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151. Augmenting hippocampal-prefrontal neuronal synchrony during sleep enhances memory consolidation in humans.

Author

Geva-Sagiv, Maya, Mankin, Emily A, Eliashiv, Dawn, Epstein, Shdema, Cherry, Natalie, Kalender, Guldamla, Tchemodanov, Natalia, Nir, Yuval, and Fried, Itzhak

Subjects
Hippocampus, Prefrontal Cortex, Temporal Lobe, Humans, Electroencephalography, Sleep, Memory Consolidation, Sleep Research, Mental Health, Behavioral and Social Science, Clinical Research, Basic Behavioral and Social Science, Neurosciences, 1.1 Normal biological development and functioning, Underpinning research, Neurological, Mental health, Psychology, Cognitive Sciences, Neurology & Neurosurgery
Abstract

Memory consolidation during sleep is thought to depend on the coordinated interplay between cortical slow waves, thalamocortical sleep spindles and hippocampal ripples, but direct evidence is lacking. Here, we implemented real-time closed-loop deep brain stimulation in human prefrontal cortex during sleep and tested its effects on sleep electrophysiology and on overnight consolidation of declarative memory. Synchronizing the stimulation to the active phases of endogenous slow waves in the medial temporal lobe (MTL) enhanced sleep spindles, boosted locking of brain-wide neural spiking activity to MTL slow waves, and improved coupling between MTL ripples and thalamocortical oscillations. Furthermore, synchronized stimulation enhanced the accuracy of recognition memory. By contrast, identical stimulation without this precise time-locking was not associated with, and sometimes even degraded, these electrophysiological and behavioral effects. Notably, individual changes in memory accuracy were highly correlated with electrophysiological effects. Our results indicate that hippocampo-thalamocortical synchronization during sleep causally supports human memory consolidation.

Published
2023

153. Grounding Language Models to Images for Multimodal Inputs and Outputs

Author

Koh, Jing Yu, Salakhutdinov, Ruslan, and Fried, Daniel

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

We propose an efficient method to ground pretrained text-only language models to the visual domain, enabling them to process arbitrarily interleaved image-and-text data, and generate text interleaved with retrieved images. Our method leverages the abilities of language models learnt from large scale text-only pretraining, such as in-context learning and free-form text generation. We keep the language model frozen, and finetune input and output linear layers to enable cross-modality interactions. This allows our model to process arbitrarily interleaved image-and-text inputs, and generate free-form text interleaved with retrieved images. We achieve strong zero-shot performance on grounded tasks such as contextual image retrieval and multimodal dialogue, and showcase compelling interactive abilities. Our approach works with any off-the-shelf language model and paves the way towards an effective, general solution for leveraging pretrained language models in visually grounded settings., Comment: Published in ICML 2023. Project page: https://jykoh.com/fromage

Published
2023

154. SantaCoder: don't reach for the stars!

Author

Allal, Loubna Ben, Li, Raymond, Kocetkov, Denis, Mou, Chenghao, Akiki, Christopher, Ferrandis, Carlos Munoz, Muennighoff, Niklas, Mishra, Mayank, Gu, Alex, Dey, Manan, Umapathi, Logesh Kumar, Anderson, Carolyn Jane, Zi, Yangtian, Poirier, Joel Lamy, Schoelkopf, Hailey, Troshin, Sergey, Abulkhanov, Dmitry, Romero, Manuel, Lappert, Michael, De Toni, Francesco, del Río, Bernardo García, Liu, Qian, Bose, Shamik, Bhattacharyya, Urvashi, Zhuo, Terry Yue, Yu, Ian, Villegas, Paulo, Zocca, Marco, Mangrulkar, Sourab, Lansky, David, Nguyen, Huu, Contractor, Danish, Villa, Luis, Li, Jia, Bahdanau, Dzmitry, Jernite, Yacine, Hughes, Sean, Fried, Daniel, Guha, Arjun, de Vries, Harm, and von Werra, Leandro

Subjects
Computer Science - Software Engineering, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

The BigCode project is an open-scientific collaboration working on the responsible development of large language models for code. This tech report describes the progress of the collaboration until December 2022, outlining the current state of the Personally Identifiable Information (PII) redaction pipeline, the experiments conducted to de-risk the model architecture, and the experiments investigating better preprocessing methods for the training data. We train 1.1B parameter models on the Java, JavaScript, and Python subsets of The Stack and evaluate them on the MultiPL-E text-to-code benchmark. We find that more aggressive filtering of near-duplicates can further boost performance and, surprisingly, that selecting files from repositories with 5+ GitHub stars deteriorates performance significantly. Our best model outperforms previous open-source multilingual code generation models (InCoder-6.7B and CodeGen-Multi-2.7B) in both left-to-right generation and infilling on the Java, JavaScript, and Python portions of MultiPL-E, despite being a substantially smaller model. All models are released under an OpenRAIL license at https://hf.co/bigcode.

Published
2023

155. Enhancing the Accuracy of Density Functional Tight Binding Models Through ChIMES Many-body Interaction Potentials

Author

Goldman, Nir, Fried, Laurence E., Lindsey, Rebecca K., Pham, C. Huy, and Dettori, R.

Subjects
Condensed Matter - Materials Science, Physics - Chemical Physics, Physics - Computational Physics
Abstract

Semi-empirical quantum models such as Density Functional Tight Binding (DFTB) are attractive methods for obtaining quantum simulation data at longer time and length scales than possible with standard approaches. However, application of these models can require lengthy effort due to the lack of a systematic approach for their development. In this work, we discuss use of the Chebyshev Interaction Model for Efficient Simulation (ChIMES) to create rapidly parameterized DFTB models which exhibit strong transferability due to the inclusion of many-body interactions that might otherwise be inaccurate. We apply our modeling approach to silicon polymorphs and review previous work on titanium hydride. We also review creation of a general purpose DFTB/ChIMES model for organic molecules and compounds that approaches hybrid functional and coupled cluster accuracy with two orders of magnitude fewer parameters than similar neural network approaches. In all cases, DFTB/ChIMES yields similar accuracy to the underlying quantum method with orders of magnitude improvement in computational cost. Our developments provide a way to create computationally efficient and highly accurate simulations over varying extreme thermodynamic conditions, where physical and chemical properties can be difficult to interrogate directly and there is historically a significant reliance on theoretical approaches for interpretation and validation of experimental results., Comment: 42 pages, 8 figures, 6 tables. In review for a special issue in J. Chem. Phys

Published
2023
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158. Execution-Based Evaluation for Open-Domain Code Generation

Author

Wang, Zhiruo, Zhou, Shuyan, Fried, Daniel, and Neubig, Graham

Subjects
Computer Science - Software Engineering, Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract

To extend the scope of coding queries to more realistic settings, we propose ODEX, the first Open-Domain EXecution-based natural language (NL) to Python code generation dataset. ODEX has 945 NL-Code pairs spanning 79 diverse libraries, along with 1,707 human-written test cases for execution. Our NL-Code pairs are harvested from StackOverflow forums to encourage natural and practical coding queries. Moreover, ODEX supports four natural languages as intents, in English, Spanish, Japanese, and Russian. ODEX unveils intriguing behavioral differences among top-performing code language models (LM). While CODEX achieves better overall results, CODEGEN improves effectively via scaling -- CODEGEN 6.1B performs comparably with CODEX 12B. Both models show substantial gaps between open and closed domains, but CODEGEN gaps tend to decrease with model size while CODEX gaps increase. We release ODEX to facilitate research into open-domain problems for the code generation community.

