Search

Your search keyword '"A. Cauchois"' showing total 10,237 results
Start Over Author "A. Cauchois"
10,237 results on '"A. Cauchois"'

1. Using graph neural networks to reconstruct charged pion showers in the CMS High Granularity Calorimeter

Author

Aamir, M., Adamov, G., Adams, T., Adloff, C., Afanasiev, S., Agrawal, C., Ahmad, A., Ahmed, H. A., Akbar, S., Akchurin, N., Akgul, B., Akgun, B., Akpinar, R. O., Aktas, E., Kadhim, A. Al, Alexakhin, V., Alimena, J., Alison, J., Alpana, A., Alshehri, W., Dominguez, P. Alvarez, Alyari, M., Amendola, C., Amir, R. B., Andersen, S. B., Andreev, Y., Antoszczuk, P. D., Aras, U., Ardila, L., Aspell, P., Avila, M., Awad, I., Aydilek, O., Azimi, Z., Pretel, A. Aznar, Bach, O. A., Bainbridge, R., Bakshi, A., Bam, B., Banerjee, S., Barney, D., Bayraktar, O., Beaudette, F., Beaujean, F., Becheva, E., Behera, P. K., Belloni, A., Bergauer, T., Besancon, M., Bylund, O. Bessidskaia, Bhatt, L., Bhattacharya, S., Bhowmil, D., Blekman, F., Blinov, P., Bloch, P., Bodek, A., Boger, a., Bonnemaison, A., Bouyjou, F., Brennan, L., Brondolin, E., Brusamolino, A., Bubanja, I., Perraguin, A. Buchot, Bunin, P., Misura, A. Burazin, Butler-nalin, A., Cakir, A., Callier, S., Campbell, S., Candemir, Y. B., Canderan, K., Cankocak, K., Cappati, A., Caregari, S., Carron, S., Carty, C., Cauchois, A., Ceard, L., Cerci, S., Chang, P. J., Chatterjee, R. M., Chatterjee, S., Chattopadhyay, P., Chatzistavrou, T., Chaudhary, M. S., Chen, J. A., Chen, J., Chen, Y., Cheng, K., Cheung, H., Chhikara, J., Chiron, A., Chiusi, M., Chokheli, D., Chudasama, R., Clement, E., Mendez, S. Coco, Coko, D., Coskun, K., Couderc, F., Crossman, B., Cui, Z., Cuisset, T., Cummings, G., Curtis, E. M., D'Alfonso, M., Döhler-Ball, J., Dadazhanova, O., Damgov, J., Das, I., Gupta, S. Das, Dauncey, P., Mendes, A. David Tinoco, Davies, G., Davignon, O., de Barbaro, P., De La Taille, C., De Silva, M., De Wit, A., Debbins, P., Defranchis, M. M., Delagnes, E., Devouge, P., Di Guglielmo, G., Diehl, L., Dilsiz, K., Dincer, G. G., Dittmann, J., Dragicevic, M., Du, D., Dubinchik, B., Dugad, S., Dulucq, F., Dumanoglu, I., Duran, B., Dutta, S., Dutta, V., Dychkant, A., Dünser, M., Edberg, T., Ehle, I. T., Berni, A. El, Elias, F., Eno, S. C., Erdogan, E. N., Erkmen, B., Ershov, Y., Ertorer, E. Y., Extier, S., Eychenne, L., Fedar, Y. E., Fedi, G., De Almeida, J. P. Figueiredo De Sá Sousa, Alves, B. A. Fontana Santos, Frahm, E., Francis, K., Freeman, J., French, T., Gaede, F., Gandhi, P. K., Ganjour, S., Garcia-Bellido, A., Gastaldi, F., Gazi, L., Gecse, Z., Gerwig, H., Gevin, O., Ghosh, S., Gill, K., Gingu, C., Gleyzer, S., Godinovic, N., Goettlicher, P., Goff, R., Gok, M., Golunov, A., Gonultas, B., Martínez, J. D. González, Gorbounov, N., Gouskos, L., Gray, A., Gray, L., Grieco, C., Groenroos, S., Groner, D., Gruber, A., Grummer, A., Grönroos, S., Guerrero, D., Guilloux, F., Guler, Y., Gungordu, A. D., Guo, J., Guo, K., Guler, E. Gurpinar, Gutti, H. K., Guvenli, A. A., Gülmez, E., Hacisahinoglu, B., Halkin, Y., Machado, G. Hamilton Ilha, Hare, H. S., Hatakeyama, K., Heering, A. H., Hegde, V., Heintz, U., Hinton, N., Hinzmann, A., Hirschauer, J., Hitlin, D., Hoff, J., Hos, İ., Hou, B., Hou, X., Howard, A., Howe, C., Hsieh, H., Hsu, T., Hua, H., Hummer, F., Imran, M., Incandela, J., Iren, E., Isildak, B., Jackson, P. S., Jackson, W. J., Jain, S., Jana, P., Jaroslavceva, J., Jena, S., Jige, A., Jordano, P. P., Joshi, U., Kaadze, K., Kachanov, V., Kafizov, A., Kalipoliti, L., Tharayil, A. Kallil, Kaluzinska, O., Kamble, S., Kaminskiy, A., Kanemura, M., Kanso, H., Kao, Y., Kapic, A., Kapsiak, C., Karjavine, V., Karmakar, S., Karneyeu, A., Kaya, M., Topaksu, A. Kayis, Kaynak, B., Kazhykarim, Y., Khan, F. A., Khudiakov, A., Kieseler, J., Kim, R. S., Klijnsma, T., Kloiber, E. G., Klute, M., Kocak, Z., Kodali, K. R., Koetz, K., Kolberg, T., Kolcu, O. B., Komaragiri, J. R., Komm, M., Kopsalis, I., Krause, H. A., Krawczyk, M. A., Vinayakam, T. R. Krishnaswamy, Kristiansen, K., Kristic, A., Krohn, M., Kronheim, B., Krüger, K., Kudtarkar, C., Kulis, S., Kumar, M., Kumar, N., Kumar, S., Verma, R. Kumar, Kunori, S., Kunts, A., Kuo, C., Kurenkov, A., Kuryatkov, V., Kyre, S., Ladenson, J., Lamichhane, K., Landsberg, G., Langford, J., Laudrain, A., Laughlin, R., Lawhorn, J., Dortz, O. Le, Lee, S. W., Lektauers, A., Lelas, D., Leon, M., Levchuk, L., Li, A. J., Li, J., Li, Y., Liang, Z., Liao, H., Lin, K., Lin, W., Lin, Z., Lincoln, D., Linssen, L., Litomin, A., Liu, G., Liu, Y., Lobanov, A., Lohezic, V., Loiseau, T., Lu, C., Lu, R., Lu, S. Y., Lukens, P., Mackenzie, M., Magnan, A., Magniette, F., Mahjoub, A., Mahon, D., Majumder, G., Makarenko, V., Malakhov, A., Malgeri, L., Mallios, S., Mandloi, C., Mankel, A., Mannelli, M., Mans, J., Mantilla, C., Martinez, G., Massa, C., Masterson, P., Matthewman, M., Matveev, V., Mayekar, S., Mazlov, I., Mehta, A., Mestvirishvili, A., Miao, Y., Milella, G., Mirza, I. R., Mitra, P., Moccia, S., Mohanty, G. B., Monti, F., Moortgat, F., Murthy, S., Music, J., Musienko, Y., Nabili, S., Nelson, J. W., Nema, A., Neutelings, I., Niedziela, J., Nikitenko, A., Noonan, D., Noy, M., Nurdan, K., Obraztsov, S., Ochando, C., Ogul, H., Olsson, J., Onel, Y., Ozkorucuklu, S., Paganis, E., Palit, P., Pan, R., Pandey, S., Pantaleo, F., Papageorgakis, C., Paramesvaran, S., Paranjpe, M. M., Parolia, S., Parsons, A. G., Parygin, P., Pastika, J., Paulini, M., Paus, C., Castillo, K. Peñaló, Pedro, K., Pekic, V., Peltola, T., Peng, B., Perego, A., Perini, D., Petrilli, A., Pham, H., Podem, S. K., Popov, V., Portales, L., Potok, O., Pradeep, P. B., Pramanik, R., Prosper, H., Prvan, M., Qasim, S. R., Qu, H., Quast, T., Trivio, A. Quiroga, Rabour, L., Raicevic, N., Rao, M. A., Rapacz, K., Redjeb, W., Reinecke, M., Revering, M., Roberts, A., Rohlf, J., Rosado, P., Rose, A., Rothman, S., Rout, P. K., Rovere, M., Roy, A., Rubinov, P., Rumerio, P., Rusack, R., Rygaard, L., Ryjov, V., Sadivnycha, S., Sahin, M. Ö., Sakarya, U., Salerno, R., Saradhy, R., Saraf, M., Sarbandi, K., Sarkisla, M. A., Satyshev, I., Saud, N., Sauvan, J., Schindler, G., Schmidt, A., Schmidt, I., Schmitt, M. H., Sculac, A., Sculac, T., Sedelnikov, A., Seez, C., Sefkow, F., Selivanova, D., Selvaggi, M., Sergeychik, V., Sert, H., Shahid, M., Sharma, P., Sharma, R., Sharma, S., Shelake, M., Shenai, A., Shih, C. W., Shinde, R., Shmygol, D., Shukla, R., Sicking, E., Silva, P., Simsek, C., Simsek, E., Sirasva, B. K., Sirois, Y., Song, S., Song, Y., Soudais, G., Sriram, S., Jacques, R. R. St, Leiton, A. G. Stahl, Steen, A., Stein, J., Strait, J., Strobbe, N., Su, X., Sukhov, E., Suleiman, A., Cerci, D. Sunar, Suryadevara, P., Swain, K., Syal, C., Tali, B., Tanay, K., Tang, W., Tanvir, A., Tao, J., Tarabini, A., Tatli, T., Taylor, R., Taysi, Z. C., Teafoe, G., Tee, C. Z., Terrill, W., Thienpont, D., Thomas, P. E., Thomas, R., Titov, M., Todd, C., Todd, E., Toms, M., Tosun, A., Troska, J., Tsai, L., Tsamalaidze, Z., Tsionou, D., Tsipolitis, G., Tsirigoti, M., Tu, R., Polat, S. N. Tural, Undleeb, S., Usai, E., Uslan, E., Ustinov, V., Uzunian, A., Vernazza, E., Viahin, O., Viazlo, O., Vichoudis, P., Vijay, A., Virdee, T., Voirin, E., Vojinovic, M., Vámi, T. Á., Wade, A., Walter, D., Wang, C., Wang, F., Wang, J., Wang, K., Wang, X., Wang, Y., Wang, Z., Wanlin, E., Wayne, M., Wetzel, J., Whitbeck, A., Wickwire, R., Wilmot, D., Wilson, J., Wu, H., Xiao, M., Yang, J., Yazici, B., Ye, Y., Yerli, B., Yetkin, T., Yi, R., Yohay, R., Yu, T., Yuan, C., Yuan, X., Yuksel, O., YushmanoV, I., Yusuff, I., Zabi, A., Zareckis, D., Zehetner, P., Zghiche, A., Zhang, C., Zhang, D., Zhang, H., Zhang, J., Zhang, Z., Zhao, X., Zhong, J., Zhou, Y., and Zorbilmez, Ç.

