Hannah, Melisha, Nester, Carla, Chishti, Aftab, Klein, Jon, Katsoufis, Chryso, Seeherunvong, Wacharee, Rheault, Michelle, Wong, Craig, Mathews, Nisha, Barcia, John, Swiatecka-Urban, Agnes, Bartosh, Sharon, Hunley, Tracy, Dharnidharka, Vikas, Gaut, Joseph, Laurin, Louis-Philippe, Royal, Virginie, Achanti, Anand, Budisavljevic, Milos, Self, Sally, Ghossein, Cybele, Wadhwani, Shikha, Ayoub, Isabelle, Nadasdy, Tibor, Parikh, Samir, Rovin, Brad, Chang, Anthony, Fatima, Huma, Novak, Jan, Renfrow, Matthew, Rizk, Dana, Chen, Dhruti, Derebail, Vimal, Falk, Ronald, Gibson, Keisha, Hogan, Susan, Jain, Koyal, Jennette, J. Charles, Mottl, Amy, Poulton, Caroline, Saha, Manish Kanti, Fogo, Agnes, Sanghani, Neil, Kidd, Jason, Massey, Hugh, Muthusamy, Selvaraj, Ganesan, Santhi, Gonzalez-Vicente, Agustin, Schelling, Jeffrey, Hou, Jean, Lemley, Kevin, Mika, Warren, Russo, Pierre, Denburg, Michelle, Kogon, Amy, Meyers, Kevin, Pradhan, Madhura, Matar, Raed Bou, O’Toole, John, Sedor, John, Bagnasco, Serena, Neu, Alicia, Adler, Sharon, Dai, Tiane, Dukkipati, Ram, Fervenza, Fernando, Sethi, Sanjeev, Kaskel, Frederick, Vento, Suzanne, Weisstuch, Joseph, Wu, Ming, Zhdanova, Olga, Heymann, Jurgen, Waldman, Meryl, Winkler, Cheryl, Hladunewich, Michelle, Avila-Casado, Carmen, Cattran, Daniel, Heather, Reich, Boll, Philip, Drexler, Yelena, Fornoni, Alessia, Gipson, Patrick, Hodgin, Jeffrey, Oliverio, Andrew, Hogan, Jon, Holzman, Lawrence, Palmer, Matthew, Abromovitz, Blaise, Mortiz, Michael, Alpers, Charles, Jefferson, J. Ashley, Brown, Elizabeth, Sambandam, Kamal, Robinson, Bruce, Nast, Cynthia, Barisoni, Laura, Gillespie, Brenda, Gipson, Deb, Hicken, Maggie, Kretzler, Matthias, Mariani, Laura, Guay-Woodford, Lisa M., Krissberg, Jill R., O’Shaughnessy, Michelle M., Smith, Abigail R., Helmuth, Margaret E., Almaani, Salem, Aviles, Diego H., Brathwaite, Kaye E., Cai, Yi, Gbadegesin, Rasheed, Glenn, Dorey A., Greenbaum, Larry A., Iragorri, Sandra, Khalid, Myda, Kopp, Jeffrey, Lafayette, Richard, Lane, Jerome C., Lugani, Francesca, Nestor, Jordan G., Parekh, Rulan S., Reidy, Kimberly, Selewski, David T., Sethna, Christine B., Sperati, C. John, Tuttle, Katherine, Twombley, Katherine, Vasylyeva, Tetyana L., Weaver, Donald J., Jr., and Wenderfer, Scott E.