Published
2022

159. Highly-parallelized simulation of a pixelated LArTPC on a GPU

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., Ahmad, Z., Ahmed, J., Aimard, B., Akbar, F., Allison, K., Monsalve, S. Alonso, Alrashed, M., Alt, C., Alton, A., Alvarez, R., Amedo, P., Anderson, J., Andrade, D. A., Andreopoulos, C., Andreotti, M., Andrews, M. P., Andrianala, F., Andringa, S., Anfimov, N., Campanelli, W. L. Anicézio, Ankowski, A., Antoniassi, M., Antonova, M., Antoshkin, A., Antusch, S., Aranda-Fernandez, A., Arellano, L., Arnold, L. O., Arroyave, M. A., Asaadi, J., Ashkenazi, A., Asquith, L., Aurisano, A., Aushev, V., Autiero, D., Ayala-Torres, M., 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., Barao, F., Barenboim, G., Alzás, P. Barham, Barker, G. J., Barkhouse, W., Barnes, C., Barr, G., Monarca, J. Barranco, Barros, A., Barros, N., Barrow, J. L., Basharina-Freshville, A., Bashyal, A., Basque, V., Batchelor, C., 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., Bellantoni, L., Bellettini, G., Bellini, V., Beltramello, O., Benekos, N., Montiel, C. Benitez, Benjamin, D., Neves, F. Bento, Berger, J., Berkman, S., Bernardini, P., Berner, R. M., Bersani, A., Bertolucci, S., Betancourt, M., Rodríguez, A. Betancur, Bevan, A., Bezawada, Y., Bezerra, A. T., Bezerra, T. J., Bhambure, J., Bhardwaj, A., Bhatnagar, V., Bhattacharjee, M., Bhattacharya, M., Bhattarai, D., Bhuller, S., Bhuyan, B., Biagi, S., Bian, J., Biassoni, M., Biery, K., Bilki, B., Bishai, M., Bisignani, V., Bitadze, A., Blake, A., Blaszczyk, F. D., Blazey, G. C., Blend, D., Blucher, E., Boissevain, J., Bolognesi, S., Bolton, T., Bomben, L., Bonesini, M., Bonilla-Diaz, C., Bonini, F., Booth, A., Boran, F., Bordoni, S., Borkum, A., Bostan, N., Bour, P., Boyden, D., Bracinik, J., Braga, D., Brailsford, D., Branca, A., Brandt, A., Bravo-Moreno, M., Bremer, J., Brew, C., Brice, S. J., Brizzolari, C., Bromberg, C., Brooke, J., Bross, A., Brunetti, G., Brunetti, M., Buchanan, N., Budd, H., Buergi, J., V., G. Caceres, Cagnoli, I., Cai, T., Caiulo, D., Calabrese, R., Calafiura, P., Calcutt, J., Calin, M., Calivers, L., Calvez, S., Calvo, E., Caminata, A., Caratelli, D., Carber, D., Carceller, J. C., Carini, G., Carlus, B., Carneiro, M. F., Carniti, P., Terrazas, I. Caro, Carranza, H., Carrara, N., Carroll, L., Carroll, T., Carter, A., Forero, J. F. Castaño, Castillo, A., Catano-Mur, E., Cattadori, C., Cavalier, F., Cavallaro, G., Cavanna, F., Centro, S., Cerati, G., Cervelli, A., Villanueva, A. Cervera, Chakraborty, K., Chalifour, M., Chappell, A., Chardonnet, E., Charitonidis, N., Chatterjee, A., Chattopadhyay, S., Chen, H., Chen, M., Chen, Y., Chen, Z., Chen-Wishart, Z., Cheon, Y., Cherdack, D., Chi, C., Childress, S., Chirco, R., Chiriacescu, A., Chitirasreemadam, N., Cho, K., Choate, S., Chokheli, D., Chong, P. S., Chowdhury, B., Christensen, A., Christian, D., Christodoulou, G., Chukanov, A., Chung, M., Church, E., Cicero, V., Clapa, D., 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., 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, C., David, Q., Davies, G. S., Davini, S., Dawson, J., De, K., De, S., 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, Deisting, A., De Jong, P., De la Torre, A., Delbart, A., De Leo, V., Delepine, D., Delgado, M., Dell'Acqua, A., Delmonte, N., De Lurgio, P., Neto, J. R. T. de Mello, DeMuth, D. M., Dennis, S., Densham, C., Denton, P., Deptuch, G. W., De Roeck, A., De Romeri, V., De Souza, G., Detje, J. P., Devi, R., 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., 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., Eads, M., Earle, A., Edmunds, D., Eisch, J., Emberger, L., 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., Fields, L., Filip, P., Filkins, A., Filthaut, F., Fine, R., Fiorillo, G., Fiorini, M., Fischer, V., Fitzpatrick, R. S., Flanagan, W., Fleming, B., Flight, R., Fogarty, S., Foreman, W., Fowler, J., Franc, J., Franco, D., Freeman, J., Fried, J., Friedland, A., Fuess, S., Furic, I. K., Furman, K., Furmanski, A. P., Gabrielli, A., Gago, A., Gallagher, H., Gallas, A., Gallego-Ros, 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., Gardiner, S., Gastler, D., Gauch, A., Gauvreau, J., Gauzzi, P., Ge, G., Geffroy, N., Gelli, B., Gendotti, A., Gent, S., Gerlach, L., Ghorbani-Moghaddam, Z., Giammaria, P., Giammaria, T., Giangiacomi, N., Gibin, D., Gil-Botella, I., Gilligan, S., Gioiosa, A., Giovannella, S., Girerd, C., Giri, A. K., Gnani, D., Gogota, O., Gold, M., 