6,121 results on '"Betz P"'
Search Results
102. A Randomized Control Trial of a Digital Health Tool for Safer Firearm and Medication Storage for Patients with Suicide Risk
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Boggs, Jennifer M., Quintana, LeeAnn M., Beck, Arne, Clarke, Christina L., Richardson, Laura, Conley, Amy, Buckingham, Edward T., Richards, Julie E., and Betz, Marian E.
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- 2024
- Full Text
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103. „Radikale“ vs. „funktionelle“ Chirurgie der Nasennebenhöhlen – ein Widerspruch?
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Weber, Rainer K., Hildenbrand, Tanja, Kühnel, Thomas, Hoffmann, Thomas K., Betz, Christian, and Sommer, Fabian
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- 2024
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104. Multiple instance ensembling for paranasal anomaly classification in the maxillary sinus
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Bhattacharya, Debayan, Behrendt, Finn, Becker, Benjamin Tobias, Beyersdorff, Dirk, Petersen, Elina, Petersen, Marvin, Cheng, Bastian, Eggert, Dennis, Betz, Christian, Hoffmann, Anna Sophie, and Schlaefer, Alexander
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- 2024
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105. Prähospitaler Kreislaufstillstand im Lockdown: Auswirkungen der übergreifenden Infektionspräventionsmaßnahmen während der ersten SARS-CoV-2-Welle
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Grübl, T., Plöger, B., Sassen, M. C., Jerrentrup, A., Schieffer, B., and Betz, S.
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- 2024
- Full Text
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106. Sustainability Competencies and Skills in Software Engineering: An Industry Perspective
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Heldal, Rogardt, Nguyen, Ngoc-Thanh, Moreira, Ana, Lago, Patricia, Duboc, Leticia, Betz, Stefanie, Coroama, Vlad C., Penzenstadler, Birgit, Porras, Jari, Capilla, Rafael, Brooks, Ian, Oyedeji, Shola, and Venters, Colin C.
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Computer Science - Software Engineering - Abstract
Achieving the UN Sustainable Development Goals (SDGs) demands adequate levels of awareness and actions to address sustainability challenges. Software systems will play an important role in moving towards these targets. Sustainability skills are necessary to support the development of software systems and to provide sustainable IT-supported services for citizens. While there is a growing number of academic bodies, including sustainability education in engineering and computer science curricula, there is not yet comprehensive research on the competencies and skills required by IT professionals to develop such systems. This study aims to identify the industrial sustainability needs for education and training from software engineers' perspective. We conducted interviews and focus groups with experts from twenty-eight organisations with an IT division from nine countries to understand their interests, goals and achievements related to sustainability, and the skills and competencies needed to achieve their goals. Our findings show that organisations are interested in sustainability, both idealistically and increasingly for core business reasons. They seek to improve the sustainability of processes and products but encounter difficulties, like the trade-off between short-term financial profitability and long-term sustainability goals. To fill the gaps, they have promoted in-house training courses, collaborated with universities, and sent employees to external training. The acquired competencies make sustainability an integral part of software development. We conclude that educational programs should include knowledge and skills on core sustainability concepts, system thinking, soft skills, technical sustainability, sustainability impact and measurements, values and ethics, standards and legal aspects, and advocacy and lobbying.
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- 2023
107. Tissue Classification During Needle Insertion Using Self-Supervised Contrastive Learning and Optical Coherence Tomography
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Bhattacharya, Debayan, Latus, Sarah, Behrendt, Finn, Thimm, Florin, Eggert, Dennis, Betz, Christian, and Schlaefer, Alexander
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Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Needle positioning is essential for various medical applications such as epidural anaesthesia. Physicians rely on their instincts while navigating the needle in epidural spaces. Thereby, identifying the tissue structures may be helpful to the physician as they can provide additional feedback in the needle insertion process. To this end, we propose a deep neural network that classifies the tissues from the phase and intensity data of complex OCT signals acquired at the needle tip. We investigate the performance of the deep neural network in a limited labelled dataset scenario and propose a novel contrastive pretraining strategy that learns invariant representation for phase and intensity data. We show that with 10% of the training set, our proposed pretraining strategy helps the model achieve an F1 score of 0.84 whereas the model achieves an F1 score of 0.60 without it. Further, we analyse the importance of phase and intensity individually towards tissue classification.
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- 2023
108. ESID: Exploring the Design and Development of a Visual Analytics Tool for Epidemiological Emergencies
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Betz, Pawandeep Kaur, Stoll, Julien, Grappendorf, Valerie, Gilg, Jonas, Zeumer, Moritz, Klitz, Margrit, Spataro, Luca, Klein, Anna, Rothenhäusler, Lena, Bohnacker, Hartmut, Krämer, Hans, Meyer-Hermann, Michael, Somogyi, Sybille, Gerndt, Andreas, and Kühn, Martin J.
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Computer Science - Human-Computer Interaction ,I.3 - Abstract
Visual analytics tools can help illustrate the spread of infectious diseases and enable informed decisions on epidemiological and public health issues. To create visualisation tools that are intuitive, easy to use, and effective in communicating information, continued research and development focusing on user-centric and methodological design models is extremely important. As a contribution to this topic, this paper presents the design and development process of the visual analytics application ESID (Epidemiological Scenarios for Infectious Diseases). ESID is a visual analytics tool aimed at projecting the future developments of infectious disease spread using reported and simulated data based on sound mathematical-epidemiological models. The development process involved a collaborative and participatory design approach with project partners from diverse scientific fields. The findings from these studies, along with the guidelines derived from them, played a pivotal role in shaping the visualisation tool., Comment: 7 pages, 4 images and 2 table, IEEE VIS Workshop on Visualization for Pandemic and Emergency Responses 2023 (Vis4PandEmRes)
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- 2023
109. The James Webb Space Telescope Mission
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Gardner, Jonathan P., Mather, John C., Abbott, Randy, Abell, James S., Abernathy, Mark, Abney, Faith E., Abraham, John G., Abraham, Roberto, Abul-Huda, Yasin M., Acton, Scott, Adams, Cynthia K., Adams, Evan, Adler, David S., Adriaensen, Maarten, Aguilar, Jonathan Albert, Ahmed, Mansoor, Ahmed, Nasif S., Ahmed, Tanjira, Albat, Rüdeger, Albert, Loïc, Alberts, Stacey, Aldridge, David, Allen, Mary Marsha, Allen, Shaune S., Altenburg, Martin, Altunc, Serhat, Alvarez, Jose Lorenzo, Álvarez-Márquez, Javier, de Oliveira, Catarina Alves, Ambrose, Leslie L., Anandakrishnan, Satya M., Andersen, Gregory C., Anderson, Harry James, Anderson, Jay, Anderson, Kristen, Anderson, Sara M., Aprea, Julio, Archer, Benita J., Arenberg, Jonathan W., Argyriou, Ioannis, Arribas, Santiago, Artigau, Étienne, Arvai, Amanda Rose, Atcheson, Paul, Atkinson, Charles B., Averbukh, Jesse, Aymergen, Cagatay, Bacinski, John J., Baggett, Wayne E., Bagnasco, Giorgio, Baker, Lynn L., Balzano, Vicki Ann, Banks, Kimberly A., Baran, David A., Barker, Elizabeth A., Barrett, Larry K., Barringer, Bruce O., Barto, Allison, Bast, William, Baudoz, Pierre, Baum, Stefi, Beatty, Thomas G., Beaulieu, Mathilde, Bechtold, Kathryn, Beck, Tracy, Beddard, Megan M., Beichman, Charles, Bellagama, Larry, Bely, Pierre, Berger, Timothy W., Bergeron, Louis E., Darveau-Bernier, Antoine, Bertch, Maria D., Beskow, Charlotte, Betz, Laura E., Biagetti, Carl P., Birkmann, Stephan, Bjorklund, Kurt F., Blackwood, James D., Blazek, Ronald Paul, Blossfeld, Stephen, Bluth, Marcel, Boccaletti, Anthony, Boegner Jr., Martin E., Bohlin, Ralph C., Boia, John Joseph, Böker, Torsten, Bonaventura, N., Bond, Nicholas A., Bosley, Kari Ann, Boucarut, Rene A., Bouchet, Patrice, Bouwman, Jeroen, Bower, Gary, Bowers, Ariel S., Bowers, Charles W., Boyce, Leslye A., Boyer, Christine T., Boyer, Martha L., Boyer, Michael, Boyer, Robert, Bradley, Larry D., Brady, Gregory R., Brandl, Bernhard R., Brannen, Judith L., Breda, David, Bremmer, Harold G., Brennan, David, Bresnahan, Pamela A., Bright, Stacey N., Broiles, Brian J., Bromenschenkel, Asa, Brooks, Brian H., Brooks, Keira J., Brown, Bob, Brown, Bruce, Brown, Thomas