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9,897 results on '"Leach, A. P."'

1. COMCAT: Leveraging Human Judgment to Improve Automatic Documentation and Summarization

Author: Grandel, Skyler, Andersen, Scott Thomas, Huang, Yu, and Leach, Kevin
Subjects: Computer Science - Software Engineering
Abstract: Software maintenance constitutes a substantial portion of the total lifetime costs of software, with a significant portion attributed to code comprehension. Software comprehension is eased by documentation such as comments that summarize and explain code. We present COMCAT, an approach to automate comment generation by augmenting Large Language Models (LLMs) with expertise-guided context to target the annotation of source code with comments that improve comprehension. Our approach enables the selection of the most relevant and informative comments for a given snippet or file containing source code. We develop the COMCAT pipeline to comment C/C++ files by (1) automatically identifying suitable locations in which to place comments, (2) predicting the most helpful type of comment for each location, and (3) generating a comment based on the selected location and comment type. In a human subject evaluation, we demonstrate that COMCAT-generated comments significantly improve developer code comprehension across three indicative software engineering tasks by up to 12% for 87% of participants. In addition, we demonstrate that COMCAT-generated comments are at least as accurate and readable as human-generated comments and are preferred over standard ChatGPT-generated comments for up to 92% of snippets of code. Furthermore, we develop and release a dataset containing source code snippets, human-written comments, and human-annotated comment categories. COMCAT leverages LLMs to offer a significant improvement in code comprehension across a variety of human software engineering tasks., Comment: 12 pages, 6 figures
Published: 2024

2. Imaging of single barium atoms in a second matrix site in solid xenon for barium tagging in a $^{136}$Xe double beta decay experiment

Author: Yvaine, M., Fairbank, D., Soderstrom, J., Taylor, C., Stanley, J., Walton, T., Chambers, C., Iverson, A., Fairbank, W., Kharusi, S. Al, Amy, A., Angelico, E., Anker, A., Arnquist, I. J., Atencio, A., Bane, J., Belov, V., Bernard, E. P., Bhatta, T., Bolotnikov, A., Breslin, J., Breur, P. A., Brodsky, J. P., Brown, E., Brunner, T., Caden, E., Cao, G. F., Cesmecioglu, D., Chambers, E., Chana, B., Chernyak, D., Chiu, M., Collister, R., Cvitan, M., Daniels, T., Darroch, L., DeVoe, R., di Vacri, M. L., Dolinski, M. J., Eckert, B., Elbeltagi, M., Elmansali, R., Fatemighomi, N., Foust, B., Fu, Y. S., Gallacher, D., Gallice, N., Giacomini, G., Gillis, W., Gingras, C., Gornea, R., Gratta, G., Hardy, C. A., Hedges, S., Hein, E., Holt, J. D., Hoppe, E. W., Karelin, A., Keblbeck, D., Kotov, I., Kuchenkov, A., Kumar, K. S., Kwiatkowski, A. A., Larson, A., Latif, M. B., Leach, K. G., Lennarz, A., Leonard, D. S., Lewis, H., Li, G., Li, Z., Licciardi, C., Lindsay, R., MacLellan, R., Majidi, S., Malbrunot, C., Masbou, J., McMichael, K., Peregrina, M. Medina, Moe, M., Mong, B., Moore, D. C., Natzke, C. R., Ngwadla, X. E., Ni, K., Nolan, A., Nowicki, S. C., Ondze, J. C. Nzobadila, Odian, A., Orrell, J. L., Ortega, G. S., Overman, C. T., Pagani, L., Smalley, H. Peltz, Perna, A., Pocar, A., Radeka, V., Raguzin, E., Rasiwala, H., Ray, D., Rescia, S., Richardson, G., Ross, R., Rowson, P. C., Saldanha, R., Sangiorgio, S., Schwartz, S., Sekula, S., Si, L., Soma, A. K., Spadoni, F., Stekhanov, V., Sun, X. L., Thibado, S., Tidball, A., Totev, T., Triambak, S., Tsang, T., Tyuka, O. A., van Bruggen, E., Vidal, M., Walent, M., Wamba, K., Wang, H. W., Wang, Q. D., Wang, W., Wang, Y. G., Watts, M., Wehrfritz, M., Wen, L. J., Wichoski, U., Wilde, S., Worcester, M., Xu, H., Yang, L., Yu, M., and Zeldovich, O.
Subjects: Physics - Atomic Physics, High Energy Physics - Experiment, Nuclear Experiment
Abstract: Neutrinoless double beta decay is one of the most sensitive probes for new physics beyond the Standard Model of particle physics. One of the isotopes under investigation is $^{136}$Xe, which would double beta decay into $^{136}$Ba. Detecting the single $^{136}$Ba daughter provides a sort of ultimate tool in the discrimination against backgrounds. Previous work demonstrated the ability to perform single atom imaging of Ba atoms in a single-vacancy site of a solid xenon matrix. In this paper, the effort to identify signal from individual barium atoms is extended to Ba atoms in a hexa-vacancy site in the matrix and is achieved despite increased photobleaching in this site. Abrupt fluorescence turn-off of a single Ba atom is also observed. Significant recovery of fluorescence signal lost through photobleaching is demonstrated upon annealing of Ba deposits in the Xe ice. Following annealing, it is observed that Ba atoms in the hexa-vacancy site exhibit antibleaching while Ba atoms in the tetra-vacancy site exhibit bleaching. This may be evidence for a matrix site transfer upon laser excitation. Our findings offer a path of continued research toward tagging of Ba daughters in all significant sites in solid xenon., Comment: 9 pages, 8 figures
Published: 2024

3. A Variational Approach to Learning Photonic Unitary Operators

Author: Bezuidenhout, Hadrian, Koni, Mwezi, Leach, Jonathan, Obando, Paola Concha, Forbes, Andrew, and Nape, Isaac
Subjects: Physics - Optics, Quantum Physics
Abstract: Structured light, light tailored in its internal degrees of freedom, has become topical in numerous quantum and classical information processing protocols. In this work, we harness the high dimensional nature of structured light modulated in the transverse spatial degree of freedom to realise an adaptable scheme for learning unitary operations. Our approach borrows from concepts in variational quantum computing, where a search or optimisation problem is mapped onto the task of finding a minimum ground state energy for a given energy/goal function. We achieve this by a pseudo-random walk procedure over the parameter space of the unitary operation, implemented with optical matrix-vector multiplication enacted on arrays of Gaussian modes by exploiting the partial Fourier transforming capabilities of a cylindrical lens in the transverse degree of freedom for the measurement. We outline the concept theoretically, and experimentally demonstrate that we are able to learn optical unitary matrices for dimensions d = 2, 4, 8 and 16 with average fidelities of >90%. Our work advances high dimensional information processing and can be adapted to both process and quantum state tomography of unknown states and channels.
Published: 2024

4. EngineBench: Flow Reconstruction in the Transparent Combustion Chamber III Optical Engine

Author: Baker, Samuel J., Hobley, Michael A., Scherl, Isabel, Fang, Xiaohang, Leach, Felix C. P., and Davy, Martin H.
Subjects: Physics - Fluid Dynamics, Computer Science - Computer Vision and Pattern Recognition
Abstract: We present EngineBench, the first machine learning (ML) oriented database to use high quality experimental data for the study of turbulent flows inside combustion machinery. Prior datasets for ML in fluid mechanics are synthetic or use overly simplistic geometries. EngineBench is comprised of real-world particle image velocimetry (PIV) data that captures the turbulent airflow patterns in a specially-designed optical engine. However, in PIV data from internal flows, such as from engines, it is often challenging to achieve a full field of view and large occlusions can be present. In order to design optimal combustion systems, insight into the turbulent flows in these obscured areas is needed, which can be provided via inpainting models. Here we propose a novel inpainting task using random edge gaps, a technique that emphasises realism by introducing occlusions at random sizes and orientations at the edges of the PIV images. We test five ML methods on random edge gaps using pixel-wise, vector-based, and multi-scale performance metrics. We find that UNet-based models are more accurate than the industry-norm non-parametric approach and the context encoder at this task on both small and large gap sizes. The dataset and inpainting task presented in this paper support the development of more general-purpose pre-trained ML models for engine design problems. The method comparisons allow for more informed selection of ML models for problems in experimental flow diagnostics. All data and code are publicly available at https://eng.ox.ac.uk/tpsrg/research/enginebench/.
Published: 2024

5. Supernova Electron-Neutrino Interactions with Xenon in the nEXO Detector

Author: nEXO Collaboration, Hedges, S., Kharusi, S. Al, Angelico, E., Brodsky, J. P., Richardson, G., Wilde, S., Amy, A., Anker, A., Arnquist, I. J., Arsenault, P., Atencio, A., Badhrees, I., Bane, J., Belov, V., Bernard, E. P., Bhatta, T., Bolotnikov, A., Breslin, J., Breur, P. A., Brown, E., Brunner, T., Caden, E., Cao, G. F., Cao, L. Q., Cesmecioglu, D., Chambers, E., Chana, B., Charlebois, S. A., Chernyak, D., Chiu, M., Collister, R., Cvitan, M., Dalmasson, J., Daniels, T., Darroch, L., DeVoe, R., di Vacri, M. L., Ding, Y. Y., Dolinski, M. J., Eckert, B., Elbeltagi, M., Elmansali, R., Fabris, L., Fairbank, W., Farine, J., Fatemighomi, N., Foust, B., Fu, Y. S., Gallacher, D., Gallice, N., Gillis, W., Goeldi, D., Gorham, A., Gornea, R., Gratta, G., Guan, Y. D., Hardy, C. A., Heffner, M., Hein, E., Holt, J. D., Hoppe, E. W., House, A., Hunt, W., Iverson, A., Kachru, P., Karelin, A., Keblbeck, D., Kuchenkov, A., Kumar, K. S., Larson, A., Latif, M. B., Leach, K. G., Lenardo, B. G., Leonard, D. S., Lewis, H., Li, G., Li, Z., Licciardi, C., Lindsay, R., MacLellan, R., Majidi, S., Malbrunot, C., Martel-Dion, P., Masbou, J., McMichael, K., Medina-Peregrina, M., Mong, B., Moore, D. C., Nattress, J., Natzke, C. R., Ngwadla, X. E., Ni, K., Nolan, A., Nowicki, S. C., Ondze, J. C. Nzobadila, Orrell, J. L., Ortega, G. S., Overman, C. T., Pagani, L., Smalley, H. Peltz, Perna, A., Piepke, A., Franco, T. Pinto, Pocar, A., Pratte, J. -F., Rasiwala, H., Ray, D., Raymond, K., Rescia, S., Riot, V., Ross, R., Saldanha, R., Sangiorgio, S., Schwartz, S., Sekula, S., Soderstrom, J., Soma, A. K., Spadoni, F., Sun, X. L., Thibado, S., Tidball, A., Totev, T., Triambak, S., Tsang, R. H. M., Tyuka, O. A., van Bruggen, E., Vidal, M., Viel, S., Walent, M., Wang, Q. D., Wang, W., Wang, Y. G., Watts, M., Wehrfritz, M., Wei, W., Wen, L. J., Wichoski, U., Wu, X. M., Xu, H., Yang, H. B., Yang, L., Yu, M., Yvaine, M., Zeldovich, O., and Zhao, J.
Subjects: High Energy Physics - Phenomenology, High Energy Physics - Experiment, Nuclear Theory
Abstract: Electron-neutrino charged-current interactions with xenon nuclei were modeled in the nEXO neutrinoless double-beta decay detector (~5-tonne, 90% ${}^{136}$Xe, 10% ${}^{134}$Xe) to evaluate its sensitivity to supernova neutrinos. Predictions for event rates and detectable signatures were modeled using the MARLEY event generator. We find good agreement between MARLEY's predictions and existing theoretical calculations of the inclusive cross sections at supernova neutrino energies. The interactions modeled by MARLEY were simulated within the nEXO simulation framework and were run through an example reconstruction algorithm to determine the detector's efficiency for reconstructing these events. The simulated data, incorporating the detector response, were used to study the ability of nEXO to reconstruct the incident electron-neutrino spectrum and these results were extended to a larger xenon detector of the same isotope enrichment. We estimate that nEXO will be able to observe electron-neutrino interactions with xenon from supernovae as far as 5 to 8 kpc from earth, while the ability to reconstruct incident electron-neutrino spectrum parameters from observed interactions in nEXO is limited to closer supernovae., Comment: 17 pages, 16 figures
Published: 2024