Published
2023
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37. Mapping European Association of Urology Guideline Practice Across Europe: An Audit of Androgen Deprivation Therapy Use Before Prostate Cancer Surgery in 6598 Cases in 187 Hospitals Across 31 European Countries

Author

Heidegger, Isabel, Resch, Johannes Mischinger Irene, Turba, Simon, Zeder, Robin, Lodeta, Braninimir, Van Praet, Charles, Ghysel, Christophe, Arentsen, Harm C., Beysens, Matthias, Vinckier, Marie-Hélène, Mottrie, Alexandre, de Groote, Ruben, Timev, Aleksandar Ivanov, Georgiev, Marincho Ivanov, Yanev, Krassimir Prodanov, Mladenov, Boris, Ivanov, Atanas Slavchev, Antonov, Petar, Valkanov, Stanislav, Tomašković, Igor, Kulis, Tomislav, Bokarica, Pero, Pavlović, Oliver, Krajina, Vinko, Situm, Marijan, Boban, Toni, Soric, Tomislav, Vidic, Ivan, Benko, Goran, Peršec, Zoran, Sović, Tomislav, Zachoval, Roman, Stejskal, Jiri, Capoun, Otakar, Pitra, Tomáš, Gojdič, Marek, Babjuk, Marek, Novák, Vojtěch, Grepl, Michal, Broul, Marek, Novák, Jan, Lund, Lars, Nordström Joensen, Ulla, Borre, Michael, Veskimäe, Priit, Baum, Peep, Tamm, Toomas, Okas, Rauno, Jämsä, Pyry, Lahdensuo, Kanerva, Siltari, Sirkku, Seikkula, Heikki, Palmberg, Christian, Isotalo, Taina, Fiard, Gaelle, Verrier, Cecile, Wiedemann, Laura, Lecornet, Emilie, Leon, Priscilla, Millet, Clementine, Ponzio, Charles, Ploussard, Guillaume, Xylinas, Evanguelos, Ingels, Alexandre, Bigot, Pierre, Le Corre, Vincent, Audenet, François, Berg, Sebastian, Palisaar, Rein-Jueri, Heidenreich, Axel, Seelemeyer, Felix, Krege, Susanne, Leyh-Bannurah, Sami-Ramzi, Witt, Jörn H., Abdirahman, Ayanle, Truß, Michael C., Kranz, Jennifer, Andreas, Karagiannis, Vassileios, Tzortzis, Andreas, Andreou, Paparidis, Spyridon, Davis, Nikolaos Ferakis Niall F., Keane, Kevin G., Fuentes, Adrian, Scuderi, Simone, Barletta, Francesco, Manfredi, Matteo, Porpiglia, Francesco, Cerruto, Maria Angela, Antonelli, Alessandro, Esperto, Francesco, Rossanese, Marta, Veneziano, Domenico, Castelli, Tommaso, Mantica, Roberto La Rocca Marcello Scarcia Guglielmo, Rebuffo, Silvia, Pomara, Giorgio, Pavan, Nicola, Silvestri, Tommaso, Reale, Giulio Francesco, Polara, Andrea, Falagario, Ugo Giovanni, Carrieri, Giuseppe, Ferrari, Giovanni, Brausi, Maurizio, Orecchia, Luca, Annino, Filippo, Kazlauskas, Gražvydas, Stavridis, Sotir, Radovic, Nenad, Vukovic, Marko, van