M., Bruce, Barry W., Bryson, Jonathan G., Bujanda, Edwin D., Bullock, Blake M., Bunker, A. J., Bureo, Rafael, Burt, Irving J., Bush, James Aaron, Bushouse, Howard A., Bussman, Marie C., Cabaud, Olivier, Cale, Steven, Calhoon, Charles D., Calvani, Humberto, Canipe, Alicia M., Caputo, Francis M., Cara, Mihai, Carey, Larkin, Case, Michael Eli, Cesari, Thaddeus, Cetorelli, Lee D., Chance, Don R., Chandler, Lynn, Chaney, Dave, Chapman, George N., Charlot, S., Chayer, Pierre, Cheezum, Jeffrey I., Chen, Bin, Chen, Christine H., Cherinka, Brian, Chichester, Sarah C., Chilton, Zachary S., Chittiraibalan, Dharini, Clampin, Mark, Clark, Charles R., Clark, Kerry W., Clark, Stephanie M., Claybrooks, Edward E., Cleveland, Keith A., Cohen, Andrew L., Cohen, Lester M., Colón, Knicole D., Coleman, Benee L., Colina, Luis, Comber, Brian J., Comeau, Thomas M., Comer, Thomas, Reis, Alain Conde, Connolly, Dennis C., Conroy, Kyle E., Contos, Adam R., Contreras, James, Cook, Neil J., Cooper, James L., Cooper, Rachel Aviva, Correia, Michael F., Correnti, Matteo, Cossou, Christophe, Costanza, Brian F., Coulais, Alain, Cox, Colin R., Coyle, Ray T., Cracraft, Misty M., Noriega-Crespo, Alberto, Crew, Keith A., Curtis, Gary J., Cusveller, Bianca, Maciel, Cleyciane Da Costa, Dailey, Christopher T., Daugeron, Frédéric, Davidson, Greg S., Davies, James E., Davis, Katherine Anne, Davis, Michael S., Day, Ratna, de Chambure, Daniel, de Jong, Pauline, De Marchi, Guido, Dean, Bruce H., Decker, John E., Delisa, Amy S., Dell, Lawrence C., Dellagatta, Gail, Dembinska, Franciszka, Demosthenes, Sandor, Dencheva, Nadezhda M., Deneu, Philippe, DePriest, William W., Deschenes, Jeremy, Dethienne, Nathalie, Detre, Örs Hunor, Diaz, Rosa Izela, Dicken, Daniel, DiFelice, Audrey S., Dillman, Matthew, Disharoon, Maureen O., van Dishoeck, Ewine F., Dixon, William V., Doggett, Jesse B., Dominguez, Keisha L., Donaldson, Thomas S., Doria-Warner, Cristina M., Santos, Tony Dos, Doty, Heather, Douglas Jr., Robert E., Doyon, René, Dressler, Alan, Driggers, Jennifer, Driggers, Phillip A., Dunn, Jamie L., DuPrie, Kimberly C., Dupuis, Jean, Durning, John, Dutta, Sanghamitra B., Earl, Nicholas M., Eccleston, Paul, Ecobichon, Pascal, Egami, Eiichi, Ehrenwinkler, Ralf, Eisenhamer, Jonathan D., Eisenhower, Michael, Eisenstein, Daniel J., Hamel, Zaky El, Elie, Michelle L., Elliott, James, Elliott, Kyle Wesley, Engesser, Michael, Espinoza, Néstor, Etienne, Odessa, Etxaluze, Mireya, Evans, Leah, Fabreguettes, Luce, Falcolini, Massimo, Falini, Patrick R., Fatig, Curtis, Feeney, Matthew, Feinberg, Lee D., Fels, Raymond, Ferdous, Nazma, Ferguson, Henry C., Ferrarese, Laura, Ferreira, Marie-Héléne, Ferruit, Pierre, Ferry, Malcolm, Filippazzo, Joseph Charles, Firre, Daniel, Fix, Mees, Flagey, Nicolas, Flanagan, Kathryn A., Fleming, Scott W., Florian, Michael, Flynn, James R., Foiadelli, Luca, Fontaine, Mark R., Fontanella, Erin Marie, Forshay, Peter Randolph, Fortner, Elizabeth A., Fox, Ori D., Framarini, Alexandro P., Francisco, John I., Franck, Randy, Franx, Marijn, Franz, David E., Friedman, Scott D., Friend, Katheryn E., Frost, James R., Fu, Henry, Fullerton, Alexander W., Gaillard, Lionel, Galkin, Sergey, Gallagher, Ben, Galyer, Anthony D., Marín, Macarena García, Gardner, Lisa E., Garland, Dennis, Garrett, Bruce Albert, Gasman, Danny, Gáspár, András, Gastaud, René, Gaudreau, Daniel, Gauthier, Peter Timothy, Geers, Vincent, Geithner, Paul H., Gennaro, Mario, Gerber, John, Gereau, John C., Giampaoli, Robert, Giardino, Giovanna, Gibbons, Paul C., Gilbert, Karolina, Gilman, Larry, Girard, Julien H., Giuliano, Mark E., Gkountis, Konstantinos, Glasse, Alistair, Glassmire, Kirk Zachary, Glauser, Adrian Michael, Glazer, Stuart D., Goldberg, Joshua, Golimowski, David A., Gonzaga, Shireen P., Gordon, Karl D., Gordon, Shawn J., Goudfrooij, Paul, Gough, Michael J., Graham, Adrian J., Grau, Christopher M., Green, Joel David, Greene, Gretchen R., Greene, Thomas P., Greenfield, Perry E., Greenhouse, Matthew A., Greve, Thomas R., Greville, Edgar M., Grimaldi, Stefano, Groe, Frank E., Groebner, Andrew, Grumm, David M., Grundy, Timothy, Güdel, Manuel, Guillard, Pierre, Guldalian, John, Gunn, Christopher A., Gurule, Anthony, Gutman, Irvin Meyer, Guy, Paul D., Guyot, Benjamin, Hack, Warren J., Haderlein, Peter, Hagan, James B., Hagedorn, Andria, Hainline, Kevin, Haley, Craig, Hami, Maryam, Hamilton, Forrest Clifford, Hammann, Jeffrey, Hammel, Heidi B., Hanley, Christopher J., Hansen, Carl August, Hardy, Bruce, Harnisch, Bernd, Harr, Michael Hunter, Harris, Pamela, Hart, Jessica Ann, Hartig, George F., Hasan, Hashima, Hashim, Kathleen Marie, Hashimoto, Ryan, Haskins, Sujee J., Hawkins, Robert Edward, Hayden, Brian, Hayden, William L., Healy, Mike, Hecht, Karen, Heeg, Vince J., Hejal, Reem, Helm, Kristopher A., Hengemihle, Nicholas J., Henning, Thomas, Henry, Alaina, Henry, Ronald L., Henshaw, Katherine, Hernandez, Scarlin, Herrington, Donald C., Heske, Astrid, Hesman, Brigette Emily, Hickey, David L., Hilbert, Bryan N., Hines, Dean C., Hinz, Michael R., Hirsch, Michael, Hitcho, Robert S., Hodapp, Klaus, Hodge, Philip E., Hoffman, Melissa, Holfeltz, Sherie T., Holler, Bryan Jason, Hoppa, Jennifer Rose, Horner, Scott, Howard, Joseph M., Howard, Richard J., Huber, Jean M., Hunkeler, Joseph S., Hunter, Alexander, Hunter, David Gavin, Hurd, Spencer W., Hurst, Brendan J., Hutchings, John B., Hylan, Jason E., Ignat, Luminita Ilinca, Illingworth, Garth, Irish, Sandra M., Isaacs III, John C., Jackson Jr., Wallace C., Jaffe, Daniel T., Jahic, Jasmin, Jahromi, Amir, Jakobsen, Peter, James, Bryan, James, John C., James, LeAndrea Rae, Jamieson, William Brian, Jandra, Raymond D., Jayawardhana, Ray, Jedrzejewski, Robert, Jeffers, Basil S., Jensen, Peter, Joanne, Egges, Johns, Alan T., Johnson, Carl A., Johnson, Eric L., Johnson, Patricia, Johnson, Phillip Stephen, Johnson, Thomas K., Johnson, Timothy W., Johnstone, Doug, Jollet, Delphine, Jones, Danny P., Jones, Gregory S., Jones, Olivia C., Jones, Ronald A., Jones, Vicki, Jordan, Ian J., Jordan, Margaret E., Jue, Reginald, Jurkowski, Mark H., Justis, Grant, Justtanont, Kay, Kaleida, Catherine C., Kalirai, Jason S., Kalmanson, Phillip Cabrales, Kaltenegger, Lisa, Kammerer, Jens, Kan, Samuel K., Kanarek, Graham Childs, Kao, Shaw-Hong, Karakla, Diane M., Karl, Hermann, Kassin, Susan A., Kauffman, David D., Kavanagh, Patrick, Kelley, Leigh L., Kelly, Douglas M., Kendrew, Sarah, Kennedy, Herbert V., Kenny, Deborah A., Keski-Kuha, Ritva A., Keyes, Charles D., Khan, Ali, Kidwell, Richard C., Kimble, Randy A., King, James S., King, Richard C., Kinzel, Wayne M., Kirk, Jeffrey R., Kirkpatrick, Marc E., Klaassen, Pamela, Klingemann, Lana, Klintworth, Paul U., Knapp, Bryan Adam, Knight, Scott, Knollenberg, Perry J., Knutsen, Daniel Mark, Koehler, Robert, Koekemoer, Anton M., Kofler, Earl T., Kontson, Vicki L., Kovacs, Aiden Rose, Kozhurina-Platais, Vera, Krause, Oliver, Kriss, Gerard A., Krist, John, Kristoffersen, Monica R., Krogel, Claudia, Krueger, Anthony P., Kulp, Bernard A., Kumari, Nimisha, Kwan, Sandy W., Kyprianou, Mark, Labador, Aurora Gadiano, Labiano, Álvaro, Lafrenière, David, Lagage, Pierre-Olivier, Laidler, Victoria G., Laine, Benoit, Laird, Simon, Lajoie, Charles-Philippe, Lallo, Matthew D., Lam, May Yen, LaMassa, Stephanie Marie, Lambros, Scott D., Lampenfield, Richard Joseph, Lander, Matthew Ed, Langston, James Hutton, Larson, Kirsten, Larson, Melora, LaVerghetta, Robert Joseph, Law, David R., Lawrence, Jon F., Lee, David W., Lee, Janice, Lee, Yat-Ning Paul, Leisenring, Jarron, Leveille, Michael Dunlap, Levenson, Nancy A., Levi, Joshua S., Levine, Marie B., Lewis, Dan, Lewis, Jake, Lewis, Nikole, Libralato, Mattia, Lidon, Norbert, Liebrecht, Paula Louisa, Lightsey, Paul, Lilly, Simon, Lim, Frederick C., Lim, Pey Lian, Ling, Sai-Kwong, Link, Lisa J., Link, Miranda Nicole, Lipinski, Jamie L., Liu, XiaoLi, Lo, Amy S., Lobmeyer, Lynette, Logue, Ryan M., Long, Chris A., Long, Douglas R., Long, Ilana D., Long, Knox S., López-Caniego, Marcos, Lotz, Jennifer M., Love-Pruitt, Jennifer M., Lubskiy, Michael, Luers, Edward B., Luetgens, Robert A., Luevano, Annetta J., Lui, Sarah Marie G. Flores, Lund III, James M., Lundquist, Ray A., Lunine, Jonathan, Lützgendorf, Nora, Lynch, Richard J., MacDonald, Alex J., MacDonald, Kenneth, Macias, Matthew J., Macklis, Keith I., Maghami, Peiman, Maharaja, Rishabh Y., Maiolino, Roberto, Makrygiannis, Konstantinos G., Malla, Sunita Giri, Malumuth, Eliot M., Manjavacas, Elena, Marini, Andrea, Marrione, Amanda, Marston, Anthony, Martel, André R, Martin, Didier, Martin, Peter G., Martinez, Kristin L., Maschmann, Marc, Masci, Gregory L., Masetti, Margaret E., Maszkiewicz, Michael, Matthews, Gary, Matuskey, Jacob E., McBrayer, Glen A., McCarthy, Donald W., McCaughrean, Mark J., McClare, Leslie A., McClare, Michael D., McCloskey, John C., McClurg, Taylore D., McCoy, Martin, McElwain, Michael W., McGregor, Roy D., McGuffey, Douglas B., McKay, Andrew G., McKenzie, William K., McLean, Brian, McMaster, Matthew, McNeil, Warren, De Meester, Wim, Mehalick, Kimberly L., Meixner, Margaret, Meléndez, Marcio, Menzel, Michael P., Menzel, Michael T., Merz, Matthew, Mesterharm, David D., Meyer, Michael R., Meyett, Michele L., Meza, Luis E., Midwinter, Calvin, Milam, Stefanie N., Miller, Jay Todd, Miller, William C., Miskey, Cherie L., Misselt, Karl, Mitchell, Eileen P., Mohan, Martin, Montoya, Emily E., Moran, Michael J., Morishita, Takahiro, Moro-Martín, Amaya, Morrison, Debra L., Morrison, Jane, Morse, Ernie C., Moschos, Michael, Moseley, S. H., Mosier, Gary E., Mosner, Peter, Mountain, Matt, Muckenthaler, Jason