Subjects
Physics - Instrumentation and Detectors, High Energy Physics - Experiment, Physics - Data Analysis, Statistics and Probability
Abstract

A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated.

Published
2024
Full Text
View/download PDF

2. Simplified Modelling Based on an Ontology of Users with Cerebral Palsy Interacting with People or Systems: Case Study of an Assistive System Used in Mobility

Author

Guerrier, Yohan, Bosquet, Siegrid, Valadier, Octave, Cauchois, Nicolas, Delcroix, Véronique, de Oliveira, Káthia Marçal, Kolski, Christophe, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Antona, Margherita, editor, Stephanidis, Constantine, editor, Gao, Qin, editor, and Zhou, Jia, editor

Published
2025
Full Text
View/download PDF

4. Modeling of the driver transverse profile for laser wakefield electron acceleration at APOLLON Research Facility

Author

Moulanier, Ioaquin, Dickson, Lewis, Ballage, Charles, Vasilovici, Ovidiu, Gremaud, Aubin, Dufrenoy, Sandrine Dobosz, Delerue, Nicolas, Bernardi, Lorenzo, Mahjoub, Ali, Cauchois, Antoine, Specka, Arnd, Massimo, Francesco, Maynard, Gilles, and Cros, Brigitte

Subjects
Physics - Plasma Physics, Physics - Accelerator Physics
Abstract

The quality of electron bunches accelerated by laser wakefields is highly dependant on the temporal and spatial features of the laser driver. Analysis of experiments performed at APOLLON PW-class laser facility shows that spatial instabilities of the focal spot, such as shot-to-shot pointing fluctuations or asymmetry of the transverse fluence, lead to charge and energy degradation of the accelerated electron bunch. It is shown that PIC simulations can reproduce experimental results with a significantly higher accuracy when the measured laser asymmetries are included in the simulated laser's transverse profile, compared to simulations with ideal, symmetric laser profile. A method based on a modified Gerchberg-Saxton iterative algorithm is used to retrieve the laser electric field from fluence measurements in vacuum in the focal volume, and accurately reproduce experimental results using PIC simulations, leading to simulated electron spectra in close agreement with experimental results, for the accelerated charge, energy distribution and pointing of the electron beam at the exit of the plasma.

Published
2023

5. Unique in the Smart Grid -The Privacy Cost of Fine-Grained Electrical Consumption Data

Author

Voyez, Antonin, Allard, Tristan, Avoine, Gildas, Cauchois, Pierre, Fromont, Elisa, and Simonin, Matthieu

Subjects
Computer Science - Cryptography and Security, Computer Science - Databases
Abstract

The collection of electrical consumption time series through smart meters grows with ambitious nationwide smart grid programs. This data is both highly sensitive and highly valuable: strong laws about personal data protect it while laws about open data aim at making it public after a privacy-preserving data publishing process. In this work, we study the uniqueness of large scale real-life fine-grained electrical consumption time-series and show its link to privacy threats. Our results show a worryingly high uniqueness rate in such datasets. In particular, we show that knowing 5 consecutive electric measures allows to re-identify on average more than 90% of households in our 2.5M half-hourly electric time series dataset. Moreover, uniqueness remains high even when data is severely degraded. For example, when data is rounded to the nearest 100 watts, knowing 7 consecutive electric measures allows to re-identify on average more than 40% of the households (same dataset). We also study the relationship between uniqueness and entropy, uniqueness and electric consumption, and electric consumption and temperatures, showing their strong correlation.