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., Goodwin, O., Goswami, S., Gotti, C., Goudeau, J., Goudzovski, E., Grace, C., Gran, R., Granados, E., Granger, P., Grant, C., Gratieri, D., Green, P., Greenberg, S., Greenler, L., Greer, J., Grenard, J., Griffith, W. C., Groetschla, F. T., Groh, M., Grzelak, K., Gu, W., Guarino, V., Guarise, M., Guenette, R., Guerard, E., Guerzoni, M., Guffanti, D., Guglielmi, A., Guo, B., Gupta, A., Gupta, V., Guthikonda, K. K., Guzowski, P., Guzzo, M. M., Gwon, S., Ha, C., Haaf, K., Habig, A., Hadavand, H., Hadef, A., Haenni, R., Hagaman, L., Hahn, A., Haiston, J., Hamacher-Baumann, P., Hamernik, T., Hamilton, P., Han, J., Hancock, J., Happacher, F., Harris, D. A., Hartnell, J., Hartnett, T., Harton, J., Hasegawa, T., Hasnip, C., Hatcher, R., Hatfield, K. W., Hatzikoutelis, A., Hayes, C., 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., Hilgenberg, C., Hill, T., 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., Hourlier, A., Howard, B., Howell, R., Barrios, J. Hoyos, Hristova, I., Hronek, M. S., Huang, J., Huang, R. G., Hulcher, Z., 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., Jediny, F., Jena, D., Jeong, Y. S., Jesús-Valls, C., Ji, X., Jiang, J., Jiang, L., Jipa, A., Jo, J. H., Joaquim, F. R., Johnson, W., Jones, B., Jones, R., Jovancevic, N., Judah, M., Jung, C. K., Junk, T., Jwa, Y., Kabirnezhad, M., Kaboth, A., Kadenko, I., Kakorin, I., Kalitkina, A., Kalra, D., Koseyan, O. Kamer, Kamiya, F., Kaplan, D. M., Karagiorgi, G., Karaman, G., Karcher, A., Karyotakis, Y., Kasai, S., Kasetti, S. P., Kashur, L., Katsioulas, I., Kazaryan, N., Kearns, E., Keener, P., Kelly, K. J., Kemp, E., Kemularia, O., Kermaidic, Y., Ketchum, W., Kettell, S. H., Khabibullin, M., Khotjantsev, A., Khvedelidze, A., Kim, D., King, B., Kirby, B., Kirby, M., Klein, J., Kleykamp, J., Klustova, A., Kobilarcik, T., Koehler, K., Koerner, L. W., Koh, D. H., Kohn, S., Koller, P. P., Kolupaeva, L., Korablev, D., Kordosky, M., Kosc, T., Kose, U., Kostelecký, V. A., Kothekar, K., Kotler, I., Kozhukalov, V., Kralik, R., Kreczko, L., Krennrich, F., Kreslo, I., Kropp, W., Kroupova, T., Kubu, M., Kudenko, Y., Kudryavtsev, V. A., Kuhlmann, S., Kulagin, S., Kumar, J., Kumar, P., Kunze, P., Kuravi, R., Kurita, N., Kuruppu, C., Kus, V., Kutter, T., Kvasnicka, J., Kwak, D., Lambert, A., Land, B. J., Lane, C. E., Lang, K., Langford, T., Langstaff, M., Lanni, F., Lantwin, O., Larkin, J., Lasorak, P., Last, D., Laundrie, A., Laurenti, G., Lawrence, A., Laycock, P., Lazanu, I., Lazzaroni, M., Le, T., Leardini, S., Learned, J., LeBrun, P., LeCompte, T., Lee, C., Legin, V., Miotto, G. Lehmann, Lehnert, R., de Oliveira, M. A. Leigui, Leitner, M., Lepin, L. M., Li, S. W., Li, Y., Liao, H., Lin, C. S., Lin, S., Lindebaum, D., Lineros, R. A., Ling, J., Lister, A., Littlejohn, B. R., Liu, J., Liu, Y., Lockwitz, S., Loew, T., Lokajicek, M., Lomidze, I., Long, K., Lord, T., LoSecco, J. M., Louis, W. C., Lu, X. -G., Luk, K. B., Lunday, B., Luo, X., Luppi, E., Lux, T., Luzio, V. P., 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., Mahn, K., Maio, A., Major, A., Majumdar, K., Maloney, J. A., Man, M., Mandrioli, G., Mandujano, R. C., Maneira, J., Manenti, L., Manly, S., Mann, A., Manolopoulos, K., Plata, M. Manrique, Manyam, V. N., Marchan, M., Marchionni, A., Marciano, W., Marfatia, D., Mariani, C., Maricic, J., Marinho, F., Marino, A. D., Markiewicz, T., Marsden, D., Marshak, M., Marshall, C. M., Marshall, J., Marteau, J., Martín-Albo, J., Martinez, N., Caicedo, D. A. Martinez, López, F. Martínez, Miravé, P. Martínez, Martynenko, S., Mascagna, V., Mason, K., Mastbaum, A., Matichard, F., Matsuno, S., Matthews, J., Mauger, C., Mauri, N., Mavrokoridis, K., Mawby, I., Mazza, R., Mazzacane, A., McAskill, T., McCluskey, E., McConkey, N., McFarland, K. S., McGrew, C., McNab, A., Mefodiev, A., Mehta, P., Melas, P., Mena, O., Mendez, H., Mendez, P., Méndez, D. P., Menegolli, A., Meng, G., Messier, M. D., Metcalf, W., Mewes, M., Meyer, H., Miao, T., Michna, G., Mikola, V., Milincic, R., Miller, G., Miller, W., Mills, J., Mineev, O., Minotti, A., Miranda, O. G., Miryala, S., Miscetti, S., Mishra, C. S., Mishra, S. R., Mislivec, A., Mitchell, M., Mladenov, D., Mocioiu, I., Moffat, K., 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, Moon, S. H., Mooney, M., Moor, A. F., Moreno, D., Morescalchi, L., Moretti, D., Morris, C., Mossey, C., Mote, M., Motuk, E., Moura, C. A., Mousseau, J., 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, S., Musser, J., Nachtman, J., Nagai, Y., Nagu, S., Nalbandyan, M., Nandakumar, R., Naples, D., Narita, S., Nath, A., Navrer-Agasson, A., Nayak, N., Nebot-Guinot, M., Negishi, K., Nelson, J. K., Nelson, M., Nesbit, J., Nessi, M., Newbold, D., Newcomer, M., Newton, H., 