S., Mueller, Donald G., Mueller, Migo, Muhiem, Daniella, Mühlmann, Prisca, Mullally, Susan Elizabeth, Mullen, Stephanie M., Munger, Alan J, Murphy, Jess, Murray, Katherine T., Muzerolle, James C., Mycroft, Matthew, Myers, Andrew, Myers, Carey R., Myers, Fred Richard R., Myers, Richard, Myrick, Kaila, Nagle IV, Adrian F., Nayak, Omnarayani, Naylor, Bret, Neff, Susan G., Nelan, Edmund P., Nella, John, Nguyen, Duy Tuong, Nguyen, Michael N., Nickson, Bryony, Nidhiry, John Joseph, Niedner, Malcolm B., Nieto-Santisteban, Maria, Nikolov, Nikolay K., Nishisaka, Mary Ann, Nota, Antonella, O'Mara, Robyn C., Oboryshko, Michael, O'Brien, Marcus B., Ochs, William R., Offenberg, Joel D., Ogle, Patrick Michael, Ohl, Raymond G., Olmsted, Joseph Hamden, Osborne, Shannon Barbara, O'Shaughnessy, Brian Patrick, Östlin, Göran, O'Sullivan, Brian, Otor, O. Justin, Ottens, Richard, Ouellette, Nathalie N. -Q., Outlaw, Daria J., Owens, Beverly A., Pacifici, Camilla, Page, James Christophe, Paranilam, James G., Park, Sang, Parrish, Keith A., Paschal, Laura, Patapis, Polychronis, Patel, Jignasha, Patrick, Keith, Pattishall Jr., Robert A., Paul, Douglas William, Paul, Shirley J., Pauly, Tyler Andrew, Pavlovsky, Cheryl M., Peña-Guerrero, Maria, Pedder, Andrew H., Peek, Matthew Weldon, Pelham, Patricia A., Penanen, Konstantin, Perriello, Beth A., Perrin, Marshall D., Perrine, Richard F., Perrygo, Chuck, Peslier, Muriel, Petach, Michael, Peterson, Karla A., Pfarr, Tom, Pierson, James M., Pietraszkiewicz, Martin, Pilchen, Guy, Pipher, Judy L., Pirzkal, Norbert, Pitman, Joseph T., Player, Danielle M., Plesha, Rachel, Plitzke, Anja, Pohner, John A., Poletis, Karyn Konstantin, Pollizzi, Joseph A., Polster, Ethan, Pontius, James T., Pontoppidan, Klaus, Porges, Susana C., Potter, Gregg D., Prescott, Stephen, Proffitt, Charles R., Pueyo, Laurent, Neira, Irma Aracely Quispe, Radich, Armando, Rager, Reiko T., Rameau, Julien, Ramey, Deborah D., Alarcon, Rafael Ramos, Rampini, Riccardo, Rapp, Robert, Rashford, Robert A., Rauscher, Bernard J., Ravindranath, Swara, Rawle, Timothy, Rawlings, Tynika N., Ray, Tom, Regan, Michael W., Rehm, Brian, Rehm, Kenneth D., Reid, Neill, Reis, Carl A., Renk, Florian, Reoch, Tom B., Ressler, Michael, Rest, Armin W., Reynolds, Paul J., Richon, Joel G., Richon, Karen V., Ridgaway, Michael, Riedel, Adric Richard, Rieke, George H., Rieke, Marcia, Rifelli, Richard E., Rigby, Jane R., Riggs, Catherine S., Ringel, Nancy J., Ritchie, Christine E., Rix, Hans-Walter, Robberto, Massimo, Robinson, Michael S., Robinson, Orion, Rock, Frank W., Rodriguez, David R., del Pino, Bruno Rodríguez, Roellig, Thomas, Rohrbach, Scott O., Roman, Anthony J., Romelfanger, Frederick J., Romo Jr., Felipe P., Rosales, Jose J., Rose, Perry, Roteliuk, Anthony F., Roth, Marc N., Rothwell, Braden Quinn, Rouzaud, Sylvain, Rowe, Jason, Rowlands, Neil, Roy, Arpita, Royer, Pierre, Rui, Chunlei, Rumler, Peter, Rumpl, William, Russ, Melissa L., Ryan, Michael B., Ryan, Richard M., Saad, Karl, Sabata, Modhumita, Sabatino, Rick, Sabbi, Elena, Sabelhaus, Phillip A., Sabia, Stephen, Sahu, Kailash C., Saif, Babak N., Salvignol, Jean-Christophe, Samara-Ratna, Piyal, Samuelson, Bridget S., Sanders, Felicia A., Sappington, Bradley, Sargent, B. A., Sauer, Arne, Savadkin, Bruce J., Sawicki, Marcin, Schappell, Tina M., Scheffer, Caroline, Scheithauer, Silvia, Scherer, Ron, Schiff, Conrad, Schlawin, Everett, Schmeitzky, Olivier, Schmitz, Tyler S., Schmude, Donald J., Schneider, Analyn, Schreiber, Jürgen, Schroeven-Deceuninck, Hilde, Schultz, John J., Schwab, Ryan, Schwartz, Curtis H., Scoccimarro, Dario, Scott, John F., Scott, Michelle B., Seaton, Bonita L., Seely, Bruce S., Seery, Bernard, Seidleck, Mark, Sembach, Kenneth, Shanahan, Clare Elizabeth, Shaughnessy, Bryan, Shaw, Richard A., Shay, Christopher Michael, Sheehan, Even, Sheth, Kartik, Shih, Hsin-Yi, Shivaei, Irene, Siegel, Noah, Sienkiewicz, Matthew G., Simmons, Debra D., Simon, Bernard P., Sirianni, Marco, Sivaramakrishnan, Anand, Slade, Jeffrey E., Sloan, G. C., Slocum, Christine E., Slowinski, Steven E., Smith, Corbett T., Smith, Eric P., Smith, Erin C., Smith, Koby, Smith, Robert, Smith, Stephanie J., Smolik, John L., Soderblom, David R., Sohn, Sangmo Tony, Sokol, Jeff, Sonneborn, George, Sontag, Christopher D., Sooy, Peter R., Soummer, Remi, Southwood, Dana M., Spain, Kay, Sparmo, Joseph, Speer, David T., Spencer, Richard, Sprofera, Joseph D., Stallcup, Scott S., Stanley, Marcia K., Stansberry, John A., Stark, Christopher C., Starr, Carl W., Stassi, Diane Y., Steck, Jane A., Steeley, Christine D., Stephens, Matthew A., Stephenson, Ralph J., Stewart, Alphonso C., Stiavelli, Massimo, Stockman Jr., Hervey, Strada, Paolo, Straughn, Amber N., Streetman, Scott, Strickland, David Kendal, Strobele, Jingping F., Stuhlinger, Martin, Stys, Jeffrey Edward, Such, Miguel, Sukhatme, Kalyani, Sullivan, Joseph F., Sullivan, Pamela C., Sumner, Sandra M., Sun, Fengwu, Sunnquist, Benjamin Dale, Swade, Daryl Allen, Swam, Michael S., Swenton, Diane F., Swoish, Robby A., Litten, Oi In Tam, Tamas, Laszlo, Tao, Andrew, Taylor, David K., Taylor, Joanna M., Plate, Maurice te, Van Tea, Mason, Teague, Kelly K., Telfer, Randal C., Temim, Tea, Texter, Scott C., Thatte, Deepashri G., Thompson, Christopher Lee, Thompson, Linda M., Thomson, Shaun R., Thronson, Harley, Tierney, C. M., Tikkanen, Tuomo, Tinnin, Lee, Tippet, William Thomas, Todd, Connor William, Tran, Hien D., Trauger, John, Trejo, Edwin Gregorio, Truong, Justin Hoang Vinh, Tsukamoto, Christine L., Tufail, Yasir, Tumlinson, Jason, Tustain, Samuel, Tyra, Harrison, Ubeda, Leonardo, Underwood, Kelli, Uzzo, Michael A., Vaclavik, Steven, Valenduc, Frida, Valenti, Jeff A., Van Campen, Julie, van de Wetering, Inge, Van Der Marel, Roeland P., van Haarlem, Remy, Vandenbussche, Bart, Vanterpool, Dona D., Vernoy, Michael R., Costas, Maria Begoña Vila, Volk, Kevin, Voorzaat, Piet, Voyton, Mark F., Vydra, Ekaterina, Waddy, Darryl J., Waelkens, Christoffel, Wahlgren, Glenn Michael, Walker Jr., Frederick E., Wander, Michel, Warfield, Christine K., Warner, Gerald, Wasiak, Francis C., Wasiak, Matthew F., Wehner, James, Weiler, Kevin R., Weilert, Mark, Weiss, Stanley B., Wells, Martyn, Welty, Alan D., Wheate, Lauren, Wheeler, Thomas P., White, Christy L., Whitehouse, Paul, Whiteleather, Jennifer Margaret, Whitman, William Russell, Williams, Christina C., Willmer, Christopher N. A., Willott, Chris J., Willoughby, Scott P., Wilson, Andrew, Wilson, Debra, Wilson, Donna V., Windhorst, Rogier, Wislowski, Emily Christine, Wolfe, David J., Wolfe, Michael A., Wolff, Schuyler, Wondel, Amancio, Woo, Cindy, Woods, Robert T., Worden, Elaine, Workman, William, Wright, Gillian S., Wu, Carl, Wu, Chi-Rai, Wun, Dakin D., Wymer, Kristen B., Yadetie, Thomas, Yan, Isabelle C., Yang, Keith C., Yates, Kayla L., Yeager, Christopher R., Yerger, Ethan John, Young, Erick T., Young, Gary, Yu, Gene, Yu, Susan, Zak, Dean S., Zeidler, Peter, Zepp, Robert, Zhou, Julia, Zincke, Christian A., Zonak, Stephanie, and Zondag, Elisabeth
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Astrophysics - Instrumentation and Methods for Astrophysics - Abstract
Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit., Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figures
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- 2023
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110. Multiple Instance Ensembling For Paranasal Anomaly Classification In The Maxillary Sinus
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Bhattacharya, Debayan, Behrendt, Finn, Becker, Benjamin Tobias, Beyersdorff, Dirk, Petersen, Elina, Petersen, Marvin, Cheng, Bastian, Eggert, Dennis, Betz, Christian, Hoffmann, Anna Sophie, and Schlaefer, Alexander
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Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Paranasal anomalies are commonly discovered during routine radiological screenings and can present with a wide range of morphological features. This diversity can make it difficult for convolutional neural networks (CNNs) to accurately classify these anomalies, especially when working with limited datasets. Additionally, current approaches to paranasal anomaly classification are constrained to identifying a single anomaly at a time. These challenges necessitate the need for further research and development in this area. In this study, we investigate the feasibility of using a 3D convolutional neural network (CNN) to classify healthy maxillary sinuses (MS) and MS with polyps or cysts. The task of accurately identifying the relevant MS volume within larger head and neck Magnetic Resonance Imaging (MRI) scans can be difficult, but we develop a straightforward strategy to tackle this challenge. Our end-to-end solution includes the use of a novel sampling technique that not only effectively localizes the relevant MS volume, but also increases the size of the training dataset and improves classification results. Additionally, we employ a multiple instance ensemble prediction method to further boost classification performance. Finally, we identify the optimal size of MS volumes to achieve the highest possible classification performance on our dataset. With our multiple instance ensemble prediction strategy and sampling strategy, our 3D CNNs achieve an F1 of 0.85 whereas without it, they achieve an F1 of 0.70. We demonstrate the feasibility of classifying anomalies in the MS. We propose a data enlarging strategy alongside a novel ensembling strategy that proves to be beneficial for paranasal anomaly classification in the MS.