Published
2022

6. Performance of the CMS High Granularity Calorimeter prototype to charged pion beams of 20$-$300 GeV/c

Author

Acar, B., Adamov, G., Adloff, C., Afanasiev, S., Akchurin, N., Akgün, B., Alhusseini, M., Alison, J., de Almeida, J. P. Figueiredo de sa Sousa, de Almeida, P. G. Dias, Alpana, A., Alyari, M., Andreev, I., Aras, U., Aspell, P., Atakisi, I. O., Bach, O., Baden, A., Bakas, G., Bakshi, A., Banerjee, S., DeBarbaro, P., Bargassa, P., Barney, D., Beaudette, F., Beaujean, F., Becheva, E., Becker, A., Behera, P., Belloni, A., Bergauer, T., Berni, M. El, Besancon, M., Bhattacharya, S., Bhowmik, D., Bilki, B., Bilokin, S., Blazey, G. C., Blekman, F., Bloch, P., Bodek, A., Bonanomi, M., Bonis, J., Bonnemaison, A., Bonomally, S., Borg, J., Bouyjou, F., Bower, N., Braga, D., Brennan, L., Brianne, E., Brondolin, E., Bryant, P., Buhmann, E., Buhmann, P., Butler-Nalin, A., Bychkova, O., Callier, S., Calvet, D., Canderan, K., Cankocak, K., Cao, X., Cappati, A., Caraway, B., Caregari, S., Carty, C., Cauchois, A., Ceard, L., Cerci, D. S., Cerci, S., Cerminara, G., Chadeeva, M., Charitonidis, N., Chatterjee, R., Chen, J. A., Chen, Y. M., Cheng, H. J., Cheng, K. Y., Cheung, H., Chokheli, D., Cipriani, M., Čoko, D., Couderc, F., Cuba, E., Danilov, M., Dannheim, D., Daoud, W., Das, I., Dauncey, P., Davies, G., Davignon, O., Day, E., Debbins, P., Defranchis, M. M., Delagnes, E., Demiragli, Z., Demirbas, U., Derylo, G., Diaz, D., Diehl, L., Dinaucourt, P., Dincer, G. G., Dittmann, J., Dragicevic, M., Dugad, S., Dulucq, F., Dumanoglu, I., Dünser, M., Dutta, S., Dutta, V., Edberg, T. K., Elias, F., Emberger, L., Eno, S. C., Ershov, Yu., Extier, S., Fahim, F., Fallon, C., Fard, K. Sarbandi, Fedi, G., Ferragina, L., Forthomme, L., Frahm, E., Franzoni, G., Freeman, J., French, T., Gadow, K., Gandhi, P., Ganjour, S., Gao, X., Garcia, M. T. Ramos, Garcia-Bellido, A., Garutti, E., Gastaldi, F., Gastler, D., Gecse, Z., Germer, A., Gerwig, H., Gevin, O., Ghosh, S., Gilbert, A., Gilbert, W., Gill, K., Gingu, C., Gninenko, S., Golunov, A., Golutvin, I., Gonultas, B., Gorbounov, N., Göttlicher, P., Gouskos, L., Graf, C., Gray, A. B., Grieco, C., Gr\"önroos, S., Gu, Y., Guilloux, F., Guler, E. Gurpinar, Guler, Y., Gülmez, E., Guo, J., Gutti, H., Hakimi, A., Hammer, M., Hartbrich, O., Hassanshahi, H. M., Hatakeyama, K., Hazen, E., Heering, A., Hegde, V., Heintz, U., Heuchel, D., Hinton, N., Hirschauer, J., Hoff, J., Hou, W. S., Hou, X., Hua, H., Huck, S., Hussain, A., Incandela, J., Irles, A., Irshad, A., Isik, C., Jain, S., Jaroslavceva, J., Jheng, H. R., Joshi, U., Kaadze, K., Kachanov, V., Kalipoliti, L., Kaminskiy, A., Kanuganti, A. R., Kao, Y. W., Kapoor, A., Kara, O., Karneyeu, A., Kałuzińska, O., Kaya, M., Kaya, O., Kazhykharim, Y., Khan, F. A., Khukhunaishvili, A., Kieseler, J., Kilpatrick, M., Kim, S., Koetz, K., Kolberg, T., Komm, M., Köseyan, O. K., Kraus, V., Krawczyk, M., Kristiansen, K., Kristić, A., Krohn, M., Kronheim, B., Krüger, K., Kulis, S., Kumar, M., Kunori, S., Kuo, C. M., Kuryatkov, V., Kvasnicka, J., Kyre, S., Lai, Y., Lamichhane, K., Landsberg, G., Lange, C., Langford, J., Laurien, S., Lee, M. Y., Lee, S. W., Leiton, A. G. Stahl, Levin, A., Li, A., Li, J. H., Li, Y. Y., Liang, Z., Liao, H., Lin, Z., Lincoln, D., Linssen, L., Lipton, R., Liu, G., Liu, Y., Lobanov, A., Lohezic, V., Lomidze, D., Lu, R. S., Lu, S., Lupi, M., Lysova, I., Magnan, A. -M., Magniette, F., Mahjoub, A., Martens, S., Matysek, M., Meier, B., Malakhov, A., Mallios, S., Mandjavize, I., Mannelli, M., Mans, J., Marchioro, A., Martelli, A., Martinez, G., Masterson, P., Matthewman, M., Mayekar, S. N., David, A., Coco, S., Meng, B., Menkel, A ., Mestvirishvili, A., Milella, G., Mirza, I., Moccia, S., Mohanty, G. B., Monti, F., Moortgat, F. W., Morrissey, I., Motta, J., Murthy, S., Musić, J., Musienko, Y., Nabili, S., Nguyen, M., Nikitenko, A., Noonan, D., Noy, M., Nurdan, K., Nursanto, M. Wulansatiti, Ochando, C., Odell, N., Okawa, H., Onel, Y., Ortez, W., Ozegović, J., Ozkorucuklu, S., Paganis, E., Palmer, C. A., Pandey, S., Pantaleo, F., Papageorgakis, C., Papakrivopoulos, I., Paranjpe, M., Parshook, J., Pastika, N., Paulini, M., Peitzmann, T., Peltola, T., Peng, N., Perraguin, A. Buchot, Petiot, P., Pierre-Emile, T., Pinto, M. Vicente Barreto, Popova, E., Pöschl, R., Prosper, H., Prvan, M., Puljak, I., Qasim, S. R., Qu, H., Quast, T., Quinn, R., Quinnan, M., Rane, A., Rao, K. K., Rapacz, K., Raux, L., Redjeb, W., Reinecke, M., Revering, M., Richard, F., Roberts, A., Sanchez, A. M., Rohlf, J., Rolph, J., Romanteau, T., Rosado, M., Rose, A., Rovere, M., Roy, A., Rubinov, P., Rusack, R., Rusinov, V., Ryjov, V., Sahin, O. M., Salerno, R., Saradhy, R., Sarkar, T., Sarkisla, M. A., Sauvan, J. B., Schmidt, I., Schmitt, M., Schuwalow, S., Scott, E., Seez, C., Sefkow, F., Selivanova, D., Sharma, S., Shelake, M., Shenai, A., Shukla, R., Sicking, E., De, M., Silva, P., Simkina, P., Simon, F., Simsek, A. E., Sirois, Y., Smirnov, V., Sobering, T. J., Spencer, E., Srimanobhas, N., Steen, A., Strait, J., Strobbe, N., Su, X. F., Sudo, Y., Suarez, C. Mantilla, Sukhov, E., Sulak, L., Sun, L., Suryadevara, P., Syal, C., de La Taille, C., Tali, B., Tan, C. L., Tao, J., Tarabini, A., Tatli, T., Thaus, R., Taylor, R. D., Tekten, S., Thiebault, A., Thienpont, D., Tiley, C., Tiras, E., Titov, M., Tlisov, D., Tok, U. G., Kayis, A., Troska, J., Tsai, L. S., Tsamalaidze, Z., Tsipolitis, G., Tsirou, A., Undleeb, S., Urbanski, D., Uslan, E., Ustinov, V., Uzunian, A., Varela, J., Velasco, M., Vernazza, E., Viazlo, O., Vichoudis, P., Virdee, T., Voirin, E., Vojinovi\c, M., Vojinovic, M., Wade, A., Wang, C., Wang, C. C., Wang, D., Wang, F., Wang, X., Wang, Z., Wayne, M., Webb, S. N., Whitbeck, A., Wickwire, R., Wilson, J. S., Wu, H. Y., Wu, L., Xiao, M., Yang, J., Yeh, C. H, Yohay, R., Yu, D., Yu, S. S., Yuan, C., Miao, Y., Yumiceva, F., Yusuff, I., Zabi, A., Zacharopoulou, A., Zamiatin, N., Zarubin, A., Zehetner, P., Zerwas, D., Zhang, H., Zhang, J., Zhang, Y., Zhang, Z., and Zhao, X.

Subjects
Physics - Instrumentation and Detectors
Abstract

The upgrade of the CMS experiment for the high luminosity operation of the LHC comprises the replacement of the current endcap calorimeter by a high granularity sampling calorimeter (HGCAL). The electromagnetic section of the HGCAL is based on silicon sensors interspersed between lead and copper (or copper tungsten) absorbers. The hadronic section uses layers of stainless steel as an absorbing medium and silicon sensors as an active medium in the regions of high radiation exposure, and scintillator tiles directly readout by silicon photomultipliers in the remaining regions. As part of the development of the detector and its readout electronic components, a section of a silicon-based HGCAL prototype detector along with a section of the CALICE AHCAL prototype was exposed to muons, electrons and charged pions in beam test experiments at the H2 beamline at the CERN SPS in October 2018. The AHCAL uses the same technology as foreseen for the HGCAL but with much finer longitudinal segmentation. The performance of the calorimeters in terms of energy response and resolution, longitudinal and transverse shower profiles is studied using negatively charged pions, and is compared to GEANT4 predictions. This is the first report summarizing results of hadronic showers measured by the HGCAL prototype using beam test data., Comment: Accepted for publication by JINST

Published
2022

8. Query-Adaptive Predictive Inference with Partial Labels

Author

Cauchois, Maxime and Duchi, John

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

The cost and scarcity of fully supervised labels in statistical machine learning encourage using partially labeled data for model validation as a cheaper and more accessible alternative. Effectively collecting and leveraging weakly supervised data for large-space structured prediction tasks thus becomes an important part of an end-to-end learning system. We propose a new computationally-friendly methodology to construct predictive sets using only partially labeled data on top of black-box predictive models. To do so, we introduce "probe" functions as a way to describe weakly supervised instances and define a false discovery proportion-type loss, both of which seamlessly adapt to partial supervision and structured prediction -- ranking, matching, segmentation, multilabel or multiclass classification. Our experiments highlight the validity of our predictive set construction as well as the attractiveness of a more flexible user-dependent loss framework.

Published
2022

9. Link21 Transportation Planning and Funding

Author

Berman, Josh, Cauchois, Camille, Lucchesi, Dominic, McGee, Mary, and Peck, Christina

Abstract

The looming mass transit fiscal cliff threatens the viability of long term operations of BART and future Link21 projects. BART’s historic reliance on farebox recovery for financial stability necessitates an evaluation of available funding sources in an effort to increase funding for both long term project planning and ongoing operations. Given existing State and Federal policies that call for increased investment in public transit and rail and California’s stated goals around greenhouse gas emission reductions and equity improvements, the time is ripe for changes to the current funding mechanisms which have long favored highway and road projects over transit and rail. A streamlined process to ensure continuous and advanced planning is necessary for the successful completion of megaregional transit and rail projects that cross political jurisdictions. Further, this type of planning and funding is necessary for California to remain competitive for Federal funding opportunities, especially given the unprecedented amount of funding currently available from the Infrastructure Investment and Jobs Act (2021).

Published
2022

10. The Lifecycle of a Statistical Model: Model Failure Detection, Identification, and Refitting

Author

Ali, Alnur, Cauchois, Maxime, and Duchi, John C.

Subjects
Statistics - Methodology, Statistics - Machine Learning
Abstract

The statistical machine learning community has demonstrated considerable resourcefulness over the years in developing highly expressive tools for estimation, prediction, and inference. The bedrock assumptions underlying these developments are that the data comes from a fixed population and displays little heterogeneity. But reality is significantly more complex: statistical models now routinely fail when released into real-world systems and scientific applications, where such assumptions rarely hold. Consequently, we pursue a different path in this paper vis-a-vis the well-worn trail of developing new methodology for estimation and prediction. In this paper, we develop tools and theory for detecting and identifying regions of the covariate space (subpopulations) where model performance has begun to degrade, and study intervening to fix these failures through refitting. We present empirical results with three real-world data sets -- including a time series involving forecasting the incidence of COVID-19 -- showing that our methodology generates interpretable results, is useful for tracking model performance, and can boost model performance through refitting. We complement these empirical results with theory proving that our methodology is minimax optimal for recovering anomalous subpopulations as well as refitting to improve accuracy in a structured normal means setting.