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., Olivier, A., Olshevskiy, A., Onel, Y., Onishchuk, Y., Ott, J., Pagani, L., Palacio, G., Palamara, O., Palestini, S., Paley, J. M., Pallavicini, M., Palomares, C., Pan, S., Vazquez, W. Panduro, Pantic, E., Paolone, V., Papadimitriou, V., Papaleo, R., Papanestis, A., Paramesvaran, S., Parke, S., Parozzi, E., Parsa, S., Parsa, Z., Parveen, S., Parvu, M., Pasciuto, D., Pascoli, S., Pasqualini, L., Pasternak, J., Pater, J., Patrick, C., Patrizii, L., Patterson, R. B., Patton, S. J., Patzak, T., Paudel, A., Paulucci, L., Pavlovic, Z., Pawloski, G., Payne, D., Pec, V., 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., Piastra, F., Pickering, L., Pietropaolo, F., Pimentel, V. L., Pinaroli, G., Plows, K., Plunkett, R., Pollman, T., Pompa, F., Pons, X., Poonthottathil, N., Poppi, F., Pordes, S., Porter, J., Potekhin, M., Potenza, R., Potukuchi, B. V. K. S., 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., Radev, R., Radics, B., Rafique, A., Raguzin, E., Rai, M., Rajaoalisoa, M., Rakhno, I., Rakotonandrasana, A., Rakotondravohitra, L., Rameika, R., Delgado, M. A. Ramirez, Ramson, B., Rappoldi, A., Raselli, G., Ratoff, P., Raut, S., Razafinime, H., Razakamiandra, R. F., Rea, E. M., Real, J. S., Rebel, B., Rechenmacher, R., Reggiani-Guzzo, M., Reichenbacher, J., Reitzner, S. D., Sfar, H. Rejeb, Renshaw, A., Rescia, S., Resnati, F., Ribas, M., Riboldi, S., Riccio, C., Riccobene, G., Rice, L. C. J., Ricol, J. S., Rigamonti, A., Rigaut, Y., Rincón, E. V., Ritchie-Yates, A., Rivera, D., Rivera, R., Robert, A., Rocha, J. L. Rocabado, Rochester, L., Roda, M., Rodrigues, P., Alonso, M. J. Rodriguez, Rondon, J. Rodriguez, Romeo, E., Rosauro-Alcaraz, S., Rosier, P., Rossella, M., Rossi, M., Ross-Lonergan, M., Rout, J., Roy, P., Rubbia, A., Rubbia, C., Ferreira, G. Ruiz, Russell, B., Ruterbories, D., Rybnikov, A., Saa-Hernandez, A., Saakyan, R., Sacerdoti, S., Sahu, N., Sala, P., Samios, N., Samoylov, O., Sanchez, M. C., 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., Scarpelli, A., Schefke, T., Schellman, H., Schifano, S., Schlabach, P., Schmitz, D., Schneider, A. W., Scholberg, K., Schukraft, A., Segreto, E., Selyunin, A., Senise, C. R., Sensenig, J., Sgalaberna, D., Shaevitz, M. H., Shafaq, S., Shaker, F., Shamma, M., Shanahan, P., Sharankova, R., Sharma, H. R., Sharma, R., Kumar, R., Shaw, K., Shaw, T., Shchablo, K., Shepherd-Themistocleous, C., Sheshukov, A., Shi, W., Shin, S., Shoemaker, I., Shooltz, D., Shrock, R., Silber, J., Simard, L., Sinclair, J., Sinev, G., Singh, Jaydip, Singh, J., Singh, L., Singh, P., Singh, V., Chauhan, S. Singh, Sipos, R., Sirri, G., Sitraka, A., Siyeon, K., Skarpaas, K., Smith, E., Smith, P., Smolik, J., Smy, 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., Spagliardi, F., Spanu, M., 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., Susic, V., Suter, L., Sutera, C. M., Sutton, K., Suvorov, Y., Svoboda, R., Swain, S. K., Szczerbinska, B., Szelc, A. M., Taffara, A., Talukdar, N., Tamara, J., Tanaka, H. A., Tang, S., Taniuchi, N., Oregui, B. Tapia, Tapper, A., Tariq, S., Tarpara, E., Tata, N., Tatar, E., Tayloe, R., Teklu, A. M., Tennessen, P., Tenti, M., Terao, K., Terranova, F., Testera, G., Thakore, T., Thea, A., Thompson, A., Thorn, C., Timm, S. C., Tishchenko, V., Todorović, N., Tomassetti, L., Tonazzo, A., Torbunov, D., Torti, M., Tortola, M., Tortorici, F., Tosi, N., Totani, D., Toups, M., Touramanis, C., Travaglini, R., Trevor, J., Trilov, S., Trzaska, W. H., Tsai, Y., Tsai, Y. -T., Tsamalaidze, Z., Tsang, K. V., Tsverava, N., Tu, S. Z., Tufanli, S., Tull, C., Turner, J., Tuzi, M., Tyler, J., Tyley, E., Tzanov, M., Uboldi, L., Uchida, M. A., 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., Vallecorsa, S., Van Berg, R., Van de Water, R. G., Forero, D. Vanegas, Vannerom, D., Varanini, F., Oliva, D. Vargas, Varner, G., Vasina, S., Vaughan, N., Vaziri, K., Vega, J., Ventura, S., Verdugo, A., Vergani, S., Vermeulen, M. A., Verzocchi, M., Vicenzi, M., de Souza, H. Vieira, Vignoli, C., Vilela, C., Viren, B., Vizcaya-Hernandez, A., Vrba, T., Vuong, Q., Wachala, T., Waldron, A. V., Wallbank, M., Walton, T., Wang, H., Wang, J., Wang, L., Wang, M. H. L. S., Wang, X., Wang, Y., Warburton, K., Warner, D., Wascko, M. O., Waters, D., Watson, A., Wawrowska, K., Weatherly, P., Weber, A., Weber, M., Wei, H., Weinstein, A., Wenman, D., Wetstein, M., Whilhelmi, J., White, A., Whitehead, L. H., Whittington, D., Wilking, M. J., Wilkinson, A., Wilkinson, C., Williams, Z., Wilson, F., Wilson, R. J., 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, G., Yang, K., Yang, T., Yankelevich, A., Yershov, N., Yonehara, K., Yoon, Y. S., Young, T., Yu, B., Yu, H., Yu, J., Yu, Y., Yuan, W., Zaki, R., Zalesak, J., Zambelli, L., Zamorano, B., Zani, A., Zazueta, L., Zeller, G. P., Zennamo, J., Zeug, K., Zhang, C., Zhang, S., Zhang, Y., Zhao, M., Zhivun, E., Zimmerman, E. D., Zucchelli, S., Zuklin, J., Zutshi, V., and Zwaska, R.

