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129,007 results on '"Ritter A"'

64. White coat syndrome and its variations: differences and clinical impact

Author

Pioli MR, Ritter AMV, de Faria AP, and Modolo R

Subjects
Hypertension, white coat effect, white coat hypertension, masked hypertension, cardiovascular risk, Internal medicine, RC31-1245
Abstract

Mariana R Pioli,1 Alessandra MV Ritter,1 Ana Paula de Faria,1 Rodrigo Modolo1,2 1Department of Pharmacology, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Sao Paulo, Brazil; 2Laboratory of Cardiac Catheterization, Department of Internal Medicine, Cardiology Division, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Sao Paulo, Brazil Abstract: Hypertension is closely linked to increased cardiovascular risk and development of target organ damage (TOD). Therefore, proper clinical follow-up and treatment of hypertensive subjects are mandatory. A great number of individuals present a variation on blood pressure (BP) levels when they are assessed either in the office or in the out-of-office settings. This phenomenon is defined as white coat syndrome – a change in BP levels due to the presence of a physician or other health professional. In this context, the term “white coat syndrome” may refer to three important and different clinical conditions: 1) white coat hypertension, 2) white coat effect, and 3) masked hypertension. The development of TOD and the increased cardiovascular risk play different roles in these specific subgroups of white coat syndrome. Correct diagnose and clinical guidance are essential to improve the prognosis of these patients. The aim of this review was to elucidate contemporary aspects of these types of white coat syndrome on general and hypertensive population. Keywords: hypertension, white coat effect, white coat hypertension, masked hypertension, cardiovascular risk

Published
2018

65. Quantitative investigation of quantum emitter yield in drop-casted hexagonal boron nitride nanoflakes

Author

Kretzschmar, Tom, Ritter, Sebastian, Kumar, Anand, Vogl, Tobias, Eilenberger, Falk, and Schmidt, Falko

Subjects
Physics - Optics, Quantum Physics
Abstract

Single photon emitters (SPEs) are a key component for their use as pure photon source in quantum technologies. In this study, we investigate the generation of SPEs from drop-casted hexagonal boron nitride (hBN) nanoflakes, examining the influence of the immersion solution and the source of hBN. We show that, depending on the utilized supplier and solution the number and quality of the emitters changes. We perform a comprehensive optical characterization of the deposited nanoflakes to assess the quality of the generated SPEs. We show quantitative data on SPE yields, highlighting significant variations among solvents and different sources of hBN. This holds particular significance for employing drop-casted nanoflakes as SPE sources in quantum communication, sensing, and imaging. Our method is easily expandable to all kinds of surfaces and can be done without requiring complex fabrication steps and equipment, thus providing the necessary scalability required for industrial quantum applications., Comment: 29 pages, 15 figures

Published
2024

66. GraphMatch: Subgraph Query Processing on FPGAs

Author

Dann, Jonas, Götz, Tobias, Ritter, Daniel, Giceva, Jana, and Fröning, Holger

Subjects
Computer Science - Databases, Computer Science - Hardware Architecture
Abstract

Efficiently finding subgraph embeddings in large graphs is crucial for many application areas like biology and social network analysis. Set intersections are the predominant and most challenging aspect of current join-based subgraph query processing systems for CPUs. Previous work has shown the viability of utilizing FPGAs for acceleration of graph and join processing. In this work, we propose GraphMatch, the first genearl-purpose stand-alone subgraph query processing accelerator based on worst-case optimal joins (WCOJ) that is fully designed for modern, field programmable gate array (FPGA) hardware. For efficient processing of various graph data sets and query graph patterns, it leverages a novel set intersection approach, called AllCompare, tailor-made for FPGAs. We show that this set intersection approach efficiently solves multi-set intersections in subgraph query processing, superior to CPU-based approaches. Overall, GraphMatch achieves a speedup of over 2.68x and 5.16x, compared to the state-of-the-art systems GraphFlow and RapidMatch, respectively.

Published
2024

67. NEO-BENCH: Evaluating Robustness of Large Language Models with Neologisms

Author

Zheng, Jonathan, Ritter, Alan, and Xu, Wei

Subjects
Computer Science - Computation and Language
Abstract

The performance of Large Language Models (LLMs) degrades from the temporal drift between data used for model training and newer text seen during inference. One understudied avenue of language change causing data drift is the emergence of neologisms -- new word forms -- over time. We create a diverse resource of recent English neologisms by using several popular collection methods. We analyze temporal drift using neologisms by comparing sentences containing new words with near-identical sentences that replace neologisms with existing substitute words. Model performance is nearly halved in machine translation when a single neologism is introduced in a sentence. Motivated by these results, we construct a benchmark to evaluate LLMs' ability to generalize to neologisms with various natural language understanding tasks and model perplexity. Models with later knowledge cutoff dates yield lower perplexities and perform better in downstream tasks. LLMs are also affected differently based on the linguistic origins of words, indicating that neologisms are complex for static LLMs to address. We will release our benchmark and code for reproducing our experiments., Comment: accepted to ACL 2024 main conference, 9 pages

Published
2024

68. Introducing RSESS: An Open Source Enumerative Sphere Shaping Implementation Coded in Rust

Author

Ritter, Frederik, Rode, Andrej, and Schmalen, Laurent

Subjects
Computer Science - Information Theory, Electrical Engineering and Systems Science - Signal Processing
Abstract

In this work, we present an open-source implementation of the enumerative sphere shaping (ESS) algorithm used for probabilistic constellation shaping (PCS). PCS aims at closing the shaping gap caused by using uniformly distributed modulation symbols in channels for which information theory shows non-uniformly distributed signaling to be optimal. ESS is one such PCS algorithm that sets itself apart as it operates on a trellis representation of a subset of the possible symbol sequences. ESS leads to an empirical distribution of the symbols that closely approximates the optimal distribution for the additive white Gaussian noise (AWGN) channel. We provide an open-source implementation of this algorithm in the compiled language Rust, as well as Python bindings with which our Rust code can be called in a regular Python script. We also compare simulation results on the AWGN channel using our implementation with previous works on this topic., Comment: Accepted for presentation at the 13th GNU Radio conference (GRCon)

Published
2024

69. Stanceosaurus 2.0: Classifying Stance Towards Russian and Spanish Misinformation

Author

Lavrouk, Anton, Ligon, Ian, Naous, Tarek, Zheng, Jonathan, Ritter, Alan, and Xu, Wei

Subjects
Computer Science - Computation and Language, Computer Science - Computers and Society, Computer Science - Machine Learning, Computer Science - Social and Information Networks
Abstract

The Stanceosaurus corpus (Zheng et al., 2022) was designed to provide high-quality, annotated, 5-way stance data extracted from Twitter, suitable for analyzing cross-cultural and cross-lingual misinformation. In the Stanceosaurus 2.0 iteration, we extend this framework to encompass Russian and Spanish. The former is of current significance due to prevalent misinformation amid escalating tensions with the West and the violent incursion into Ukraine. The latter, meanwhile, represents an enormous community that has been largely overlooked on major social media platforms. By incorporating an additional 3,874 Spanish and Russian tweets over 41 misinformation claims, our objective is to support research focused on these issues. To demonstrate the value of this data, we employed zero-shot cross-lingual transfer on multilingual BERT, yielding results on par with the initial Stanceosaurus study with a macro F1 score of 43 for both languages. This underlines the viability of stance classification as an effective tool for identifying multicultural misinformation., Comment: WNUT2024

Published
2024

70. Constrained Decoding for Cross-lingual Label Projection

Author

Le, Duong Minh, Chen, Yang, Ritter, Alan, and Xu, Wei

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

Zero-shot cross-lingual transfer utilizing multilingual LLMs has become a popular learning paradigm for low-resource languages with no labeled training data. However, for NLP tasks that involve fine-grained predictions on words and phrases, the performance of zero-shot cross-lingual transfer learning lags far behind supervised fine-tuning methods. Therefore, it is common to exploit translation and label projection to further improve the performance by (1) translating training data that is available in a high-resource language (e.g., English) together with the gold labels into low-resource languages, and/or (2) translating test data in low-resource languages to a high-source language to run inference on, then projecting the predicted span-level labels back onto the original test data. However, state-of-the-art marker-based label projection methods suffer from translation quality degradation due to the extra label markers injected in the input to the translation model. In this work, we explore a new direction that leverages constrained decoding for label projection to overcome the aforementioned issues. Our new method not only can preserve the quality of translated texts but also has the versatility of being applicable to both translating training and translating test data strategies. This versatility is crucial as our experiments reveal that translating test data can lead to a considerable boost in performance compared to translating only training data. We evaluate on two cross-lingual transfer tasks, namely Named Entity Recognition and Event Argument Extraction, spanning 20 languages. The results demonstrate that our approach outperforms the state-of-the-art marker-based method by a large margin and also shows better performance than other label projection methods that rely on external word alignment., Comment: Accepted at ICLR 2024