Published
2022

11. Predictive Inference with Weak Supervision

Author

Cauchois, Maxime, Gupta, Suyash, Ali, Alnur, and Duchi, John

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

The expense of acquiring labels in large-scale statistical machine learning makes partially and weakly-labeled data attractive, though it is not always apparent how to leverage such data for model fitting or validation. We present a methodology to bridge the gap between partial supervision and validation, developing a conformal prediction framework to provide valid predictive confidence sets -- sets that cover a true label with a prescribed probability, independent of the underlying distribution -- using weakly labeled data. To do so, we introduce a (necessary) new notion of coverage and predictive validity, then develop several application scenarios, providing efficient algorithms for classification and several large-scale structured prediction problems. We corroborate the hypothesis that the new coverage definition allows for tighter and more informative (but valid) confidence sets through several experiments.

Published
2022

12. A comment and erratum on 'Excess Optimism: How Biased is the Apparent Error of an Estimator Tuned by SURE?'

Author

Cauchois, Maxime, Ali, Alnur, and Duchi, John

Subjects
Mathematics - Statistics Theory
Abstract

We identify and correct an error in the paper "Excess Optimism: How Biased is the Apparent Error of an Estimator Tuned by SURE?" This correction allows new guarantees on the excess degrees of freedom--the bias in the error estimate of Stein's unbiased risk estimate (SURE) for an estimator tuned by directly minimizing the SURE criterion--for arbitrary SURE-tuned linear estimators. Oracle inequalities follow as a consequence of these results for such estimators.

Published
2021

13. Response of a CMS HGCAL silicon-pad electromagnetic calorimeter prototype to 20-300 GeV positrons

Author

Acar, B., Adamov, G., Adloff, C., Afanasiev, S., Akchurin, N., Akgün, B., Khan, F. Alam, Alhusseini, M., Alison, J., Alpana, A., Altopp, G., Alyari, M., An, S., Anagul, S., Andreev, I., Aspell, P., Atakisi, I. O., Bach, O., Baden, A., Bakas, G., Bakshi, A., Bannerjee, S., Bargassa, P., Barney, D., Beaudette, F., Beaujean, F., Becheva, E., Becker, A., Behera, P., Belloni, A., Bergauer, T., Besancon, M., Bhattacharya, S., Bhowmik, D., Bilki, B., Bloch, P., Bodek, A., Bonanomi, M., Bonnemaison, A., Bonomally, S., Borg, J., Bouyjou, F., Bower, N., Braga, D., Brashear, J., Brondolin, E., Bryant, P., Perraguin, A. Buchot, Bueghly, J., Burkle, B., Butler-Nalin, A., Bychkova, O., Callier, S., Calvet, D., Cao, X., Cappati, A., Caraway, B., Caregari, S., Cauchois, A., Ceard, L., Cekmecelioglu, Y. C., Cerci, S., Cerminara, G., Chadeeva, M., Charitonidis, N., Chatterjee, R., Chen, Y. M., Chen, Z., Cheng, H. J., Cheng, K. y., Chernichenko, S., Cheung, H., Chien, C. H., Choudhury, S., Čoko, D., Collura, G., Couderc, F., Danilov, M., Dannheim, D., Daoud, W., Dauncey, P., David, A., Davies, G., Davignon, O., Day, E., DeBarbaro, P., De Guio, F., de La Taille, C., De Silva, M., Debbins, P., Defranchis, M. M., Delagnes, E., Berrio, J. M. Deltoro, Derylo, G., de Almeida, P. G. Dias, Diaz, D., Dinaucourt, P., Dittmann, J., Dragicevic, M., Dugad, S., Dulucq, F., Dumanoglu, I., Dutta, V., Dutta, S., Dünser, M., Eckdahl, J., Edberg, T. K., Berni, M. El, Elias, F., Eno, S. C., Ershov, Yu., Everaerts, P., Extier, S., Fahim, F., Fallon, C., Fedi, G., Alves, B. A. Fontana Santos, Frahm, E., Franzoni, G., Freeman, J., French, T., Gandhi, P., Ganjour, S., Gao, X., Garcia-Bellido, A., Gastaldi, F., Gecse, Z., Geerebaert, Y., Gerwig, H., Gevin, O., Ghosh, S., Gilbert, A., Gilbert, W., Gill, K., Gingu, C., Gninenko, S., Golunov, A., Golutvin, I., Gonzalez, T., Gorbounov, N., Gouskos, L., Gray, A. B., Gu, Y., Guilloux, F., Guler, Y., Gülmez, E., Guo, J., Guler, E. Gurpinar, Hammer, M., Hassanshahi, H. M., Hatakeyama, K., Heering, A., Hegde, V., Heintz, U., Hinton, N., Hirschauer, J., Hoff, J., Hou, W. -S., Hou, X., Hua, H., Incandela, J., Irshad, A., Isik, C., Jain, S., Jheng, H. R., Joshi, U., Kachanov, V., Kalinin, A., Kalipoliti, L., Kaminskiy, A., Kapoor, A., Kara, O., Karneyeu, A., Kaya, M., Kaya, O., Topaksu, A. Kayis, Khukhunaishvili, A., Kiesler, J., Kilpatrick, M., Kim, S., Koetz, K., Kolberg, T., Köseyan, O. K., Kristić, A., Krohn, M., Krüger, K., Kulagin, N., Kulis, S., Kunori, S., Kuo, C. M., Kuryatkov, V., Kyre, S., Lai, Y., Lamichhane, K., Landsberg, G., Lange, C., Langford, J., Lee, M. Y., Levin, A., Li, A., Li, B., Li, J. H., Li, Y. y., Liao, H., Lincoln, D., Linssen, L., Lipton, R., Liu, Y., Lobanov, A., Lu, R. -S., Lupi, M., Lysova, I., Magnan, A. -M., Magniette, F., Mahjoub, A., Maier, A. A., Malakhov, A., Mallios, S., Mandjavize, I., Mannelli, M., Mans, J., Marchioro, A., Martelli, A., Martinez, G., Masterson, P., Meng, B., Mengke, T., Mestvirishvili, A., Mirza, I., Moccia, S., Mohanty, G. B., Monti, F., Morrissey, I., Murthy, S., Musić, J., Musienko, Y., Nabili, S., Nagar, A., Nguyen, M., Nikitenko, A., Noonan, D., Noy, M., Nurdan, K., Ochando, C., Odegard, B., Odell, N., Okawa, H., Onel, Y., Ortez, W., Ozegović, J., Ozkorucuklu, S., Paganis, E., Pagenkopf, D., Palladino, V., Pandey, S., Pantaleo, F., Papageorgakis, C., Papakrivopoulos, I., Parshook, J., Pastika, N., Paulini, M., Paulitsch, P., Peltola, T., Gomes, R. Pereira, Perkins, H., Petiot, P., Pierre-Emile, T., Pitters, F., Popova, E., Prosper, H., Prvan, M., Puljak, I., Qu, H., Quast, T., Quinn, R., Quinnan, M., Garcia, M. T. Ramos, Rao, K. K., Rapacz, K., Raux, L., Reichenbach, G., Reinecke, M., Revering, M., Roberts, A., Romanteau, T., Rose, A., Rovere, M., Roy, A., Rubinov, P., Rusack, R., Rusinov, V., Ryjov, V., Sahin, O. M., Salerno, R., Rodriguez, A. M. Sanchez, Saradhy, R., Sarkar, T., Sarkisla, M. A., Sauvan, J. B., Schmidt, I., Schmitt, M., Scott, E., Seez, C., Sefkow, F., Sharma, S., Shein, I., Shenai, A., Shukla, R., Sicking, E., Sieberer, P., Silva, P., Simsek, A. E., Sirois, Y., Smirnov, V., Sozbilir, U., Spencer, E., Steen, A., Strait, J., Strobbe, N., Su, J. W., Sukhov, E., Sun, L., Cerci, D. Sunar, Syal, C., Tali, B., Tan, C. L., Tao, J., Tastan, I., Tatli, T., Thaus, R., Tekten, S., Thienpont, D., Tiras, E., Titov, M., Tlisov, D., Tok, U. G., Troska, J., Tsai, L. -S., Tsamalaidze, Z., Tsipolitis, G., Tsirou, A., Tyurin, N., Undleeb, S., Urbanski, D., Ustinov, V., Uzunian, A., Van de Klundert, M., Varela, J., Velasco, M., Viazlo, O., Pinto, M. Vicente Barreto, Virdee, P. Vichoudis T., de Oliveira, R. Vizinho, Voelker, J., Voirin, E., Vojinovic, M., Wade, A., Wang, C., Wang, F., Wang, X., Wang, Z., Wayne, M., Webb, S. N., Whitbeck, A., White, D., Wickwire, R., Wilson, J. S., Winter, D., Wu, H. y., Wu, L., Nursanto, M. Wulansatiti, Yeh, C. H, Yohay, R., Yu, D., Yu, G. B., Yu, S. S., Yuan, C., Yumiceva, F., Yusuff, I., Zacharopoulou, A., Zamiatin, N., Zarubin, A., Zenz, S., Zghiche, A., Zhang, H., Zhang, J., Zhang, Y., and Zhang, Z.