Subjects
Physics - Computational Physics, Physics - Instrumentation and Detectors
Abstract

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on $10^3$ pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype., Comment: 26 pages, 15 figures

Published
2022

160. Prediction of Scene Plausibility

Author

Nachmias, Or, Fried, Ohad, and Shamir, Ariel

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Understanding the 3D world from 2D images involves more than detection and segmentation of the objects within the scene. It also includes the interpretation of the structure and arrangement of the scene elements. Such understanding is often rooted in recognizing the physical world and its limitations, and in prior knowledge as to how similar typical scenes are arranged. In this research we pose a new challenge for neural network (or other) scene understanding algorithms - can they distinguish between plausible and implausible scenes? Plausibility can be defined both in terms of physical properties and in terms of functional and typical arrangements. Hence, we define plausibility as the probability of encountering a given scene in the real physical world. We build a dataset of synthetic images containing both plausible and implausible scenes, and test the success of various vision models in the task of recognizing and understanding plausibility.

Published
2022

161. FakeOut: Leveraging Out-of-domain Self-supervision for Multi-modal Video Deepfake Detection

Author

Knafo, Gil and Fried, Ohad

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Video synthesis methods rapidly improved in recent years, allowing easy creation of synthetic humans. This poses a problem, especially in the era of social media, as synthetic videos of speaking humans can be used to spread misinformation in a convincing manner. Thus, there is a pressing need for accurate and robust deepfake detection methods, that can detect forgery techniques not seen during training. In this work, we explore whether this can be done by leveraging a multi-modal, out-of-domain backbone trained in a self-supervised manner, adapted to the video deepfake domain. We propose FakeOut; a novel approach that relies on multi-modal data throughout both the pre-training phase and the adaption phase. We demonstrate the efficacy and robustness of FakeOut in detecting various types of deepfakes, especially manipulations which were not seen during training. Our method achieves state-of-the-art results in cross-dataset generalization on audio-visual datasets. This study shows that, perhaps surprisingly, training on out-of-domain videos (i.e., not especially featuring speaking humans), can lead to better deepfake detection systems. Code is available on GitHub.

Published
2022

162. Coder Reviewer Reranking for Code Generation

Author

Zhang, Tianyi, Yu, Tao, Hashimoto, Tatsunori B., Lewis, Mike, Yih, Wen-tau, Fried, Daniel, and Wang, Sida I.

Subjects
Computer Science - Machine Learning, Computer Science - Computation and Language, Computer Science - Programming Languages, Computer Science - Software Engineering
Abstract

Sampling diverse programs from a code language model and reranking with model likelihood is a popular method for code generation but it is prone to preferring degenerate solutions. Inspired by collaborative programming, we propose Coder-Reviewer reranking. We augment Coder language models from past work, which generate programs given language instructions, with Reviewer models, which evaluate the likelihood of the instruction given the generated programs. We perform an extensive study across six datasets with eight models from three model families. Experimental results show that Coder-Reviewer reranking leads to consistent and significant improvement (up to 17% absolute accuracy gain) over reranking with the Coder model only. When combined with executability filtering, Coder-Reviewer reranking can often outperform the minimum Bayes risk method. Coder-Reviewer reranking is easy to implement by prompting, can generalize to different programming languages, and works well with off-the-shelf hyperparameters.

Published
2022

163. Taming Normalizing Flows

Author

Malnick, Shimon, Avidan, Shai, and Fried, Ohad

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We propose an algorithm for taming Normalizing Flow models - changing the probability that the model will produce a specific image or image category. We focus on Normalizing Flows because they can calculate the exact generation probability likelihood for a given image. We demonstrate taming using models that generate human faces, a subdomain with many interesting privacy and bias considerations. Our method can be used in the context of privacy, e.g., removing a specific person from the output of a model, and also in the context of debiasing by forcing a model to output specific image categories according to a given target distribution. Taming is achieved with a fast fine-tuning process without retraining the model from scratch, achieving the goal in a matter of minutes. We evaluate our method qualitatively and quantitatively, showing that the generation quality remains intact, while the desired changes are applied.

Published
2022

164. G^3: Geolocation via Guidebook Grounding

Author

Luo, Grace, Biamby, Giscard, Darrell, Trevor, Fried, Daniel, and Rohrbach, Anna

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Computation and Language
Abstract

We demonstrate how language can improve geolocation: the task of predicting the location where an image was taken. Here we study explicit knowledge from human-written guidebooks that describe the salient and class-discriminative visual features humans use for geolocation. We propose the task of Geolocation via Guidebook Grounding that uses a dataset of StreetView images from a diverse set of locations and an associated textual guidebook for GeoGuessr, a popular interactive geolocation game. Our approach predicts a country for each image by attending over the clues automatically extracted from the guidebook. Supervising attention with country-level pseudo labels achieves the best performance. Our approach substantially outperforms a state-of-the-art image-only geolocation method, with an improvement of over 5% in Top-1 accuracy. Our dataset and code can be found at https://github.com/g-luo/geolocation_via_guidebook_grounding., Comment: Findings of EMNLP 2022

Published
2022

165. Neural Font Rendering

Author

Anderson, Daniel, Shamir, Ariel, and Fried, Ohad

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Recent advances in deep learning techniques and applications have revolutionized artistic creation and manipulation in many domains (text, images, music); however, fonts have not yet been integrated with deep learning architectures in a manner that supports their multi-scale nature. In this work we aim to bridge this gap, proposing a network architecture capable of rasterizing glyphs in multiple sizes, potentially paving the way for easy and accessible creation and manipulation of fonts.

Published
2022

166. SpaText: Spatio-Textual Representation for Controllable Image Generation

Author

Avrahami, Omri, Hayes, Thomas, Gafni, Oran, Gupta, Sonal, Taigman, Yaniv, Parikh, Devi, Lischinski, Dani, Fried, Ohad, and Yin, Xi

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

Recent text-to-image diffusion models are able to generate convincing results of unprecedented quality. However, it is nearly impossible to control the shapes of different regions/objects or their layout in a fine-grained fashion. Previous attempts to provide such controls were hindered by their reliance on a fixed set of labels. To this end, we present SpaText - a new method for text-to-image generation using open-vocabulary scene control. In addition to a global text prompt that describes the entire scene, the user provides a segmentation map where each region of interest is annotated by a free-form natural language description. Due to lack of large-scale datasets that have a detailed textual description for each region in the image, we choose to leverage the current large-scale text-to-image datasets and base our approach on a novel CLIP-based spatio-textual representation, and show its effectiveness on two state-of-the-art diffusion models: pixel-based and latent-based. In addition, we show how to extend the classifier-free guidance method in diffusion models to the multi-conditional case and present an alternative accelerated inference algorithm. Finally, we offer several automatic evaluation metrics and use them, in addition to FID scores and a user study, to evaluate our method and show that it achieves state-of-the-art results on image generation with free-form textual scene control., Comment: CVPR 2023. Project page available at: https://omriavrahami.com/spatext

Published
2022
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167. GEFF: Improving Any Clothes-Changing Person ReID Model using Gallery Enrichment with Face Features