6. Maternal Education Prospectively Predicts Child Neurocognitive Function: An Environmental Influences on Child Health Outcomes Study

Author: Santiago Morales, Maureen E. Bowers, Lauren Shuffrey, Katherine Ziegler, Sonya Troller-Renfree, Alexis Hernandez, Stephanie C. Leach, Monica McGrath, Cindy Ola, Leslie D. Leve, Sara S. Nozadi, Margaret M. Swingler, Jin-Shei Lai, Julie B. Schweitzer, William Fifer, Carlos A. Camargo, Gurjit K. Khurana Hershey, Allison L. B. Shapiro, Daniel P. Keating, Tina V. Hartert, Sean Deoni, Assiamira Ferrara, and Amy J. Elliott
Abstract: A large body of research has established a relation between maternal education and children's neurocognitive functions, such as executive function and language. However, most studies have focused on early childhood and relatively few studies have examined associations with changes in maternal education over time. Consequently, it remains unclear if early maternal education is longitudinally related to neurocognitive functions in children, adolescents, and young adults. In addition, the associations between changes in maternal education across development and more broadly defined neurocognitive outcomes remain relatively untested. The current study leveraged a large multicohort sample to examine the longitudinal relations between perinatal maternal education and changes in maternal education during development with children's, adolescents', and young adults' neurocognitive functions (N = 2,688; M[subscript age] = 10.32 years; SD[subscript age] = 4.26; range = 3-20 years). Moreover, we examined the differential effects of perinatal maternal education and changes in maternal education across development on executive function and language performance. Perinatal maternal education was positively associated with children's later overall neurocognitive function. This longitudinal relation was stronger for language than executive function. In addition, increases in maternal education were related to improved language performance but were not associated with executive functioning performance. Our findings support perinatal maternal education as an important predictor of neurocognitive outcomes later in development. Moreover, our results suggest that examining how maternal education changes across development can provide important insights that can help inform policies and interventions designed to foster neurocognitive development. [This paper was written on behalf of program collaborators for Environmental Influences on Child Health Outcomes.]
Published: 2024
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7. The Unexpected Benefits of a Research-Practice Partnership's Efforts to Strengthen Budgetary Decision-Making

Author: Robert Shand, Stephen M. Leach, Fiona M. Hollands, Bo Yan, Dena Dossett, Florence Chang, and Yilin Pan
Abstract: This research-practice partnership (RPP) focused on developing and testing metrics and tools to foster improved evidence-based budgetary decision-making. The expectation was that research findings would directly influence decisions about program expansion, contraction, or elimination. Instead, unexpected findings led to unexpected uses: changes in program implementation, administrative data collection processes, and the kinds of information provided to inform budget and program improvement decisions. A content analysis of two rounds of interviews with senior district budget decision-makers identified recommendations for improving the budget decision process and document changes in the budget process over time. The study highlights the sometimes unanticipated ways that RPPs can inform educational decisions. [This is the online first version of an article published in "Peabody Journal of Education."]
Published: 2024
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8. Case Study: Neural Network Malware Detection Verification for Feature and Image Datasets

Author: Robinette, Preston K., Lopez, Diego Manzanas, Serbinowska, Serena, Leach, Kevin, and Johnson, Taylor T.
Subjects: Computer Science - Cryptography and Security
Abstract: Malware, or software designed with harmful intent, is an ever-evolving threat that can have drastic effects on both individuals and institutions. Neural network malware classification systems are key tools for combating these threats but are vulnerable to adversarial machine learning attacks. These attacks perturb input data to cause misclassification, bypassing protective systems. Existing defenses often rely on enhancing the training process, thereby increasing the model's robustness to these perturbations, which is quantified using verification. While training improvements are necessary, we propose focusing on the verification process used to evaluate improvements to training. As such, we present a case study that evaluates a novel verification domain that will help to ensure tangible safeguards against adversaries and provide a more reliable means of evaluating the robustness and effectiveness of anti-malware systems. To do so, we describe malware classification and two types of common malware datasets (feature and image datasets), demonstrate the certified robustness accuracy of malware classifiers using the Neural Network Verification (NNV) and Neural Network Enumeration (nnenum) tools, and outline the challenges and future considerations necessary for the improvement and refinement of the verification of malware classification. By evaluating this novel domain as a case study, we hope to increase its visibility, encourage further research and scrutiny, and ultimately enhance the resilience of digital systems against malicious attacks., Comment: In International Conference On Formal Methods in Software Engineering, 2024; (FormaliSE'24)
Published: 2024

9. Direct Experimental Constraints on the Spatial Extent of a Neutrino Wavepacket

Author: Smolsky, Joseph, Leach, Kyle G, Abells, Ryan, Amaro, Pedro, Andoche, Adrien, Borbridge, Keith, Bray, Connor, Cantor, Robin, Diercks, David, Fretwell, Spencer, Friedrich, Stephan, Gillespie, Abigail, Guerra, Mauro, Hall, Ad, Harris, Cameron N, Harris, Jackson T, Hinkle, Calvin, Lamm, Amii, Hayen, Leendert M, Hervieux, Paul-Antoine, Kim, Geon-Bo, Kim, Inwook, Lennarz, Annika, Lordi, Vincenzo, Machado, Jorge, Marino, Andrew, McKeen, David, Mougeot, Xavier, Ponce, Francisco, Ruiz, Chris, Samanta, Amit, Santos, José Paulo, Stone-Whitehead, Caitlyn, Taylor, John, Templet, Joseph, Upadhyayula, Sriteja, Wagner, Louis, and Warburton, William K
Subjects: Nuclear Experiment, High Energy Physics - Experiment, Quantum Physics
Abstract: Despite their high relative abundance in our Universe, neutrinos are the least understood fundamental particles of nature. They also provide a unique system to study quantum coherence and the wavelike nature of particles in fundamental systems due to their extremely weak interaction probabilities. In fact, the quantum properties of neutrinos emitted in experimentally relevant sources are virtually unknown and the spatial extent of the neutrino wavepacket is only loosely constrained by reactor neutrino oscillation data with a spread of 13 orders of magnitude. Here, we present the first direct limits of this quantity through a new experimental concept to extract the energy width, $\sigma_{\textrm{N},E}$, of the recoil daughter nucleus emitted in the nuclear electron capture (EC) decay of $^7$Be. The final state in the EC decay process contains a recoiling $^7$Li nucleus and an electron neutrino ($\nu_e$) which are entangled at their creation. The $^7$Li energy spectrum is measured to high precision by directly embedding $^7$Be radioisotopes into a high resolution superconducting tunnel junction that is operated as a cryogenic sensor. The lower limit on the spatial uncertainty of the recoil daughter was found to be $\sigma_{\textrm{N}, x} \geq 6.2$\,pm, which implies the final-state system is localized at a scale more than a thousand times larger than the nucleus itself. From this measurement, the first direct lower limits on the spatial extent of the neutrino wavepacket were extracted using two different theoretical methods. These results have wide-reaching implications in several areas including the nature of spatial localization at sub-atomic scales, interpretation of neutrino physics data, and the potential reach of future large-scale experiments., Comment: 20 pages, 3 figures, v3 corrects and updates one of the wavepacket width calculations
Published: 2024

10. Generating Hard-Negative Out-of-Scope Data with ChatGPT for Intent Classification

Author: Li, Zhijian, Larson, Stefan, and Leach, Kevin
Subjects: Computer Science - Computation and Language
Abstract: Intent classifiers must be able to distinguish when a user's utterance does not belong to any supported intent to avoid producing incorrect and unrelated system responses. Although out-of-scope (OOS) detection for intent classifiers has been studied, previous work has not yet studied changes in classifier performance against hard-negative out-of-scope utterances (i.e., inputs that share common features with in-scope data, but are actually out-of-scope). We present an automated technique to generate hard-negative OOS data using ChatGPT. We use our technique to build five new hard-negative OOS datasets, and evaluate each against three benchmark intent classifiers. We show that classifiers struggle to correctly identify hard-negative OOS utterances more than general OOS utterances. Finally, we show that incorporating hard-negative OOS data for training improves model robustness when detecting hard-negative OOS data and general OOS data. Our technique, datasets, and evaluation address an important void in the field, offering a straightforward and inexpensive way to collect hard-negative OOS data and improve intent classifiers' robustness., Comment: LREC-COLING 2024
Published: 2024

11. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Author: Gemini Team, Georgiev, Petko, Lei, Ving Ian, Burnell, Ryan, Bai, Libin, Gulati, Anmol, Tanzer, Garrett, Vincent, Damien, Pan, Zhufeng, Wang, Shibo, Mariooryad, Soroosh, Ding, Yifan, Geng, Xinyang, Alcober, Fred, Frostig, Roy, Omernick, Mark, Walker, Lexi, Paduraru, Cosmin, Sorokin, Christina, Tacchetti, Andrea, Gaffney, Colin, Daruki, Samira, Sercinoglu, Olcan, Gleicher, Zach, Love, Juliette, Voigtlaender, Paul, Jain, Rohan, Surita, Gabriela, Mohamed, Kareem, Blevins, Rory, Ahn, Junwhan, Zhu, Tao, Kawintiranon, Kornraphop, Firat, Orhan, Gu, Yiming, Zhang, Yujing, Rahtz, Matthew, Faruqui, Manaal, Clay, Natalie, Gilmer, Justin, Co-Reyes, JD, Penchev, Ivo, Zhu, Rui, Morioka, Nobuyuki, Hui, Kevin, Haridasan, Krishna, Campos, Victor, Mahdieh, Mahdis, Guo, Mandy, Hassan, Samer, Kilgour, Kevin, Vezer, Arpi, Cheng, Heng-Tze, de Liedekerke, Raoul, Goyal, Siddharth, Barham, Paul, Strouse, DJ, Noury, Seb, Adler, Jonas, Sundararajan, Mukund, Vikram, Sharad, Lepikhin, Dmitry, Paganini, Michela, Garcia, Xavier, Yang, Fan, Valter, Dasha, Trebacz, Maja, Vodrahalli, Kiran, Asawaroengchai, Chulayuth, Ring, Roman, Kalb, Norbert, Soares, Livio Baldini, Brahma, Siddhartha, Steiner, David, Yu, Tianhe, Mentzer, Fabian, He, Antoine, Gonzalez, Lucas, Xu, Bibo, Kaufman, Raphael Lopez, Shafey, Laurent El, Oh, Junhyuk, Hennigan, Tom, Driessche, George van den, Odoom, Seth, Lucic, Mario, Roelofs, Becca, Lall, Sid, Marathe, Amit, Chan, Betty, Ontanon, Santiago, He, Luheng, Teplyashin, Denis, Lai, Jonathan, Crone, Phil, Damoc, Bogdan, Ho, Lewis, Riedel, Sebastian, Lenc, Karel, Yeh, Chih-Kuan, Chowdhery, Aakanksha, Xu, Yang, Kazemi, Mehran, Amid, Ehsan, Petrushkina, Anastasia, Swersky, Kevin, Khodaei, Ali, Chen, Gowoon, Larkin, Chris, Pinto, Mario, Yan, Geng, Badia, Adria Puigdomenech, Patil, Piyush, Hansen, Steven, Orr, Dave, Arnold, Sebastien M. R., Grimstad, Jordan, Dai, Andrew, Douglas, Sholto, Sinha, Rishika, Yadav, Vikas, Chen, Xi, Gribovskaya, Elena, Austin, Jacob, Zhao, Jeffrey, Patel, Kaushal, Komarek, Paul, Austin, Sophia, Borgeaud, Sebastian, Friso, Linda, Goyal, Abhimanyu, Caine, Ben, Cao, Kris, Chung, Da-Woon, Lamm, Matthew, Barth-Maron, Gabe, Kagohara, Thais, Olszewska, Kate, Chen, Mia, Shivakumar, Kaushik, Agarwal, Rishabh, Godhia, Harshal, Rajwar, Ravi, Snaider, Javier, Dotiwalla, Xerxes, Liu, Yuan, Barua, Aditya, Ungureanu, Victor, Zhang, Yuan, Batsaikhan, Bat-Orgil, Wirth, Mateo, Qin, James, Danihelka, Ivo, Doshi, Tulsee, Chadwick, Martin, Chen, Jilin, Jain, Sanil, Le, Quoc, Kar, Arjun, Gurumurthy, Madhu, Li, Cheng, Sang, Ruoxin, Liu, Fangyu, Lamprou, Lampros, Munoz, Rich, Lintz, Nathan, Mehta, Harsh, Howard, Heidi, Reynolds, Malcolm, Aroyo, Lora, Wang, Quan, Blanco, Lorenzo, Cassirer, Albin, Griffith, Jordan, Das, Dipanjan, Lee, Stephan, Sygnowski, Jakub, Fisher, Zach, Besley, James, Powell, Richard, Ahmed, Zafarali, Paulus, Dominik, Reitter, David, Borsos, Zalan, Joshi, Rishabh, Pope, Aedan, Hand, Steven, Selo, Vittorio, Jain, Vihan, Sethi, Nikhil, Goel, Megha, Makino, Takaki, May, Rhys, Yang, Zhen, Schalkwyk, Johan, Butterfield, Christina, Hauth, Anja, Goldin, Alex, Hawkins, Will, Senter, Evan, Brin, Sergey, Woodman, Oliver, Ritter, Marvin, Noland, Eric, Giang, Minh, Bolina, Vijay, Lee, Lisa, Blyth, Tim, Mackinnon, Ian, Reid, Machel, Sarvana, Obaid, Silver, David, Chen, Alexander, Wang, Lily, Maggiore, Loren, Chang, Oscar, Attaluri, Nithya, Thornton, Gregory, Chiu, Chung-Cheng, Bunyan, Oskar, Levine, Nir, Chung, Timothy, Eltyshev, Evgenii, Si, Xiance, Lillicrap, Timothy, Brady, Demetra, Aggarwal, Vaibhav, Wu, Boxi, Xu, Yuanzhong, McIlroy, Ross, Badola, Kartikeya, Sandhu, Paramjit, Moreira, Erica, Stokowiec, Wojciech, Hemsley, Ross, Li, Dong, Tudor, Alex, Shyam, Pranav, Rahimtoroghi, Elahe, Haykal, Salem, Sprechmann, Pablo, Zhou, Xiang, Mincu, Diana, Li, Yujia, Addanki, Ravi, Krishna, Kalpesh, Wu, Xiao, Frechette, Alexandre, Eyal, Matan, Dafoe, Allan, Lacey, Dave, Whang, Jay, Avrahami, Thi, Zhang, Ye, Taropa, Emanuel, Lin, Hanzhao, Toyama, Daniel, Rutherford, Eliza, Sano, Motoki, Choe, HyunJeong, Tomala, Alex, Safranek-Shrader, Chalence, Kassner, Nora, Pajarskas, Mantas, Harvey, Matt, Sechrist, Sean, Fortunato, Meire, Lyu, Christina, Elsayed, Gamaleldin, Kuang, Chenkai, Lottes, James, Chu, Eric, Jia, Chao, Chen, Chih-Wei, Humphreys, Peter, Baumli, Kate, Tao, Connie, Samuel, Rajkumar, Santos, Cicero Nogueira dos, Andreassen, Anders, Rakićević, Nemanja, Grewe, Dominik, Kumar, Aviral, Winkler, Stephanie, Caton, Jonathan, Brock, Andrew, Dalmia, Sid, Sheahan, Hannah, Barr, Iain, Miao, Yingjie, Natsev, Paul, Devlin, Jacob, Behbahani, Feryal, Prost, Flavien, Sun, Yanhua, Myaskovsky, Artiom, Pillai, Thanumalayan Sankaranarayana, Hurt, Dan, Lazaridou, Angeliki, Xiong, Xi, Zheng, Ce, Pardo, Fabio, Li, Xiaowei, Horgan, Dan, Stanton, Joe, Ambar, Moran, Xia, Fei, Lince, Alejandro, Wang, Mingqiu, Mustafa, Basil, Webson, Albert, Lee, Hyo, Anil, Rohan, Wicke, Martin, Dozat, Timothy, Sinha, Abhishek, Piqueras, Enrique, Dabir, Elahe, Upadhyay, Shyam, Boral, Anudhyan, Hendricks, Lisa Anne, Fry, Corey, Djolonga, Josip, Su, Yi, Walker, Jake, Labanowski, Jane, Huang, Ronny, Misra, Vedant, Chen, Jeremy, Skerry-Ryan, RJ, Singh, Avi, Rijhwani, Shruti, Yu, Dian, Castro-Ros, Alex, Changpinyo, Beer, Datta, Romina, Bagri, Sumit, Hrafnkelsson, Arnar Mar, Maggioni, Marcello, Zheng, Daniel, Sulsky, Yury, Hou, Shaobo, Paine, Tom Le, Yang, Antoine, Riesa, Jason, Rogozinska, Dominika, Marcus, Dror, Badawy, Dalia El, Zhang, Qiao, Wang, Luyu, Miller, Helen, Greer, Jeremy, Sjos, Lars Lowe, Nova, Azade, Zen, Heiga, Chaabouni, Rahma, Rosca, Mihaela, Jiang, Jiepu, Chen, Charlie, Liu, Ruibo, Sainath, Tara, Krikun, Maxim, Polozov, Alex, Lespiau, Jean-Baptiste, Newlan, Josh, Cankara, Zeyncep, Kwak, Soo, Xu, Yunhan, Chen, Phil, Coenen, Andy, Meyer, Clemens, Tsihlas, Katerina, Ma, Ada, Gottweis, Juraj, Xing, Jinwei, Gu, Chenjie, Miao, Jin, Frank, Christian, Cankara, Zeynep, Ganapathy, Sanjay, Dasgupta, Ishita, Hughes-Fitt, Steph, Chen, Heng, Reid, David, Rong, Keran, Fan, Hongmin, van Amersfoort, Joost, Zhuang, Vincent, Cohen, Aaron, Gu, Shixiang Shane, Mohananey, Anhad, Ilic, Anastasija, Tobin, Taylor, Wieting, John, Bortsova, Anna, Thacker, Phoebe, Wang, Emma, Caveness, Emily, Chiu, Justin, Sezener, Eren, Kaskasoli, Alex, Baker, Steven, Millican, Katie, Elhawaty, Mohamed, Aisopos, Kostas, Lebsack, Carl, Byrd, Nathan, Dai, Hanjun, Jia, Wenhao, Wiethoff, Matthew, Davoodi, Elnaz, Weston, Albert, Yagati, Lakshman, Ahuja, Arun, Gao, Isabel, Pundak, Golan, Zhang, Susan, Azzam, Michael, Sim, Khe Chai, Caelles, Sergi, Keeling, James, Sharma, Abhanshu, Swing, Andy, Li, YaGuang, Liu, Chenxi, Bostock, Carrie Grimes, Bansal, Yamini, Nado, Zachary, Anand, Ankesh, Lipschultz, Josh, Karmarkar, Abhijit, Proleev, Lev, Ittycheriah, Abe, Yeganeh, Soheil Hassas, Polovets, George, Faust, Aleksandra, Sun, Jiao, Rrustemi, Alban, Li, Pen, Shivanna, Rakesh, Liu, Jeremiah, Welty, Chris, Lebron, Federico, Baddepudi, Anirudh, Krause, Sebastian, Parisotto, Emilio, Soricut, Radu, Xu, Zheng, Bloxwich, Dawn, Johnson, Melvin, Neyshabur, Behnam, Mao-Jones, Justin, Wang, Renshen, Ramasesh, Vinay, Abbas, Zaheer, Guez, Arthur, Segal, Constant, Nguyen, Duc Dung, Svensson, James, Hou, Le, York, Sarah, Milan, Kieran, Bridgers, Sophie, Gworek, Wiktor, Tagliasacchi, Marco, Lee-Thorp, James, Chang, Michael, Guseynov, Alexey, Hartman, Ale Jakse, Kwong, Michael, Zhao, Ruizhe, Kashem, Sheleem, Cole, Elizabeth, Miech, Antoine, Tanburn, Richard, Phuong, Mary, Pavetic, Filip, Cevey, Sebastien, Comanescu, Ramona, Ives, Richard, Yang, Sherry, Du, Cosmo, Li, Bo, Zhang, Zizhao, Iinuma, Mariko, Hu, Clara Huiyi, Roy, Aurko, Bijwadia, Shaan, Zhu, Zhenkai, Martins, Danilo, Saputro, Rachel, Gergely, Anita, Zheng, Steven, Jia, Dawei, Antonoglou, Ioannis, Sadovsky, Adam, Gu, Shane, Bi, Yingying, Andreev, Alek, Samangooei, Sina, Khan, Mina, Kocisky, Tomas, Filos, Angelos, Kumar, Chintu, Bishop, Colton, Yu, Adams, Hodkinson, Sarah, Mittal, Sid, Shah, Premal, Moufarek, Alexandre, Cheng, Yong, Bloniarz, Adam, Lee, Jaehoon, Pejman, Pedram, Michel, Paul, Spencer, Stephen, Feinberg, Vladimir, Xiong, Xuehan, Savinov, Nikolay, Smith, Charlotte, Shakeri, Siamak, Tran, Dustin, Chesus, Mary, Bohnet, Bernd, Tucker, George, von Glehn, Tamara, Muir, Carrie, Mao, Yiran, Kazawa, Hideto, Slone, Ambrose, Soparkar, Kedar, Shrivastava, Disha, Cobon-Kerr, James, Sharman, Michael, Pavagadhi, Jay, Araya, Carlos, Misiunas, Karolis, Ghelani, Nimesh, Laskin, Michael, Barker, David, Li, Qiujia, Briukhov, Anton, Houlsby, Neil, Glaese, Mia, Lakshminarayanan, Balaji, Schucher, Nathan, Tang, Yunhao, Collins, Eli, Lim, Hyeontaek, Feng, Fangxiaoyu, Recasens, Adria, Lai, Guangda, Magni, Alberto, De Cao, Nicola, Siddhant, Aditya, Ashwood, Zoe, Orbay, Jordi, Dehghani, Mostafa, Brennan, Jenny, He, Yifan, Xu, Kelvin, Gao, Yang, Saroufim, Carl, Molloy, James, Wu, Xinyi, Arnold, Seb, Chang, Solomon, Schrittwieser, Julian, Buchatskaya, Elena, Radpour, Soroush, Polacek, Martin, Giordano, Skye, Bapna, Ankur, Tokumine, Simon, Hellendoorn, Vincent, Sottiaux, Thibault, Cogan, Sarah, Severyn, Aliaksei, Saleh, Mohammad, Thakoor, Shantanu, Shefey, Laurent, Qiao, Siyuan, Gaba, Meenu, Chang, Shuo-yiin, Swanson, Craig, Zhang, Biao, Lee, Benjamin, Rubenstein, Paul Kishan, Song, Gan, Kwiatkowski, Tom, Koop, Anna, Kannan, Ajay, Kao, David, Schuh, Parker, Stjerngren, Axel, Ghiasi, Golnaz, Gibson, Gena, Vilnis, Luke, Yuan, Ye, Ferreira, Felipe