Subjects
Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract

The Compact Muon Solenoid Collaboration is designing a new high-granularity endcap calorimeter, HGCAL, to be installed later this decade. As part of this development work, a prototype system was built, with an electromagnetic section consisting of 14 double-sided structures, providing 28 sampling layers. Each sampling layer has an hexagonal module, where a multipad large-area silicon sensor is glued between an electronics circuit board and a metal baseplate. The sensor pads of approximately 1 cm$^2$ are wire-bonded to the circuit board and are readout by custom integrated circuits. The prototype was extensively tested with beams at CERN's Super Proton Synchrotron in 2018. Based on the data collected with beams of positrons, with energies ranging from 20 to 300 GeV, measurements of the energy resolution and linearity, the position and angular resolutions, and the shower shapes are presented and compared to a detailed Geant4 simulation.

Published
2021
Full Text
View/download PDF

20. Robust Validation: Confident Predictions Even When Distributions Shift

Author

Cauchois, Maxime, Gupta, Suyash, Ali, Alnur, and Duchi, John C.

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, Statistics - Methodology
Abstract

While the traditional viewpoint in machine learning and statistics assumes training and testing samples come from the same population, practice belies this fiction. One strategy -- coming from robust statistics and optimization -- is thus to build a model robust to distributional perturbations. In this paper, we take a different approach to describe procedures for robust predictive inference, where a model provides uncertainty estimates on its predictions rather than point predictions. We present a method that produces prediction sets (almost exactly) giving the right coverage level for any test distribution in an $f$-divergence ball around the training population. The method, based on conformal inference, achieves (nearly) valid coverage in finite samples, under only the condition that the training data be exchangeable. An essential component of our methodology is to estimate the amount of expected future data shift and build robustness to it; we develop estimators and prove their consistency for protection and validity of uncertainty estimates under shifts. By experimenting on several large-scale benchmark datasets, including Recht et al.'s CIFAR-v4 and ImageNet-V2 datasets, we provide complementary empirical results that highlight the importance of robust predictive validity., Comment: Published in the Journal of the American Statistical Association (JASA 2024)

Published
2020
Full Text
View/download PDF

21. Knowing what you know: valid and validated confidence sets in multiclass and multilabel prediction

Author

Cauchois, Maxime, Gupta, Suyash, and Duchi, John

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, Statistics - Methodology
Abstract

We develop conformal prediction methods for constructing valid predictive confidence sets in multiclass and multilabel problems without assumptions on the data generating distribution. A challenge here is that typical conformal prediction methods---which give marginal validity (coverage) guarantees---provide uneven coverage, in that they address easy examples at the expense of essentially ignoring difficult examples. By leveraging ideas from quantile regression, we build methods that always guarantee correct coverage but additionally provide (asymptotically optimal) conditional coverage for both multiclass and multilabel prediction problems. To address the potential challenge of exponentially large confidence sets in multilabel prediction, we build tree-structured classifiers that efficiently account for interactions between labels. Our methods can be bolted on top of any classification model---neural network, random forest, boosted tree---to guarantee its validity. We also provide an empirical evaluation, simultaneously providing new validation methods, that suggests the more robust coverage of our confidence sets., Comment: Updated section on multilabel settings addressing the cases when labels may repel each other

Published
2020

22. Social Data to Enhance Typical Consumer Energy Profile Estimation on a National Level

Author

Alyafi, Amr, Cauchois, Pierre, Delinchant, Benoit, Berges, Alain, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Pierfederici, Serge, editor, and Martin, Jean-Philippe, editor

Published
2023
Full Text
View/download PDF

23. Multilingual Question Answering from Formatted Text applied to Conversational Agents

Author

Siblini, Wissam, Pasqual, Charlotte, Lavielle, Axel, Challal, Mohamed, and Cauchois, Cyril

Subjects
Computer Science - Computation and Language
Abstract

Recent advances with language models (e.g. BERT, XLNet, ...), have allowed surpassing human performance on complex NLP tasks such as Reading Comprehension. However, labeled datasets for training are available mostly in English which makes it difficult to acknowledge progress in other languages. Fortunately, models are now pre-trained on unlabeled data from hundreds of languages and exhibit interesting transfer abilities from one language to another. In this paper, we show that multilingual BERT is naturally capable of zero-shot transfer for an extractive Question Answering task (eQA) from English to other languages. More specifically, it outperforms the best previously known baseline for transfer to Japanese and French. Moreover, using a recently published large eQA French dataset, we are able to further show that (1) zero-shot transfer provides results really close to a direct training on the target language and (2) combination of transfer and training on target is the best option overall. We finally present a practical application: a multilingual conversational agent called Kate which answers to HR-related questions in several languages directly from the content of intranet pages.

Published
2019

27. Tocilizumab versus anakinra in COVID-19: results from propensity score matching

Author

Robin Arcani, Florian Correard, Pierre Suchon, Gilles Kaplanski, Rodolphe Jean, Raphael Cauchois, Marine Leprince, Vincent Arcani, Julie Seguier, Benjamin De Sainte Marie, Baptiste Andre, Marie Koubi, Pascal Rossi, Stéphane Gayet, Nirvina Gobin, Victoria Garrido, Joris Weiland, Elisabeth Jouve, Anne-Laure Couderc, Patrick Villani, and Aurélie Daumas

Subjects
anakinra, tocilizumab, anti-interleukin drugs, COVID-19, mortality, SARS-CoV-2, Immunologic diseases. Allergy, RC581-607
Abstract

BackgroundTocilizumab and anakinra are anti-interleukin drugs to treat severe coronavirus disease 2019 (COVID-19) refractory to corticosteroids. However, no studies compared the efficacy of tocilizumab versus anakinra to guide the choice of the therapy in clinical practice. We aimed to compare the outcomes of COVID-19 patients treated with tocilizumab or anakinra.MethodsOur retrospective study was conducted in three French university hospitals between February 2021 and February 2022 and included all the consecutive hospitalized patients with a laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection assessed by RT-PCR who were treated with tocilizumab or anakinra. A propensity score matching was performed to minimize confounding effects due to the non-random allocation.ResultsAmong 235 patients (mean age, 72 years; 60.9% of male patients), the 28-day mortality (29.4% vs. 31.2%, p = 0.76), the in-hospital mortality (31.7% vs. 33.0%, p = 0.83), the high-flow oxygen requirement (17.5% vs. 18.3%, p = 0.86), the intensive care unit admission rate (30.8% vs. 22.2%, p = 0.30), and the mechanical ventilation rate (15.4% vs. 11.1%, p = 0.50) were similar in patients receiving tocilizumab and those receiving anakinra. After propensity score matching, the 28-day mortality (29.1% vs. 30.4%, p = 1) and the rate of high-flow oxygen requirement (10.1% vs. 21.5%, p = 0.081) did not differ between patients receiving tocilizumab or anakinra. Secondary infection rates were similar between the tocilizumab and anakinra groups (6.3% vs. 9.2%, p = 0.44).ConclusionOur study showed comparable efficacy and safety profiles of tocilizumab and anakinra to treat severe COVID-19.