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- 2023
111. Strong stationarity for the control of viscous history-dependent evolutionary VIs arising in applications
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Betz, Livia
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Mathematics - Optimization and Control ,34G25, 34K35, 49J40, 49K21, 74R99 - Abstract
This paper addresses optimal control problems governed by history-dependent EVIs with viscosity. One of the prominent properties of the state system is its non-smooth nature, so that the application of standard adjoint calculus is excluded. We extend the results from [7] by showing that history-dependent EVIs with viscosity can be formulated as non-smooth ODEs in Hilbert space in a general setting. The Hadamard directional differentiability of the solution map is investigated. Based on previous results, this allows us to establish strong stationary conditions for two different viscous damage models with fatigue., Comment: 25 pages. arXiv admin note: text overlap with arXiv:2302.05627
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- 2023
112. Drive Right: Promoting Autonomous Vehicle Education Through an Integrated Simulation Platform
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Qiao, Zhijie, Loeb, Helen, Gurrla, Venkata, Lebermann, Matt, Betz, Johannes, and Mangharam, Rahul
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Computer Science - Human-Computer Interaction - Abstract
Autonomous vehicles (AVs) are being rapidly introduced into our lives. However, public misunderstanding and mistrust have become prominent issues hindering the acceptance of these driverless technologies. The primary objective of this study is to evaluate the effectiveness of a driving simulator to help the public gain an understanding of AVs and build trust in them. To achieve this aim, we built an integrated simulation platform, designed various driving scenarios, and recruited 28 participants for the experiment. The study results indicate that a driving simulator effectively decreases the participants' perceived risk of AVs and increases perceived usefulness. The proposed methodologies and findings of this study can be further explored by auto manufacturers and policymakers to provide user-friendly AV design.
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- 2023
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113. Variance Sum Rule for Entropy Production
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Di Terlizzi, I., Gironella, M., Herraez-Aguilar, D., Betz, T., Monroy, F., Baiesi, M., and Ritort, F.
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Physics - Biological Physics ,Condensed Matter - Soft Condensed Matter ,Condensed Matter - Statistical Mechanics ,82A02 - Abstract
Entropy production is the hallmark of nonequilibrium physics, quantifying irreversibility, dissipation, and the efficiency of energy transduction processes. Despite many efforts, its measurement at the nanoscale remains challenging. We introduce a variance sum rule for displacement and force variances that permits us to measure the entropy production rate $\sigma$ in nonequilibrium steady states. We first illustrate it for directly measurable forces, such as an active Brownian particle in an optical trap. We then apply the variance sum rule to flickering experiments in human red blood cells. We find that $\sigma$ is spatially heterogeneous with a finite correlation length and its average value agrees with calorimetry measurements. The VSR paves the way to derive $\sigma$ using force spectroscopy and time-resolved imaging in living and active matter., Comment: 5 pages and 4 figures. It also contains Supp. Info. with 6 additional figures and 4 tables
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- 2023
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114. Entropy bound for time reversal markers
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Knotz, Gabriel, Muenker, Till M., Betz, Timo, and Krüger, Matthias
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Condensed Matter - Statistical Mechanics ,Condensed Matter - Soft Condensed Matter - Abstract
We derive a bound for entropy production in terms of the mean of normalizable path-antisymmetric observables. The optimal observable for this bound is shown to be the signum of entropy production, which is often easier determined or estimated than entropy production itself. It can be preserved under coarse graining by use of a simple path grouping algorithm. We demonstrate this relation and its properties using a driven network on a ring, for which the bound saturates for short times for any driving strength. This work can open a way to systematic coarse graining of entropy production., Comment: 5 pages, 2 figures
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- 2023
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115. Optimal control of a viscous damage model with fatigue
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Betz, Livia
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Mathematics - Optimization and Control ,34G25, 34K35, 49J20, 49J27, 74R99 - Abstract
Motivated by fatigue damage models, this paper addresses optimal control problems governed by a non-smooth system featuring two non-differentiable mappings. This consists of a coupling between a doubly non-smooth history-dependent evolution and an elliptic PDE. After proving the directional differentiability of the associated solution mapping, an optimality system which is stronger than the one obtained by classical smoothening procedures is derived. If one of the non-differentiable mappings becomes smooth, the optimality conditions are of strong stationary type, i.e., equivalent to the primal necessary optimality condition., Comment: 31 pages
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- 2023
116. Strong stationarity for optimal control problems with non-smooth integral equation constraints: Application to continuous DNNs
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Antil, Harbir, Betz, Livia, and Wachsmuth, Daniel
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Mathematics - Optimization and Control - Abstract
Motivated by the residual type neural networks (ResNet), this paper studies optimal control problems constrained by a non-smooth integral equation. Such non-smooth equations, for instance, arise in the continuous representation of fractional deep neural networks (DNNs). Here the underlying non-differentiable function is the ReLU or max function. The control enters in a nonlinear and multiplicative manner and we additionally impose control constraints. Because of the presence of the non-differentiable mapping, the application of standard adjoint calculus is excluded. We derive strong stationary conditions by relying on the limited differentiability properties of the non-smooth map. While traditional approaches smoothen the non-differentiable function, no such smoothness is retained in our final strong stationarity system. Thus, this work also closes a gap which currently exists in continuous neural networks with ReLU type activation function.
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- 2023
117. Rapid characterisation of mixtures of hydrogen and natural gas by means of ultrasonic time-delay estimation
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J. M. Monsalve, U. Völz, M. Jongmanns, B. Betz, S. Langa, C. Ruffert, J. Amelung, and M. Wiersig
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Technology - Abstract
The implementation of the “power-to-gas” concept, where hydrogen and natural gas are blended and transported in the existing network, requires a quick, on-site method to monitor the content of hydrogen in the mixture. We evaluate a rapid characterisation of this mixture based on the measurement of the speed of sound, using micromachined ultrasonic transducers (MUTs). Two MUT-based prototypes were implemented to analyse a mixture of natural gas and hydrogen under controlled conditions. Changes in the hydrogen content below 2 mol % (in a mixture that was adjusted between 6 mol % and 16 mol %) were discriminated by both devices, including the uncertainty due to the temperature compensation and the time-delay estimation. The obtained values of the speed of sound were consistent with those calculated from independent, non-acoustic measurements performed with a gas chromatograph and a density sensor. An MUT-based flow meter is thus capable of reporting both gas intake and the molar fraction of hydrogen, provided that the source of natural gas is kept constant.
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- 2024
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118. Reduced olfactory bulb volume accompanies olfactory dysfunction after mild SARS-CoV-2 infection
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Marvin Petersen, Benjamin Becker, Maximilian Schell, Carola Mayer, Felix L. Naegele, Elina Petersen, Raphael Twerenbold, Götz Thomalla, Bastian Cheng, Christian Betz, and Anna S. Hoffmann
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Medicine ,Science - Abstract
Abstract Despite its high prevalence, the determinants of smelling impairment in COVID-19 remain not fully understood. In this work, we aimed to examine the association between olfactory bulb volume and the clinical trajectory of COVID-19-related smelling impairment in a large-scale magnetic resonance imaging (MRI) analysis. Data of non-vaccinated COVID-19 convalescents recruited within the framework of the prospective Hamburg City Health Study COVID Program between March and December 2020 were analyzed. At baseline, 233 participants underwent MRI and neuropsychological testing as well as a structured questionnaire for olfactory function. Between March and April 2022, olfactory function was assessed at follow-up including quantitative olfactometric testing with Sniffin’ Sticks. This study included 233 individuals recovered from mainly mild to moderate SARS-CoV-2 infections. Longitudinal assessment demonstrated a declining prevalence of self-reported olfactory dysfunction from 67.1% at acute infection, 21.0% at baseline examination and 17.5% at follow-up. Participants with post-acute self-reported olfactory dysfunction had a significantly lower olfactory bulb volume at baseline than normally smelling individuals. Olfactory bulb volume at baseline predicted olfactometric scores at follow-up. Performance in neuropsychological testing was not significantly associated with the olfactory bulb volume. Our work demonstrates an association of long-term self-reported smelling dysfunction and olfactory bulb integrity in a sample of individuals recovered from mainly mild to moderate COVID-19. Collectively, our results highlight olfactory bulb volume as a surrogate marker that may inform diagnosis and guide rehabilitation strategies in COVID-19.