Tiengo, Kamath, Aishwarya, Klimenko, Ted, Franko, Ken, Xiao, Kefan, Bhattacharya, Indro, Patel, Miteyan, Wang, Rui, Morris, Alex, Strudel, Robin, Sharma, Vivek, Choy, Peter, Hashemi, Sayed Hadi, Landon, Jessica, Finkelstein, Mara, Jhakra, Priya, Frye, Justin, Barnes, Megan, Mauger, Matthew, Daun, Dennis, Baatarsukh, Khuslen, Tung, Matthew, Farhan, Wael, Michalewski, Henryk, Viola, Fabio, Quitry, Felix de Chaumont, Lan, Charline Le, Hudson, Tom, Wang, Qingze, Fischer, Felix, Zheng, Ivy, White, Elspeth, Dragan, Anca, Alayrac, Jean-baptiste, Ni, Eric, Pritzel, Alexander, Iwanicki, Adam, Isard, Michael, Bulanova, Anna, Zilka, Lukas, Dyer, Ethan, Sachan, Devendra, Srinivasan, Srivatsan, Muckenhirn, Hannah, Cai, Honglong, Mandhane, Amol, Tariq, Mukarram, Rae, Jack W., Wang, Gary, Ayoub, Kareem, FitzGerald, Nicholas, Zhao, Yao, Han, Woohyun, Alberti, Chris, Garrette, Dan, Krishnakumar, Kashyap, Gimenez, Mai, Levskaya, Anselm, Sohn, Daniel, Matak, Josip, Iturrate, Inaki, Chang, Michael B., Xiang, Jackie, Cao, Yuan, Ranka, Nishant, Brown, Geoff, Hutter, Adrian, Mirrokni, Vahab, Chen, Nanxin, Yao, Kaisheng, Egyed, Zoltan, Galilee, Francois, Liechty, Tyler, Kallakuri, Praveen, Palmer, Evan, Ghemawat, Sanjay, Liu, Jasmine, Tao, David, Thornton, Chloe, Green, Tim, Jasarevic, Mimi, Lin, Sharon, Cotruta, Victor, Tan, Yi-Xuan, Fiedel, Noah, Yu, Hongkun, Chi, Ed, Neitz, Alexander, Heitkaemper, Jens, Sinha, Anu, Zhou, Denny, Sun, Yi, Kaed, Charbel, Hulse, Brice, Mishra, Swaroop, Georgaki, Maria, Kudugunta, Sneha, Farabet, Clement, Shafran, Izhak, Vlasic, Daniel, Tsitsulin, Anton, Ananthanarayanan, Rajagopal, Carin, Alen, Su, Guolong, Sun, Pei, V, Shashank, Carvajal, Gabriel, Broder, Josef, Comsa, Iulia, Repina, Alena, Wong, William, Chen, Warren Weilun, Hawkins, Peter, Filonov, Egor, Loher, Lucia, Hirnschall, Christoph, Wang, Weiyi, Ye, Jingchen, Burns, Andrea, Cate, Hardie, Wright, Diana Gage, Piccinini, Federico, Zhang, Lei, Lin, Chu-Cheng, Gog, Ionel, Kulizhskaya, Yana, Sreevatsa, Ashwin, Song, Shuang, Cobo, Luis C., Iyer, Anand, Tekur, Chetan, Garrido, Guillermo, Xiao, Zhuyun, Kemp, Rupert, Zheng, Huaixiu Steven, Li, Hui, Agarwal, Ananth, Ngani, Christel, Goshvadi, Kati, Santamaria-Fernandez, Rebeca, Fica, Wojciech, Chen, Xinyun, Gorgolewski, Chris, Sun, Sean, Garg, Roopal, Ye, Xinyu, Eslami, S. M. Ali, Hua, Nan, Simon, Jon, Joshi, Pratik, Kim, Yelin, Tenney, Ian, Potluri, Sahitya, Thiet, Lam Nguyen, Yuan, Quan, Luisier, Florian, Chronopoulou, Alexandra, Scellato, Salvatore, Srinivasan, Praveen, Chen, Minmin, Koverkathu, Vinod, Dalibard, Valentin, Xu, Yaming, Saeta, Brennan, Anderson, Keith, Sellam, Thibault, Fernando, Nick, Huot, Fantine, Jung, Junehyuk, Varadarajan, Mani, Quinn, Michael, Raul, Amit, Le, Maigo, Habalov, Ruslan, Clark, Jon, Jalan, Komal, Bullard, Kalesha, Singhal, Achintya, Luong, Thang, Wang, Boyu, Rajayogam, Sujeevan, Eisenschlos, Julian, Jia, Johnson, Finchelstein, Daniel, Yakubovich, Alex, Balle, Daniel, Fink, Michael, Agarwal, Sameer, Li, Jing, Dvijotham, Dj, Pal, Shalini, Kang, Kai, Konzelmann, Jaclyn, Beattie, Jennifer, Dousse, Olivier, Wu, Diane, Crocker, Remi, Elkind, Chen, Jonnalagadda, Siddhartha Reddy, Lee, Jong, Holtmann-Rice, Dan, Kallarackal, Krystal, Liu, Rosanne, Vnukov, Denis, Vats, Neera, Invernizzi, Luca, Jafari, Mohsen, Zhou, Huanjie, Taylor, Lilly, Prendki, Jennifer, Wu, Marcus, Eccles, Tom, Liu, Tianqi, Kopparapu, Kavya, Beaufays, Francoise, Angermueller, Christof, Marzoca, Andreea, Sarcar, Shourya, Dib, Hilal, Stanway, Jeff, Perbet, Frank, Trdin, Nejc, Sterneck, Rachel, Khorlin, Andrey, Li, Dinghua, Wu, Xihui, Goenka, Sonam, Madras, David, Goldshtein, Sasha, Gierke, Willi, Zhou, Tong, Liu, Yaxin, Liang, Yannie, White, Anais, Li, Yunjie, Singh, Shreya, Bahargam, Sanaz, Epstein, Mark, Basu, Sujoy, Lao, Li, Ozturel, Adnan, Crous, Carl, Zhai, Alex, Lu, Han, Tung, Zora, Gaur, Neeraj, Walton, Alanna, Dixon, Lucas, Zhang, Ming, Globerson, Amir, Uy, Grant, Bolt, Andrew, Wiles, Olivia, Nasr, Milad, Shumailov, Ilia, Selvi, Marco, Piccinno, Francesco, Aguilar, Ricardo, McCarthy, Sara, Khalman, Misha, Shukla, Mrinal, Galic, Vlado, Carpenter, John, Villela, Kevin, Zhang, Haibin, Richardson, Harry, Martens, James, Bosnjak, Matko, Belle, Shreyas Rammohan, Seibert, Jeff, Alnahlawi, Mahmoud, McWilliams, Brian, Singh, Sankalp, Louis, Annie, Ding, Wen, Popovici, Dan, Simicich, Lenin, Knight, Laura, Mehta, Pulkit, Gupta, Nishesh, Shi, Chongyang, Fatehi, Saaber, Mitrovic, Jovana, Grills, Alex, Pagadora, Joseph, Petrova, Dessie, Eisenbud, Danielle, Zhang, Zhishuai, Yates, Damion, Mittal, Bhavishya, Tripuraneni, Nilesh, Assael, Yannis, Brovelli, Thomas, Jain, Prateek, Velimirovic, Mihajlo, Akbulut, Canfer, Mu, Jiaqi, Macherey, Wolfgang, Kumar, Ravin, Xu, Jun, Qureshi, Haroon, Comanici, Gheorghe, Wiesner, Jeremy, Gong, Zhitao, Ruddock, Anton, Bauer, Matthias, Felt, Nick, GP, Anirudh, Arnab, Anurag, Zelle, Dustin, Rothfuss, Jonas, Rosgen, Bill, Shenoy, Ashish, Seybold, Bryan, Li, Xinjian, Mudigonda, Jayaram, Erdogan, Goker, Xia, Jiawei, Simsa, Jiri, Michi, Andrea, Yao, Yi, Yew, Christopher, Kan, Steven, Caswell, Isaac, Radebaugh, Carey, Elisseeff, Andre, Valenzuela, Pedro, McKinney, Kay, Paterson, Kim, Cui, Albert, Latorre-Chimoto, Eri, Kim, Solomon, Zeng, William, Durden, Ken, Ponnapalli, Priya, Sosea, Tiberiu, Choquette-Choo, Christopher A., Manyika, James, Robenek, Brona, Vashisht, Harsha, Pereira, Sebastien, Lam, Hoi, Velic, Marko, Owusu-Afriyie, Denese, Lee, Katherine, Bolukbasi, Tolga, Parrish, Alicia, Lu, Shawn, Park, Jane, Venkatraman, Balaji, Talbert, Alice, Rosique, Lambert, Cheng, Yuchung, Sozanschi, Andrei, Paszke, Adam, Kumar, Praveen, Austin, Jessica, Li, Lu, Salama, Khalid, Kim, Wooyeol, Dukkipati, Nandita, Baryshnikov, Anthony, Kaplanis, Christos, Sheng, XiangHai, Chervonyi, Yuri, Unlu, Caglar, Casas, Diego de Las, Askham, Harry, Tunyasuvunakool, Kathryn, Gimeno, Felix, Poder, Siim, Kwak, Chester, Miecnikowski, Matt, Dimitriev, Alek, Parisi, Aaron, Liu, Dangyi, Tsai, Tomy, Shevlane, Toby, Kouridi, Christina, Garmon, Drew, Goedeckemeyer, Adrian, Brown, Adam R., Vijayakumar, Anitha, Elqursh, Ali, Jazayeri, Sadegh, Huang, Jin, Carthy, Sara Mc, Hoover, Jay, Kim, Lucy, Kumar, Sandeep, Chen, Wei, Biles, Courtney, Bingham, Garrett, Rosen, Evan, Wang, Lisa, Tan, Qijun, Engel, David, Pongetti, Francesco, de Cesare, Dario, Hwang, Dongseong, Yu, Lily, Pullman, Jennifer, Narayanan, Srini, Levin, Kyle, Gopal, Siddharth, Li, Megan, Aharoni, Asaf, Trinh, Trieu, Lo, Jessica, Casagrande, Norman, Vij, Roopali, Matthey, Loic, Ramadhana, Bramandia, Matthews, Austin, Carey, CJ, Johnson, Matthew, Goranova, Kremena, Shah, Rohin, Ashraf, Shereen, Dasgupta, Kingshuk, Larsen, Rasmus, Wang, Yicheng, Vuyyuru, Manish Reddy, Jiang, Chong, Ijazi, Joana, Osawa, Kazuki, Smith, Celine, Boppana, Ramya Sree, Bilal, Taylan, Koizumi, Yuma, Xu, Ying, Altun, Yasemin, Shabat, Nir, Bariach, Ben, Korchemniy, Alex, Choo, Kiam, Ronneberger, Olaf, Iwuanyanwu, Chimezie, Zhao, Shubin, Soergel, David, Hsieh, Cho-Jui, Cai, Irene, Iqbal, Shariq, Sundermeyer, Martin, Chen, Zhe, Bursztein, Elie, Malaviya, Chaitanya, Biadsy, Fadi, Shroff, Prakash, Dhillon, Inderjit, Latkar, Tejasi, Dyer, Chris, Forbes, Hannah, Nicosia, Massimo, Nikolaev, Vitaly, Greene, Somer, Georgiev, Marin, Wang, Pidong, Martin, Nina, Sedghi, Hanie, Zhang, John, Banzal, Praseem, Fritz, Doug, Rao, Vikram, Wang, Xuezhi, Zhang, Jiageng, Patraucean, Viorica, Du, Dayou, Mordatch, Igor, Jurin, Ivan, Liu, Lewis, Dubey, Ayush, Mohan, Abhi, Nowakowski, Janek, Ion, Vlad-Doru, Wei, Nan, Tojo, Reiko, Raad, Maria Abi, Hudson, Drew A., Keshava, Vaishakh, Agrawal, Shubham, Ramirez, Kevin, Wu, Zhichun, Nguyen, Hoang, Liu, Ji, Sewak, Madhavi, Petrini, Bryce, Choi, DongHyun, Philips, Ivan, Wang, Ziyue, Bica, Ioana, Garg, Ankush, Wilkiewicz, Jarek, Agrawal, Priyanka, Guo, Danhao, Xue, Emily, Shaik, Naseer, Leach, Andrew, Khan, Sadh MNM, Wiesinger, Julia, Jerome, Sammy, Chakladar, Abhishek, Wang, Alek Wenjiao, Ornduff, Tina, Abu, Folake, Ghaffarkhah, Alireza, Wainwright, Marcus, Cortes, Mario, Liu, Frederick, Maynez, Joshua, Petrov, Slav, Wu, Yonghui, Hassabis, Demis, Kavukcuoglu, Koray, Dean, Jeffrey, and Vinyals, Oriol
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. The family includes two new models: (1) an updated Gemini 1.5 Pro, which exceeds the February version on the great majority of capabilities and benchmarks; (2) Gemini 1.5 Flash, a more lightweight variant designed for efficiency with minimal regression in quality. Gemini 1.5 models achieve near-perfect recall on long-context retrieval tasks across modalities, improve the state-of-the-art in long-document QA, long-video QA and long-context ASR, and match or surpass Gemini 1.0 Ultra's state-of-the-art performance across a broad set of benchmarks. Studying the limits of Gemini 1.5's long-context ability, we find continued improvement in next-token prediction and near-perfect retrieval (>99%) up to at least 10M tokens, a generational leap over existing models such as Claude 3.0 (200k) and GPT-4 Turbo (128k). Finally, we highlight real-world use cases, such as Gemini 1.5 collaborating with professionals on completing their tasks achieving 26 to 75% time savings across 10 different job categories, as well as surprising new capabilities of large language models at the frontier; when given a grammar manual for Kalamang, a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person who learned from the same content.
Published: 2024