Published
2023
Full Text
View/download PDF

28. Timing performance of the CMS High Granularity Calorimeter prototype

Author

Acar, B, Adamov, G, Adloff, C, Afanasiev, S, Akchurin, N, Akgun, B, Khan, F, Alhusseini, M, Alison, J, Alpana, A, Altopp, G, Alyari, M, An, S, Anagul, S, Andreev, I, Aspell, P, Atakisi, I, Bach, O, Baden, A, Bakas, G, Bakshi, A, Bannerjee, S, Bargassa, P, Barney, D, Beaudette, F, Beaujean, F, Becheva, E, Becker, A, Behera, P, Belloni, A, Bergauer, T, Besancon, M, Bhattacharya, S, Bhowmik, D, Bilki, B, Bloch, P, Bodek, A, Bonanomi, M, Bonnemaison, A, Bonomally, S, Borg, J, Bouyjou, F, Bower, N, Braga, D, Brashear, J, Brondolin, E, Bryant, P, Buchot Perraguin, A, Bueghly, J, Burkle, B, Butler-Nalin, A, Bychkova, O, Callier, S, Calvet, D, Cao, X, Cappati, A, Caraway, B, Caregari, S, Cauchois, A, Ceard, L, Cekmecelioglu, Y, Cerci, S, Cerminara, G, Chadeeva, M, Charitonidis, N, Chatterjee, R, Chen, Y, Chen, Z, Cheng, H, Cheng, K, Chernichenko, S, Cheung, H, Chien, C, Choudhury, S, Coko, D, Collura, G, Couderc, F, Danilov, M, Dannheim, D, Daoud, W, Dauncey, P, David, A, Davies, G, Davignon, O, Day, E, De Barbaro, P, De Guio, F, de La Taille, C, De Silva, M, Debbins, P, Defranchis, M, Delagnes, E, Deltoro Berrio, J, Derylo, G, Dias de Almeida, P, Diaz, D, Dinaucourt, P, Dittmann, J, Dragicevic, M, Dugad, S, Dulucq, F, Dumanoglu, I, Dutta, V, Dutta, S, Dunser, M, Eckdahl, J, Edberg, T, El Berni, M, Elias, F, Eno, S, Ershov, Y, Everaerts, P, Extier, S, Fahim, F, Fallon, C, Fedi, G, Fontana Santos Alves, B, Frahm, E, Franzoni, G, Freeman, J, French, T, Gandhi, P, Ganjour, S, Gao, X, Garcia-Bellido, A, Gastaldi, F, Gecse, Z, Geerebaert, Y, Gerwig, H, Gevin, O, Ghosh, S, Gilbert, A, Gilbert, W, Gill, K, Gingu, C, Gninenko, S, Golunov, A, Golutvin, I, Gonzalez, T, Gorbounov, N, Gouskos, L, Gray, A, Gu, Y, Guilloux, F, Guler, Y, Gulmez, E, Guo, J, Gurpinar Guler, E, Hammer, M, Hassanshahi, H, Hatakeyama, K, Heering, A, Hegde, V, Heintz, U, Hinton, N, Hirschauer, J, Hoff, J, Hou, W, Hou, X, Hua, H, Incandela, J, Irshad, A, Isik, C, Jain, S, Jheng, H, Joshi, U, Kachanov, V, Kalinin, A, Kalipoliti, L, Kaminskiy, A, Kapoor, A, Kara, O, Karneyeu, A, Kaya, M, Kaya, O, Kayis Topaksu, A, Khukhunaishvili, A, Kieseler, J, Kilpatrick, M, Kim, S, Koetz, K, Kolberg, T, Koseyan, O, Kristic, A, Krohn, M, Kruger, K, Kulagin, N, Kulis, S, Kunori, S, Kuo, C, Kuryatkov, V, Kyre, S, Lai, Y, Lamichhane, K, Landsberg, G, Lange, C, Langford, J, Lee, M, Levin, A, Li, A, Li, B, Li, J, Li, Y, Liao, H, Lincoln, D, Linssen, L, Lipton, R, Liu, Y, Lobanov, A, Lu, R, Lupi, M, Lysova, I, Magnan, A, Magniette, F, Mahjoub, A, Maier, A, Malakhov, A, Mallios, S, Mannelli, M, Mans, J, Marchioro, A, Martelli, A, Martinez, G, Masterson, P, Meng, B, Mengke, T, Mestvirishvili, A, Mirza, I, Moccia, S, Mohanty, G, Monti, F, Morrissey, I, Murthy, S, Music, J, Musienko, Y, Nabili, S, Nagar, A, Nguyen, M, Nikitenko, A, Noonan, D, Noy, M, Nurdan, K, Ochando, C, Odegard, B, Odell, N, Okawa, H, Onel, Y, Ortez, W, Ozegovic, J, Ozkorucuklu, S, Paganis, E, Pagenkopf, D, Palladino, V, Pandey, S, Pantaleo, F, Papageorgakis, C, Papakrivopoulos, I, Parshook, J, Pastika, N, Paulini, M, Paulitsch, P, Peltola, T, Pereira Gomes, R, Perkins, H, Petiot, P, Pierre-Emile, T, Pitters, F, Popova, E, Prosper, H, Prvan, M, Puljak, I, Qu, H, Quast, T, Quinn, R, Quinnan, M, Ramos Garcia, M, Rao, K, Rapacz, K, Raux, L, Reichenbach, G, Reinecke, M, Revering, M, Roberts, A, Romanteau, T, Rose, A, Rovere, M, Roy, A, Rubinov, P, Rusack, R, Rusinov, V, Ryjov, V, Sahin, M, Salerno, R, Sanchez Rodriguez, A, Saradhy, R, Sarkar, T, Sarkisla, M, Sauvan, J, Schmidt, I, Schmitt, M, Scott, E, Seez, C, Sefkow, F, Sharma, S, Shein, I, Shenai, A, Shukla, R, Sicking, E, Sieberer, P, Silva, P, Simsek, A, Sirois, Y, Smirnov, V, Sozbilir, U, Spencer, E, Steen, A, Strait, J, Strobbe, N, Su, J, Sukhov, E, Sun, L, Sunar Cerci, D, Syal, C, Tali, B, Tan, C, Tao, J, Tastan, I, Tatli, T, Thaus, R, Tekten, S, Thienpont, D, Tiras, E, Titov, M, Tlisov, D, Tok, U, Troska, J, Tsai, L, Tsamalaidze, Z, Tsipolitis, G, Tsirou, A, Tyurin, N, Undleeb, S, Urbanski, D, Ustinov, V, Uzunian, A, Van de Klundert, M, Varela, J, Velasco, M, Viazlo, O, Vicente Barreto Pinto, M, Vichoudis, P, Virdee, T, Vizinho de Oliveira, R, Voelker, J, Voirin, E, Vojinovic, M, Wade, A, Wang, C, Wang, F, Wang, X, Wang, Z, Wayne, M, Webb, S, Whitbeck, A, White, D, Wickwire, R, Wilson, J, Winter, D, Wu, H, Wu, L, Wulansatiti Nursanto, M, Yeh, C, Yohay, R, Yu, D, Yu, G, Yu, S, Yuan, C, Yumiceva, F, Yusuff, I, Zacharopoulou, A, Zamiatin, N, Zarubin, A, Zenz, S, Zghiche, A, Zhang, H, Zhang, J, Zhang, Y, Zhang, Z, Acar B., Adamov G., Adloff C., Afanasiev S., Akchurin N., Akgun B., Khan F., Alhusseini M., Alison J., Alpana A., Altopp G., Alyari M., An S., Anagul S., Andreev I., Aspell P., Atakisi I., Bach O., Baden A., Bakas G., Bakshi A., Bannerjee S., Bargassa P., Barney D., Beaudette F., Beaujean F., Becheva E., Becker A., Behera P., Belloni A., Bergauer T., Besancon M., Bhattacharya S., Bhowmik D., Bilki B., Bloch P., Bodek A., Bonanomi M., Bonnemaison A., Bonomally S., Borg J., Bouyjou F., Bower N., Braga D., Brashear J., Brondolin E., Bryant P., Buchot Perraguin A., Bueghly J., Burkle B., Butler-Nalin A., Bychkova O., Callier S., Calvet D., Cao X., Cappati A., Caraway B., Caregari S., Cauchois A., Ceard L., Cekmecelioglu Y., Cerci S., Cerminara G., Chadeeva M., Charitonidis N., Chatterjee R., Chen Y., Chen Z., Cheng H., Cheng K., Chernichenko S., Cheung H., Chien C., Choudhury S., Coko D., Collura G., Couderc F., Danilov M., Dannheim D., Daoud W., Dauncey P., David A., Davies G., Davignon O., Day E., De Barbaro P., De Guio F., de La Taille C., De Silva M., Debbins P., Defranchis M., Delagnes E., Deltoro Berrio J., Derylo G., Dias de Almeida P., Diaz D., Dinaucourt P., Dittmann J., Dragicevic M., Dugad S., Dulucq F., Dumanoglu I., Dutta V., Dutta S., Dunser M., Eckdahl J., Edberg T., El Berni M., Elias F., Eno S., Ershov Y., Everaerts P., Extier S., Fahim F., Fallon C., Fedi G., Fontana Santos Alves B., Frahm E., Franzoni G., Freeman J., French T., Gandhi P., Ganjour S., Gao X., Garcia-Bellido A., Gastaldi F., Gecse Z., Geerebaert Y., Gerwig H., Gevin O., Ghosh S., Gilbert A., Gilbert W., Gill K., Gingu C., Gninenko S., Golunov A., Golutvin I., Gonzalez T., Gorbounov N., Gouskos L., Gray A., Gu Y., Guilloux F., Guler Y., Gulmez E., Guo J., Gurpinar Guler E., Hammer M., Hassanshahi H., Hatakeyama K., Heering A., Hegde V., Heintz U., Hinton N., Hirschauer J., Hoff J., Hou W. -S., Hou X., Hua H., Incandela J., Irshad A., Isik C., Jain S., Jheng H., Joshi U., Kachanov V., Kalinin A., Kalipoliti L., Kaminskiy A., Kapoor A., Kara O., Karneyeu A., Kaya M., Kaya O., Kayis Topaksu A., Khukhunaishvili A., Kieseler J., Kilpatrick M., Kim S., Koetz K., Kolberg T., Koseyan O., Kristic A., Krohn M., Kruger K., Kulagin N., Kulis S., Kunori S., Kuo C., Kuryatkov V., Kyre S., Lai Y., Lamichhane K., Landsberg G., Lange C., Langford J., Lee M., Levin A., Li A., Li B., Li J., Li Y., Liao H., Lincoln D., Linssen L., Lipton R., Liu Y., Lobanov A., Lu R. -S., Lupi M., Lysova I., Magnan A. -M., Magniette F., Mahjoub A., Maier A., Malakhov A., Mallios S., Mannelli M., Mans J., Marchioro A., Martelli A., Martinez G., Masterson P., Meng B., Mengke T., Mestvirishvili A., Mirza I., Moccia S., Mohanty G., Monti F., Morrissey I., Murthy S., Music J., Musienko Y., Nabili S., Nagar A., Nguyen M., Nikitenko A., Noonan D., Noy M., Nurdan K., Ochando C., Odegard B., Odell N., Okawa H., Onel Y., Ortez W., Ozegovic J., Ozkorucuklu S., Paganis E., Pagenkopf D., Palladino V., Pandey S., Pantaleo F., Papageorgakis C., Papakrivopoulos I., Parshook J., Pastika N., Paulini M., Paulitsch P., Peltola T., Pereira Gomes R., Perkins H., Petiot P., Pierre-Emile T., Pitters F., Popova E., Prosper H., Prvan M., Puljak I., Qu H., Quast T., Quinn R., Quinnan M., Ramos Garcia M., Rao K., Rapacz K., Raux L., Reichenbach G., Reinecke M., Revering M., Roberts A., Romanteau T., Rose A., Rovere M., Roy A., Rubinov P., Rusack R., Rusinov V., Ryjov V., Sahin M., Salerno R., Sanchez Rodriguez A., Saradhy R., Sarkar T., Sarkisla M., Sauvan J., Schmidt I., Schmitt M., Scott E., Seez C., Sefkow F., Sharma S., Shein I., Shenai A., Shukla R., Sicking E., Sieberer P., Silva P., Simsek A., Sirois Y., Smirnov V., Sozbilir U., Spencer E., Steen A., Strait J., Strobbe N., Su J., Sukhov E., Sun L., Sunar Cerci D., Syal C., Tali B., Tan C., Tao J., Tastan I., Tatli T., Thaus R., Tekten S., Thienpont D., Tiras E., Titov M., Tlisov D., Tok U., Troska J., Tsai L. -S., Tsamalaidze Z., Tsipolitis G., Tsirou A., Tyurin N., Undleeb S., Urbanski D., Ustinov V., Uzunian A., Van de Klundert M., Varela J., Velasco M., Viazlo O., Vicente Barreto Pinto M., Vichoudis P., Virdee T., Vizinho de Oliveira R., Voelker J., Voirin E., Vojinovic M., Wade A., Wang C., Wang F., Wang X., Wang Z., Wayne M., Webb S., Whitbeck A., White D., Wickwire R., Wilson J., Winter D., Wu H., Wu L., Wulansatiti Nursanto M., Yeh C., Yohay R., Yu D., Yu G., Yu S., Yuan C., Yumiceva F., Yusuff I., Zacharopoulou A., Zamiatin N., Zarubin A., Zenz S., Zghiche A., Zhang H., Zhang J., Zhang Y., Zhang Z., Acar, B, Adamov, G, Adloff, C, Afanasiev, S, Akchurin, N, Akgun, B, Khan, F, Alhusseini, M, Alison, J, Alpana, A, Altopp, G, Alyari, M, An, S, Anagul, S, Andreev, I, Aspell, P, Atakisi, I, Bach, O, Baden, A, Bakas, G, Bakshi, A, Bannerjee, S, Bargassa, P, Barney, D, Beaudette, F, Beaujean, F, Becheva, E, Becker, A, Behera, P, Belloni, A, Bergauer, T, Besancon, M, Bhattacharya, S, Bhowmik, D, Bilki, B, Bloch, P, Bodek, A, Bonanomi, M, Bonnemaison, A, Bonomally, S, Borg, J, Bouyjou, F, Bower, N, Braga, D, Brashear, J, Brondolin, E, Bryant, P, Buchot Perraguin, A, Bueghly, J, Burkle, B, Butler-Nalin, A, Bychkova, O, Callier, S, Calvet, D, Cao, X, Cappati, A, Caraway, B, Caregari, S, Cauchois, A, Ceard, L, Cekmecelioglu, Y, Cerci, S, Cerminara, G, Chadeeva, M, Charitonidis, N, Chatterjee, R, Chen, Y, Chen, Z, Cheng, H, Cheng, K, Chernichenko, S, Cheung, H, Chien, C, Choudhury, S, Coko, D, Collura, G, Couderc, F, Danilov, M, Dannheim, D, Daoud, W, Dauncey, P, David, A, Davies, G, Davignon, O, Day, E, De Barbaro, P, De Guio, F, de La Taille, C, De Silva, M, Debbins, P, Defranchis, M, Delagnes, E, Deltoro Berrio, J, Derylo, G, Dias de Almeida, P, Diaz, D, Dinaucourt, P, Dittmann, J, Dragicevic, M, Dugad, S, Dulucq, F, Dumanoglu, I, Dutta, V, Dutta, S, Dunser, M, Eckdahl, J, Edberg, T, El Berni, M, Elias, F, Eno, S, Ershov, Y, Everaerts, P, Extier, S, Fahim, F, Fallon, C, Fedi, G, Fontana Santos Alves, B, Frahm, E, Franzoni, G, Freeman, J, French, T, Gandhi, P, Ganjour, S, Gao, X, Garcia-Bellido, A, Gastaldi, F, Gecse, Z, Geerebaert, Y, Gerwig, H, Gevin, O, Ghosh, S, Gilbert, A, Gilbert, W, Gill, K, Gingu, C, Gninenko, S, Golunov, A, Golutvin, I, Gonzalez, T, Gorbounov, N, Gouskos, L, Gray, A, Gu, Y, Guilloux, F, Guler, Y, Gulmez, E, Guo, J, Gurpinar Guler, E, Hammer, M, Hassanshahi, H, Hatakeyama, K, Heering, A, Hegde, V, Heintz, U, Hinton, N, Hirschauer, J, Hoff, J, Hou, W, Hou, X, Hua, H, Incandela, J, Irshad, A, Isik, C, Jain, S, Jheng, H, Joshi, U, Kachanov, V, Kalinin, A, Kalipoliti, L, Kaminskiy, A, Kapoor, A, Kara, O, Karneyeu, A, Kaya, M, Kaya, O, Kayis Topaksu, A, Khukhunaishvili, A, Kieseler, J, Kilpatrick, M, Kim, S, Koetz, K, Kolberg, T, Koseyan, O, Kristic, A, Krohn, M, Kruger, K, Kulagin, N, Kulis, S, Kunori, S, Kuo, C, Kuryatkov, V, Kyre, S, Lai, Y, Lamichhane, K, Landsberg, G, Lange, C, Langford, J, Lee, M, Levin, A, Li, A, Li, B, Li, J, Li, Y, Liao, H, Lincoln, D, Linssen, L, Lipton, R, Liu, Y, Lobanov, A, Lu, R, Lupi, M, Lysova, I, Magnan, A, Magniette, F, Mahjoub, A, Maier, A, Malakhov, A, Mallios, S, Mannelli, M, Mans, J, Marchioro, A, Martelli, A, Martinez, G, Masterson, P, Meng, B, Mengke, T, Mestvirishvili, A, Mirza, I, Moccia, S, Mohanty, G, Monti, F, Morrissey, I, Murthy, S, Music, J, Musienko, Y, Nabili, S, Nagar, A, Nguyen, M, Nikitenko, A, Noonan, D, Noy, M, Nurdan, K, Ochando, C, Odegard, B, Odell, N, Okawa, H, Onel, Y, Ortez, W, Ozegovic, J, Ozkorucuklu, S, Paganis, E, Pagenkopf, D, Palladino, V, Pandey, S, Pantaleo, F, Papageorgakis, C, Papakrivopoulos, I, Parshook, J, Pastika, N, Paulini, M, Paulitsch, P, Peltola, T, Pereira Gomes, R, Perkins, H, Petiot, P, Pierre-Emile, T, Pitters, F, Popova, E, Prosper, H, Prvan, M, Puljak, I, Qu, H, Quast, T, Quinn, R, Quinnan, M, Ramos Garcia, M, Rao, K, Rapacz, K, Raux, L, Reichenbach, G, Reinecke, M, Revering, M, Roberts, A, Romanteau, T, Rose, A, Rovere, M, Roy, A, Rubinov, P, Rusack, R, Rusinov, V, Ryjov, V, Sahin, M, Salerno, R, Sanchez Rodriguez, A, Saradhy, R, Sarkar, T, Sarkisla, M, Sauvan, J, Schmidt, I, Schmitt, M, Scott, E, Seez, C, Sefkow, F, Sharma, S, Shein, I, Shenai, A, Shukla, R, Sicking, E, Sieberer, P, Silva, P, Simsek, A, Sirois, Y, Smirnov, V, Sozbilir, U, Spencer, E, Steen, A, Strait, J, Strobbe, N, Su, J, Sukhov, E, Sun, L, Sunar Cerci, D, Syal, C, Tali, B, Tan, C, Tao, J, Tastan, I, Tatli, T, Thaus, R, Tekten, S, Thienpont, D, Tiras, E, Titov, M, Tlisov, D, Tok, U, Troska, J, Tsai, L, Tsamalaidze, Z, Tsipolitis, G, Tsirou, A, Tyurin, N, Undleeb, S, Urbanski, D, Ustinov, V, Uzunian, A, Van de Klundert, M, Varela, J, Velasco, M, Viazlo, O, Vicente Barreto Pinto, M, Vichoudis, P, Virdee, T, Vizinho de Oliveira, R, Voelker, J, Voirin, E, Vojinovic, M, Wade, A, Wang, C, Wang, F, Wang, X, Wang, Z, Wayne, M, Webb, S, Whitbeck, A, White, D, Wickwire, R, Wilson, J, Winter, D, Wu, H, Wu, L, Wulansatiti Nursanto, M, Yeh, C, Yohay, R, Yu, D, Yu, G, Yu, S, Yuan, C, Yumiceva, F, Yusuff, I, Zacharopoulou, A, Zamiatin, N, Zarubin, A, Zenz, S, Zghiche, A, Zhang, H, Zhang, J, Zhang, Y, Zhang, Z, Acar B., Adamov G., Adloff C., Afanasiev S., Akchurin N., Akgun B., Khan F., Alhusseini M., Alison J., Alpana A., Altopp G., Alyari M., An S., Anagul S., Andreev I., Aspell P., Atakisi I., Bach O., Baden A., Bakas G., Bakshi A., Bannerjee S., Bargassa P., Barney D., Beaudette F., Beaujean F., Becheva E., Becker A., Behera P., Belloni A., Bergauer T., Besancon M., Bhattacharya S., Bhowmik D., Bilki B., Bloch P., Bodek A., Bonanomi M., Bonnemaison A., Bonomally S., Borg J., Bouyjou F., Bower N., Braga D., Brashear J., Brondolin E., Bryant P., Buchot Perraguin A., Bueghly J., Burkle B., Butler-Nalin A., Bychkova O., Callier S., Calvet D., Cao X., Cappati A., Caraway B., Caregari S., Cauchois A., Ceard L., Cekmecelioglu Y., Cerci S., Cerminara G., Chadeeva M., Charitonidis N., Chatterjee R., Chen Y., Chen Z., Cheng H., Cheng K., Chernichenko S., Cheung H., Chien C., Choudhury S., Coko D., Collura G., Couderc F., Danilov M., Dannheim D., Daoud W., Dauncey P., David A., Davies G., Davignon O., Day E., De Barbaro P., De Guio F., de La Taille C., De Silva M., Debbins P., Defranchis M., Delagnes E., Deltoro Berrio J., Derylo G., Dias de Almeida P., Diaz D., Dinaucourt P., Dittmann J., Dragicevic M., Dugad S., Dulucq F., Dumanoglu I., Dutta V., Dutta S., Dunser M., Eckdahl J., Edberg T., El Berni M., Elias F., Eno S., Ershov Y., Everaerts P., Extier S., Fahim F., Fallon C., Fedi G., Fontana Santos Alves B., Frahm E., Franzoni G., Freeman J., French T., Gandhi P., Ganjour S., Gao X., Garcia-Bellido A., Gastaldi F., Gecse Z., Geerebaert Y., Gerwig H., Gevin O., Ghosh S., Gilbert A., Gilbert W., Gill K., Gingu C., Gninenko S., Golunov A., Golutvin I., Gonzalez T., Gorbounov N., Gouskos L., Gray A., Gu Y., Guilloux F., Guler Y., Gulmez E., Guo J., Gurpinar Guler E., Hammer M., Hassanshahi H., Hatakeyama K., Heering A., Hegde V., Heintz U., Hinton N., Hirschauer J., Hoff J., Hou W. -S., Hou X., Hua H., Incandela J., Irshad A., Isik C., Jain S., Jheng H., Joshi U., Kachanov V., Kalinin A., Kalipoliti L., Kaminskiy A., Kapoor A., Kara O., Karneyeu A., Kaya M., Kaya O., Kayis Topaksu A., Khukhunaishvili A., Kieseler J., Kilpatrick M., Kim S., Koetz K., Kolberg T., Koseyan O., Kristic A., Krohn M., Kruger K., Kulagin N., Kulis S., Kunori S., Kuo C., Kuryatkov V., Kyre S., Lai Y., Lamichhane K., Landsberg G., Lange C., Langford J., Lee M., Levin A., Li A., Li B., Li J., Li Y., Liao H., Lincoln D., Linssen L., Lipton R., Liu Y., Lobanov A., Lu R. -S., Lupi M., Lysova I., Magnan A. -M., Magniette F., Mahjoub A., Maier A., Malakhov A., Mallios S., Mannelli M., Mans J., Marchioro A., Martelli A., Martinez G., Masterson P., Meng B., Mengke T., Mestvirishvili A., Mirza I., Moccia S., Mohanty G., Monti F., Morrissey I., Murthy S., Music J., Musienko Y., Nabili S., Nagar A., Nguyen M., Nikitenko A., Noonan D., Noy M., Nurdan K., Ochando C., Odegard B., Odell N., Okawa H., Onel Y., Ortez W., Ozegovic J., Ozkorucuklu S., Paganis E., Pagenkopf D., Palladino V., Pandey S., Pantaleo F., Papageorgakis C., Papakrivopoulos I., Parshook J., Pastika N., Paulini M., Paulitsch P., Peltola T., Pereira Gomes R., Perkins H., Petiot P., Pierre-Emile T., Pitters F., Popova E., Prosper H., Prvan M., Puljak I., Qu H., Quast T., Quinn R., Quinnan M., Ramos Garcia M., Rao K., Rapacz K., Raux L., Reichenbach G., Reinecke M., Revering M., Roberts A., Romanteau T., Rose A., Rovere M., Roy A., Rubinov P., Rusack R., Rusinov V., Ryjov V., Sahin M., Salerno R., Sanchez Rodriguez A., Saradhy R., Sarkar T., Sarkisla M., Sauvan J., Schmidt I., Schmitt M., Scott E., Seez C., Sefkow F., Sharma S., Shein I., Shenai A., Shukla R., Sicking E., Sieberer P., Silva P., Simsek A., Sirois Y., Smirnov V., Sozbilir U., Spencer E., Steen A., Strait J., Strobbe N., Su J., Sukhov E., Sun L., Sunar Cerci D., Syal C., Tali B., Tan C., Tao J., Tastan I., Tatli T., Thaus R., Tekten S., Thienpont D., Tiras E., Titov M., Tlisov D., Tok U., Troska J., Tsai L. -S., Tsamalaidze Z., Tsipolitis G., Tsirou A., Tyurin N., Undleeb S., Urbanski D., Ustinov V., Uzunian A., Van de Klundert M., Varela J., Velasco M., Viazlo O., Vicente Barreto Pinto M., Vichoudis P., Virdee T., Vizinho de Oliveira R., Voelker J., Voirin E., Vojinovic M., Wade A., Wang C., Wang F., Wang X., Wang Z., Wayne M., Webb S., Whitbeck A., White D., Wickwire R., Wilson J., Winter D., Wu H., Wu L., Wulansatiti Nursanto M., Yeh C., Yohay R., Yu D., Yu G., Yu S., Yuan C., Yumiceva F., Yusuff I., Zacharopoulou A., Zamiatin N., Zarubin A., Zenz S., Zghiche A., Zhang H., Zhang J., Zhang Y., and Zhang Z.