Published
2024

71. Doping Liquid Argon with Xenon in ProtoDUNE Single-Phase: Effects on Scintillation Light

Author

DUNE Collaboration, Abud, A. Abed, Abi, B., Acciarri, R., Acero, M. A., Adames, M. R., Adamov, G., Adamowski, M., Adams, D., Adinolfi, M., Adriano, C., Aduszkiewicz, A., Aguilar, J., Aimard, B., Akbar, F., Allison, K., Monsalve, S. Alonso, Alrashed, M., Alton, A., Alvarez, R., Es-sghir, H. Amar, Amedo, P., Anderson, J., Andrade, D. A., Andreopoulos, C., Andreotti, M., Andrews, M. P., Andrianala, F., Andringa, S., Anfimov, N., Ankowski, A., Antoniassi, M., Antonova, M., Antoshkin, A., Aranda-Fernandez, A., Arellano, L., Diaz, E. Arrieta, Arroyave, M. A., Asaadi, J., Ashkenazi, A., Asquith, L., Atkin, E., Auguste, D., Aurisano, A., Aushev, V., Autiero, D., Azfar, F., Back, A., Back, H., Back, J. J., Bagaturia, I., Bagby, L., Balashov, N., Balasubramanian, S., Baldi, P., Baldini, W., Baller, B., Bambah, B., Banerjee, R., Barao, F., Barenboim, G., BarhamAlzás, P., Barker, G. J., Barkhouse, W., Barr, G., Monarca, J. Barranco, Barros, A., Barros, N., Barrow, D., Barrow, J. L., Basharina-Freshville, A., Bashyal, A., Basque, V., Batchelor, C., Bathe-Peters, L., Battat, J. B. R., Battisti, F., Bay, F., Bazetto, M. C. Q., Alba, J. L. L. Bazo, Beacom, J. F., Bechetoille, E., Behera, B., Belchior, E., Bell, G., Bellantoni, L., Bellettini, G., Bellini, V., Beltramello, O., Benekos, N., Montiel, C. Benitez, Benjamin, D., Neves, F. Bento, Berger, J., Berkman, S., Bernardini, P., Bersani, A., Bertolucci, S., Betancourt, M., Rodríguez, A. Betancur, Bevan, A., Bezawada, Y., Bezerra, A. T., Bezerra, T. J., Bhat, A., Bhatnagar, V., Bhatt, J., Bhattacharjee, M., Bhattacharya, M., Bhuller, S., Bhuyan, B., Biagi, S., Bian, J., Biery, K., Bilki, B., Bishai, M., Bitadze, A., Blake, A., Blaszczyk, F. D., Blazey, G. C., Blucher, E., Boissevain, J., Bolognesi, S., Bolton, T., Bomben, L., Bonesini, M., Bonilla-Diaz, C., Bonini, F., Booth, A., Boran, F., Bordoni, S., Merlo, R. Borges, Borkum, A., Bostan, N., Bracinik, J., Braga, D., Brahma, B., Brailsford, D., Bramati, F., Branca, A., Brandt, A., Bremer, J., Brew, C., Brice, S. J., Brio, V., Brizzolari, C., Bromberg, C., Brooke, J., Bross, A., Brunetti, G., Brunetti, M., Buchanan, N., Budd, H., Buergi, J., Burgardt, D., Butchart, S., V., G. Caceres, Cagnoli, I., Cai, T., Calabrese, R., Calcutt, J., Calin, M., Calivers, L., Calvo, E., Caminata, A., Campanelli, W., Benitez, A. Campos, Canci, N., Capó, J., Caracas, I., Caratelli, D., Carber, D., Carceller, J. M., Carini, G., Carlus, B., Carneiro, M. F., Carniti, P., Terrazas, I. Caro, Carranza, H., Carrara, N., Carroll, L., Carroll, T., Carter, A., Casazza, D., Forero, J. F. Castaño, Castaño, F. A., Castillo, A., Castromonte, C., Catano-Mur, E., Cattadori, C., Cavalier, F., Cavanna, F., Centro, S., Cerati, G., Cervelli, A., Villanueva, A. Cervera, Chakraborty, K., Chalifour, M., Chappell, A., Charitonidis, N., Chatterjee, A., Chen, H., Chen, M., Chen, W. C., Chen, Y., Chen-Wishart, Z., Cherdack, D., Chi, C., Chirco, R., Chitirasreemadam, N., Cho, K., Choate, S., Chokheli, D., Chong, P. S., Chowdhury, B., Christian, D., Chukanov, A., Chung, M., Church, E., Cicala, M. F., Cicerchia, M., Cicero, V., Ciolini, R., Clair, J., Clarke, P., Cline, G., Coan, T. E., Cocco, A. G., Coelho, J. A. B., Cohen, A., Collot, J., Conley, E., Conrad, J. M., Convery, M., Cooke, P., Copello, S., Cova, P., Cox, C., Cremaldi, L., Cremonesi, L., Crespo-Anadón, J. I., Crisler, M., Cristaldo, E., Crnkovic, J., Crone, G., Cross, R., Cudd, A., Cuesta, C., Cui, Y., Cussans, D., Dai, J., Dalager, O., Dallavalle, R., da Motta, H., Dar, Z. A., Darby, R., Peres, L. Da Silva, David, Q., Davies, G. S., Davini, S., Dawson, J., De Aguiar, R., De Almeida, P., Debbins, P., De Bonis, I., Decowski, M. P., de Gouvêa, A., De Holanda, P. C., Astiz, I. L. De Icaza, De Jong, P., De la Torre, A., Delbart, A., Delepine, D., Delgado, M., Dell'Acqua, A., Monache, G. Delle, Delmonte, N., De Lurgio, P., Demario, R., Neto, J. R. T. de Mello, DeMuth, D. M., Dennis, S., Densham, C., Denton, P., Deptuch, G. W., De Roeck, A., De Romeri, V., Detje, J. P., Devine, J., Dharmapalan, R., Dias, M., Díaz, J. S., Díaz, F., Di Capua, F., Di Domenico, A., Di Domizio, S., Di Falco, S., Di Giulio, L., Ding, P., Di Noto, L., Diociaiuti, E., Distefano, C., Diurba, R., Diwan, M., Djurcic, Z., Doering, D., Dolan, S., Dolek, F., Dolinski, M. J., Domenici, D., Domine, L., Donati, S., Donon, Y., Doran, S., Douglas, D., Doyle, T. A., Dragone, A., Drielsma, F., Duarte, L., Duchesneau, D., Duffy, K., Dugas, K., Dunne, P., Dutta, B., Duyang, H., Dvornikov, O., Dwyer, D. A., Dyshkant, A. S., Dytman, S., Eads, M., Earle, A., Edayath, S., Edmunds, D., Eisch, J., Englezos, P., Ereditato, A., Erjavec, T., Escobar, C. O., Evans, J. J., Ewart, E., Ezeribe, A. C., Fahey, K., Fajt, L., Falcone, A., Fani', M., Farnese, C., Farzan, Y., Fedoseev, D., Felix, J., Feng, Y., Fernandez-Martinez, E., Ferraro, F., Ferry, G., Fields, L., Filip, P., Filkins, A., Filthaut, F., Fine, R., Fiorillo, G., Fiorini, M., Fogarty, S., Foreman, W., Fowler, J., Franc, J., Francis, K., Franco, D., Franklin, J., Freeman, J., Fried, J., Friedland, A., Fuess, S., Furic, I. K., Furman, K., Furmanski, A. P., Gabrielli, A., Gago, A. M., Galizzi, F., Gallagher, H., Gallas, A., Gallice, N., Galymov, V., Gamberini, E., Gamble, T., Ganacim, F., Gandhi, R., Ganguly, S., Gao, F., Gao, S., Garcia-Gamez, D., García-Peris, M. Á., Gardim, F., Gardiner, S., Gastler, D., Gauch, A., Gauvreau, J., Gauzzi, P., Ge, G., Geffroy, N., Gelli, B., Gent, S., Gerlach, L., Ghorbani-Moghaddam, Z., Giammaria, P., Giammaria, T., Gibin, D., Gil-Botella, I., Gilligan, S., Gioiosa, A., Giovannella, S., Girerd, C., Giri, A. K., Giugliano, C., Giusti, V., Gnani, D., Gogota, O., Gollapinni, S., Gollwitzer, K., Gomes, R. A., Bermeo, L. V. Gomez, Fajardo, L. S. Gomez, Gonnella, F., Gonzalez-Diaz, D., Gonzalez-Lopez, M., Goodman, M. C., Goswami, S., Gotti, C., Goudeau, J., Goudzovski, E., Grace, C., Gramellini, E., Gran, R., Granados, E., Granger, P., Grant, C., Gratieri, D. R., Grauso, G., Green, P., Greenberg, S., Greer, J., Griffith, W. C., Groetschla, F. T., Grzelak, K., Gu, W., Guarino, V., Guarise, M., Guenette, R., Guerard, E., Guerzoni, M., Guffanti, D., Guglielmi, A., Guo, B., Guo, Y., Gupta, A., Gupta, V., Gurung, G., Gutierrez, D., Guzowski, P., Guzzo, M. M., Gwon, S., Haaf, K., Habig, A., Hadavand, H., Haenni, R., Hagaman, L., Hahn, A., Haiston, J., Hakenmueller, J., Hamernik, T., Hamilton, P., Hancock, J., Happacher, F., Harris, D. A., Hartnell, J., Hartnett, T., Harton, J., Hasegawa, T., Hasnip, C., Hatcher, R., Hayrapetyan, K., Hays, J., Hazen, E., He, M., Heavey, A., Heeger, K. M., Heise, J., Henry, S., Morquecho, M. A. Hernandez, Herner, K., Hewes, V., Higuera, A., Hilgenberg, C., Hillier, S. J., Himmel, A., Hinkle, E., Hirsch, L. R., Ho, J., Hoff, J., Holin, A., Holvey, T., Hoppe, E., Horton-Smith, G. A., Hostert, M., Houdy, T., Howard, B., Howell, R., Hristova, I., Hronek, M. S., Huang, J., Huang, R. G., Hulcher, Z., Ibrahim, M., Iles, G., Ilic, N., Iliescu, A. M., Illingworth, R., Ingratta, G., Ioannisian, A., Irwin, B., Isenhower, L., Oliveira, M. Ismerio, Itay, R., Jackson, C. M., Jain, V., James, E., Jang, W., Jargowsky, B., Jena, D., Ji, X., Jiang, C., Jiang, J., Jiang, L., Jipa, A., Joaquim, F. R., Johnson, W., Jones, B., Jones, R., Fernández, D. José, Jovancevic, N., Judah, M., Jung, C. K., Junk, T., Jwa, Y., Kabirnezhad, M., Kaboth, A. C., Kadenko, I., Kakorin, I., Kalitkina, A., Kalra, D., Kamiya, F., Kandemir, M., Kaplan, D. M., Karagiorgi, G., Karaman, G., Karcher, A., Karyotakis, Y., Kasai, S., Kasetti, S. P., Kashur, L., Katsioulas, I., Kauther, A., Kazaryan, N., Ke, L., Kearns, E., Keener, P. T., Kelly, K. J., Kemp, E., Kemularia, O., Kermaidic, Y., Ketchum, W., Kettell, S. H., Khabibullin, M., Khan, N., Khvedelidze, A., Kim, D., Kim, J., King, B., Kirby, B., Kirby, M., Klein, J., Kleykamp, J., Klustova, A., Kobilarcik, T., Koch, L., Koehler, K., Koerner, L. W., Koh, D. H., Kolupaeva, L., Korablev, D., Kordosky, M., Kosc, T., Kose, U., Kostelecký, V. A., Kothekar, K., Kotler, I., Kovalcuk, M., Kozhukalov, V., Krah, W., Kralik, R., Kramer, M., Kreczko, L., Krennrich, F., Kreslo, I., Kroupova, T., Kubota, S., Kubu, M., Kudenko, Y., Kudryavtsev, V. A., Kuhlmann, S., Kumar, J., Kumar, P., Kumaran, S., Kunze, P., Kunzmann, J., Kuravi, R., Kurita, N., Kuruppu, C., Kus, V., Kutter, T., Kvasnicka, J., Labree, T., Lackey, T., Lambert, A., Land, B. J., Lane, C. E., Lane, N., Lang, K., Langford, T., Langstaff, M., Lanni, F., Lantwin, O., Larkin, J., Lasorak, P., Last, D., Laudrain, A., Laundrie, A., Laurenti, G., Lavaut, E., Lawrence, A., Laycock, P., Lazanu, I., Lazzaroni, M., Le, T., Leardini, S., Learned, J., LeCompte, T., Lee, C., Legin, V., Miotto, G. Lehmann, Lehnert, R., de Oliveira, M. A. Leigui, Leitner, M., Silverio, D. Leon, Lepin, L. M., Li, J. -Y., Li, S. W., Li, Y., Liao, H., Lin, C. S., Lindebaum, D., Lineros, R. A., Ling, J., Lister, A., Littlejohn, B. R., Liu, H., Liu, J., Liu, Y., Lockwitz, S., Lokajicek, M., Lomidze, I., Long, K., Lopes, T. V., Lopez, J., de Rego, I. López, March, N. López, Lord, T., LoSecco, J. M., Louis, W. C., Sanchez, A. Lozano, Lu, X. -G., Luk, K. B., Lunday, B., Luo, X., Luppi, E., Maalmi, J., MacFarlane, D., Machado, A. A., Machado, P., Macias, C. T., Macier, J. R., MacMahon, M., Maddalena, A., Madera, A., Madigan, P., Magill, S., Magueur, C., Mahn, K., Maio, A., Major, A., Majumdar, K., Man, M., Mandujano, R. C., Maneira, J., Manly, S., Mann, A., Manolopoulos, K., Plata, M. Manrique, Corchado, S. Manthey, Manyam, V. N., Marchan, M., Marchionni, A., Marciano, W., Marfatia, D., Mariani, C., Maricic, J., Marinho, F., Marino, A. D., Markiewicz, T., Marques, F. Das Chagas, Marsden, D., Marshak, M., Marshall, C. M., Marshall, J., Martín-Albo, J., Martinez, N., Caicedo, D. A. Martinez, López, F. Martínez, Miravé, P. Martínez, Martynenko, S., Mascagna, V., Massari, C., Mastbaum, A., Matichard, F., Matsuno, S., Matteucci, G., Matthews, J., Mauger, C., Mauri, N., Mavrokoridis, K., Mawby, I., Mazza, R., Mazzacane, A., McAskill, T., McConkey, N., McFarland, K. S., McGrew, C., McNab, A., Meazza, L., Meddage, V. C. N., Mehta, B., Mehta, P., Melas, P., Mena, O., Mendez, H., Mendez, P., Méndez, D. P., Menegolli, A., Meng, G., Messier, M. D., Metallo, S., Metcalf, J., Metcalf, W., Mewes, M., Meyer, H., Miao, T., Miccoli, A., Michna, G., Mikola, V., Milincic, R., Miller, G., Miller, W., Mineev, O., Minotti, A., Miralles, L., Miranda, O. G., Mironov, C., Miryala, S., Miscetti, S., Mishra, C. S., Mishra, S. R., Mislivec, A., Mitchell, M., Mladenov, D., Mocioiu, I., Mogan, A., Moggi, N., Mohanta, R., Mohayai, T. A., Mokhov, N., Molina, J., Bueno, L. Molina, Montagna, E., Montanari, A., Montanari, C., Montanari, D., Montanino, D., Zetina, L. M. Montaño, Mooney, M., Moor, A. F., Moore, Z., Moreno, D., Moreno-Palacios, O., Morescalchi, L., Moretti, D., Moretti, R., Morris, C., Mossey, C., Mote, M., Moura, C. A., Mouster, G., Mu, W., Mualem, L., Mueller, J., Muether, M., Muheim, F., Muir, A., Mulhearn, M., Munford, D., Munteanu, L. J., Muramatsu, H., Muraz, J., Murphy, M., Murphy, T., Muse, J., Mytilinaki, A., Nachtman, J., Nagai, Y., Nagu, S., Nalbandyan, M., Nandakumar, R., Naples, D., Narita, S., Nath, A., Navrer-Agasson, A., Nayak, N., Nebot-Guinot, M., Nehm, A., Nelson, J. K., Neogi, O., Nesbit, J., Nessi, M., Newbold, D., Newcomer, M., Nichol, R., Nicolas-Arnaldos, F., Nikolica, A., Nikolov, J., Niner, E., Nishimura, K., Norman, A., Norrick, A., Novella, P., Nowak, J. A., Oberling, M., Ochoa-Ricoux, J. P., Oh, S., Oh, S. B., Olivier, A., Olshevskiy, A., Olson, T., Onel, Y., Onishchuk, Y., Oranday, A., Osbiston, M., Vélez, J. A. Osorio, Ormachea, L. Otiniano, Ott, J., Pagani, L., Palacio, G., Palamara, O., Palestini, S., Paley, J. M., Pallavicini, M., Palomares, C., Pan, S., Panda, P., Vazquez, W. Panduro, Pantic, E., Paolone, V., Papadimitriou, V., Papaleo, R., Papanestis, A., Papoulias, D., Paramesvaran, S., Paris, A., Parke, S., Parozzi, E., Parsa, S., Parsa, Z., Parveen, S., Parvu, M., Pasciuto, D., Pascoli, S., Pasqualini, L., Pasternak, J., Patrick, C., Patrizii, L., Patterson, R. B., Patzak, T., Paudel, A., Paulucci, L., Pavlovic, Z., Pawloski, G., Payne, D., Pec, V., Pedreschi, E., Peeters, S. J. M., Perez, A. Pena, Pennacchio, E., Penzo, A., Peres, O. L. G., Gonzalez, Y. F. Perez, Pérez-Molina, L., Pernas, C., Perry, J., Pershey, D., Pessina, G., Petrillo, G., Petta, C., Petti, R., Pia, V., Pickering, L., Pietropaolo, F., Pimentel, V. L., Pinaroli, G., Pinchault, J., Plows, K., Plunkett, R., Pollack, C., Pollman, T., Pompa, F., Pons, X., Poonthottathil, N., Poppi, F., Pordes, S., Porter, J., Potekhin, M., Potenza, R., Pozimski, J., Pozzato, M., Prakash, S., Prakash, T., Pratt, C., Prest, M., Psihas, F., Pugnere, D., Qian, X., Raaf, J. L., Radeka, V., Rademacker, J., Radics, B., Rafique, A., Raguzin, E., Rai, M., Rajaoalisoa, M., Rakhno, I., Rakotondravohitra, L., Ralte, L., Delgado, M. A. Ramirez, Ramson, B., Rappoldi, A., Raselli, G., Ratoff, P., Ray, R., Razafinime, H., Rea, E. M., Real, J. S., Rebel, B., Rechenmacher, R., Reggiani-Guzzo, M., Reichenbacher, J., Reitzner, S. D., Sfar, H. Rejeb, Renner, E., Renshaw, A., Rescia, S., Resnati, F., Restrepo, D., Reynolds, C., Ribas, M., Riboldi, S., Riccio, C., Riccobene, G., Ricol, J. S., Rigan, M., Rincón, E. V., Ritchie-Yates, A., Ritter, S., Rivera, D., Rivera, R., Robert, A., Rocha, J. L. Rocabado, Rochester, L., Roda, M., Rodrigues, P., Alonso, M. J. Rodriguez, Rondon, J. Rodriguez, Rosauro-Alcaraz, S., Rosier, P., Ross, D., Rossella, M., Rossi, M., Ross-Lonergan, M., Roy, N., Roy, P., Rubbia, C., Ruggeri, A., Ferreira, G. Ruiz, Russell, B., Ruterbories, D., Rybnikov, A., Saa-Hernandez, A., Saakyan, R., Sacerdoti, S., Sahoo, S. K., Sahu, N., Sala, P., Samios, N., Samoylov, O., Sanchez, M. C., Bravo, A. Sánchez, Sanchez-Lucas, P., Sandberg, V., Sanders, D. A., Sankey, D., Santoro, D., Saoulidou, N., Sapienza, P., Sarasty, C., Sarcevic, I., Sarra, I., Savage, G., Savinov, V., Scanavini, G., Scaramelli, A., Scarff, A., Schefke, T., Schellman, H., Schifano, S., Schlabach, P., Schmitz, D., Schneider, A. W., Scholberg, K., Schukraft, A., Schuld, B., Segreto, E., Selyunin, A., Senise, C. R., Sensenig, J., Shaevitz, M. H., Shanahan, P., Sharma, P., Kumar, R., Shaw, K., Shaw, T., Shchablo, K., Shepherd-Themistocleous, C., Sheshukov, A., Shi, W., Shin, S., Shivakoti, S., Shoemaker, I., Shooltz, D., Shrock, R., Siddi, B., Silber, J., Simard, L., Sinclair, J., Sinev, G., Singh, Jaydip, Singh, J., Singh, L., Singh, P., Singh, V., Chauhan, S. Singh, Sipos, R., Sironneau, C., Sirri, G., Siyeon, K., Skarpaas, K., Smedley, J., Smith, E., Smith, J., Smith, P., Smolik, J., Smy, M., Snape, M., Snider, E. L., Snopok, P., Snowden-Ifft, D., Nunes, M. Soares, Sobel, H., Soderberg, M., Sokolov, S., Salinas, C. J. Solano, Söldner-Rembold, S., Soleti, S. R., Solomey, N., Solovov, V., Sondheim, W. E., Sorel, M., Sotnikov, A., Soto-Oton, J., Sousa, A., Soustruznik, K., Spinella, F., Spitz, J., Spooner, N. J. C., Spurgeon, K., Stalder, D., Stancari, M., Stanco, L., Steenis, J., Stein, R., Steiner, H. M., Lisbôa, A. F. Steklain, Stepanova, A., Stewart, J., Stillwell, B., Stock, J., Stocker, F., Stokes, T., Strait, M., Strauss, T., Strigari, L., Stuart, A., Suarez, J. G., Subash, J., Surdo, A., Suter, L., Sutera, C. M., Sutton, K., Suvorov, Y., Svoboda, R., Swain, S. K., Szczerbinska, B., Szelc, A. M., Sztuc, A., Taffara, A., Talukdar, N., Tamara, J., Tanaka, H. A., Tang, S., Taniuchi, N., Casanova, A. M. Tapia, Oregui, B. Tapia, Tapper, A., Tariq, S., Tarpara, E., Tatar, E., Tayloe, R., Tedeschi, D., Teklu, A. M., Vidal, J. Tena, Tennessen, P., Tenti, M., Terao, K., Terranova, F., Testera, G., Thakore, T., Thea, A., Thiebault, A., Thompson, A., Thorn, C., Timm, S. C., Tiras, E., Tishchenko, V., Todorović, N., Tomassetti, L., Tonazzo, A., Torbunov, D., Torti, M., Tortola, M., Tortorici, F., Tosi, N., Totani, D., Toups, M., Touramanis, C., Tran, D., Travaglini, R., Trevor, J., Triller, E., Trilov, S., Truncali, D., Trzaska, W. H., Tsai, Y., Tsai, Y. -T., Tsamalaidze, Z., Tsang, K. V., Tsverava, N., Tu, S. Z., Tufanli, S., Turner, J., Tuzi, M., Tyler, J., Tyley, E., Tzanov, M., Uchida, M. A., González, J. Ureña, Urheim, J., Usher, T., Utaegbulam, H., Uzunyan, S., Vagins, M. R., Vahle, P., Valder, S., Valdiviesso, G. A., Valencia, E., Valentim, R., Vallari, Z., Vallazza, E., Valle, J. W. F., Van Berg, R., Van de Water, R. G., Forero, D. V., Van Nuland-Troost, M., Varanini, F., Oliva, D. Vargas, Varner, G., Vasina, S., Vaughan, N., Vaziri, K., Vega, J., Ventura, S., Verdugo, A., Vergani, S., Verzocchi, M., Vetter, K., Vicenzi, M., de Souza, H. Vieira, Vignoli, C., Villa, E., Viren, B., Vizcaya-Hernandez, A., Vrba, T., Vuong, Q., Waldron, A. V., Wallbank, M., Walsh, J., Walton, T., Wang, H., Wang, J., Wang, L., Wang, M. H. L. S., Wang, X., Wang, Y., Warburton, K., Warner, D., Warsame, L., Wascko, M. O., Waters, D., Watson, A., Wawrowska, K., Weber, A., Weber, M., Wei, H., Weinstein, A., Wenzel, H., Westerdale, S., Wetstein, M., Whalen, K., Whilhelmi, J., White, A., Whitehead, L. H., Whittington, D., Wilking, M. J., Wilkinson, A., Wilkinson, C., Wilson, F., Wilson, R. J., Winter, P., Wisniewski, W., Wolcott, J., Wolfs, J., Wongjirad, T., Wood, A., Wood, K., Worcester, E., Worcester, M., Wospakrik, M., Wresilo, K., Wret, C., Wu, S., Wu, W., Wurm, M., Wyenberg, J., Xiao, Y., Xiotidis, I., Yaeggy, B., Yahlali, N., Yandel, E., Yang, K., Yang, T., Yankelevich, A., Yershov, N., Yonehara, K., Young, T., Yu, B., Yu, H., Yu, J., Yu, Y., Yuan, W., Zaki, R., Zalesak, J., Zambelli, L., Zamorano, B., Zani, A., Zapata, O., Zazueta, L., Zeller, G. P., Zennamo, J., Zeug, K., Zhang, C., Zhang, S., Zhao, M., Zhivun, E., Zimmerman, E. D., Zucchelli, S., Zuklin, J., Zutshi, V., and Zwaska, R.