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- 2024
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119. Diabetes mellitus and hard braking events in older adult drivers
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Difei Liu, Stanford Chihuri, Howard F. Andrews, Marian E. Betz, Carolyn DiGuiseppi, David W. Eby, Linda L. Hill, Vanya Jones, Thelma J. Mielenz, Lisa J. Molnar, David Strogatz, Barbara H. Lang, and Guohua Li
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Aging ,Cohort study ,Diabetes ,Driving safety ,Hard braking event ,Motor vehicle accident ,Medical emergencies. Critical care. Intensive care. First aid ,RC86-88.9 ,Public aspects of medicine ,RA1-1270 - Abstract
Abstract Background Diabetes mellitus (DM) can impair driving safety due to hypoglycemia, hyperglycemia, diabetic peripheral neuropathy, and diabetic eye diseases. However, few studies have examined the association between DM and driving safety in older adults based on naturalistic driving data. Methods Data for this study came from a multisite naturalistic driving study of drivers aged 65–79 years at baseline. Driving data for the study participants were recorded by in-vehicle recording devices for up to 44 months. We used multivariable negative binomial modeling to estimate adjusted incidence rate ratios (aIRRs) and 95% confidence intervals (CIs) of hard braking events (HBEs, defined as maneuvers with deceleration rates ≥ 0.4 g) associated with DM. Results Of the 2856 study participants eligible for this analysis, 482 (16.9%) reported having DM at baseline, including 354 (12.4%) insulin non-users and 128 (4.5%) insulin users. The incidence rates of HBEs per 1000 miles were 1.13 for drivers without DM, 1.15 for drivers with DM not using insulin, and 1.77 for drivers with DM using insulin. Compared to drivers without DM, the risk of HBEs was 48% higher for drivers with DM using insulin (aIRR 1.48; 95% CI: 1.43, 1.53). Conclusion Older adult drivers with DM using insulin appear to be at increased proneness to vehicular crashes. Driving safety should be taken into consideration in DM care and management.
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- 2024
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120. Spatiotemporal changes in fine particulate matter and ozone in the oasis city of Korla, northeastern Tarim Basin of China
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Tayierjiang Aishan, Yaxin Sun, Ümüt Halik, Florian Betz, Asadilla Yusup, and Remila Rezhake
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Particulate matter (PM) ,Ozone ,Desert Oasis Cities ,Korla ,Medicine ,Science - Abstract
Abstract Air pollution is a serious environmental health concern for humans and other living organisms. This study analyzes the spatial and temporal characteristics of air pollutant concentrations, changes in the degree of pollution, and the wavelet coherence of the air quality index (AQI) with pollutants in various monitoring stations. The analysis is based on long-term time series data (January 2016 to December 2023) of air pollutants (PM2.5, PM10, and O3) from Korla, an oasis city in the northeastern part of the Tarim Basin, China. The concentrations of PM2.5, PM10, and O3 in Korla showed a cyclical trend from 2016 to 2023; PM10 concentrations exhibited all-season exceedance and PM2.5 exhibited exceedance only in spring. PM2.5 and PM10 showed a seasonal distribution of spring > winter > fall > summer; O3 concentrations showed a seasonal distribution of summer > spring > fall > winter. Strong positive wavelet coherence between PM and Air Quality Index (AQI) data series suggests that the AQI data series can effectively characterize fluctuating trends in PM concentrations. Moreover, PM10 levels IV and VI were maintained at approximately 10%, indicating that sand and dust have a substantial influence on air quality and pose potential threats to the health of urban inhabitants. Based on the results of this study, future efforts must strengthen relative countermeasures for sand prevention and control, select urban greening species with anti-pollution capabilities, rationally expand urban green spaces, and restrict regulations for reducing particulate matter emissions within city areas.
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- 2024
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121. Canine transmissible venereal tumour in Morocco: Clinical and pathological findings in 64 dogs – Insights from a descriptive epidemiological study (2020-2023)
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Nadia Laissaoui, Yolanda Millan, Daniela Simon Betz, Meryem El Mrini, Ghita Bouayad, Najat Lamalmi, Noursaid Tligui, and Rahma Azrib
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diagnosis ,dogs ,epidemiology ,transmissible venereal tumour ,Zoology ,QL1-991 - Abstract
Background: Canine transmissible venereal tumour (CTVT) is a widely spread, contagious neoplasm commonly found in dogs. Mostly affects the external genitalia, however, it may also exhibit unusual clinical presentations. Aim: To describe the epidemiology, clinical appearance, cytologic and histopathologic features of dogs with TVT in Morocco. Methods: Within the realm of a nation-wide study on canine and feline tumours in Morocco between September 2020 and March 2023, dogs with histologically diagnosed TVT were identified and data on epidemiologic, clinical as well as cytologic and histologic features compiled and analyzed. Results: A total of 64 cases of canine TVT were diagnosed. 52 dogs were cross-breed (81.2%) while 4 Siberian Huskies (6.2%) and 3 German shepherds (4.7%) were the most affected pure-breed dogs. Median age of dogs at diagnosis was 3 years (range, 1-10years) and male gender was more common (male:female ratio; 1.3:1). Tumour was located exclusively in the genital area in 58 cases (90.6%), whereas 6 dogs (9.4%) had an atypical occurrence of TVT with locations including skin and nasal cavity. Cytology allowed for an early diagnosis in 2 cases. Histology revealed no differences between the genital and extragenital forms. Immunohistochemistry was necessary in 4 cases and revealed positive staining for vimentin and Alpha-1-antitrypsin, negative marking for CD3, CD20 and AE1/AE3, and low cytoplasmic labeling for lysozyme. Conclusion: CTVT is a widely distributed neoplasm in Morocco, mostly showing presence in young, cross-breed, and oftentimes stray dogs. Adequate understanding of this tumour's epidemiological features is necessary for its management and eradication. [Open Vet J 2024; 14(5.000): 1206-1215]
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- 2024
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122. Dynamic Geospatial Data Integration: A Case Study of Moving Objects in Munakata City, Japan Using OGC API Moving Features and Sensorthings API
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T. Santhanavanich, R. Padsala, M. Betz, and V. Coors
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Technology ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Applied optics. Photonics ,TA1501-1820 - Abstract
The effective tracking and analysis of moving objects within urban environments presents a complex challenge that necessitates robust geospatial data integration. Open Geospatial Consortium (OGC) APIs offer standardized approaches to managing dynamic geospatial information. This paper presents a case study of real-time moving object tracking including buses and trains in the city of Munakata, Japan, utilizing two prominent OGC APIs: OGC API Moving Features and OGC SensorThings API. The study explores the implementation of both APIs, examining their strengths and limitations in handling real-time location updates and associated sensor data generated by moving buses. The research provides insights into the practical suitability of each API model for dynamic object tracking, offering valuable guidance for practitioners seeking to optimize geospatial data integration within smart cities and intelligent transportation systems.
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- 2024
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123. TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge
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Betz, Johannes, Betz, Tobias, Fent, Felix, Geisslinger, Maximilian, Heilmeier, Alexander, Hermansdorfer, Leonhard, Herrmann, Thomas, Huch, Sebastian, Karle, Phillip, Lienkamp, Markus, Lohmann, Boris, Nobis, Felix, Ögretmen, Levent, Rowold, Matthias, Sauerbeck, Florian, Stahl, Tim, Trauth, Rainer, Werner, Frederik, and Wischnewski, Alexander
- Subjects
Computer Science - Robotics - Abstract
For decades, motorsport has been an incubator for innovations in the automotive sector and brought forth systems like disk brakes or rearview mirrors. Autonomous racing series such as Roborace, F1Tenth, or the Indy Autonomous Challenge (IAC) are envisioned as playing a similar role within the autonomous vehicle sector, serving as a proving ground for new technology at the limits of the autonomous systems capabilities. This paper outlines the software stack and approach of the TUM Autonomous Motorsport team for their participation in the Indy Autonomous Challenge, which holds two competitions: A single-vehicle competition on the Indianapolis Motor Speedway and a passing competition at the Las Vegas Motor Speedway. Nine university teams used an identical vehicle platform: A modified Indy Lights chassis equipped with sensors, a computing platform, and actuators. All the teams developed different algorithms for object detection, localization, planning, prediction, and control of the race cars. The team from TUM placed first in Indianapolis and secured second place in Las Vegas. During the final of the passing competition, the TUM team reached speeds and accelerations close to the limit of the vehicle, peaking at around 270 km/h and 28 ms2. This paper will present details of the vehicle hardware platform, the developed algorithms, and the workflow to test and enhance the software applied during the two-year project. We derive deep insights into the autonomous vehicle's behavior at high speed and high acceleration by providing a detailed competition analysis. Based on this, we deduce a list of lessons learned and provide insights on promising areas of future work based on the real-world evaluation of the displayed concepts., Comment: 37 pages, 18 figures, 2 tables
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- 2022
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124. Connecting the Brain and Body to Support Equity Work: A Toolkit for Education Leaders
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WestEd, Pate, Christina, Tilley-Gyado, Terna, and Betz, Jenny
- Abstract
Education leaders have varying power to change inequitable structures and systems, but all need safety, support, skills, strategies, and practices to sustain change efforts--especially when doing this work in a stressful or oppressive context. Often overlooked, the health and wellbeing of staff (all types) is as important as the health and wellbeing of students and families. To help education leaders in equity work, this toolkit offers evidence-based information on the brain and behavior in the context of leadership and educational equity work, then offers brain-body and somatic strategies, with specific examples, to guide leaders in recognizing and responding to physical, social, and emotional needs in the education agency context.