Author

Arkushin, Daniel, Cohen, Bar, Peleg, Shmuel, and Fried, Ohad

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In the Clothes-Changing Re-Identification (CC-ReID) problem, given a query sample of a person, the goal is to determine the correct identity based on a labeled gallery in which the person appears in different clothes. Several models tackle this challenge by extracting clothes-independent features. However, the performance of these models is still lower for the clothes-changing setting compared to the same-clothes setting in which the person appears with the same clothes in the labeled gallery. As clothing-related features are often dominant features in the data, we propose a new process we call Gallery Enrichment, to utilize these features. In this process, we enrich the original gallery by adding to it query samples based on their face features, using an unsupervised algorithm. Additionally, we show that combining ReID and face feature extraction modules alongside an enriched gallery results in a more accurate ReID model, even for query samples with new outfits that do not include faces. Moreover, we claim that existing CC-ReID benchmarks do not fully represent real-world scenarios, and propose a new video CC-ReID dataset called 42Street, based on a theater play that includes crowded scenes and numerous clothes changes. When applied to multiple ReID models, our method (GEFF) achieves an average improvement of 33.5% and 6.7% in the Top-1 clothes-changing metric on the PRCC and LTCC benchmarks. Combined with the latest ReID models, our method achieves new SOTA results on the PRCC, LTCC, CCVID, LaST and VC-Clothes benchmarks and the proposed 42Street dataset.

Published
2022

168. Ham2Pose: Animating Sign Language Notation into Pose Sequences

Author

Shalev-Arkushin, Rotem, Moryossef, Amit, and Fried, Ohad

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Computation and Language
Abstract

Translating spoken languages into Sign languages is necessary for open communication between the hearing and hearing-impaired communities. To achieve this goal, we propose the first method for animating a text written in HamNoSys, a lexical Sign language notation, into signed pose sequences. As HamNoSys is universal by design, our proposed method offers a generic solution invariant to the target Sign language. Our method gradually generates pose predictions using transformer encoders that create meaningful representations of the text and poses while considering their spatial and temporal information. We use weak supervision for the training process and show that our method succeeds in learning from partial and inaccurate data. Additionally, we offer a new distance measurement that considers missing keypoints, to measure the distance between pose sequences using DTW-MJE. We validate its correctness using AUTSL, a large-scale Sign language dataset, show that it measures the distance between pose sequences more accurately than existing measurements, and use it to assess the quality of our generated pose sequences. Code for the data pre-processing, the model, and the distance measurement is publicly released for future research.

Published
2022

169. AutoReply: Detecting Nonsense in Dialogue Introspectively with Discriminative Replies

Author

Shi, Weiyan, Dinan, Emily, Renduchintala, Adi, Fried, Daniel, Jacob, Athul Paul, Yu, Zhou, and Lewis, Mike

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Existing approaches built separate classifiers to detect nonsense in dialogues. In this paper, we show that without external classifiers, dialogue models can detect errors in their own messages introspectively, by calculating the likelihood of replies that are indicative of poor messages. For example, if an agent believes its partner is likely to respond "I don't understand" to a candidate message, that message may not make sense, so an alternative message should be chosen. We evaluate our approach on a dataset from the game Diplomacy, which contains long dialogues richly grounded in the game state, on which existing models make many errors. We first show that hand-crafted replies can be effective for the task of detecting nonsense in applications as complex as Diplomacy. We then design AutoReply, an algorithm to search for such discriminative replies automatically, given a small number of annotated dialogue examples. We find that AutoReply-generated replies outperform handcrafted replies and perform on par with carefully fine-tuned large supervised models. Results also show that one single reply without much computation overheads can also detect dialogue nonsense reasonably well.

Published
2022

170. DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation

Author

Lai, Yuhang, Li, Chengxi, Wang, Yiming, Zhang, Tianyi, Zhong, Ruiqi, Zettlemoyer, Luke, Yih, Scott Wen-tau, Fried, Daniel, Wang, Sida, and Yu, Tao

Subjects
Computer Science - Software Engineering, Computer Science - Computation and Language
Abstract

We introduce DS-1000, a code generation benchmark with a thousand data science problems spanning seven Python libraries, such as NumPy and Pandas. Compared to prior works, DS-1000 incorporates three core features. First, our problems reflect diverse, realistic, and practical use cases since we collected them from StackOverflow. Second, our automatic evaluation is highly specific (reliable) -- across all Codex-002-predicted solutions that our evaluation accept, only 1.8% of them are incorrect; we achieve this with multi-criteria metrics, checking both functional correctness by running test cases and surface-form constraints by restricting API usages or keywords. Finally, we proactively defend against memorization by slightly modifying our problems to be different from the original StackOverflow source; consequently, models cannot answer them correctly by memorizing the solutions from pre-training. The current best public system (Codex-002) achieves 43.3% accuracy, leaving ample room for improvement. We release our benchmark at https://ds1000-code-gen.github.io.

Published
2022

171. Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approaches

Author

Fried, Daniel, Tomlin, Nicholas, Hu, Jennifer, Patel, Roma, and Nematzadeh, Aida

Subjects
Computer Science - Computation and Language
Abstract

People rely heavily on context to enrich meaning beyond what is literally said, enabling concise but effective communication. To interact successfully and naturally with people, user-facing artificial intelligence systems will require similar skills in pragmatics: relying on various types of context -- from shared linguistic goals and conventions, to the visual and embodied world -- to use language effectively. We survey existing grounded settings and pragmatic modeling approaches and analyze how the task goals, environmental contexts, and communicative affordances in each work enrich linguistic meaning. We present recommendations for future grounded task design to naturally elicit pragmatic phenomena, and suggest directions that focus on a broader range of communicative contexts and affordances., Comment: Findings of EMNLP 2023

Published
2022

172. Autonomous Medical Needle Steering In Vivo

Author

Kuntz, Alan, Emerson, Maxwell, Ertop, Tayfun Efe, Fried, Inbar, Fu, Mengyu, Hoelscher, Janine, Rox, Margaret, Akulian, Jason, Gillaspie, Erin A., Lee, Yueh Z., Maldonado, Fabien, Webster III, Robert J., and Alterovitz, Ron

Subjects
Computer Science - Robotics
Abstract

The use of needles to access sites within organs is fundamental to many interventional medical procedures both for diagnosis and treatment. Safe and accurate navigation of a needle through living tissue to an intra-tissue target is currently often challenging or infeasible due to the presence of anatomical obstacles in the tissue, high levels of uncertainty, and natural tissue motion (e.g., due to breathing). Medical robots capable of automating needle-based procedures in vivo have the potential to overcome these challenges and enable an enhanced level of patient care and safety. In this paper, we show the first medical robot that autonomously navigates a needle inside living tissue around anatomical obstacles to an intra-tissue target. Our system leverages an aiming device and a laser-patterned highly flexible steerable needle, a type of needle capable of maneuvering along curvilinear trajectories to avoid obstacles. The autonomous robot accounts for anatomical obstacles and uncertainty in living tissue/needle interaction with replanning and control and accounts for respiratory motion by defining safe insertion time windows during the breathing cycle. We apply the system to lung biopsy, which is critical in the diagnosis of lung cancer, the leading cause of cancer-related death in the United States. We demonstrate successful performance of our system in multiple in vivo porcine studies and also demonstrate that our approach leveraging autonomous needle steering outperforms a standard manual clinical technique for lung nodule access., Comment: 22 pages, 6 figures