der Slot, Margaretha Adriana, Bruins, Harman Maxim, van Oort, Inge, Witjes, Fred, van der Poel, Henk, Beisland, Christian, Lilleåsenm, Gunder, Müller, Stig, Haug, Erik S., Dimmen, Magne, Czech, Anna K., Nyk, Lukasz, Jaskulski, Jaroslaw, Ratajczyk, Krzysztof, Azevedo, Nuno, Braga, Isaac, Pereira, João, Lúcio, Rui, Pina, João, da Silva, Edgar Miguel Calvo Loureiro Tavares, Furriel, Frederico, Mota, Paulo, Rodrigues, Miguel, Radavoi, George Daniel, Crisan, Nicolae, Andras, Iulia, Robert, Stoica, Bratu, Ovidiu, Surcel, Cristian, Kotov, Sergei, Malkhasyan, Vigen, Petrov, Sergei, Reva, Sergei, Bumbasirevic, Uros, Kováčik, Viktor, Perečinský, Ivan, Rybár, Ľuboš, Šulgan, Ján, Briš, Lukáš, Jursová, Katarína, Chovan, Miroslav, Kička, Tomáš, Taskovska, Milena, Kovačič, Rok, Miklavžina, Andraž, Alvarez-Maestro, Mario, De Castro, Javier Mayor, Aragón-Chamizo, Juan, Sutil, Raquel Sopeña, Perrello, Carmen Garau, Vilaseca, Antoni, Perez, Jorge Huguet, Ovide, Julia Aumatell, Planas, Jacques, Borque-Fernando, Angel, Sánchez-Izquierdo, Elena, Jimenez, Jose Luis Marenco, Lendínez-Cano, Guillermo, Puche-Sanz, Ignacio, Garcia-Baquero, Rodrigo, Esteban, Mario Domínguez, Pérez-Fentes, Daniel, Serván, Patricia Parra, Koskela, Lotta Renström, Stranne, Johan, Scholtz, Bianca, Torbrand, Christian, Wagenius, Magnus, Ugge, Henrik, Örtegren, Joakim, Langenauer, Janine, Zumstein, Valentin, Schmid, Hans Peter, Rieken, Malte, Saba, Karim, Strebel, Raeto T., Mortezavi, Ashkan, Rentsch, Cyrill, Roth, Beat, Eberli, Daniel, Pascal, Oechslin, Auer, Rebecca, John, Hubert, Thalmann, George N., Baltacı, Sümer, Mungan, Aydın, Sözen, Sinan, Cetin, Serhat, Aslan, Guven, Türkeri, Levent, İzol, Volkan, Demirdağ, Çetin, Ozden, Sami Berk, Toktaş, Gökhan, Sarikaya, Şaban, Tinay, İlker, Müezzinoğlu, Talha, Erbatu, Oguzcan, Sagnak, Levent, Akdoğan, Bülent, Can, Cavit, Şahin, Hayrettin, Yazıcı, Cenk Murat, Volkov, Serhii, Shulyak, Olexandr, Douglas, David, Hemmant, Joshua, El-Taji, Omar, Ahmad, Imran, Nalagatla, Sarika, Janebdar, Husay, Veeratterapillay, Rajan, Rai, Bhavan, Conroy, Samantha, Cumberbatch, Marcus, Malde, Sachin, MacLennan, Steven, Duncan, Eilidh, Dunsmore, Jennifer, Fullwood, Louise, Lumen, Nicolaas, Plass, Karin, Ribal, Maria J., Roobol, Monique J., Nieboer, Daan, Schouten, Natasha, Skolarus, Ted A., Smith, Emma Jane, N'Dow, James, Mottet, Nicolas, and Briganti, Alberto