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- 2022
125. RAD-Sim: Rapid Architecture Exploration for Novel Reconfigurable Acceleration Devices
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Boutros, Andrew, Nurvitadhi, Eriko, and Betz, Vaughn
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Computer Science - Hardware Architecture - Abstract
With the continued growth in field-programmable gate array (FPGA) capacity and their incorporation into new environments such as datacenters, we have witnessed the introduction of a new class of reconfigurable acceleration devices (RADs) that go beyond conventional FPGA architectures. These devices combine a reconfigurable fabric with coarse-grained domain-specialized accelerator blocks all connected via a high-performance packet-switched network-on-chip (NoC) for efficient system-wide communication. However, we lack the tools necessary to efficiently explore the huge design space for RADs, study the complex interactions between their different components and evaluate various combinations of design choices. In this work, we develop RAD-Sim, a cycle-level architecture simulator that allows rapid application-driven exploration of the design space of novel RADs. To showcase the capabilities of RADSim, we map and simulate a state-of-the-art deep learning (DL) inference overlay on a RAD instance incorporating an FPGA fabric and a complex of hard matrix-vector multiplication engines, communicating over a system-wide NoC. Through this example, we show how RAD-Sim can help architects quantify the effect of changing specific architecture parameters on end-to-end application performance., Comment: Published in the 2022 proceedings of the International Conference on Field-Programmable Logic and Applications (FPL)
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- 2023
126. Resonance expansion of quadratic quantities with regularized quasinormal modes
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Betz, Fridtjof, Binkowski, Felix, Hammerschmidt, Martin, Zschiedrich, Lin, and Burger, Sven
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Physics - Optics ,Physics - Computational Physics - Abstract
Resonance expansions are an intuitive approach to capture the interaction of an optical resonator with light. Here, we present a quasinormal mode expansion approach for quadratic observables exploiting the rigorous Riesz projection method. We demonstrate the approach by a numerical implementation of a state-of-the-art quantum light source and emphasize the ability of the approach to provide modal expansions outside the underlying nanophotonic resonator.
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- 2022
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127. High-performance designs for fiber-pigtailed quantum-light sources based on quantum dots in electrically-controlled circular Bragg gratings
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Rickert, Lucas, Betz, Fridtjof, Plock, Matthias, Burger, Sven, and Heindel, Tobias
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Quantum Physics ,Physics - Optics - Abstract
We present a numerical investigation of directly fiber-coupled hybrid circular Bragg gratings (CBGs) featuring electrical control for operation in the application relevant wavelength regimes around 930 nm as well as the telecom O- and C-band. We use a surrogate model combined with a Bayesian optimization approach to perform numerical optimization of the device performance which takes into account robustness with respect to fabrication tolerances. The proposed high-performance designs combine hCBGs with a dielectric planarization and a transparent contact material, enabling >86% direct fiber coupling efficiency (up to >93% efficiency into NA 0.8) while exhibiting Purcell Factors >20. Especially the proposed designs for the telecom range prove robust and can sustain expected fiber efficiencies of more than $(82.2\pm4.1)^{+2.2}_{-5.5}$% and expected average Purcell Factors of up to $(23.2\pm2.3)^{+3.2}_{-3.0}$ assuming conservative fabrication accuracies. The wavelength of maximum Purcell enhancement proves to be the most affected performance parameter by the deviations. Finally, we show that electrical field strengths suitable for Stark-tuning of an embedded quantum dot can be reached in the identified designs., Comment: Main text including Method section, (15 pages, 5 figures, and 50 references). The data sets and used code in this work is available on Zenodo (see reference in the main text)
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- 2022
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128. RGB-L: Enhancing Indirect Visual SLAM using LiDAR-based Dense Depth Maps
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Sauerbeck, Florian, Obermeier, Benjamin, Rudolph, Martin, and Betz, Johannes
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Computer Science - Robotics - Abstract
In this paper, we present a novel method for integrating 3D LiDAR depth measurements into the existing ORB-SLAM3 by building upon the RGB-D mode. We propose and compare two methods of depth map generation: conventional computer vision methods, namely an inverse dilation operation, and a supervised deep learning-based approach. We integrate the former directly into the ORB-SLAM3 framework by adding a so-called RGB-L (LiDAR) mode that directly reads LiDAR point clouds. The proposed methods are evaluated on the KITTI Odometry dataset and compared to each other and the standard ORB-SLAM3 stereo method. We demonstrate that, depending on the environment, advantages in trajectory accuracy and robustness can be achieved. Furthermore, we demonstrate that the runtime of the ORB-SLAM3 algorithm can be reduced by more than 40 % compared to the stereo mode. The related code for the ORB-SLAM3 RGB-L mode will be available as open-source software under https://github.com/TUMFTM/ORB SLAM3 RGBL., Comment: Accepted at ICCCR 2023
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- 2022
129. Unsupervised Anomaly Detection of Paranasal Anomalies in the Maxillary Sinus
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Bhattacharya, Debayan, Behrendt, Finn, Becker, Benjamin Tobias, Beyersdorff, Dirk, Petersen, Elina, Petersen, Marvin, Cheng, Bastian, Eggert, Dennis, Betz, Christian, Hoffmann, Anna Sophie, and Schlaefer, Alexander
- Subjects
Electrical Engineering and Systems Science - Image and Video Processing - Abstract
Deep learning (DL) algorithms can be used to automate paranasal anomaly detection from Magnetic Resonance Imaging (MRI). However, previous works relied on supervised learning techniques to distinguish between normal and abnormal samples. This method limits the type of anomalies that can be classified as the anomalies need to be present in the training data. Further, many data points from normal and anomaly class are needed for the model to achieve satisfactory classification performance. However, experienced clinicians can segregate between normal samples (healthy maxillary sinus) and anomalous samples (anomalous maxillary sinus) after looking at a few normal samples. We mimic the clinicians ability by learning the distribution of healthy maxillary sinuses using a 3D convolutional auto-encoder (cAE) and its variant, a 3D variational autoencoder (VAE) architecture and evaluate cAE and VAE for this task. Concretely, we pose the paranasal anomaly detection as an unsupervised anomaly detection problem. Thereby, we are able to reduce the labelling effort of the clinicians as we only use healthy samples during training. Additionally, we can classify any type of anomaly that differs from the training distribution. We train our 3D cAE and VAE to learn a latent representation of healthy maxillary sinus volumes using L1 reconstruction loss. During inference, we use the reconstruction error to classify between normal and anomalous maxillary sinuses. We extract sub-volumes from larger head and neck MRIs and analyse the effect of different fields of view on the detection performance. Finally, we report which anomalies are easiest and hardest to classify using our approach. Our results demonstrate the feasibility of unsupervised detection of paranasal anomalies from MRIs with an AUPRC of 85% and 80% for cAE and VAE, respectively.
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- 2022
130. High-speed whole-genome sequencing of a Whippet: Rapid chromosome-level assembly and annotation of an extremely fast dog’s genome
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Marcel Nebenführ, David Prochotta, Alexander Ben Hamadou, Axel Janke, Charlotte Gerheim, Christian Betz, Carola Greve, and Hanno Jörn Bolz
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Electronic computers. Computer science ,QA75.5-76.95 - Abstract
The time required for genome sequencing and de novo assembly depends on the interaction between laboratory work, sequencing capacity, and the bioinformatics workflow, often constrained by external sequencing services. Bringing together academic biodiversity institutes and a medical diagnostics company with extensive sequencing capabilities, we aimed at generating a high-quality mammalian de novo genome in minimal time. We present the first chromosome-level genome assembly of the Whippet, using PacBio long-read high-fidelity sequencing and reference-guided scaffolding. The final assembly has a contig N50 of 55 Mbp and a scaffold N50 of 65.7 Mbp. The total assembly length is 2.47 Gbp, of which 2.43 Gpb were scaffolded into 39 chromosome-length scaffolds. Annotation using mammalian genomes and transcriptome data yielded 28,383 transcripts, 90.9% complete BUSCO genes, and identified 36.5% repeat content. Sequencing, assembling, and scaffolding the chromosome-level genome of the Whippet took less than a week, adding another high-quality reference genome to the available sequences of domestic dog breeds.
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- 2024
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131. EPOS-OHCA: Early Predictors of Outcome and Survival after non-traumatic Out-of-Hospital Cardiac Arrest
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Julian Kreutz, Nikolaos Patsalis, Charlotte Müller, Georgios Chatzis, Styliani Syntila, Kiarash Sassani, Susanne Betz, Bernhard Schieffer, and Birgit Markus
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Out-of-hospital cardiac arrest (OHCA) ,Post-resuscitation management ,Prognostic parameters ,Outcome ,Specialties of internal medicine ,RC581-951 - Abstract
Background: Post-cardiac arrest syndrome (PCAS) after out-of-hospital cardiac arrest (OHCA) poses significant challenges due to its complex pathomechanisms involving inflammation, ischemia, and reperfusion injury. The identification of early available prognostic indicators is essential for optimizing therapeutic decisions and improving patient outcomes. Methods: In this retrospective single-center study, we analyzed real-world data from 463 OHCA patients with either prehospital or in-hospital return of spontaneous circulation (ROSC), treated at the Cardiac Arrest Center of the University Hospital of Marburg (MCAC) from January 2018 to December 2022. We evaluated demographic, prehospital, and clinical variables, including initial rhythms, resuscitation details, and early laboratory results. Statistical analyses included logistic regression to identify predictors of survival and neurological outcomes. Results: Overall, 46.9% (n = 217) of patients survived to discharge, with 70.1% (n = 152) achieving favorable neurological status (CPC 1 or 2). Age, initial shockable rhythm, resuscitation time to return of spontaneous circulation (ROSC), and early laboratory parameters like lactate, C-reactive protein, and glomerular filtration rate were identified as independent and combined Early Predictors of Outcome and Survival (EPOS), with high significant predictive value for survival (AUC 0.86 [95% CI 0.82–0.89]) and favorable neurological outcome (AUC 0.84 [95% CI 0.80–0.88]). Conclusion: Integration of EPOS into clinical procedures may significantly improve clinical decision making and thus patient prognosis in the early time-crucial period after OHCA. However, further validation in other patient cohorts is needed.
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- 2024
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132. Pyridinium tosylate
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Eric Cyriel Hosten and Richard Betz
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crystal structure ,hydrogen bond ,pyridinium salt ,Crystallography ,QD901-999 - Abstract
The title compound (systematic name: pyridinium 4-methylbenzenesulfonate), C5H6N+·C7H7O3S−, is the pyridinium salt of para-toluenesulfonic acid. In the crystal, classical N—H...O hydrogen bonds as well as C—H...O contacts connect the cationic and anionic entities into sheets lying parallel to the ab plane.