12. Social Orientation: A New Feature for Dialogue Analysis

Author: Morrill, Todd, Deng, Zhaoyuan, Chen, Yanda, Ananthram, Amith, Leach, Colin Wayne, and McKeown, Kathleen
Subjects: Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract: There are many settings where it is useful to predict and explain the success or failure of a dialogue. Circumplex theory from psychology models the social orientations (e.g., Warm-Agreeable, Arrogant-Calculating) of conversation participants and can be used to predict and explain the outcome of social interactions. Our work is novel in its systematic application of social orientation tags to modeling conversation outcomes. In this paper, we introduce a new data set of dialogue utterances machine-labeled with social orientation tags. We show that social orientation tags improve task performance, especially in low-resource settings, on both English and Chinese language benchmarks. We also demonstrate how social orientation tags help explain the outcomes of social interactions when used in neural models. Based on these results showing the utility of social orientation tags for dialogue outcome prediction tasks, we release our data sets, code, and models that are fine-tuned to predict social orientation tags on dialogue utterances., Comment: Accepted to LREC-COLING 2024
Published: 2024

13. Do Machines and Humans Focus on Similar Code? Exploring Explainability of Large Language Models in Code Summarization

Author: Li, Jiliang, Zhang, Yifan, Karas, Zachary, McMillan, Collin, Leach, Kevin, and Huang, Yu
Subjects: Computer Science - Software Engineering, Computer Science - Artificial Intelligence
Abstract: Recent language models have demonstrated proficiency in summarizing source code. However, as in many other domains of machine learning, language models of code lack sufficient explainability. Informally, we lack a formulaic or intuitive understanding of what and how models learn from code. Explainability of language models can be partially provided if, as the models learn to produce higher-quality code summaries, they also align in deeming the same code parts important as those identified by human programmers. In this paper, we report negative results from our investigation of explainability of language models in code summarization through the lens of human comprehension. We measure human focus on code using eye-tracking metrics such as fixation counts and duration in code summarization tasks. To approximate language model focus, we employ a state-of-the-art model-agnostic, black-box, perturbation-based approach, SHAP (SHapley Additive exPlanations), to identify which code tokens influence that generation of summaries. Using these settings, we find no statistically significant relationship between language models' focus and human programmers' attention. Furthermore, alignment between model and human foci in this setting does not seem to dictate the quality of the LLM-generated summaries. Our study highlights an inability to align human focus with SHAP-based model focus measures. This result calls for future investigation of multiple open questions for explainable language models for code summarization and software engineering tasks in general, including the training mechanisms of language models for code, whether there is an alignment between human and model attention on code, whether human attention can improve the development of language models, and what other model focus measures are appropriate for improving explainability.
Published: 2024
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14. EyeTrans: Merging Human and Machine Attention for Neural Code Summarization

Author: Zhang, Yifan, Li, Jiliang, Karas, Zachary, Bansal, Aakash, Li, Toby Jia-Jun, McMillan, Collin, Leach, Kevin, and Huang, Yu
Subjects: Computer Science - Software Engineering, Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction
Abstract: Neural code summarization leverages deep learning models to automatically generate brief natural language summaries of code snippets. The development of Transformer models has led to extensive use of attention during model design. While existing work has primarily and almost exclusively focused on static properties of source code and related structural representations like the Abstract Syntax Tree (AST), few studies have considered human attention, that is, where programmers focus while examining and comprehending code. In this paper, we develop a method for incorporating human attention into machine attention to enhance neural code summarization. To facilitate this incorporation and vindicate this hypothesis, we introduce EyeTrans, which consists of three steps: (1) we conduct an extensive eye-tracking human study to collect and pre-analyze data for model training, (2) we devise a data-centric approach to integrate human attention with machine attention in the Transformer architecture, and (3) we conduct comprehensive experiments on two code summarization tasks to demonstrate the effectiveness of incorporating human attention into Transformers. Integrating human attention leads to an improvement of up to 29.91% in Functional Summarization and up to 6.39% in General Code Summarization performance, demonstrating the substantial benefits of this combination. We further explore performance in terms of robustness and efficiency by creating challenging summarization scenarios in which EyeTrans exhibits interesting properties. We also visualize the attention map to depict the simplifying effect of machine attention in the Transformer by incorporating human attention. This work has the potential to propel AI research in software engineering by introducing more human-centered approaches and data.
Published: 2024
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15. Edible mycelium as proliferation and differentiation support for anchorage-dependent animal cells in cultivated meat production.

Author: Ogawa, Minami, Kermani, Alex, Huynh, Mayrene, Baar, Keith, Leach, J, and Block, David
Abstract: Cultivated meat production requires bioprocess optimization to achieve cell densities that are multiple orders of magnitude higher compared to conventional cell culture techniques. These processes must maximize resource efficiency and cost-effectiveness by attaining high cell growth productivity per unit of medium. Microcarriers, or carriers, are compatible with large-scale bioreactor use, and offer a large surface-area-to-volume ratio for the adhesion and proliferation of anchorage-dependent animal cells. An ongoing challenge persists in the efficient retrieval of cells from the carriers, with conflicting reports on the effectiveness of trypsinization and the need for additional optimization measures such as carrier sieving. To surmount this issue, edible carriers have been proposed, offering the advantage of integration into the final food product while providing opportunities for texture, flavor, and nutritional incorporation. Recently, a proof of concept (POC) utilizing inactivated mycelium biomass derived from edible filamentous fungus demonstrated its potential as a support structure for myoblasts. However, this POC relied on a model mammalian cell line combination with a single mycelium species, limiting realistic applicability to cultivated meat production. This study aims to advance the POC. We found that the species of fungi composing the carriers impacts C2C12 myoblast cell attachment-with carriers derived from Aspergillus oryzae promoting the best proliferation. C2C12 myoblasts effectively differentiated on mycelium carriers when induced in myogenic differentiation media. Mycelium carriers also supported proliferation and differentiation of bovine satellite cells. These findings demonstrate the potential of edible mycelium carrier technology to be readily adapted in product development within the cultivated meat industry.
Published: 2024

16. Height and body surface area versus wall stress for stratification of mid-term outcomes in ascending aortic aneurysm

Author: Zamirpour, Siavash, Xuan, Yue, Wang, Zhongjie, Gomez, Axel, Leach, Joseph R, Mitsouras, Dimitrios, Saloner, David A, Guccione, Julius M, Ge, Liang, and Tseng, Elaine E
Subjects: Biomedical and Clinical Sciences, Clinical Sciences, Cardiovascular, Good Health and Well Being, Ascending aortic aneurysm, Biomechanics, Computed tomography, Finite element analysis, Outcomes, Risk factors, Cardiorespiratory Medicine and Haematology, Cardiovascular medicine and haematology
Abstract: ObjectivesCurrent diameter-based guidelines for ascending thoracic aortic aneurysms (aTAA) do not consistently predict risk of dissection/rupture. ATAA wall stresses may enhance risk stratification independent of diameter. The relation of wall stresses and diameter indexed to height and body surface area (BSA) is unknown. Our objective was to compare aTAA wall stresses with indexed diameters in relation to all-cause mortality at 3.75 years follow-up.MethodsFinite element analyses were performed in a veteran population with aortas ≥ 4.0 cm. Three-dimensional geometries were reconstructed from computed tomography with models accounting for pre-stress geometries. A fiber-embedded hyperelastic material model was applied to obtain wall stress distributions under systolic pressure. Peak wall stresses were compared across guideline thresholds for diameter/BSA and diameter/height. Hazard ratios for all-cause mortality and surgical aneurysm repair were estimated using cause-specific Cox proportional hazards models.ResultsOf 253 veterans, 54 (21 %) had aneurysm repair at 3.75 years. Indexed diameter alone would have prompted repair at baseline in 17/253 (6.7 %) patients, including only 4/230 (1.7 %) with diameter
Published: 2024

17. Mechanoregulation of MSC spheroid immunomodulation

Author: Thai, Victoria L, Mierswa, Sabrina, Griffin, Katherine H, Boerckel, Joel D, and Leach, J Kent
Subjects: Engineering, Biomedical Engineering, Regenerative Medicine, Underpinning research, 5.2 Cellular and gene therapies, 2.1 Biological and endogenous factors, 1.1 Normal biological development and functioning, Aetiology, Development of treatments and therapeutic interventions, Biomedical engineering
Abstract: Mesenchymal stromal cells (MSCs) are widely used in cell-based therapies and tissue regeneration for their potent secretome, which promotes host cell recruitment and modulates inflammation. Compared to monodisperse cells, MSC spheroids exhibit improved viability and increased secretion of immunomodulatory cytokines. While mechanical stimulation of monodisperse cells can increase cytokine production, the influence of mechanical loading on MSC spheroids is unknown. Here, we evaluated the effect of controlled, uniaxial cyclic compression on the secretion of immunomodulatory cytokines by human MSC spheroids and tested the influence of load-induced gene expression on MSC mechanoresponsiveness. We exposed MSC spheroids, entrapped in alginate hydrogels, to three cyclic compressive regimes with varying stress (L) magnitudes (i.e., 5 and 10 kPa) and hold (H) durations (i.e., 30 and 250 s) L5H30, L10H30, and L10H250. We observed changes in cytokine and chemokine expression dependent on the loading regime, where higher stress regimes tended to result in more exaggerated changes. However, only MSC spheroids exposed to L10H30 induced human THP-1 macrophage polarization toward an M2 phenotype compared to static conditions. Static and L10H30 loading facilitated a strong, interlinked F-actin arrangement, while L5H30 and L10H250 disrupted the structure of actin filaments. This was further examined when the actin cytoskeleton was disrupted via Y-27632. We observed downregulation of YAP-related genes, and the levels of secreted inflammatory cytokines were globally decreased. These findings emphasize the essential role of mechanosignaling in mediating the immunomodulatory potential of MSC spheroids.
Published: 2024

18. The Data Acquisition System for Phase-III of the BeEST Experiment

Author: Bray, C., Fretwell, S., Kim, I., Warburton, W. K., Ponce, F., Leach, K. G., Friedrich, S., Abells, R., Amaro, P., Andoche, A., Cantor, R., Diercks, D., Guerra, M., Hall, A., Harris, C., Harris, J., Hayen, L., Hervieux, P. A., Kim, G. B., Lennarz, A., Lordi, V., Machado, J., Machule, P., Marino, A., McKeen, D., Mougeot, X., Ruiz, C., Samanta, A., Santos, J. P., and Stone-Whitehead, C.
Subjects: Physics - Instrumentation and Detectors, Nuclear Experiment, Quantum Physics
Abstract: The BeEST experiment is a precision laboratory search for physics beyond the standard model that measures the electron capture decay of $^7$Be implanted into superconducting tunnel junction (STJ) detectors. For Phase-III of the experiment, we constructed a continuously sampling data acquisition system to extract pulse shape and timing information from 16 STJ pixels offline. Four additional pixels are read out with a fast list-mode digitizer, and one with a nuclear MCA already used in the earlier limit-setting phases of the experiment. We present the performance of the data acquisition system and discuss the relative advantages of the different digitizers., Comment: 8 pages, 4 figures, proceedings for The 20th International Conference on Low Temperature Detectors
Published: 2023

19. Masculinity and Afrocentric Worldview: Assessing Risk and Protective Factors of Self-Reliance and Ubuntu on Young Black Men’s Suicide Ideation

Author: Lateef, Husain, Adams, Leslie, Leach, Benjamin, Boahen-Boaten, Baffour, Jallesma, Francine, Bernard, Donte, and Williams, Ed-Dee
Published: 2024
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20. Evaluation of 3D T1-weighted spoiled gradient echo MR image quality using artificial intelligence image reconstruction techniques in the pediatric brain

Author: Nagaraj, Usha D., Dillman, Jonathan R., Tkach, Jean A., Greer, Joshua S., and Leach, James L.
Published: 2024
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21. Abusive head trauma and the eye in infants and children - clinical guideline update by the royal college of ophthalmologists and the royal college of paediatrics and child health: executive summary

Author: Watts, Patrick, Adams, Gillian, Biswas, Susmito, Davis, Paul, Leach, Paul, Lloyd, I. Christopher, McPartland, Jo, and Mulvihill, Alan
Published: 2024
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22. Evaluation of T2W FLAIR MR image quality using artificial intelligence image reconstruction techniques in the pediatric brain