Published
2022

173. Identification and reconstruction of low-energy electrons in the ProtoDUNE-SP detector

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., Ahmad, Z., Ahmed, J., Aimard, B., Akbar, F., Allison, K., Monsalve, S. Alonso, Alrashed, M., Alt, C., Alton, A., Alvarez, R., Amedo, P., Anderson, J., Andrade, D. A., Andreopoulos, C., Andreotti, M., Andrews, M. P., Andrianala, F., Andringa, S., Anfimov, N., Campanelli, W. L. Anicézio, Ankowski, A., Antoniassi, M., Antonova, M., Antoshkin, A., Antusch, S., Aranda-Fernandez, A., Arellano, L., Arnold, L. O., Arroyave, M. A., Asaadi, J., Asquith, L., Aurisano, A., Aushev, V., Autiero, D., Ayala-Torres, M., Azfar, F., Back, A., Back, H., Back, J. J., Bagaturia, I., Bagby, L., Balashov, N., Balasubramanian, S., Baldi, P., Baller, B., Bambah, B., Barao, F., Barenboim, G., Barker, G. J., Barkhouse, W., Barnes, C., Barr, G., Monarca, J. Barranco, Barros, A., Barros, N., Barrow, J. L., Basharina-Freshville, A., Bashyal, A., Basque, V., Batchelor, C., 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., Bellantoni, L., Bellettini, G., Bellini, V., Beltramello, O., Benekos, N., Montiel, C. Benitez, Benjamin, D., Neves, F. Bento, Berger, J., Berkman, S., Bernardini, P., Berner, R. M., Bersani, A., Bertolucci, S., Betancourt, M., Rodríguez, A. Betancur, Bevan, A., Bezawada, Y., Bezerra, A. T., Bezerra, T. J., Bhambure, J., Bhardwaj, A., Bhatnagar, V., Bhattacharjee, M., Bhattarai, D., Bhuller, S., Bhuyan, B., Biagi, S., Bian, J., Biassoni, M., Biery, K., Bilki, B., Bishai, M., Bisignani, V., Bitadze, A., Blake, A., Blaszczyk, F. D., Blazey, G. C., Blend, D., Blucher, E., Boissevain, J., Bolognesi, S., Bolton, T., Bomben, L., Bonesini, M., Bonilla-Diaz, C., Bonini, F., Booth, A., Boran, F., Bordoni, S., Borkum, A., Bostan, N., Bour, P., Boyden, D., Bracinik, J., Braga, D., Brailsford, D., Branca, A., Brandt, A., Bremer, J., Brew, C., Brice, S. J., Brizzolari, C., Bromberg, C., Brooke, J., Bross, A., Brunetti, G., Brunetti, M., Buchanan, N., Budd, H., Buergi, J., V., G. Caceres, Cagnoli, I., Cai, T., Caiulo, D., Calabrese, R., Calafiura, P., Calcutt, J., Calin, M., Calivers, L., Calvez, S., Calvo, E., Caminata, A., Caratelli, D., Carber, D., Carceller, J. C., Carini, G., Carlus, B., Carneiro, M. F., Carniti, P., Terrazas, I. Caro, Carranza, H., Carrara, N., Carroll, L., Carroll, T., Forero, J. F. Castaño, Castillo, A., Catano-Mur, E., Cattadori, C., Cavalier, F., Cavallaro, G., Cavanna, F., Centro, S., Cerati, G., Cervelli, A., Villanueva, A. 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Subjects
High Energy Physics - Experiment, Physics - Instrumentation and Detectors
Abstract

Measurements of electrons from $\nu_e$ interactions are crucial for the Deep Underground Neutrino Experiment (DUNE) neutrino oscillation program, as well as searches for physics beyond the standard model, supernova neutrino detection, and solar neutrino measurements. This article describes the selection and reconstruction of low-energy (Michel) electrons in the ProtoDUNE-SP detector. ProtoDUNE-SP is one of the prototypes for the DUNE far detector, built and operated at CERN as a charged particle test beam experiment. A sample of low-energy electrons produced by the decay of cosmic muons is selected with a purity of 95%. This sample is used to calibrate the low-energy electron energy scale with two techniques. An electron energy calibration based on a cosmic ray muon sample uses calibration constants derived from measured and simulated cosmic ray muon events. Another calibration technique makes use of the theoretically well-understood Michel electron energy spectrum to convert reconstructed charge to electron energy. In addition, the effects of detector response to low-energy electron energy scale and its resolution including readout electronics threshold effects are quantified. Finally, the relation between the theoretical and reconstructed low-energy electron energy spectrum is derived and the energy resolution is characterized. The low-energy electron selection presented here accounts for about 75% of the total electron deposited energy. After the addition of lost energy using a Monte Carlo simulation, the energy resolution improves from about 40% to 25% at 50~MeV. These results are used to validate the expected capabilities of the DUNE far detector to reconstruct low-energy electrons., Comment: 19 pages, 10 figures

Published
2022

174. Consensus for Operating Room Multimodal Data Management: Identifying Research Priorities for Data-Driven Surgery

Author

Alain Garcia Vazquez, MD, Juan Verde, MD, Ariosto Hernandez Lara, MD, Didier Mutter, MD, Lee Swanstrom, MD, G-OR Research Committee, 5G-OR Consensus Panel, Ariosto Hernandez Lara, Barbara Seeliger, Daniel Hashimoto, Deepak Alapatt, Joel Lavanchy, Juan Verde, Lise Lecointre, Pietro Mascagni, Pr.Danail Stoyanov, Dirk Willhelm, MD, Pr.Gerald Fried, MD, Gretchen Jackson, MD, PhD, Jean-Paul Mazellier, PhD, Pr.Lena Maier-Hein, Pr.Nicolas Padoy, PhD, Pr.Sascha Treskatsch, MD, Pr.Silvana Perretta, MD, PhD, Pr.Stefanie Speidel, Pr.Teodor Grantcharov, Annika Mareike Engel, BSc, MEng, Axel Boese, DrIng, Carla M. Pugh, MD, PhD, Cesare Hassan, Fabian Dietrich, PhD, Felix Nickel, MD, MME, Franziska Jurosch, MSc, Guido Beldi, MD, Henriette Hegermann, Dr, Johannes Horsch, Dipl-Ing, Julian Rosenkranz, Ing, MSc, Keno Sponheuer, DrMed, Luca Milone, MD, PhD, FACS, Nariaki Okamoto, MD, PhD, Patrick Seeling, PhD, Pedro Filipe Pereira Gouveia, MD, PhD, Roland Croner, Prof.Dr, Sandra Keller, PhD, Sharona B Ross, Taiga Wakabayashi, MD, Ph.D, Takeaki Ishizawa, MD, PhD, Takeshi Urade, MD, PhD, Thomas Schnelldorfer, MD, PhD, and Thorge Lackner, MSc, and MEng