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- 2024
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133. FAIR ADCP data with OSADCP: a workflow to process ocean current data from vessel-mounted ADCPs
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Robert Kopte, Marius Becker, Tim Fischer, Peter Brandt, Gerd Krahmann, Maximilian Betz, Claas Faber, Christian Winter, Johannes Karstensen, and Gauvain Wiemer
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ADCP ,underway data ,FAIR data ,python ,ocean currents ,Science ,General. Including nature conservation, geographical distribution ,QH1-199.5 - Abstract
This paper presents the open-source Python software OSADCP developed for the processing of vessel-mounted Acoustic Doppler Current Profiler (VMADCP) data. At this stage, the toolbox is designed for processing VMADCP measurements from open-ocean applications of Teledyne RDI Ocean Surveyor ADCPs and the data acquisition software VMDAS. Based on the VMDAS ENX binary output format, the software contains implementations for cleaning and vector-averaging of single-ping velocity data, verification of the position data, and applying misalignment and amplitude corrections. The procedures of OSADCP are described in detail to encourage the scientific community to use it for their own purposes. The toolbox is an integral part of a workflow implemented on the German marine research vessels in the framework of the Underway Research Data project of the German Marine Research Alliance (DAM). It aims to ensure standardized data acquisition measures, reliable data transfer from the ADCP to shore both near-real-time and in delayed-mode, processing and quality control, and dissemination of the curated data product in the data repository PANGAEA. From PANGAEA, data sets are forwarded to the European marine data hubs Copernicus Marine Service and EMODnet. The workflow that forms the framework for OSADCP is described here as an example of scientific data management that follows the FAIR data guidelines.
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- 2024
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134. Implementation of Extreme Risk Protection Orders in Colorado from 2020 to 2022: Firearm relinquishment and return and petitioner characteristics
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Leslie M Barnard, Nisha Batta, Megan McCarthy, Kimberly Thies, Caitlin Robinson, Marcus Schultze, Marian E. Betz, and Christopher E. Knoepke
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Medicine - Abstract
Introduction: Firearm injury remains a public health problem, with nearly 50,000 firearm-related deaths in the US in 2021. Extreme risk protection orders (ERPOs) are civil restraining orders that intend to reduce firearm deaths by temporarily removing firearms from individuals who are threatening violence to themselves or others. We described ERPO use by petitioner type and implementation including firearm removal. Methods: All ERPO petitions filed in Colorado (1/1/2020–12/31/2022) were analyzed using an established abstraction tool and team-based approach. Case data abstracted from petitions and court documents were analyzed descriptively. Results: Over three years, there were 353 ERPO petitions filed in Colorado. Only 39 % percent of granted petitions had documentation of firearms being relinquished. The average number firearms relinquished was 1.8 with a range of 1 to 31 firearms. One third (37.7 %) of petitions mentioned a mental health issue, 10 % had a renewal request, and half (54.6 %) of petitions were filed by law enforcement (LE). LE petitions filed were more likely to be granted temporary ERPOs (94.3 % vs 35.0 %, p
- Published
- 2024
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135. Indy Autonomous Challenge -- Autonomous Race Cars at the Handling Limits
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Wischnewski, Alexander, Geisslinger, Maximilian, Betz, Johannes, Betz, Tobias, Fent, Felix, Heilmeier, Alexander, Hermansdorfer, Leonhard, Herrmann, Thomas, Huch, Sebastian, Karle, Phillip, Nobis, Felix, Ögretmen, Levent, Rowold, Matthias, Sauerbeck, Florian, Stahl, Tim, Trauth, Rainer, Lienkamp, Markus, and Lohmann, Boris
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Computer Science - Robotics ,Computer Science - Artificial Intelligence - Abstract
Motorsport has always been an enabler for technological advancement, and the same applies to the autonomous driving industry. The team TUM Auton-omous Motorsports will participate in the Indy Autonomous Challenge in Octo-ber 2021 to benchmark its self-driving software-stack by racing one out of ten autonomous Dallara AV-21 racecars at the Indianapolis Motor Speedway. The first part of this paper explains the reasons for entering an autonomous vehicle race from an academic perspective: It allows focusing on several edge cases en-countered by autonomous vehicles, such as challenging evasion maneuvers and unstructured scenarios. At the same time, it is inherently safe due to the motor-sport related track safety precautions. It is therefore an ideal testing ground for the development of autonomous driving algorithms capable of mastering the most challenging and rare situations. In addition, we provide insight into our soft-ware development workflow and present our Hardware-in-the-Loop simulation setup. It is capable of running simulations of up to eight autonomous vehicles in real time. The second part of the paper gives a high-level overview of the soft-ware architecture and covers our development priorities in building a high-per-formance autonomous racing software: maximum sensor detection range, relia-ble handling of multi-vehicle situations, as well as reliable motion control under uncertainty.
- Published
- 2022
136. Understanding the Experiences of LGBTQ High School Students in California by Race/Ethnicity
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WestEd, Cerna, Rebeca, Stern, Alexis, Austin, Greg, Betz, Jenny, Zhang, Gary, and Hashmi, Shazia
- Abstract
To better understand the intersections of race, ethnicity, gender identity, and sexual orientation, this report presents data drawn from the California Healthy Kids Survey (2017-2019) regarding high school students' social and emotional well-being, school experiences, engagement, and their perceptions of the supports they received at school. With over 559,120 student responses, results are disaggregated by race and ethnicity as well as by gender identity and sexual orientation. The report also further disaggregates the data for Asian students by Asian group to reveal differences often masked by aggregate data. Educators can use this report to better understand how the provision of key supports may help mitigate disparities in student outcomes and promote positive student experiences in school for LGBTQ students and students of color.
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- 2021
137. Roadmap for optical tweezers
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Volpe, Giovanni, Maragò, Onofrio M, Rubinsztein-Dunlop, Halina, Pesce, Giuseppe, Stilgoe, Alexander B, Volpe, Giorgio, Tkachenko, Georgiy, Truong, Viet Giang, Chormaic, Síle Nic, Kalantarifard, Fatemeh, Elahi, Parviz, Käll, Mikael, Callegari, Agnese, Marqués, Manuel I, Neves, Antonio AR, Moreira, Wendel L, Fontes, Adriana, Cesar, Carlos L, Saija, Rosalba, Saidi, Abir, Beck, Paul, Eismann, Jörg S, Banzer, Peter, Fernandes, Thales FD, Pedaci, Francesco, Bowen, Warwick P, Vaippully, Rahul, Lokesh, Muruga, Roy, Basudev, Thalhammer-Thurner, Gregor, Ritsch-Marte, Monika, García, Laura Pérez, Arzola, Alejandro V, Castillo, Isaac Pérez, Argun, Aykut, Muenker, Till M, Vos, Bart E, Betz, Timo, Cristiani, Ilaria, Minzioni, Paolo, Reece, Peter J, Wang, Fan, McGloin, David, Ndukaife, Justus C, Quidant, Romain, Roberts, Reece P, Laplane, Cyril, Volz, Thomas, Gordon, Reuven, Hanstorp, Dag, Marmolejo, Javier Tello, Bruce, Graham D, Dholakia, Kishan, Li, Tongcang, Brzobohatý, Oto, Simpson, Stephen H, Zemánek, Pavel, Ritort, Felix, Roichman, Yael, Bobkova, Valeriia, Wittkowski, Raphael, Denz, Cornelia, Kumar, GV Pavan, Foti, Antonino, Donato, Maria Grazia, Gucciardi, Pietro G, Gardini, Lucia, Bianchi, Giulio, Kashchuk, Anatolii V, Capitanio, Marco, Paterson, Lynn, Jones, Philip H, Berg-Sørensen, Kirstine, Barooji, Younes F, Oddershede, Lene B, Pouladian, Pegah, Preece, Daryl, Adiels, Caroline Beck, De Luca, Anna Chiara, Magazzù, Alessandro, Ciriza, David Bronte, Iatì, Maria Antonia, and Swartzlander, Grover A
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Physical Sciences ,Classical Physics ,Nanotechnology ,Bioengineering ,optical tweezers ,optical trapping ,optical manipulation ,Atomic ,molecular and optical physics ,Quantum physics - Abstract
Optical tweezers are tools made of light that enable contactless pushing, trapping, and manipulation of objects, ranging from atoms to space light sails. Since the pioneering work by Arthur Ashkin in the 1970s, optical tweezers have evolved into sophisticated instruments and have been employed in a broad range of applications in the life sciences, physics, and engineering. These include accurate force and torque measurement at the femtonewton level, microrheology of complex fluids, single micro- and nano-particle spectroscopy, single-cell analysis, and statistical-physics experiments. This roadmap provides insights into current investigations involving optical forces and optical tweezers from their theoretical foundations to designs and setups. It also offers perspectives for applications to a wide range of research fields, from biophysics to space exploration.
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- 2023
138. Digital Low-Level RF control system for Accumulator Ring at Advanced Light Source Upgrade Project
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Du, Qiang, Murthy, Shreeharshini, Betz, Michael, Bender, Kevin, Lewis, Wayne, Saqib, Najm Us, Paiagua, Sergio, Doolittle, Lawrence, Serrano, Carlos, Flugstad, Benjamin, and Baptiste, Kenneth
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Physics - Accelerator Physics - Abstract
Currently ALS is undergoing an upgrade to ALSU to produce 100 times brighter soft X-ray light. The LLRF system for Accumulator Ring (AR) is composed of two identical LLRF stations, for driving RF amplifiers. The closed loop RF amplitude and phase stability is measured as $< 0.1\%$ and $< 0.1^\circ$ respectively, using the non-IQ digital down conversion together with analog up/down conversion, under a system-on-chip architecture. Realtime interlock system is implemented with $< 2 \mu$s latency, for machine protection against arc flash and unexpected RF power. Control interfaces are developed to enable PLC-FPGA-EPICS communication to support operation, timing, cavity tuning, and interlock systems. The LLRF system handles alignment of buckets to swap beams between AR and Storage Ring by synchronous phase loop ramping between the two cavities. The system also includes an optimization routine to characterize the loop dynamics and determine optimal operating point using a built-in network analyzer feature. A cavity emulator of 31 kHz bandwidth is integrated with the LLRF system to validate the performance of the overall system being developed., Comment: Talk presented at LLRF Workshop 2022 (LLRF2022, arXiv:2208.13680)
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- 2022
139. A Benchmark Comparison of Imitation Learning-based Control Policies for Autonomous Racing
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Sun, Xiatao, Zhou, Mingyan, Zhuang, Zhijun, Yang, Shuo, Betz, Johannes, and Mangharam, Rahul
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Computer Science - Robotics - Abstract
Autonomous racing with scaled race cars has gained increasing attention as an effective approach for developing perception, planning and control algorithms for safe autonomous driving at the limits of the vehicle's handling. To train agile control policies for autonomous racing, learning-based approaches largely utilize reinforcement learning, albeit with mixed results. In this study, we benchmark a variety of imitation learning policies for racing vehicles that are applied directly or for bootstrapping reinforcement learning both in simulation and on scaled real-world environments. We show that interactive imitation learning techniques outperform traditional imitation learning methods and can greatly improve the performance of reinforcement learning policies by bootstrapping thanks to its better sample efficiency. Our benchmarks provide a foundation for future research on autonomous racing using Imitation Learning and Reinforcement Learning.