Subjects
Physics - Instrumentation and Detectors
Abstract

Doping of liquid argon TPCs (LArTPCs) with a small concentration of xenon is a technique for light-shifting and facilitates the detection of the liquid argon scintillation light. In this paper, we present the results of the first doping test ever performed in a kiloton-scale LArTPC. From February to May 2020, we carried out this special run in the single-phase DUNE Far Detector prototype (ProtoDUNE-SP) at CERN, featuring 720 t of total liquid argon mass with 410 t of fiducial mass. A 5.4 ppm nitrogen contamination was present during the xenon doping campaign. The goal of the run was to measure the light and charge response of the detector to the addition of xenon, up to a concentration of 18.8 ppm. The main purpose was to test the possibility for reduction of non-uniformities in light collection, caused by deployment of photon detectors only within the anode planes. Light collection was analysed as a function of the xenon concentration, by using the pre-existing photon detection system (PDS) of ProtoDUNE-SP and an additional smaller set-up installed specifically for this run. In this paper we first summarize our current understanding of the argon-xenon energy transfer process and the impact of the presence of nitrogen in argon with and without xenon dopant. We then describe the key elements of ProtoDUNE-SP and the injection method deployed. Two dedicated photon detectors were able to collect the light produced by xenon and the total light. The ratio of these components was measured to be about 0.65 as 18.8 ppm of xenon were injected. We performed studies of the collection efficiency as a function of the distance between tracks and light detectors, demonstrating enhanced uniformity of response for the anode-mounted PDS. We also show that xenon doping can substantially recover light losses due to contamination of the liquid argon by nitrogen., Comment: 36 pages, 20 figures. Corrected author list; corrected typos across paper and polished text

Published
2024

72. Diverse Science from VLT imagery and spectroscopy of PNe in the Galactic Bulge

Author

Parker, Quentin, Tan, Shuyu, Ritter, Andreas, and Zijlstra, Albert

Subjects
Astrophysics - Astrophysics of Galaxies, Astrophysics - Solar and Stellar Astrophysics
Abstract

We have undertaken a deep investigation of a well defined sample of 136 PNe located in a 10x10 degree central region of the Galactic Bulge observed with the ESO VLT and supplemented by archival HST imagery. These studies have provided precise morphologies, major axes position angles and the most robust sample of consistently derived chemical abundances available to date. Using these data we have statistically confirmed, at 5-sigma, the precise PNe population that provides the PNe alignment of major axes previously suggested in the Galactic Bulge, revealed a partial solution to the sulfur anomaly and uncovered interesting morphological, abundance and kinematic features. We summarise the most significant findings here with detailed results appearing in a series of related publications., Comment: 6 pages, 5 figures

Published
2024

73. Evaluation of the performance of a CdZnTe-based soft $\gamma$-ray detector for CubeSat payloads

Author

de Kuijper, Kees, Diwan, Rishank, Pal, Partha Sarathi, Ritter, Andreas, Parkinson, Pablo M. Saz, Kong, Andy C. T., and Parker, Quentin A.

Subjects
Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The low-energy $\gamma$-ray (0.1-30 MeV) sky has been relatively unexplored since the decommissioning of the COMPTEL instrument on the Compton Gamma-Ray Observatory (CGRO) satellite in 2000. However, the study of this part of the energy spectrum (the ``MeV gap") is crucial for addressing numerous unresolved questions in high-energy and multi-messenger astrophysics. Although several large MeV $\gamma$-ray missions like AMEGO and e-ASTROGAM are being proposed, they are predominantly in the developmental phase, with launches not anticipated until the next decade at the earliest. In recent times, there has been a surge in proposed CubeSat missions as cost-effective and rapidly implementable ``pathfinder" alternatives. A MeV CubeSat dedicated to $\gamma$-ray astronomy has the potential to serve as a demonstrator for future, larger-scale MeV payloads. This paper presents a $\gamma$-ray payload design featuring a CdZnTe crystal calorimeter module developed by IDEAS. We report the detailed results of simulations to assess the performance of this proposed payload and compare it with those of previous $\gamma$-ray instruments., Comment: Submitted in Experimental Astronomy(Springer), 25 pages, 7 figures

Published
2024
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74. Clinical physicists’ perceptions of weekly chart checks and the potential role for automated image review assessed by structured interviews

Author

Petragallo, Rachel, Luximon, Dishane C, Neylon, Jack, Bardach, Naomi S, Ritter, Timothy, and Lamb, James M

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, automated IGRT image review, thematic analysis, weekly chart checks, Other Physical Sciences, Medical Physiology, Nuclear Medicine & Medical Imaging, Medical physiology, Medical and biological physics
Abstract

BackgroundThis study utilizes interviews of clinical medical physicists to investigate self-reported shortcomings of the current weekly chart check workflow and opportunities for improvement.MethodsNineteen medical physicists were recruited for a 30-minute semi-structured interview, with a particular focus placed on image review and the use of automated tools for image review in weekly checks. Survey-type questions were used to gather quantitative information about chart check practices and importance placed on reducing chart check workloads versus increasing chart check effectiveness. Open-ended questions were used to probe respondents about their current weekly chart check workflow, opinions of the value of weekly chart checks and perceived shortcomings, and barriers and facilitators to the implementation of automated chart check tools. Thematic analysis was used to develop common themes across the interviews.ResultsPhysicists ranked highly the value of reducing the time spent on weekly chart checks (average 6.3 on a scale from 1 to 10), but placed more value on increasing the effectiveness of checks with an average of 9.2 on a 1-10 scale. Four major themes were identified: (1) weekly chart checks need to adapt to an electronic record-and-verify chart environment, (2) physicists could add value to patient care by analyzing images without duplicating the work done by physicians, (3) greater support for trending analysis is needed in weekly checks, and (4) automation has the potential to increase the value of physics checks.ConclusionThis study identified several key shortcomings of the current weekly chart check process from the perspective of the clinical medical physicist. Our results show strong support for automating components of the weekly check workflow in order to allow for more effective checks that emphasize follow-up, trending, failure modes and effects analysis, and allow time to be spent on other higher value tasks that improve patient safety.

Published
2024

75. The SocialVidStim: a video database of positive and negative social evaluation stimuli for use in social cognitive neuroscience paradigms

Author

Tully, Laura M, Blendermann, Mary, Fine, Jeffrey R, Zakskorn, Lauren N, Fritz, Matilda, Hamlett, Gabriella E, Lamb, Shannon T, Moody, Anna K, Ng, Julenne, Parakul, Narimes, Ritter, Bryn M, Rahim, Raisa, Yu, Grace, and Taylor, Sandra L

Subjects
Biological Psychology, Psychology, Behavioral and Social Science, Clinical Research, Humans, Child, Adolescent, Young Adult, Adult, Cognitive Neuroscience, Reproducibility of Results, Arousal, stimulus set, social cognition, videos, fMRI, multiracial, Neurosciences, Cognitive Sciences, Experimental Psychology, Biological psychology, Clinical and health psychology
Abstract

This paper describes the SocialVidStim-a database of video stimuli available to the scientific community depicting positive and negative social evaluative and neutral statements. The SocialVidStim comprises 53 diverse individuals reflecting the demographic makeup of the USA, ranging from 9 to 41 years old, saying 20-60 positive and 20-60 negative social evaluative statements (e.g. 'You are a very trustworthy/annoying person'), and 20-60 neutral statements (e.g. 'The sky is blue'), totaling 5793 videos post-production. The SocialVidStim are designed for use in behavioral and functional magetic resonance imaging paradigms, across developmental stages, in diverse populations. This study describes stimuli development and reports initial validity and reliability data on a subset videos (N = 1890) depicting individuals aged 18-41 years. Raters perceive videos as expected: positive videos elicit positively valenced ratings, negative videos elicit negatively valenced ratings and neutral videos are rated as neutral. Test-retest reliability data demonstrate intraclass correlations in the good-to-excellent range for negative and positive videos and the moderate range for neutral videos. We also report small effects on valence and arousal that should be considered during stimuli selection, including match between rater and actor sex and actor believability. The SocialVidStim is a resource for researchers and we offer suggestions for using the SocialVidStim in future research.