Published
2023
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40. Decoding the historical tale: COVID-19 impact on haematological malignancy patients—EPICOVIDEHA insights from 2020 to 2022

Author

Salmanton-García, Jon, primary, Marchesi, Francesco, additional, Farina, Francesca, additional, Weinbergerová, Barbora, additional, Itri, Federico, additional, Dávila-Valls, Julio, additional, Martín-Pérez, Sonia, additional, Glenthøj, Andreas, additional, Hersby, Ditte Stampe, additional, Gomes Da Silva, Maria, additional, Nunes Rodrigues, Raquel, additional, López-García, Alberto, additional, Córdoba, Raúl, additional, Bilgin, Yavuz M., additional, Falces-Romero, Iker, additional, El-Ashwah, Shaimaa, additional, Emarah, Ziad, additional, Besson, Caroline, additional, Kohn, Milena, additional, Van Doesum, Jaap, additional, Ammatuna, Emanuele, additional, Marchetti, Monia, additional, Labrador, Jorge, additional, Zambrotta, Giovanni Paolo Maria, additional, Verga, Luisa, additional, Jaksic, Ozren, additional, Nucci, Marcio, additional, Piukovics, Klára, additional, Cabirta-Touzón, Alba, additional, Jiménez, Moraima, additional, Arellano, Elena, additional, Espigado, Ildefonso, additional, Blennow, Ola, additional, Nordlander, Anna, additional, Meers, Stef, additional, Vian Praet, Jens, additional, Aiello, Tommaso Francesco, additional, Garcia-Vidal, Carolina, additional, Fracchiolla, Nicola S., additional, Sciumè, Mariarita, additional, Seval, Guldane Cengiz, additional, Žák, Pavel, additional, Buquicchio, Caterina, additional, Tascini, Carlo, additional, Gräfe, Stefanie K., additional, Schönlein, Martin, additional, Adžić-VUKIČEVIĆ, Tatjana, additional, Bonuomo, Valentina, additional, Cattaneo, Chiara, additional, Nizamuddin, Summiya, additional, Čerňan, Martin, additional, Plantefeve, Gaëtan, additional, Prin, Romane, additional, Szotkovski, Tomas, additional, Collins, Graham P., additional, Dargenio, Michelina, additional, Petzer, Verena, additional, Wolf, Dominik, additional, Čolović, Natasha, additional, Prezioso, Lucia, additional, Valković, Toni, additional, Passamonti, Francesco, additional, Méndez, Gustavo-Adolfo, additional, Sili, Uluhan, additional, Vena, Antonio, additional, Bavastro, Martina, additional, Limongelli, Alessandro, additional, Duarte, Rafael F., additional, Ledoux, Marie-Pierre, additional, Cvetanoski, Milche, additional, Stojanoski, Zlate, additional, Machado, Marina, additional, Batinić, Josip, additional, Magliano, Gabriele, additional, Biernat, Monika M., additional, Pantić, Nikola, additional, Poulsen, Christian Bjørn, additional, Cuccaro, Annarosa, additional, Del Principe, Maria Ilaria, additional, Kulasekararaj, Austin, additional, Ormazabal-Vélez, Irati, additional, Busca, Alessandro, additional, Demirkan, Fatih, additional, Ijaz, Marriyam, additional, Klimko, Nikolai, additional, Stoma, Igor, additional, Khostelidi, Sofya, additional, Fernández, Noemí, additional, Omrani, Ali S., additional, Bergantim, Rui, additional, De Jonge, Nick, additional, Fouquet, Guillemette, additional, Navrátil, Milan, additional, Abu-Zeinah, Ghaith, additional, Samarkos, Michail, additional, Maertens, Johan, additional, De Ramón, Cristina, additional, Guidetti, Anna, additional, Magyari, Ferenc, additional, González-López, Tomás José, additional, Lahmer, Tobias, additional, Finizio, Olimpia, additional, Ali, Natasha, additional, Pinczés, László Imre, additional, Lavilla-Rubira, Esperanza, additional, Romano, Alessandra, additional, Merelli, Maria, additional, Delia, Mario, additional, Calbacho, Maria, additional, Meletiadis, Joseph, additional, Antić, Darko, additional, Hernández-Rivas, José-Ángel, additional, Marques De Almeida, Joyce, additional, Al-Khabori, Murtadha, additional, Hoenigl, Martin, additional, Tisi, Maria Chiara, additional, Khanna, Nina, additional, Barać, Aleksandra, additional, Eisa, Noha, additional, Di Blasi, Roberta, additional, Liévin, Raphaël, additional, Miranda-Castillo, Carolina, additional, Bahr, Nathan C., additional, Lamure, Sylvain, additional, Papa, Mario Virgilio, additional, Yahya, Ayel, additional, Aujayeb, Avinash, additional, Novák, Jan, additional, Erben, Nurettin, additional, Fernández-Galán, María, additional, Ribera-Santa