Author: Nagaraj, Usha D., Dillman, Jonathan R., Tkach, Jean A., Greer, Joshua S., and Leach, James L.
Published: 2024
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23. Lived Experiences of Skilled Indian Women Immigrants (SIWI) Navigating the COVID-19 Pandemic: Immigration Trajectories to Canada

Author: Biswas, Urmi Nanda, Leach, Belinda, Jijina, Parisha, and Niraula, Ashika
Published: 2024
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24. MRI and pathology comparisons in Rasmussen’s encephalitis: a multi-institutional examination of hemispherotomy outcomes relative to imaging and histological severity

Author: Doherty, Alexander, Knudson, Kathleen, Fuller, Christine, Leach, James L., Wang, Anthony C., Marupudi, Neena, Han, Rowland H., Tomko, Stuart, Ojemann, Jeff, Smyth, Matthew D., Mangano, Francesco, and Skoch, Jesse
Published: 2024
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25. Investigating the association between work family conflict (WFC) and generalized anxiety disorder (GAD) in an Australian community-based cohort study

Author: Wang, Tianying, Butterworth, Peter, Cooklin, Amanda, Strazdins, Lyndall, and Leach, Liana
Published: 2024
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26. The Impact of Service-Learning on Occupational Therapy Doctoral Students

Author: Valdes, Kristin, Rider, John, Leach, Christen, and Manalang, Katie Capistran
Abstract: The aim of this study was to determine how participation in a community service project impacts entry-level OT doctoral (OTD) students at different time points in their education. This study employed a mixed-method, prospective cohort design. The Community Service Attitudes Scale (CSAS) is an instrument to measure college students' attitudes about community service. Open-ended questions were asked after participation in the experience in addition to the CSAS to gather a deeper reflection of the S-L experience. A total of 62 OTD students participated in the study. There was no statistically significant difference between the CSAS scores between the OTD first-year and third-year students. Students reported that they gained knowledge regarding occupational justice and community-based interventions, how to establish a therapeutic relationship, and the impact of occupation-based interventions when asked what they learned from the experience. Through the participation in a S-L experience, the students gained knowledge regarding occupational justice and community-based interventions, how to establish a therapeutic relationship, and an awareness of the impact of occupation-based interventions on clients. S-L enables OT educators to provide opportunities for students to gain critical skills that will translate into clinical practice.
Published: 2023

27. Learning beyond the Content: A Dissertation in Practice on Workforce Skills Development at Community Colleges

Author: Torres, William C., Leach, Lesley F., Glaman, Ryan, and Higham, J. Russell
Abstract: In this article, we present an example of a student's recently completed dissertation in practice (DiP) to demonstrate how the DiP can serve as culminating evidence of a student's successful preparation as a scholar-practitioner. The purpose of this DiP was to determine to what extent students develop workforce skills while completing a community college education and what institutions can do to better prepare students to be successful upon graduation. Results from this mixed-method study (N = 77) suggest that graduates perceived learning a considerable amount of workforce skills during their time at the institution. However, respondents also reported that more hands-on learning would have enhanced their skill development. Through this study, the doctoral candidate was able to gain substantive results while addressing key CPED guiding principles.
Published: 2023

28. Skeletal Muscle Spheroids as Building Blocks for Engineered Muscle Tissue

Author: Johnson, Nicholas, Filler, Andrea C, Sethi, Akash, Smith, Lucas R, and Leach, J Kent
Subjects: Engineering, Biomedical Engineering, Regenerative Medicine, Musculoskeletal, Animals, Cattle, Mice, Tissue Engineering, Spheroids, Cellular, Muscle, Skeletal, Cell Differentiation, Alginates, spheroids, skeletal muscle, bioprinting, muscle engineering, hydrogel, Biomedical engineering
Abstract: Spheroids exhibit enhanced cell-cell interactions that facilitate improved survival and mimic the physiological cellular environment in vivo. Cell spheroids have been successfully used as building blocks for engineered tissues, yet the viability of this approach with skeletal muscle spheroids is poorly understood, particularly when incorporated into three-dimensional (3D) constructs. Bioprinting is a promising strategy to recapitulate the hierarchical organization of native tissue that is fundamental to its function. However, the influence of bioprinting on skeletal muscle cell spheroids and their function are yet to be interrogated. Using C2C12 mouse myoblasts and primary bovine muscle stem cells (MuSCs), we characterized spheroid formation as a function of duration and cell seeding density. We then investigated the potential of skeletal muscle spheroids entrapped in alginate bioink as tissue building blocks for bioprinting myogenic tissue. Both C2C12 and primary bovine MuSCs formed spheroids of similar sizes and remained viable after bioprinting. Spheroids of both cell types fused into larger tissue clusters over time within alginate and exhibited tissue formation comparable to monodisperse cells. Compared to monodisperse cells in alginate gels, C2C12 spheroids exhibited greater MyHC expression after 2 weeks, while cells within bovine MuSC spheroids displayed increased cell spreading. Both monodisperse and MuSC spheroids exhibited increased expression of genes denoting mid- and late-stage myogenic differentiation. Together, these data suggest that skeletal muscle spheroids have the potential for generating myogenic tissue via 3D bioprinting and reveal areas of research that could enhance myogenesis and myogenic differentiation in future studies.
Published: 2024

29. Compound heterozygous MSH3 germline variants and associated tumor somatic DNA mismatch repair dysfunction

Author: Koi, Minoru, Leach, Brandie H, McGee, Sarah, Tseng-Rogenski, Stephanie S, Burke, Carol A, and Carethers, John M
Subjects: Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Immunology, Genetics, Cancer, Rare Diseases, Aetiology, 2.1 Biological and endogenous factors, Generic health relevance, Oncology and carcinogenesis
Abstract: We describe here an individual from a fourth family with germline compound heterozygous MSH3 germline variants and its observed biological consequences. The patient was initially diagnosed with invasive moderately-differentiated adenocarcinoma of the colon at the age of 43. Germline multigene panel testing revealed a pathogenic variant MSH3 c.2436-1 G > A and a variant of (initial) uncertain significance MSH3 c.3265 A > T (p.Lys1089*). Germline genetic testing of family members confirm the variants are in trans with the c.2436-1 G > A variant of paternal and the c.3265 A > T variant of maternal origin. Tumor DNA exhibits low levels of microsatellite instability and elevated microsatellite alterations at selected tetranucleotide repeats (EMAST). Tissue immunohistochemical staining for MSH3 demonstrated variant MSH3 protein is present in the cytoplasm and cell membrane but not in the nucleus of normal and tumor epithelial cells. Furthermore, variant MSH3 is accompanied by loss of nuclear MSH6 and a reduced level of nuclear MSH2 in some tumor cells, suggesting that the variant MSH3 protein may inhibit binding of MSH6 to MSH2.
Published: 2024