Subjects
Surgery, RD1-811
Abstract

Introduction:. This study aimed to identify research areas that demand attention in multimodal data-driven surgery for improving data management in minimally invasive surgery. Background:. New surgical procedures, high-tech equipment, and digital tools are increasingly being introduced, potentially benefiting patients and surgical teams. These innovations have resulted in operating rooms evolving into data-rich environments, which, in turn, requires a thorough understanding of the data pipeline for improved and more intelligent real-time data usage. As this new domain is vast, it is necessary to identify where efforts should be focused on developing seamless and practical data usage. Methods:. A modified electronic Delphi approach was used; 53 investigators were divided into the following groups: a research group (n=9) for problem identification and a narrative literature review, a medical and technical expert group (n=14) for validation, and an invited panel (n=30) for two electronic survey rounds. Round 1 focused on a consensus regarding bottlenecks in surgical data science areas and research gaps, while round 2 prioritized the statements from round 1, and a roadmap was created based on the identified essential and very important research gaps. Results:. Consensus panelists have identified key research areas, including digitizing operating room (OR) activities, improving data streaming through advanced technologies, uniform protocols for handling multimodal data, and integrating AI for efficiency and safety. The roadmap prioritizes standardizing OR data formats, integrating OR data with patient information, ensuring regulatory compliance, standardizing surgical AI models, and securing data transfers in the next generation of wireless networks. Conclusions:. This work is an international expert consensus regarding the current issues and key research targets in the promising field of data-driven surgery, highlighting the research needs of many operating room stakeholders with the aim of facilitating the implementation of novel patient care strategies in minimally invasive surgery.

Published
2024
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175. Developing a transdiagnostic Ecological Momentary Assessment protocol for psychopathology

Author

Alberto Jover Martínez, Lotte H. J. M. Lemmens, Eiko I. Fried, and Anne Roefs

Subjects
Ecological Momentary Assessment (EMA), measurement, network approach, psychopathology, transdiagnostic, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Abstract Objectives The network approach to psychopathology posits that mental disorders emerge from dynamic interactions among psychopathology‐relevant variables. Ecological Momentary Assessment (EMA) is frequently used to assess these variables in daily life. Considering the transdiagnostic nature of the network approach to psychopathology, this study describes the development of a transdiagnostic EMA protocol for psychopathology. Methods First, 96 clinicians completed an online survey, providing three EMA constructs for up to three disorders they specialize in, and three EMA constructs relevant across disorders (transdiagnostic constructs). Second, 12 focus groups were conducted with clinical experts for specific types of diagnoses (e.g., mood disorders, anxiety disorders). Finally, a selection of items was reached by consensus. Two raters independently coded the online survey responses with an inter‐rater agreement of 87.3%. Results Jaccard indices showed up to 52.6% overlap in EMA items across types of diagnoses. The most frequently reported transdiagnostic constructs were mood, sleep quality, and stress. A final set of EMA items is created based on items' frequency and informativeness, ensuring completeness across diagnoses and minimizing burden. Conclusions The described procedure resulted in a feasible EMA protocol to examine psychopathology transdiagnostically. Feasibility was helped by the overlap in mentioned symptoms across disorders. Such overlap raises questions about the validity of DSM categories.

Published
2024
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176. Concept validation of separations for thorium-based radionuclide generator systems for medical application

Author

Bianca Schacherl, Kiara Maurer, Martin Schäfer, Yvonne Remde, Frank Geyer, Annika Fried, Steffen Alexander Happel, and Martina Benešová-Schäfer

Subjects
targeted alpha therapy, alpha in vivo nanogenerators, thorium, radium, radionuclide generators, radionuclide separation, Plasma physics. Ionized gases, QC717.6-718.8, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

Targeted alpha therapy (TαT) represents an emerging and cutting-edge treatment option for patients dealing with highly challenging metastatic cancer diseases. Critically, the limited supply of alpha-particle-emitting radionuclides, so-called alpha in vivo nanogenerators, hampers wider utilization of TαT in clinical settings. This could effectively be circumvented by alternative production routes, including straightforward purification and reformulation strategies. Radionuclide generators offering great potential in simple and robust elution strategies can be provided that still adhere to high radioisotopic, radionuclidic, and radiochemical purity criteria. This study takes a first step towards novel separation strategies by providing additional sources of alpha in vivo nanogenerators for TαT through experiments with various metal surrogates. With different systems, 232Th/natBa was used as a radionuclide generator analogue to 227Th/223Ra, and 232Th/natBa/natLa was used as a triplet analogue to 229Th/225Ra/225Ac. Three selective resins (UTEVA, TEVA, DGA-N) were evaluated for the 232Th/natBa system. Two perturbations of the best-performing resin were further evaluated using a larger diameter column and 1 week of equilibration. For the 232Th/natBa/natLa separation system, a combined column with two selective resins (TK200, TK101) was employed and evaluated. The results thus obtained pave the way for alternative separation strategies in radioactive proof-of-concept validation in the near future.

Published
2024
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181. Remote Sensing Impact of Single-Photon Sensitive Airborne Lidar Systems Based on Geiger-Mode Avalanche Photodiode Arrays.

Author

M. Jalal Khan, Luke J. Skelly, Alexandru Vasile, Thomas Francis, John Drover, Diego Rivera, Robert Hatch, Daniel Dumanis, K. Alex McIntosh, Jonathan Fréchette, Miroslaw Myszka, E. Duerr, Tina Shih, Mark Bury, Paul Consalvo, John Sultana, John Zayhowski, Ken Burkett, Craig Rowe, Robert Krauss, Brandon Call, Christopher Reichert, and Dale Fried

Published
2024
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195. Upper Cervical Fractures

Author

Fried, Tristan B., Karamian, Brian A., Vaccaro, Alexander R., Slullitel, Pablo, editor, Rossi, Luciano, editor, and Camino-Willhuber, Gastón, editor

Published
2024
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196. Individual Behavioral Insights in Schizophrenia: A Network Analysis and Mobile Sensing Approach

Author

Davies, Andy, Fried, Eiko, Costilla-Reyes, Omar, Aung, Hane, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin, Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Salvi, Dario, editor, Van Gorp, Pieter, editor, and Shah, Syed Ahmar, editor

Published
2024
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