Susana, José-María, additional, Rinaldi, Ikhwan, additional, Fazzi, Rita, additional, Piedimonte, Monica, additional, Duléry, Rémy, additional, Gonzaga, Yung, additional, Soto-Silva, Andrés, additional, Sapienza, Giuseppe, additional, Serris, Alexandra, additional, Drgoňa, Ľuboš, additional, Groh, Ana, additional, Serrano, Laura, additional, Gavriilaki, Eleni, additional, Tragiannidis, Athanasios, additional, Prattes, Juergen, additional, Coppola, Nicola, additional, Otašević, Vladimir, additional, Mladenović, Miloš, additional, Mitrović, Mirjana, additional, Mišković, Bojana, additional, Jindra, Pavel, additional, Zompi, Sofia, additional, Sacchi, Maria Vittoria, additional, Krekeler, Carolin, additional, Shumilov, Evgenii, additional, Infante, Maria Stefania, additional, García-Bordallo, Daniel, additional, Çolak, Gökçe Melis, additional, Cingolani, Antonella, additional, Mayer, Jiří, additional, Criscuolo, Marianna, additional, Nygaard, Marietta, additional, Hanáková, Michaela, additional, Fianchi, Luana, additional, Ráčil, Zdeněk, additional, Giordano, Antonio, additional, Quattrone, Martina, additional, Bonanni, Matteo, additional, Koehler, Philipp, additional, Rahimli, Laman, additional, Cornely, Oliver A., additional, Pagano, Livio, additional, Martín-Vallejo, Francisco Javier, additional, Zdziarski, Przemyslaw, additional, Zarrinfer, Hossein, additional, Wittig, Jana, additional, Win, Sein, additional, Wai-Man, Vivien, additional, Víšek, Benjamín, additional, Vinh, Donald C., additional, Vehreschild, Maria, additional, Varricchio, Gina, additional, Tsirigotis, Panagiotis, additional, Torres-Tienza, Ana, additional, Tanase, Alina Daniela, additional, Tafuri, Agostino, additional, Stamouli, Maria, additional, Sramek, Jiří, additional, Soussain, Carole, additional, Shirinova, Ayten, additional, Schubert, Jörg, additional, Schalk, Enrico, additional, Salehi, Mohammad Reza, additional, Saleh, Modar, additional, Rosati, Giorgio, additional, Roldán, Elisa, additional, Reizine, Florian, additional, Rêgo, Mayara, additional, Regalado-Artamendi, Isabel, additional, Popova, Marina, additional, Pinto, Fernando, additional, Philippe, Laure, additional, Orth, Hans Martin, additional, Ommen, Hans-Beier, additional, Obr, Aleš, additional, Núñez-Martín-Buitrago, Lucía, additional, Noël, Nicolas, additional, Neuhann, Julia, additional, Nadali, Gianpaolo, additional, Nacov, Julia A., additional, Munhoz Alburquerque, Ana M., additional, Mitra, Maria Enza, additional, Mikulska, Malgorzata, additional, Mellinghoff, Sibylle, additional, Mechtel, Ben, additional, Martín-González, Juan-Alberto, additional, Malak, Sandra, additional, Loureiro-Amigo, Jorge, additional, Lorenzo De La Peña, Lisset, additional, Liberti, Giulia, additional, Landau, Marianne, additional, Lacej, Ira, additional, Kolditz, Martin, additional, Kho, Chi Shan, additional, Khedr, Reham Abdelaziz, additional, Karthaus, Meinolf, additional, Karlsson, Linda Katharina, additional, Jiménez-Lorenzo, María-Josefa, additional, Izuzquiza, Macarena, additional, Hoell-Neugebauer, Baerbel, additional, Herbrecht, Raoul, additional, Heath, Christopher H., additional, Guolo, Fabio, additional, Grothe, Jan, additional, Gerasymchuk, Sergey, additional, García-Sanz, Ramón, additional, García-Poutón, Nicole, additional, Funke, Vaneuza Araújo Moreira, additional, Fung, Monica, additional, Flasshove, Charlotte, additional, Essame, Jenna, additional, Egger, Matthias, additional, Drenou, Bernard, additional, Dragonetti, Giulia, additional, Desole, Maximilian, additional, Della Pepa, Roberta, additional, Deau Fischer, Bénédicte, additional, De Kort, Elizabeth, additional, De Cabo, Erik, additional, Danion, François, additional, Daguindau, Etienne, additional, Cushion, Tania, additional, Cremer, Louise, additional, Cordini, Gregorio, additional, Ciceri, Fabio, additional, Chowdhury, Fazle Rabbi, additional, Chelysheva, Ekaterina, additional, Chauchet, Adrien, additional, Chai, Louis Yi Ann, additional, Ceesay, M. Mansour, additional, Busch, Elena, additional, Brehon, Mathias, additional, Borducchi, Davimar M.M., additional, Booth, Stephen, additional, Bologna, Serge, additional, Berg Venemyr, Caroline, additional, Bailén-Almorox, Rebeca, additional, Antoniadou, Anastasia, additional, Anastasopoulou, Amalia N., additional, and Altuntaş, Fevzi, additional