30. Chasing Gravitational Waves with the Cherenkov Telescope Array

Author: Green, Jarred Gershon, Carosi, Alessandro, Nava, Lara, Patricelli, Barbara, Schüssler, Fabian, Seglar-Arroyo, Monica, Consortium, Cta, Abe, Kazuki, Abe, Shotaro, Acharyya, Atreya, Adam, Remi, Aguasca-Cabot, Arnau, Agudo, Ivan, Alfaro, Jorge, Alvarez-Crespo, Nuria, Batista, Rafael Alves, Amans, Jean-Philippe, Amato, Elena, Ambrosino, Filippo, Angüner, Ekrem Oguzhan, Antonelli, Lucio Angelo, Aramo, Carla, Arcaro, Cornelia, Arrabito, Luisa, Asano, Katsuaki, Aschersleben, Jann, Ashkar, Halim, Stuani, Luiz Augusto, Baack, Dominik, Backes, Michael, Balazs, Csaba, Balbo, Matteo, Larriva, Andres Baquero, Martins, Victor Barbosa, de Almeida, Ulisses Barres, Barrio, Juan Abel, Bastieri, Denis, Batista, Pedro Ivo, Batković, Ivana, Batzofin, Rowan William, Baxter, Joshua Ryo, Beck, Geoffrey, Tjus, Julia Becker, Beiske, Lukas, Belardinelli, Daniele, Benbow, Wystan, Bernardini, Elisa, Medrano, Juan Bernete, Bernlöhr, Konrad, Berti, Alessio, Beshley, Vasyl, Bhattacharjee, Pooja, Bhattacharyya, Saptashwa, Bi, Baiyang, Biederbeck, Noah, Biland, Adrian, Bissaldi, Elisabetta, Blanch, Oscar, Blazek, Jiri, Boisson, Catherine, Bolmont, Julien, Bonnoli, Giacomo, Bordas, Pol, Bošnjak, Željka, Bradascio, Federica, Braiding, Catherine, Bronzini, Ettore, Brose, Robert, Brown, Anthony M., Brun, Francois, Brunelli, Giulia, Bulgarelli, Andrea, Burelli, Irene, Burmistrov, Leonid, Burton, Michael, Bylund, Tomas, Calisse, Paolo Gherardo, Campoy-Ordaz, Anna, Cantlay, Brandon Khan, Capalbi, Milvia, Caproni, Anderson, Capuzzo-Dolcetta, Roberto, Carlile, Colin, Caroff, Sami, Carosi, Roberto, Carrasco, Marie-Sophie, Cascone, Enrico, Cassol, Franca, Castrejon, Noelia, Catalani, Fernando, Cerasole, Davide, Cerruti, Matteo, Chaty, Sylvain, Chen, Andrew W, Chernyakova, Maria, Chiavassa, Andrea, Chudoba, Jiří, Araujo, Carlos Henrique Coimbra, Conforti, Vito, Conte, Francesco, Contreras, Jose Luis, Cossou, Christophe, Costa, Alessandro, Costantini, Heide, Cristofari, Pierre, Cuevas, Omar, Curtis-Ginsberg, Zachary, D'Amico, Giacomo, D'Ammando, Filippo, Dadina, Mauro, Dalchenko, Mykhailo, David, Ludovic, Davids, Isak Delberth, Dazzi, Francesco, De Angelis, Alessandro, de Lavergne, Mathieu de Bony, De Caprio, Vincenzo, De Cesare, Giovanni, Pino, Elisabete M. de Gouveia Dal, De Lotto, Barbara, de Lucia, Mario, de Menezes, Raniere, de Naurois, Mathieu, Wilhelmi, Emma de Oña, De Simone, Nicola, de Souza, Vitor, Del Peral, Luis, del Valle, Maria Victoria, Delagnes, Eric, Giler, Andres Gabriel Delgado, Delgado, Carlos, Dell'aiera, Michael, Della Ceca, Roberto, Della Valle, Massimo, Della Volpe, Domenico, Depaoli, Davide, Dettlaff, Antonios, Di Girolamo, Tristano, Di Piano, Ambra, Di Pierro, Federico, Di Tria, Riccardo, Di Venere, Leonardo, Díaz-Bahamondes, Christian, Dib, Claudio, Diebold, Sebastian, Dima, Razvan, Dinesh, Adithiya, Djannati-Ataï, Arache, Djuvsland, Julia Isabel, Dominguez, Alberto, Dominik, Rune Michael, Donini, Alice, Dorner, Daniela, Dörner, Julien, Doro, Michele, Anjos, Rita de Cassia dos, Dournaux, Jean-Laurent, Dravins, Dainis, Duangchan, Chaimongkol, Dubos, Coline, Ducci, Lozenzo, Dwarkadas, Vikram V., Ebr, Jan, Eckner, Christopher, Egberts, Kathrin, Einecke, Sabrina, Elsässer, Dominik, Emery, Gabriel, Godoy, Miguel Escobar, Escudero, Juan, Esposito, Paolo, Falceta-Gonçalves, Diego, Ramazani, Vandad Fallah, Faure, Alice, Fedorova, Elena, Fegan, Stephen, Feijen, Kirsty, Feng, Qi, Ferrand, Gilles, Ferrarotto, Fabio, Fiandrini, Emanuele, Fiasson, Armand, Fioretti, Valentina, Foffano, Luca, Guiteras, Lluis Font, Fontaine, Gerard, Fröse, Stefan, Fukami, Satoshi, Fukui, Yasuo, Funk, Stefan, Gaggero, Daniele, Galanti, Giorgio, Galaz, Gaspar, Gallant, Yves A., Gallozzi, Stefano, Gammaldi, Viviana, Gasbarra, Claudio, Gaug, Markus, Ghalumyan, Arsen, Gianotti, Fulvio, Giarrusso, Marina, Giglietto, Nicola, Giordano, Francesco, Giuliani, Andrea, Glicenstein, Jean-Francois, Glombitza, Jonas, Goldoni, Paolo, González, José Mauricio, González, Maria Magdalena, Coelho, Jaziel Goulart, Granot, Jonathan, Grasso, Dario, Haro, Roger Grau, Green, David, Greenshaw, Tim, Grolleron, Guillaume, Grube, Jeff, Gueta, Orel, Gunji, Shuichi, Hadasch, Daniela, Hamal, Petr, Hanlon, William, Hara, Satoshi, Harvey, Violet M., Hashiyama, Kazuaki, Hassan, Tarek, Heller, Matthieu, Cadena, Sergio Hernández, Hie, Jonathan, Hiroshima, Nagisa, Hnatyk, Bohdan, Hnatyk, Roman, Hoffmann, Dirk, Hofmann, Werner, Holler, Markus, Horan, Deirdre, Horvath, Pavel, Hovatta, Talvikki, Hrupec, Dario, Hussain, Saqib, Iarlori, Marco, Inada, Tomohiro, Incardona, Federico, Inome, Yusuke, Inoue, Susumu, Iocco, Fabio, Ishio, Kazuma, Jamrozy, Marek, Janecek, Petr, Jankowsky, Felix, Jarnot, Christian, Jean, Pierre, Martínez, Irene Jiménez, Jin, Weidong, Jocou, Laurent, Juramy-Gilles, Claire, Jurysek, Jakub, KALEKIN, Oleg, Kantzas, Dimitrios, Karas, Vladimir, Kaufmann, Sarah, Kerszberg, Daniel, Khelifi, Bruno, Kieda, David B, Kleiner, Tobias Kai, Kluźniak, Włodzimierz, Kobayashi, Yukiho, Kohri, Kazunori, Komin, Nukri, Kornecki, Paula, Kosack, Karl, Kubo, Hidetoshi, Kushida, Junko, La Barbera, Antonino, La Palombara, Nicola, Lainez, Maria, Lamastra, Alessandra, Lapington, Jon S, Lazarevic, Sanja, Lazendic-Galloway, Jasmina, Leach, Steven, Lemoine-Goumard, Marianne, Lenain, Jean-Philippe, Leto, Giuseppe, Leuschner, Fabian, Lindfors, Elina, Linhoff, Maximilian, Liodakis, Ioannis, Loïc, Lozach, Lombardi, Saverio, Longo, Francesco, López-Coto, Rubén, López-Moya, Marcos, López-Oramas, Alicia, Loporchio, Serena, Bahilo, Julio Lozano, Luque-Escamilla, Pedro L., Macias, Oscar, Maier, Gernot, Majumdar, Pratik, Malyshev, Denys, Malyshev, Dmitry, Mandat, Dusan, Manicò, Giulio, Marinos, Peter David, Markoff, Sera, Márquez, Isabel, Marquez, Patricia, Marsella, Giovanni, Martí, Josep, Martin, Pierrick, Martínez, Gustavo Augusto, Martínez, Manel, Martinez, Oibar, Marty, Christophe, Mas-Aguilar, Alvaro, Mastropietro, Michele, Maurin, Gilles, Max-Moerbeck, Walter, Mazin, Daniel, Melkumyan, David, Menchiari, Stefano, Mestre, Enrique, Meunier, Jean-Luc, Meyer, Dominique M. -A., Miceli, Davide, Michailidis, Miltiadis, Michałowski, Jerzy, Miener, Tjark, Miranda, Jose Miguel, Mitchell, Alison, Mizote, Masaya, Mizuno, Tsunefumi, Moderski, Rafal, Mohrmann, Lars, Molero, Miguel, Molfese, Cesare, Molina, Edgar, Montaruli, Teresa, Moralejo, Abelardo, Morcuende, Daniel, Morik, Katharina, Morselli, Aldo, Moulin, Emmanuel, Zamanillo, Victor Moya, Mukherjee, Reshmi, Munari, Kevin, Muraczewski, Adam, Muraishi, Hiroshi, Nakamori, Takeshi, Nayak, Amrit, Nemmen, Rodrigo, Nickel, Lukas, Niemiec, Jacek, Nieto, Daniel, Rosillo, Mireia Nievas, Nikołajuk, Marek, Nishijima, Kyoshi, Noda, Koji, Nosek, Dalibor, Novosyadlyj, Bohdan, Novotný, Vladimír, Nozaki, Seiya, O'Brien, Paul, Ohishi, Michiko, Ohtani, Yoshiki, Okumura, Akira, Olive, Jean-François, Olmi, Barbara, Ong, Rene A., Orienti, Monica, Orito, Reiko, Orlandini, Mauro, Orlando, Elena, Ostrowski, Michal, Otte, Nepomuk, Oya, Igor, Pagano, Isabella, Pagliaro, Antonio, Palatiello, Michele, Panebianco, Gabriele, Paredes, Josep M., Parmiggiani, Nicolò, Patel, Sonal Ramesh, Pavlović, Dijana, Pe'er, Asaf, Pech, Miroslav, Pecimotika, Mario, Peresano, Michele, Pérez-Romero, Judit, Peron, Giada, Persic, Massimo, Petrucci, Pierre-Olivier, Petruk, Oleh, Pfeifle, Felix, Pintore, Fabio, Pirola, Giorgio, Pittori, Carlotta, Plard, Cyann, Podobnik, Franjo, Pohl, Martin, Pons, Estelle, Prandini, Elisa, Prast, Julie, Principe, Giacomo, Priyadarshi, Chaitanya, Produit, Nicolas, Prokhorov, Dmitry, Pueschel, Elisa, Pühlhofer, Gerd, Pumo, Maria Letizia, Punch, Michael, Quirrenbach, Andreas, Raino, Silvia, Randazzo, Nunzio, Rando, Riccardo, Ravel, Thierry, Razzaque, Soebur, Regeard, Maxime, Reichherzer, Patrick, Reimer, Anita, Reimer, Olaf, Reisenegger, Andreas, Reposeur, Thierry, Reville, Brian, Rhode, Wolfgang, Ribó, Marc, Richtler, Tom, Rieger, Frank, Roache, Emmet, Fernandez, Gonzalo Rodriguez, Frías, Maria Dolores Rodríguez, Rodríguez-Vázquez, Juan José, Romano, Patrizia, Romeo, Giuseppe, Rosado, Jaime, Rowell, Gavin P, Rudak, Bronislaw, Ruiter, Ashley J., Rulten, Cameron Boyd, Russo, Federico, Sadeh, Iftach, Saha, Lab, Saito, Takayuki, Sakurai, Shunsuke, Salzmann, Heiko, Sanchez, David, Sanchez-Conde, Miguel, Sangiorgi, Pierluca, Sano, Hidetoshi, Santander, Marcos, Santangelo, Andrea, Santos-Lima, Reinaldo, Sanuy, Andreu, Šarić, Toni, Sarkar, Arkadipta, Sarkar, Subir, Saturni, Francesco Gabriele, Savchenko, Volodymyr, Scherer, Andres, Schipani, Pietro, Schleicher, Bernd, Schovanek, Petr, Schubert, Jan Lukas, Schwanke, Ullrich, Schwefer, Georg, Scuderi, Salvatore, Arroyo, Monica Seglar, Seitenzahl, Ivo, Sergijenko, Olga, Sguera, Vito, Shang, Ruo-Yu, Sharma, Pooja, Sidibe, Guereguin Der Sylvestre, Sidoli, Lara, Siejkowski, Hubert, Siqueira, Clarissa, Sizun, Patrick, Sliusar, Vitalii, Slowikowska, Agnieszka, Sol, Helene, Specovius, Andreas, Spencer, Samuel Timothy, Spiga, Daniele, Stamerra, Antonio, Stanič, Samo, Starecki, Tomasz, Starling, Rhaana, Steppa, Constantin, Stolarczyk, Thierry, Strišković, Jelena, Strzys, Marcel C., Suda, Yusuke, Suomijarvi, Tiina, Tak, Donggeun, Takahashi, Mitsunari, Takeishi, Ryuji, Tam, Pak-Hin Thomas, Tanaka, Shuta J, Tanaka, Takaaki, Terauchi, Kenta, Testa, Vincenzo, Tibaldo, Luigi, Tibolla, Omar, Torradeflot, Francesc, Torres, Diego F., Torresi, Eleonora, Tothill, Nick, Toussenel, Francois, Touzard, Victoria, Tramacere, Andrea, Travnicek, Petr, Tripodo, Giovanni, Truzzi, Stefano, Tsiahina, Adellain, Tutone, Antonio, Vacula, Martin, Vallage, Bertrand, Vallania, Piero, Vallés, Ramon, van Eldik, Christopher, van Scherpenberg, Juliane, Vandenbroucke, Justin, Vassiliev, Vladimir, Venault, Philippe, Ventura, Sofia, Vercellone, Stefano, Verna, Gaia, Viana, Aion, Viaux, Nicolás, Vigliano, Alessandro, Vignatti, Jonatan, Vigorito, Carlo francesco, Vitale, Vincenzo, Vodeb, Veronika, Voisin, Vincent, Vorobiov, Serguei, Voutsinas, Georgios Gerasimos, Vovk, Ievgen, Waegebaert, Vincent, Wagner, Stefan Joachim, Walter, Roland, Ward, Martin, Wechakama, Maneenate, White, Richard, Wierzcholska, Alicja, Will, Martin, Williams, David A., Wohlleben, Frederik, Wolter, Anna, Yamamoto, Tokonatsu, Yamazaki, Ryo, Yang, Lili, Yoshida, Tatsuo, Yoshikoshi, Takanori, Zacharias, Michael, Sanchez, Ricardo Zanmar, Zavrtanik, Danilo, Zavrtanik, Marko, Zdziarski, Andrzej A., Zech, Andreas, Zhdanov, Valery I., Ziȩtara, Krzysztof, Živec, Miha, and Zuriaga-Puig, Jaume
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: The detection of gravitational waves from a binary neutron star merger by Advanced LIGO and Advanced Virgo (GW170817), along with the discovery of the electromagnetic counterparts of this gravitational wave event, ushered in a new era of multimessenger astronomy, providing the first direct evidence that BNS mergers are progenitors of short gamma-ray bursts (GRBs). Such events may also produce very-high-energy (VHE, > 100GeV) photons which have yet to be detected in coincidence with a gravitational wave signal. The Cherenkov Telescope Array (CTA) is a next-generation VHE observatory which aims to be indispensable in this search, with an unparalleled sensitivity and ability to slew anywhere on the sky within a few tens of seconds. New observing modes and follow-up strategies are being developed for CTA to rapidly cover localization areas of gravitational wave events that are typically larger than the CTA field of view. This work will evaluate and provide estimations on the expected number of of gravitational wave events that will be observable with CTA, considering both on- and off-axis emission. In addition, we will present and discuss the prospects of potential follow-up strategies with CTA., Comment: Presented at the 38th International Cosmic Ray Conference (ICRC 2023), 2023 (arXiv:2309.08219)
Published: 2023

31. First direct $^{7}$Be electron capture $Q$-value measurement towards high-precision BSM neutrino physics searches

Author: Bhandari, R., Bollen, G., Brunner, T., Gamage, N. D., Hamaker, A., Hockenbery, Z., Gamage, M. Horana, Keblbeck, D. K., Leach, K. G., Puentes, D., Redshaw, M., Ringle, R., Schwarz, S., Sumithrarachchi, C. S., and Yandow, I.
Subjects: Nuclear Experiment
Abstract: We report the first direct measurement of the nuclear electron capture (EC) decay $Q$-value of $^{7}$Be $\rightarrow$ $^{7}$Li via high-precision Penning trap mass spectrometry (PTMS). This was performed using the LEBIT Penning trap located at the National Superconducting Cyclotron Laboratory/Facility for Rare Isotope Beams (NSCL/FRIB) using the newly commissioned Batch-Mode Ion-Source (BMIS) to deliver the unstable $^{7}$Be$^{+}$ samples. With a measured value of $Q_{EC}$ = 861.963(23) keV this result is also three times more precise than any previous determination of this quantity. This improved precision, and accuracy of the $^7$Be EC decay $Q$-value is critical for ongoing experiments that measure the recoiling nucleus in this system as a signature to search for beyond Standard Model (BSM) neutrino physics using $^7$Be-doped superconducting sensors.
Published: 2023