Published
2024

76. Tensorial tomographic Fourier ptychography with applications to muscle tissue imaging

Author

Xu, Shiqi, Yang, Xi, Ritter, Paul, Dai, Xiang, Lee, Kyung Chul, Kreiss, Lucas, Zhou, Kevin C, Kim, Kanghyun, Chaware, Amey, Neff, Jadee, Glass, Carolyn, Lee, Seung Ah, Friedrich, Oliver, and Horstmeyer, Roarke

Subjects
Atomic, Molecular and Optical Physics, Physical Sciences, Heart Disease, Bioengineering, Cardiovascular, Biomedical Imaging, computational imaging, three-dimensional imaging, phase retrieval microscopy, polarization-sensitive imaging, label-free imaging, Atomic, molecular and optical physics
Published
2024

77. Results of an AI-Based Image Review System to Detect Patient Misalignment Errors in a Multi-Institutional Database of CBCT-Guided Radiotherapy Treatments

Author

Luximon, Dishane C, Neylon, Jack, Ritter, Timothy, Agazaryan, Nzhde, Hegde, John V, Steinberg, Michael L, Low, Daniel A, and Lamb, James M

Subjects
Medical and Biological Physics, Biomedical and Clinical Sciences, Clinical Sciences, Physical Sciences, Oncology and Carcinogenesis, Cancer, Biomedical Imaging, Other Physical Sciences, Oncology & Carcinogenesis, Oncology and carcinogenesis, Theoretical and computational chemistry, Medical and biological physics
Abstract

PurposePresent knowledge of patient setup and alignment errors in image guided radiation therapy (IGRT) relies on voluntary reporting, which is thought to underestimate error frequencies. A manual retrospective patient-setup misalignment error search is infeasible owing to the bulk of cases to be reviewed. We applied a deep learning-based misalignment error detection algorithm (EDA) to perform a fully automated retrospective error search of clinical IGRT databases and determine an absolute gross patient misalignment error rate.Methods and materialsThe EDA was developed to analyze the registration between planning scans and pretreatment cone beam computed tomography scans, outputting a misalignment score ranging from 0 (most unlikely) to 1 (most likely). The algorithm was trained using simulated translational errors on a data set obtained from 680 patients treated at 2 radiation therapy clinics between 2017 and 2022. A receiver operating characteristic analysis was performed to obtain target thresholds. DICOM Query and Retrieval software was integrated with the EDA to interact with the clinical database and fully automate data retrieval and analysis during a retrospective error search from 2016 to 2017 and from 2021 to 2022 for the 2 institutions, respectively. Registrations were flagged for human review using both a hard-thresholding method and a prediction trending analysis over each individual patient's treatment course. Flagged registrations were manually reviewed and categorized as errors (>1 cm misalignment at the target) or nonerrors.ResultsA total of 17,612 registrations were analyzed by the EDA, resulting in 7.7% flagged events. Three previously reported errors were successfully flagged by the EDA, and 4 previously unreported vertebral body misalignment errors were discovered during case reviews. False positive cases often displayed substantial image artifacts, patient rotation, and soft tissue anatomy changes.ConclusionsOur results validated the clinical utility of the EDA for bulk image reviews and highlighted the reliability and safety of IGRT, with an absolute gross patient misalignment error rate of 0.04% ± 0.02% per delivered fraction.

Published
2024

78. A Maturity Model for Operations in Neuroscience Research

Author

Johnson, Erik C., Nguyen, Thinh T., Dichter, Benjamin K., Zappulla, Frank, Kosma, Montgomery, Gunalan, Kabilar, Halchenko, Yaroslav O., Neufeld, Shay Q., Schirner, Michael, Ritter, Petra, Martone, Maryann E., Wester, Brock, Pestilli, Franco, and Yatsenko, Dimitri

Subjects
Quantitative Biology - Neurons and Cognition, Computer Science - Computers and Society
Abstract

Scientists are adopting new approaches to scale up their activities and goals. Progress in neurotechnologies, artificial intelligence, automation, and tools for collaboration promises new bursts of discoveries. However, compared to other disciplines and the industry, neuroscience laboratories have been slow to adopt key technologies to support collaboration, reproducibility, and automation. Drawing on progress in other fields, we define a roadmap for implementing automated research workflows for diverse research teams. We propose establishing a five-level capability maturity model for operations in neuroscience research. Achieving higher levels of operational maturity requires new technology-enabled methodologies, which we describe as ``SciOps''. The maturity model provides guidelines for evaluating and upgrading operations in multidisciplinary neuroscience teams., Comment: 10 pages, one figure

Published
2023

79. Noise robust distillation of self-supervised speech models via correlation metrics

Author

Ritter-Gutierrez, Fabian, Huang, Kuan-Po, Ng, Dianwen, Wong, Jeremy H. M., Lee, Hung-yi, Chng, Eng Siong, and Chen, Nancy F.

Subjects
Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Compared to large speech foundation models, small distilled models exhibit degraded noise robustness. The student's robustness can be improved by introducing noise at the inputs during pre-training. Despite this, using the standard distillation loss still yields a student with degraded performance. Thus, this paper proposes improving student robustness via distillation with correlation metrics. Teacher behavior is learned by maximizing the teacher and student cross-correlation matrix between their representations towards identity. Noise robustness is encouraged via the student's self-correlation minimization. The proposed method is agnostic of the teacher model and consistently outperforms the previous approach. This work also proposes an heuristic to weigh the importance of the two correlation terms automatically. Experiments show consistently better clean and noise generalization on Intent Classification, Keyword Spotting, and Automatic Speech Recognition tasks on SUPERB Challenge., Comment: 6 pages

Published
2023

80. Benchmarks for Physical Reasoning AI

Author

Melnik, Andrew, Schiewer, Robin, Lange, Moritz, Muresanu, Andrei, Saeidi, Mozhgan, Garg, Animesh, and Ritter, Helge

Subjects
Computer Science - Artificial Intelligence
Abstract

Physical reasoning is a crucial aspect in the development of general AI systems, given that human learning starts with interacting with the physical world before progressing to more complex concepts. Although researchers have studied and assessed the physical reasoning of AI approaches through various specific benchmarks, there is no comprehensive approach to evaluating and measuring progress. Therefore, we aim to offer an overview of existing benchmarks and their solution approaches and propose a unified perspective for measuring the physical reasoning capacity of AI systems. We select benchmarks that are designed to test algorithmic performance in physical reasoning tasks. While each of the selected benchmarks poses a unique challenge, their ensemble provides a comprehensive proving ground for an AI generalist agent with a measurable skill level for various physical reasoning concepts. This gives an advantage to such an ensemble of benchmarks over other holistic benchmarks that aim to simulate the real world by intertwining its complexity and many concepts. We group the presented set of physical reasoning benchmarks into subcategories so that more narrow generalist AI agents can be tested first on these groups.

Published
2023

81. The Evolution of Keylogger Technologies: A Survey from Historical Origins to Emerging Opportunities

Author

Salas-Nino, Marco, Ritter, Grant, Hamdan, Daniel, Wang, Tao, and Hou, Tao

Subjects
Computer Science - Cryptography and Security
Abstract

As the digital world evolves, so do the threats to our security do too. Keyloggers were once a large threat to the cyber world. Though undergoing many transformations alongside the technological advancements of today, it is important to raise questions about the importance of Anti-Keyloggers in our current state of cyber security. This survey dives into the historical evolution of Keyloggers and investigates their current day forms. Within this inspection of Keyloggers, we must propose whether Anti-Keyloggers serve a purpose to this ever-changing landscape before us or if emerging strategies have rendered them obsolete.