Published
2024
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45. Neural Importance Sampling

Author

Müller, Thomas, McWilliams, Brian, Rousselle, Fabrice, Gross, Markus, and Novák, Jan

Subjects
Computer Science - Machine Learning, Computer Science - Graphics, Statistics - Machine Learning
Abstract

We propose to use deep neural networks for generating samples in Monte Carlo integration. Our work is based on non-linear independent components estimation (NICE), which we extend in numerous ways to improve performance and enable its application to integration problems. First, we introduce piecewise-polynomial coupling transforms that greatly increase the modeling power of individual coupling layers. Second, we propose to preprocess the inputs of neural networks using one-blob encoding, which stimulates localization of computation and improves inference. Third, we derive a gradient-descent-based optimization for the KL and the $\chi^2$ divergence for the specific application of Monte Carlo integration with unnormalized stochastic estimates of the target distribution. Our approach enables fast and accurate inference and efficient sample generation independently of the dimensionality of the integration domain. We show its benefits on generating natural images and in two applications to light-transport simulation: first, we demonstrate learning of joint path-sampling densities in the primary sample space and importance sampling of multi-dimensional path prefixes thereof. Second, we use our technique to extract conditional directional densities driven by the product of incident illumination and the BSDF in the rendering equation, and we leverage the densities for path guiding. In all applications, our approach yields on-par or higher performance than competing techniques at equal sample count., Comment: 19 pages, 15 figures. Accepted for publication in ACM Transactions on Graphics; presented at SIGGRAPH 2019

Published
2018

48. Deep Scattering: Rendering Atmospheric Clouds with Radiance-Predicting Neural Networks

Author

Kallweit, Simon, Müller, Thomas, McWilliams, Brian, Gross, Markus, and Novák, Jan

Subjects
Computer Science - Learning, Computer Science - Graphics, Statistics - Machine Learning
Abstract

We present a technique for efficiently synthesizing images of atmospheric clouds using a combination of Monte Carlo integration and neural networks. The intricacies of Lorenz-Mie scattering and the high albedo of cloud-forming aerosols make rendering of clouds---e.g. the characteristic silverlining and the "whiteness" of the inner body---challenging for methods based solely on Monte Carlo integration or diffusion theory. We approach the problem differently. Instead of simulating all light transport during rendering, we pre-learn the spatial and directional distribution of radiant flux from tens of cloud exemplars. To render a new scene, we sample visible points of the cloud and, for each, extract a hierarchical 3D descriptor of the cloud geometry with respect to the shading location and the light source. The descriptor is input to a deep neural network that predicts the radiance function for each shading configuration. We make the key observation that progressively feeding the hierarchical descriptor into the network enhances the network's ability to learn faster and predict with high accuracy while using few coefficients. We also employ a block design with residual connections to further improve performance. A GPU implementation of our method synthesizes images of clouds that are nearly indistinguishable from the reference solution within seconds interactively. Our method thus represents a viable solution for applications such as cloud design and, thanks to its temporal stability, also for high-quality production of animated content., Comment: ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2017)

Published
2017
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49. Dexmedetomidine versus propofol for operator-directed nurse-administered procedural sedation during catheter ablation of atrial fibrillation: A randomized controlled study

Author

Servatius, Helge, Küffer, Thomas, Baldinger, Samuel H., Asatryan, Babken, Seiler, Jens, Tanner, Hildegard, Novak, Jan, Lam, Anna, Noti, Fabian, Haeberlin, Andreas, Madaffari, Antonio, Sweda, Romy, Mühl, Aline, Branca, Mattia, Dütschler, Sophie, Erdoes, Gabor, Stüber, Frank, Theiler, Lorenz, Reichlin, Tobias, and Roten, Laurent

Published
2022
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