32. The Role of Education in a Democracy: Continuing the Debate

Author: Tony Leach, Jordi Collet-Sabé, Antoni Tort Bardolet, Núria Simó Gil, and Matthew Clarke
Abstract: At a time when there are renewed expressions of concern about how our societies are organised and the health of our democracies, this paper focuses on the role of education in a democracy. Informed by John Dewey's and Martin Buber's accounts of what it is to be educated, and Homi Bhabha's concept of third space work, the paper presents the case for a progressive education for democratic citizenship. Adopting an ethnologically-informed approach, the paper provides an in-depth look at two Catalan and two English schools, focussing on the ways in which they look to provide a democracy enabling education. The findings reveal how and why mutual cooperation, collaboration and dialogue in relationships are key elements in the modelling of an education for democratic citizenship.
Published: 2024
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33. A Comparison of Three Methods for Providing Local Evidence to Inform School and District Budget Decisions

Author: F. M. Hollands, R. Shand, B. Yan, S. M. Leach, D. Dossett, F. Chang, and Y. Pan
Abstract: School and district leaders make annual decisions about investing their budgets in a multitude of educational programs. Policy directives set expectations for investing in programs that show evidence of improving student outcomes. However, evaluating many simultaneously-implemented programs under typical school operating conditions is challenging. We investigated three methods -- cost-effectiveness analysis, program value-added analysis, and academic return on investment -- to assess how each one fares against three criteria: rigor of methodology, difficulty of execution, and usability of results for decision-making. We apply each method to three programs implemented in a large, U.S. school district: Reading Recovery, Restorative Practices, and school nurses.
Published: 2024
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34. Pre-Service Teachers' Self-Efficacy during Professional Experience through COVID-19: A Large-Scale Survey of Pre-Service Teachers at a Regional University in Australia

Author: Leach, Tania, Le, Anh Hai, and Barton, Georgina M.
Abstract: Throughout 2020, the world was significantly impacted by the COVID-19 pandemic. In the context of higher education, the pandemic critically affected professional experience, a core component of teacher education. This paper shares data from a large-scale survey about teachers' sense of self-efficacy and the impact of COVID-19 on professional experience. Findings showed that the disrupted context had a moderate effect of pre-service teachers' self-efficacy in relation to their classroom management, instructional strategies, and student engagement. Qualitative results illuminated that the modes of learning, changing classroom contexts and communication between the pre-service teachers, school and their university were the underlying contextual factors that impacted pre-service teacher's self-efficacy. Implications from these findings challenge universities to consider how to best support pre-service teachers' progression in times of disruption and consider how Initial Teacher Education (ITE) programs prepare pre-service teachers to teach in disrupted classrooms.
Published: 2022

35. Are South African children on track for early learning? Findings from the South African Thrive By Five Index 2021 Survey

Author: Tredoux, Colin, Dawes, Andrew, Mattes, Frances, Schenk, Jan-Christof, Giese, Sonja, Leach, Grace, van der Berg, Servaas, and Horler, Jessica
Published: 2024
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36. Prospective comparison of restriction spectrum imaging and non-invasive biomarkers to predict upgrading on active surveillance prostate biopsy

Author: Eng, Stefan E., Basasie, Benjamin, Lam, Alfonso, John Semmes, O., Troyer, Dean A., Clarke, Geoffrey D., Sunnapwar, Abhijit G., Leach, Robin J., Johnson-Pais, Teresa L., Sokoll, Lori J., Chan, Daniel W., Tosoian, Jeffrey J., Siddiqui, Javed, Chinnaiyan, Arul M., Thompson, Jr., Ian M., Boutros, Paul C., and Liss, Michael A.
Published: 2024
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37. Patients Drive Authentic Trusted Solutions for Inclusive Clinical Trials

Author: Karmo, Maimah, Cornish, Katherine, Leach, Virginia, and Regnante, Jeanne
Published: 2024
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38. Mental Health Literacy and Education of Complementary Medicine Practitioners: A Cross-Sectional Study

Author: Harnett, Joanna E., Leach, Matthew J., Karzon, Randa, and McIntyre, Erica
Published: 2024
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39. Military Perspectives on the Provision of Spiritual Care in the Australian Defence Force: A Cross-Sectional Study

Author: Best, Megan C., Leach, Katie Tunks, Layson, Mark, and Carey, Lindsay B.
Published: 2024
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40. Reallocating Time to Physical Activity and Sleep: Associations with Body Mass Index in Cancer Survivors

Author: Hidde, Mary C., Lyden, Kate, Henry, Kim, and Leach, Heather J.
Published: 2024
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41. On Evaluation of Document Classification using RVL-CDIP

Author: Larson, Stefan, Lim, Gordon, and Leach, Kevin
Subjects: Computer Science - Computation and Language
Abstract: The RVL-CDIP benchmark is widely used for measuring performance on the task of document classification. Despite its widespread use, we reveal several undesirable characteristics of the RVL-CDIP benchmark. These include (1) substantial amounts of label noise, which we estimate to be 8.1% (ranging between 1.6% to 16.9% per document category); (2) presence of many ambiguous or multi-label documents; (3) a large overlap between test and train splits, which can inflate model performance metrics; and (4) presence of sensitive personally-identifiable information like US Social Security numbers (SSNs). We argue that there is a risk in using RVL-CDIP for benchmarking document classifiers, as its limited scope, presence of errors (state-of-the-art models now achieve accuracy error rates that are within our estimated label error rate), and lack of diversity make it less than ideal for benchmarking. We further advocate for the creation of a new document classification benchmark, and provide recommendations for what characteristics such a resource should include., Comment: EACL 2023
Published: 2023

42. Art and the science of generative AI: A deeper dive

Author: Epstein, Ziv, Hertzmann, Aaron, Herman, Laura, Mahari, Robert, Frank, Morgan R., Groh, Matthew, Schroeder, Hope, Smith, Amy, Akten, Memo, Fjeld, Jessica, Farid, Hany, Leach, Neil, Pentland, Alex, and Russakovsky, Olga
Subjects: Computer Science - Artificial Intelligence
Abstract: A new class of tools, colloquially called generative AI, can produce high-quality artistic media for visual arts, concept art, music, fiction, literature, video, and animation. The generative capabilities of these tools are likely to fundamentally alter the creative processes by which creators formulate ideas and put them into production. As creativity is reimagined, so too may be many sectors of society. Understanding the impact of generative AI - and making policy decisions around it - requires new interdisciplinary scientific inquiry into culture, economics, law, algorithms, and the interaction of technology and creativity. We argue that generative AI is not the harbinger of art's demise, but rather is a new medium with its own distinct affordances. In this vein, we consider the impacts of this new medium on creators across four themes: aesthetics and culture, legal questions of ownership and credit, the future of creative work, and impacts on the contemporary media ecosystem. Across these themes, we highlight key research questions and directions to inform policy and beneficial uses of the technology., Comment: This white paper is an expanded version of Epstein et al 2023 published in Science Perspectives on July 16, 2023 which you can find at the following DOI: 10.1126/science.adh4451
Published: 2023
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43. A method for the ethical analysis of brain-inspired AI

Author: Farisco, Michele, Baldassarre, Gianluca, Cartoni, Emilio, Leach, Antonia, Petrovici, Mihai A., Rosemann, Achim, Salles, Arleen, Stahl, Bernd, and van Albada, Sacha J.
Subjects: Computer Science - Artificial Intelligence, Quantitative Biology - Neurons and Cognition
Abstract: Despite its successes, to date Artificial Intelligence (AI) is still characterized by a number of shortcomings with regards to different application domains and goals. These limitations are arguably both conceptual (e.g., related to underlying theoretical models, such as symbolic vs. connectionist), and operational (e.g., related to robustness and ability to generalize). Biologically inspired AI, and more specifically brain-inspired AI, promises to provide further biological aspects beyond those that are already traditionally included in AI, making it possible to assess and possibly overcome some of its present shortcomings. This article examines some conceptual, technical, and ethical issues raised by the development and use of brain-inspired AI. Against this background, the paper asks whether there is anything ethically unique about brain-inspired AI. The aim of the paper is to introduce a method that has a heuristic nature and that can be applied to identify and address the ethical issues arising from brain-inspired AI. The conclusion resulting from the application of this method is that, compared to traditional AI, brain-inspired AI raises new foundational ethical issues and some new practical ethical issues, and exacerbates some of the issues raised by traditional AI., Comment: 30 pages theoretical article resulting from a multidisciplinary collaboration about technical, theoretical and ethical aspects of brain-inspired AI
Published: 2023

44. Lie symmetry classification for the 1+1 and 1+2 generalized Zoomeron equations

Author: Paliathanasis, Andronikos and Leach, P. G. L.
Subjects: Mathematical Physics, Mathematics - Analysis of PDEs
Abstract: We present a complete algebraic classification of the Lie symmetries for generalized Zoomeron equations. For the generalized 1+1 and 2+1 Zoomeron equations we solve the Lie symmetry conditions in order to constrain the free functions of the equations. We find that the differential equations of our consideration admit the same number of Lie symmetries with the non-generalized equations. The admitted Lie symmetries form the Lie algebras $2A_{1}$, $A_{3,3}$ $\ $for the 1+1 generalized Zoomeron equation, and the $% A_{4,5}^{ab}\,$, $3A_{1}\otimes _{s}2A_{1}$ in the case of the 2+1 generalized Zoomeron equation. The one-dimensional optimal system is constructed for the two equations and similarity solutions are derived.\ The similarity transformation lead to the derivation of kink solutions. Indeed, the similarity exact solutions determined in this work are asymptotic solutions near to the singular behaviour of the kink behaviour., Comment: 18 pages, no figures, to appear in Mod. Phys Lett. A
Published: 2023
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45. Revisiting Lightweight Compiler Provenance Recovery on ARM Binaries

Author: Kim, Jason, Genkin, Daniel, and Leach, Kevin
Subjects: Computer Science - Machine Learning
Abstract: A binary's behavior is greatly influenced by how the compiler builds its source code. Although most compiler configuration details are abstracted away during compilation, recovering them is useful for reverse engineering and program comprehension tasks on unknown binaries, such as code similarity detection. We observe that previous work has thoroughly explored this on x86-64 binaries. However, there has been limited investigation of ARM binaries, which are increasingly prevalent. In this paper, we extend previous work with a shallow-learning model that efficiently and accurately recovers compiler configuration properties for ARM binaries. We apply opcode and register-derived features, that have previously been effective on x86-64 binaries, to ARM binaries. Furthermore, we compare this work with Pizzolotto et al., a recent architecture-agnostic model that uses deep learning, whose dataset and code are available. We observe that the lightweight features are reproducible on ARM binaries. We achieve over 99% accuracy, on par with state-of-the-art deep learning approaches, while achieving a 583-times speedup during training and 3,826-times speedup during inference. Finally, we also discuss findings of overfitting that was previously undetected in prior work., Comment: In The 31st International Conference on Program Comprehension (ICPC 2023 RENE)
Published: 2023

46. Health-related quality of life in rural cancer survivors compared with their urban counterparts: a systematic review

Author: Latham, S., Leach, M. J., White, V. M., Webber, K., Jefford, M., Lisy, K., Davis, N., Millar, J. L., Evans, S., Emery, J. D., IJzerman, M., and Ristevski, E.
Published: 2024
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47. Retraction Note: Thermodynamic effect in Darchy–Forchheimer nanofluid flow of a single-wall carbon nanotube/multi-wall carbon nanotube suspension due to a stretching/shrinking rotating disk: Buongiorno two-phase model

Author: Nayak, M. K., Agbaje, T. M., Mondal, S., Sibanda, P., and Leach, P. G. L.
Published: 2024
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48. A method for the ethical analysis of brain-inspired AI

Author: Farisco, Michele, Baldassarre, G., Cartoni, E., Leach, A., Petrovici, M.A., Rosemann, A., Salles, A., Stahl, B., and van Albada, S. J.
Published: 2024
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49. Health selection, family division of labour and labour market participation of migrant and Australian born couples

Author: Doan, Tinh, Banwell, Cathy, Leach, Liana, and Strazdins, Lyndall
Published: 2024
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50. Adherence to dietary guidelines and associated factors among rural Australian cancer survivors: a cross-sectional study

Author: Leach, Michael James, Barber, Georgina, Monacella, Stephanie, Jamieson, Philip, Trinh, Thi, Vo, Ngan, Schmidt, Ulla, Byrne, Anny, and Ristevski, Eli
Published: 2024
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