Published
2023

82. Set-valued propagation of chaos for controlled path-dependent McKean-Vlasov SPDEs

Author

Criens, David and Ritter, Moritz

Subjects
Mathematics - Probability, Mathematics - Analysis of PDEs, Mathematics - Optimization and Control
Abstract

We develop a limit theory for controlled path-dependent mean field stochastic partial differential equations (SPDEs) within the semigroup approach of Da Prato and Zabczyk. More precisely, we prove existence results for mean field limits and particle approximations, and we establish set-valued propagation of chaos in the sense that we show convergence of sets of empirical distributions to sets of mean field limits in the Hausdorff metric topology. Furthermore, we discuss consequences of our results to stochastic optimal control. As another application, we deduce a propagation of chaos result for Peng's $G$-Brownian motion with drift interaction., Comment: some typos have been corrected

Published
2023

83. The DUNE Far Detector Vertical Drift Technology, Technical Design Report

Author

DUNE Collaboration, Abud, A. Abed, Abi, B., Acciarri, R., Acero, M. A., Adames, M. R., Adamov, G., Adamowski, M., Adams, D., Adinolfi, M., Adriano, C., Aduszkiewicz, A., Aguilar, J., Aimard, B., Akbar, F., Allison, K., Monsalve, S. Alonso, Alrashed, M., Alton, A., Alvarez, R., Amar, H., Amedo, P., Anderson, J., Andrade, D. A., Andreopoulos, C., Andreotti, M., Andrews, M. P., Andrianala, F., Andringa, S., Anfimov, N., Ankowski, A., Antoniassi, M., Antonova, M., Antoshkin, A., Aranda-Fernandez, A., Arellano, L., Diaz, E. Arrieta, Arroyave, M. A., Asaadi, J., Ashkenazi, A., Asquith, L., Atkin, E., Auguste, D., Aurisano, A., Aushev, V., Autiero, D., Azfar, F., Back, A., Back, H., Back, J. J., Bagaturia, I., Bagby, L., Balashov, N., Balasubramanian, S., Baldi, P., Baldini, W., Baller, B., Bambah, B., Banerjee, R., Barao, F., Barenboim, G., Alzás, P. Barham, Barker, G. J., Barkhouse, W., Barr, G., Monarca, J. Barranco, Barros, A., Barros, N., Barrow, D., Barrow, J. L., Basharina-Freshville, A., Bashyal, A., Basque, V., Batchelor, C., Bathe-Peters, L., Battat, J. B. R., Battisti, F., Bay, F., Bazetto, M. C. Q., Alba, J. L. L. Bazo, Beacom, J. F., Bechetoille, E., Behera, B., Belchior, E., Bell, G., Bellantoni, L., Bellettini, G., Bellini, V., Beltramello, O., Benekos, N., Montiel, C. Benitez, Benjamin, D., Neves, F. Bento, Berger, J., Berkman, S., Bernardini, P., Bersani, A., Bertolucci, S., Betancourt, M., Rodríguez, A. Betancur, Bevan, A., Bezawada, Y., Bezerra, A. T., Bezerra, T. J., Bhat, A., Bhatnagar, V., Bhatt, J., Bhattacharjee, M., Bhattacharya, M., Bhuller, S., Bhuyan, B., Biagi, S., Bian, J., Biery, K., Bilki, B., Bishai, M., Bitadze, A., Blake, A., Blaszczyk, F. D., Blazey, G. C., Blucher, E., Boissevain, J., Bolognesi, S., Bolton, T., Bomben, L., Bonesini, M., Bonilla-Diaz, C., Bonini, F., Booth, A., Boran, F., Bordoni, S., Merlo, R. 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Subjects
High Energy Physics - Experiment, Physics - Instrumentation and Detectors
Abstract

DUNE is an international experiment dedicated to addressing some of the questions at the forefront of particle physics and astrophysics, including the mystifying preponderance of matter over antimatter in the early universe. The dual-site experiment will employ an intense neutrino beam focused on a near and a far detector as it aims to determine the neutrino mass hierarchy and to make high-precision measurements of the PMNS matrix parameters, including the CP-violating phase. It will also stand ready to observe supernova neutrino bursts, and seeks to observe nucleon decay as a signature of a grand unified theory underlying the standard model. The DUNE far detector implements liquid argon time-projection chamber (LArTPC) technology, and combines the many tens-of-kiloton fiducial mass necessary for rare event searches with the sub-centimeter spatial resolution required to image those events with high precision. The addition of a photon detection system enhances physics capabilities for all DUNE physics drivers and opens prospects for further physics explorations. Given its size, the far detector will be implemented as a set of modules, with LArTPC designs that differ from one another as newer technologies arise. In the vertical drift LArTPC design, a horizontal cathode bisects the detector, creating two stacked drift volumes in which ionization charges drift towards anodes at either the top or bottom. The anodes are composed of perforated PCB layers with conductive strips, enabling reconstruction in 3D. Light-trap-style photon detection modules are placed both on the cryostat's side walls and on the central cathode where they are optically powered. This Technical Design Report describes in detail the technical implementations of each subsystem of this LArTPC that, together with the other far detector modules and the near detector, will enable DUNE to achieve its physics goals., Comment: 425 pages; 281 figures Central editing team: A. Heavey, S. Kettell, A. Marchionni, S. Palestini, S. Rajogopalan, R. J. Wilson

Published
2023

90. Overview of Sub-Project VitAM-Gust

Author

Heinrich, Ralf, Reimer, Lars, Ritter, Markus, Ernst, Britta, Hirschel, Ernst Heinrich, Founding Editor, Schröder, Wolfgang, Series Editor, Boersma, Bendiks Jan, Editorial Board Member, Fujii, Kozo, Editorial Board Member, Haase, Werner, Editorial Board Member, Leschziner, Michael A., Editorial Board Member, Periaux, Jacques, Editorial Board Member, Pirozzoli, Sergio, Editorial Board Member, Rizzi, Arthur, Editorial Board Member, Roux, Bernard, Editorial Board Member, Shokin, Yurii I., Editorial Board Member, Lagemann, Esther, Managing Editor, and Heinrich, Ralf, editor

Published
2025
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91. High-Fidelity Simulation for Identification of Steady and Dynamic Transport Aircraft Derivatives

Author

Ritter, Markus, Reimer, Lars, Hirschel, Ernst Heinrich, Founding Editor, Schröder, Wolfgang, Series Editor, Boersma, Bendiks Jan, Editorial Board Member, Fujii, Kozo, Editorial Board Member, Haase, Werner, Editorial Board Member, Leschziner, Michael A., Editorial Board Member, Periaux, Jacques, Editorial Board Member, Pirozzoli, Sergio, Editorial Board Member, Rizzi, Arthur, Editorial Board Member, Roux, Bernard, Editorial Board Member, Shokin, Yurii I., Editorial Board Member, Lagemann, Esther, Managing Editor, and Heinrich, Ralf, editor

Published
2025
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92. Elephants in Mouseholes: The Major Questions Doctrine in the Lower Courts.

Author

Ritter, Ling

Subjects
Judicial review of administrative acts -- Laws, regulations and rules, West Virginia v. EPA (142 S. Ct. 2587 (2022)), Biden v. Nebraska (143 S. Ct. 2355 (2023)), United States. Supreme Court -- Powers and duties, Government regulation
Abstract

Table of Contents Introduction I. The Supreme Court's Major Questions Doctrine A. The Major Questions Quintet B. Common Rationales and Critiques C. Unanswered Questions and Predictions for the Future II. [...], In recent years, the Supreme Court has repeatedly deployed a new doctrine with potentially seismic implications for the future of the federal administrative state. The "major questions doctrine," formally embraced by a majority of the Court for the first time in West Virginia v. EPA, requires administrative agencies to demonstrate "clear congressional authorization" when they assert authority over matters of "vast 'economic and political significance.'" But what makes a question "major" or congressional authorization "clear"? And how might this doctrine affect related principles of administrative law? As the Supreme Court has left these questions unanswered, this Note provides the first account of how lower courts and litigants are attempting to fill in the gaps. It first examines the contexts in which litigants and courts have addressed the doctrine and the strategies that challenger plaintiffs and governmental defendants have employed. It then analyzes how courts and litigants have applied the elements of the major questions test and assesses the implications of that test for two administrative law doctrines with uncertain fates: Chevron deference and nondelegation. As this Note explores, the major questions doctrine has already featured in challenges across a vast expanse of policy areas, including environmental regulation, public health, education, immigration, data privacy, labor and employment, election law, public safety, and national security, economic affairs, and anti-discrimination law. The doctrine has also been used to challenge various types of executive actions, including agency rules and regulations, enforcement actions for statutory violations, presidential (nonagency) actions, and actions that confer a public benefit rather than regulating private conduct. While the major questions doctrine remains in its early stages of development, this Note identifies emerging trends in an important group of "first movers" to illuminate the doctrine's potential impact in the years to come.