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- 2022
140. Local_INN: Implicit Map Representation and Localization with Invertible Neural Networks
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Zang, Zirui, Zheng, Hongrui, Betz, Johannes, and Mangharam, Rahul
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Computer Science - Robotics - Abstract
Robot localization is an inverse problem of finding a robot's pose using a map and sensor measurements. In recent years, Invertible Neural Networks (INNs) have successfully solved ambiguous inverse problems in various fields. This paper proposes a framework that solves the localization problem with INN. We design an INN that provides implicit map representation in the forward path and localization in the inverse path. By sampling the latent space in evaluation, Local\_INN outputs robot poses with covariance, which can be used to estimate the uncertainty. We show that the localization performance of Local\_INN is on par with current methods with much lower latency. We show detailed 2D and 3D map reconstruction from Local\_INN using poses exterior to the training set. We also provide a global localization algorithm using Local\_INN to tackle the kidnapping problem.
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- 2022
141. Bypassing the Simulation-to-reality Gap: Online Reinforcement Learning using a Supervisor
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Evans, Benjamin David, Betz, Johannes, Zheng, Hongrui, Engelbrecht, Herman A., Mangharam, Rahul, and Jordaan, Hendrik W.
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Computer Science - Robotics - Abstract
Deep reinforcement learning (DRL) is a promising method to learn control policies for robots only from demonstration and experience. To cover the whole dynamic behaviour of the robot, DRL training is an active exploration process typically performed in simulation environments. Although this simulation training is cheap and fast, applying DRL algorithms to real-world settings is difficult. If agents are trained until they perform safely in simulation, transferring them to physical systems is difficult due to the sim-to-real gap caused by the difference between the simulation dynamics and the physical robot. In this paper, we present a method of online training a DRL agent to drive autonomously on a physical vehicle by using a model-based safety supervisor. Our solution uses a supervisory system to check if the action selected by the agent is safe or unsafe and ensure that a safe action is always implemented on the vehicle. With this, we can bypass the sim-to-real problem while training the DRL algorithm safely, quickly, and efficiently. We compare our method with conventional learning in simulation and on a physical vehicle. We provide a variety of real-world experiments where we train online a small-scale vehicle to drive autonomously with no prior simulation training. The evaluation results show that our method trains agents with improved sample efficiency while never crashing, and the trained agents demonstrate better driving performance than those trained in simulation., Comment: 7 Pages, 10 Figures, 1 Table
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- 2022
142. Teaching Autonomous Systems Hands-On: Leveraging Modular Small-Scale Hardware in the Robotics Classroom
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Betz, Johannes, Zheng, Hongrui, Zang, Zirui, Sauerbeck, Florian, Walas, Krzysztof, Dimitrov, Velin, Behl, Madhur, Zheng, Rosa, Biswas, Joydeep, Krovi, Venkat, and Mangharam, Rahul
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Computer Science - Robotics ,Electrical Engineering and Systems Science - Systems and Control - Abstract
Although robotics courses are well established in higher education, the courses often focus on theory and sometimes lack the systematic coverage of the techniques involved in developing, deploying, and applying software to real hardware. Additionally, most hardware platforms for robotics teaching are low-level toys aimed at younger students at middle-school levels. To address this gap, an autonomous vehicle hardware platform, called F1TENTH, is developed for teaching autonomous systems hands-on. This article describes the teaching modules and software stack for teaching at various educational levels with the theme of "racing" and competitions that replace exams. The F1TENTH vehicles offer a modular hardware platform and its related software for teaching the fundamentals of autonomous driving algorithms. From basic reactive methods to advanced planning algorithms, the teaching modules enhance students' computational thinking through autonomous driving with the F1TENTH vehicle. The F1TENTH car fills the gap between research platforms and low-end toy cars and offers hands-on experience in learning the topics in autonomous systems. Four universities have adopted the teaching modules for their semester-long undergraduate and graduate courses for multiple years. Student feedback is used to analyze the effectiveness of the F1TENTH platform. More than 80% of the students strongly agree that the hardware platform and modules greatly motivate their learning, and more than 70% of the students strongly agree that the hardware-enhanced their understanding of the subjects. The survey results show that more than 80% of the students strongly agree that the competitions motivate them for the course., Comment: 15 pages, 12 figures, 3 tables
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- 2022
143. Game-theoretic Objective Space Planning
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Zheng, Hongrui, Zhuang, Zhijun, Betz, Johannes, and Mangharam, Rahul
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Computer Science - Robotics ,Computer Science - Artificial Intelligence - Abstract
Generating competitive strategies and performing continuous motion planning simultaneously in an adversarial setting is a challenging problem. In addition, understanding the intent of other agents is crucial to deploying autonomous systems in adversarial multi-agent environments. Existing approaches either discretize agent action by grouping similar control inputs, sacrificing performance in motion planning, or plan in uninterpretable latent spaces, producing hard-to-understand agent behaviors. Furthermore, the most popular policy optimization frameworks do not recognize the long-term effect of actions and become myopic. This paper proposes an agent action discretization method via abstraction that provides clear intentions of agent actions, an efficient offline pipeline of agent population synthesis, and a planning strategy using counterfactual regret minimization with function approximation. Finally, we experimentally validate our findings on scaled autonomous vehicles in a head-to-head racing setting. We demonstrate that using the proposed framework significantly improves learning, improves the win rate against different opponents, and the improvements can be transferred to unseen opponents in an unseen environment., Comment: Submitted to 2024 International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2024)
- Published
- 2022
144. Supervised Contrastive Learning to Classify Paranasal Anomalies in the Maxillary Sinus
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Bhattacharya, Debayan, Becker, Benjamin Tobias, Behrendt, Finn, Bengs, Marcel, Beyersdorff, Dirk, Eggert, Dennis, Petersen, Elina, Jansen, Florian, Petersen, Marvin, Cheng, Bastian, Betz, Christian, Schlaefer, Alexander, and Hoffmann, Anna Sophie
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Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning - Abstract
Using deep learning techniques, anomalies in the paranasal sinus system can be detected automatically in MRI images and can be further analyzed and classified based on their volume, shape and other parameters like local contrast. However due to limited training data, traditional supervised learning methods often fail to generalize. Existing deep learning methods in paranasal anomaly classification have been used to diagnose at most one anomaly. In our work, we consider three anomalies. Specifically, we employ a 3D CNN to separate maxillary sinus volumes without anomalies from maxillary sinus volumes with anomalies. To learn robust representations from a small labelled dataset, we propose a novel learning paradigm that combines contrastive loss and cross-entropy loss. Particularly, we use a supervised contrastive loss that encourages embeddings of maxillary sinus volumes with and without anomaly to form two distinct clusters while the cross-entropy loss encourages the 3D CNN to maintain its discriminative ability. We report that optimising with both losses is advantageous over optimising with only one loss. We also find that our training strategy leads to label efficiency. With our method, a 3D CNN classifier achieves an AUROC of 0.85 while a 3D CNN classifier optimised with cross-entropy loss achieves an AUROC of 0.66.
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- 2022
145. Speed Function for Biased Random Walks with Traps
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Betz, Volker, Meiners, Matthias, and Tomic, Ivana
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Mathematics - Probability ,Mathematical Physics ,60K37, 60F15 - Abstract
We consider a biased nearest-neighbor random walk on $\Z$ which at each step is trapped for some random time with random, site-dependent mean. We derive a simple formula for the speed function in terms of the model parameters., Comment: 10 pages, 3 figures
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- 2022
146. Anytime bottom-up rule learning for large-scale knowledge graph completion
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Meilicke, Christian, Chekol, Melisachew Wudage, Betz, Patrick, Fink, Manuel, and Stuckeschmidt, Heiner
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- 2024
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147. Division without Duress Yields High Levels of Success
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Isaacson, Kristi J. and Betz-Cahill, Christina
- Abstract
Explore the impact technology has on mathematical identity and agency when students use mathematical action technology to engage in cycles of proof and support case-based reasoning. This article showcases a mathematics task used in a fourth-grade class that allowed students to develop their conceptual understanding of division. The authors designed this mathematical challenge to address the content standard and create a learning experience during which all students meet the following objectives: (a) make conjectures about relationships among the divisor, dividend, and quotient; and (b) explain and justify their conjectures.
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- 2023
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148. Die Schädelbasischirurgie im deutschen DRG-System – Neue Zuordnung wichtiger Prozeduren
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Sommer, F., Hoffmann, T. K., Jäckel, M., Gerlach, R., Schwager, K., Deitmer, T., and Betz, C. S.
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- 2023
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149. Governments as borrowers and regulators
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Betz, Timm and Pond, Amy
- Published
- 2023
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150. Multimodal in-vehicle lighting system increases daytime light exposure and alertness in truck drivers under Arctic winter conditions
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Roland F. J. Popp, Julia Ottersbach, Thomas C. Wetter, Sebastian Schüler, Siegfried Rothe, Daniel Betz, Siegmund Staggl, and Markus Canazei
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Medicine ,Science - Abstract
Abstract Drowsiness while driving negatively impacts road safety, especially in truck drivers. The present study investigated the feasibility and alerting effects of a daylight-supplementing in-truck lighting system (DS) providing short-wavelength enriched light before, during, and after driving. In a within-participants design, eight truck drivers drove a fully-loaded truck under wintry Scandinavian conditions (low daylight levels) with a DS or placebo system for five days. Subjective and objective measures of alertness were recorded several times daily, and evening melatonin levels were recorded three times per study condition. DS significantly increased daytime light exposure without causing negative side effects while driving. In addition, no negative carry-over effects were observed on evening melatonin and sleepiness levels or on nighttime sleep quality. Moreover, objective alertness (i.e., psychomotor vigilance) before and after driving was significantly improved by bright light exposure. This effect was accompanied by improved subjective alertness in the morning. This field study demonstrated that DS was able to increase daytime light exposure in low-daylight conditions and to improve alertness in truck drivers before and after driving (e.g., during driving rest periods). Further studies are warranted to investigate the effects of daylight-supplementing in-cabin lighting on driving performance and road safety measures.
- Published
- 2024
- Full Text
- View/download PDF
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