Use of What-if Scenarios to Help Explain Artificial Intelligence Models for Neonatal Health

Autor: Mamun, Abdullah, Devoe, Lawrence D., Evans, Mark I., Britt, David W., Klein-Seetharaman, Judith, Ghasemzadeh, Hassan

Early detection of intrapartum risk enables interventions to potentially prevent or mitigate adverse labor outcomes such as cerebral palsy. Currently, there is no accurate automated system to predict such events to assist with clinical decision-makin

Externí odkaz: http://arxiv.org/abs/2410.09635

Supernova Electron-Neutrino Interactions with Xenon in the nEXO Detector

Autor: 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., Zhao, J.

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

Externí odkaz: http://arxiv.org/abs/2405.19419

An integrated online radioassay data storage and analytics tool for nEXO

Autor: Tsang, R. H. M., Piepke, A., Kharusi, S. Al, Angelico, E., Arnquist, I. J., Atencio, A., Badhrees, I., Bane, J., Belov, V., Bernard, E. P., Bhat, A., Bhatta, T., Bolotnikov, A., Breur, P. A., Brodsky, J. P., Brown, E., Brunner, T., Caden, E., Cao, G. F., Cao, L. Q., Cesmecioglu, D., Chambers, C., Chambers, E., Chana, B., Charlebois, S. A., Chernyak, D., Chiu, M., Cleveland, B., Cohen, J. R., Collister, R., Cvitan, M., Dalmasson, J., Darroch, L., Deslandes, K., DeVoe, R., di Vacri, M. L., Ding, Y. Y., Dolinski, M. J., Echevers, J., Eckert, B., Elbeltagi, M., Elmansali, R., Fabris, L., Fairbank, W., Farine, J., Fu, Y. S., Gallacher, D., Gallina, G., Gautam, P., Giacomini, G., Gillis, W., Gingras, C., Goeldi, D., Gornea, R., Gratta, G., Guan, Y. D., Hardy, C. A., Hedges, S., Heffner, M., Hein, E., Holt, J., Hoppe, E. W., House, A., Hunt, W., Iverson, A., Jamil, A., Jiang, X. S., Karelin, A., Kaufman, L. J., Kotov, I., Krücken, R., Kuchenkov, A., Kumar, K. S., Larson, A., Leach, K. G., Lenardo, B. G., Leonard, D. S., Li, G., Li, S., Li, Z., Licciardi, C., Lindsay, R., MacLellan, R., Mahtab, M., Majidi, S., Malbrunot, C., Martel-Dion, P., Masbou, J., Massacret, N., McMichael, K., Mong, B., Moore, D. C., Murray, K., 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., Peltz-Smalley, H., Perna, A., Franco, T. Pinto, Pocar, A., Pratte, J. -F., Radeka, V., Raguzin, E., Rasiwala, H., Ray, D., Rebeiro, B., Rescia, S., Retière, F., Richardson, G., Ringuette, J., Riot, V., Rowson, P. C., Roy, N., Rudolph, L., Saldanha, R., Sangiorgio, S., Schwartz, S., Soderstrom, J., Soma, A. K., Spadoni, F., Stekhanov, V., Sun, X. L., Barakoohi, E. Teimoori, Thibado, S., Tidball, A., Totev, T., Triambak, S., Tsang, T., Tyuka, O. A., Underwood, R., van Bruggen, E., Veeraraghavan, V., Vidal, M., Viel, S., Walent, M., Wamba, K., Wang, Q. D., Wang, W., Wang, Y. G., Watts, M., Wei, W., Wen, L. J., Wichoski, U., Wilde, S., Worcester, M., Wu, S., Wu, X. M., Yang, H., Yang, L., Yvaine, M., Zeldovich, O., Zhao, J., Ziegler, T.

Large-scale low-background detectors are increasingly used in rare-event searches as experimental collaborations push for enhanced sensitivity. However, building such detectors, in practice, creates an abundance of radioassay data especially during t

Externí odkaz: http://arxiv.org/abs/2304.06180

Generative Adversarial Networks for Scintillation Signal Simulation in EXO-200

Autor: Li, S., Ostrovskiy, I., Li, Z., Yang, L., Kharusi, S. Al, Anton, G., Badhrees, I., Barbeau, P. S., Beck, D., Belov, V., Bhatta, T., Breidenbach, M., Brunner, T., Cao, G. F., Cen, W. R., Chambers, C., Cleveland, B., Coon, M., Craycraft, A., Daniels, T., Darroch, L., Daugherty, S. J., Davis, J., Delaquis, S., Der Mesrobian-Kabakian, A., DeVoe, R., Dilling, J., Dolgolenko, A., Dolinski, M. J., Echevers, J., Fairbank Jr., W., Fairbank, D., Farine, J., Feyzbakhsh, S., Fierlinger, P., Fu, Y. S., Fudenberg, D., Gautam, P., Gornea, R., Gratta, G., Hall, C., Hansen, E. V., Hoessl, J., Hufschmidt, P., Hughes, M., Iverson, A., Jamil, A., Jessiman, C., Jewell, M. J., Johnson, A., Karelin, A., Kaufman, L. J., Koffas, T., Krücken, R., Kuchenkov, A., Kumar, K. S., Lan, Y., Larson, A., Lenardo, B. G., Leonard, D. S., Li, G. S., Licciardi, C., Lin, Y. H., MacLellan, R., McElroy, T., Michel, T., Mong, B., Moore, D. C., Murray, K., Njoya, O., Nusair, O., Odian, A., Perna, A., Piepke, A., Pocar, A., Retière, F., Robinson, A. L., Rowson, P. C., Runge, J., Schmidt, S., Sinclair, D., Skarpaas, K., Soma, A. K., Stekhanov, V., Tarka, M., Thibado, S., Todd, J., Tolba, T., Totev, T. I., Tsang, R.

Publikováno v: JINST 18 P06005 2023

Generative Adversarial Networks trained on samples of simulated or actual events have been proposed as a way of generating large simulated datasets at a reduced computational cost. In this work, a novel approach to perform the simulation of photodete

Externí odkaz: http://arxiv.org/abs/2303.06311

Evaluation of the precision and accuracy in the classification of breast histopathology images using the MobileNetV3 model

Autor: Kenneth DeVoe, Gary Takahashi, Ebrahim Tarshizi, Allan Sacker

Publikováno v: Journal of Pathology Informatics, Vol 15, Iss , Pp 100377- (2024)

Accurate surgical pathological assessment of breast biopsies is essential to the proper management of breast lesions. Identifying histological features, such as nuclear pleomorphism, increased mitotic activity, cellular atypia, patterns of architectu

Externí odkaz: https://doaj.org/article/1d0f1399b90e496cb97638597e9959c8