Published
2024

93. Promoting Student Competencies in Informatics Education by Combining Semantic Waves and Algorithmic Thinking

Author

Ritter, Frauke and Standl, Bernhard

Abstract

We live in a digital age, not least accelerated by the COVID-19 pandemic. It is all the more important in our society that students learn and master the key competence of algorithmic thinking to understand the informatics concepts behind every digital phenomena and thus is able to actively shape the future. For this to be successful, concepts must be identified that can convey this key competence to all students in such a way that algorithmic thinking is integrated in the subject of informatics -beyond a pure programming course. Furthermore, based on the Legitimation Code Theory, semantic waves provide a way to develop and review lesson plans. Therefore, we planned a workshop, that follow the phases of a semantic wave addressing algorithmic problems using a block-based programming language. Considering this, we suggest the so-called SWAT concept (Semantic Wave Algorithmic Thinking concept), which is carried out and analyzed in a workshop with students. The workshop was carried out in online format in an 8th grade of a high school during a coronavirus lockdown. The level of algorithmic thinking was measured using a pretest and posttest both in the treatment group and in a control group and with the help of the approximate adjusted fractional Bayes factors for testing informative hypotheses statistically and through a reductive, qualitative content analysis of the students' work results (worksheets and created programs) evaluated. The semantic wave concept was measured using several cognitive load ratings of the students during the workshop and also statistically evaluated with the approximate adjusted fractional Bayes factors for testing informative hypotheses, as well as a qualitative content analysis of the worksheets. Results of this pilot study provide first insights, that the SWAT-concept can be used in combination of unplugged and plugged parts.

Published
2023

94. Generalizing Predictive Models of Reading Ability in Adaptive Mathematics Software

Author

Almoubayyed, Husni, Fancsali, Stephen E., and Ritter, Steve

Abstract

Recent research seeks to develop more comprehensive learner models for adaptive learning software. For example, models of reading comprehension built using data from students' use of adaptive instructional software for mathematics have recently been developed. These models aim to deliver experiences that consider factors related to learning beyond performance in the target domain for instruction. We investigate the extent to which generalization is possible for a recently developed predictive model that seeks to infer students' reading comprehension ability (as measured by end-of-year standardized test scores) using an introductory learning experience in Carnegie Learning's MATHia intelligent tutoring system for mathematics. Building on a model learned on data from middle school students in a single school district in a mid-western U.S. state, using that state's end-of-year English Language Arts (ELA) standardized test score as an outcome, we consider data from a school district in a south-eastern U.S. state as well as that state's end-of-year ELA standardized test outcome. Generalization is explored by considering prediction performance when training and testing models on data from each of the individual school districts (and for their respective state's test outcomes) as well as pooling data from both districts together. We conclude with discussion of investigations of some algorithmic fairness characteristics of the learned models. The results suggest that a model trained on data from the smaller of the two school districts considered may achieve greater fairness in its predictions over models trained on data from the other district or both districts, despite broad, overall similarities in some demographic characteristics of the two school districts. This raises interesting questions for future research on generalizing these kinds of models as well as on ensuring algorithmic fairness of resulting models for use in real-world adaptive systems for learning. [For the complete proceedings, see ED630829.]

Published
2023

95. Revisiting Nebraska's Private Education Sector

Author

EdChoice, Kristof, John M., Ritter, Colyn, and Catt, Andrew D.

Abstract

Nebraska does not currently have any private educational choice programs, but recent legislative pushes have brought options such as education savings accounts (ESAs) into the spotlight. ESAs allow parents to withdraw their children from public district or charter schools and receive a deposit of public funds into government-authorized savings accounts with restricted, but multiple, uses. The characteristics of Nebraska's private school sector can inform policy debates surrounding the potential creation of educational choice programs, such as ESAs. From March 31 to May 23 2022, EdChoice administered a survey of Nebraska private school leaders. This brief provides a descriptive analysis of the results of that survey, including tuition and fees, the number of open seats, regulatory concerns, and interest in educational choice programs.

Published
2023

96. Doctoral Student Perceptions of a Project-Based Learning Approach in an Instructional Design Course

Author

McCall, Madelon, Shelton, Ryann N., Crowley, Brandy N., and Ritter, Kenley

Abstract

The purpose of this single case study was to determine how a project-based learning approach to instructional design supported education doctorate students' acquisition of new knowledge and practical application of skills in their current and future professions. Participants included 58 students in an online EdD instructional design course. We found that 72% of students credited the design project for scaffolding their learning about instructional design and 80% saw an immediate application of the instructional design project to their current professional roles. Further, 93% of students could foresee the application of new knowledge and skills to future professional opportunities. This study has implications for those who teach EdD courses and are interested in providing a project-based approach to content acquisition and teaching skills students can apply in their professional organizations, both current and future.

Published
2023

97. The 123s of School Choice: What the Research Says about Private School Choice Programs in America. 2023 Edition

Author

EdChoice, DiPerna, Paul, Kristof, John M., Lueken, Martin F., McShane, Michael Q., and Ritter, Colyn

Abstract

The goal of "The 123s" is to present the increasingly large body of private school choice research in a clear and easy-to-read format and cite the relevant studies so that anyone who is interested in the individual results can easily find them and read in more detail. This report is divided into 11 sections. The first section summarizes the number of studies and how many come to which conclusion. The following sections present the eight outcomes covered in this publication, including school safety and climate--a new subject of study. They are followed by a list of reviews that other researchers conducted about the eight outcomes covered. The last section discusses the strengths and limitations of research on school choice. Finally, tables in the Appendix present the various programs, organized by type. [For the 2022 report, see ED625418.]

Published
2023

98. Generalizing Predictive Models of Reading Ability in Adaptive Mathematics Software

Author

Husni Almoubayy, Stephen E. Fancsali, and Steve Ritter

Abstract

Recent research seeks to develop more comprehensive learner models for adaptive learning software. For example, models of reading comprehension built using data from students' use of adaptive instructional software for mathematics have recently been developed. These models aim to deliver experiences that consider factors related to learning beyond performance in the target domain for instruction. We investigate the extent to which generalization is possible for a recently developed predictive model that seeks to infer students' reading comprehension ability (as measured by end-of-year standardized test scores) using an introductory learning experience in Carnegie Learning's MATHia intelligent tutoring system for mathematics. Building on a model learned on data from middle school students in a single school district in a mid-western U.S. state, using that state's end-of-year English Language Arts (ELA) standardized test score as an outcome, we consider data from a school district in a south-eastern U.S. state as well as that state's end-of-year ELA standardized test outcome. Generalization is explored by considering prediction performance when training and testing models on data from each of the individual school districts (and for their respective state's test outcomes) as well as pooling data from both districts together. We conclude with discussion of investigations of some algorithmic fairness characteristics of the learned models. The results suggest that a model trained on data from the smaller of the two school districts considered may achieve greater fairness in its predictions over models trained on data from the other district or both districts, despite broad, overall similarities in some demographic characteristics of the two school districts. This raises interesting questions for future research on generalizing these kinds of models as well as on ensuring algorithmic fairness of resulting models for use in real-world adaptive systems for learning. [This paper was published in: "Proceedings of the 16th International Conference on Educational Data Mining," edited by M. Feng et al., International Educational Data Mining Society, 2023, pp. 207-16.]

Published
2023

99. Rewriting Math Word Problems with Large Language Models

Author

Kole Norberg, Husni Almoubayy, Stephen E. Fancsali, Logan De Ley, Kyle Weldon, April Murphy, and Steve Ritter

Abstract

Large Language Models have recently achieved high performance on many writing tasks. In a recent study, math word problems in Carnegie Learning's MATHia adaptive learning software were rewritten by human authors to improve their clarity and specificity. The randomized experiment found that emerging readers who received the rewritten word problems spent less time completing the problems and also achieved higher mastery compared to emerging readers who received the original content. We used GPT-4 to rewrite the same set of math word problems, prompting it to follow the same guidelines that the human authors followed. We lay out our prompt engineering process, comparing several prompting strategies: zero-shot, few-shot, and chain-of-thought prompting. Additionally, we overview how we leveraged GPT's ability to write python code in order to encode mathematical components of word problems. We report text analysis of the original, human-rewritten, and GPT-rewritten problems. GPT rewrites had the most optimal readability, lexical diversity, and cohesion scores but used more low frequency words. We present our plan to test the GPT outputs in upcoming randomized field trials in MATHia.

Published
2023

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