Abstract

This paper describes the experience with the calibration, reconstruction and evaluation of the timing capabilities of the CMS HGCAL prototype in the beam tests in 2018. The calibration procedure includes multiple steps and corrections ranging from tens of nanoseconds to a few hundred picoseconds. The timing performance is studied using signals from positron beam particles with energies between 20 GeV and 300 GeV. The performance is studied as a function of particle energy against an external timing reference as well as standalone by comparing the two different halves of the prototype. The timing resolution is found to be 60 ps for single-channel measurements and better than 20 ps for full showers at the highest energies, setting excellent perspectives for the HGCAL calorimeter performance at the HL-LHC.

Published
2024

29. Robust Validation: Confident Predictions Even When Distributions Shift.

Author

Cauchois, Maxime, Gupta, Suyash, Ali, Alnur, and Duchi, John C.

Subjects
ROBUST statistics, VALIDITY of statistics, MACHINE learning, STATISTICS, FORECASTING
Abstract

While the traditional viewpoint in machine learning and statistics assumes training and testing samples come from the same population, practice belies this fiction. One strategy—coming from robust statistics and optimization—is thus to build a model robust to distributional perturbations. In this article, we take a different approach to describe procedures for robust predictive inference, where a model provides uncertainty estimates on its predictions rather than point predictions. We present a method that produces prediction sets (almost exactly) giving the right coverage level for any test distribution in an f-divergence ball around the training population. The method, based on conformal inference, achieves (nearly) valid coverage in finite samples, under only the condition that the training data be exchangeable. An essential component of our methodology is to estimate the amount of expected future data shift and build robustness to it; we develop estimators and prove their consistency for protection and validity of uncertainty estimates under shifts. By experimenting on several large-scale benchmark datasets, including Recht et al.'s CIFAR-v4 and ImageNet-V2 datasets, we provide complementary empirical results that highlight the importance of robust predictive validity. for this article are available online. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

33. Immune-mediated thrombotic thrombocytopenic purpura plasma induces calcium- and IgG-dependent endothelial activation: correlations with disease severity

Author

Edwige Tellier, Agnès Widemann, Raphaël Cauchois, Julien Faccini, Marie Lagarde, Marion Brun, Philippe Robert, Stéphane Robert, Richard Bachelier, Pascale Poullin, Elien Roose, Karen Vanhoorelbeke, Paul Coppo, Françoise Dignat-George, and Gilles Kaplanski

Subjects
Diseases of the blood and blood-forming organs, RC633-647.5
Abstract

Immune-mediated thrombotic thrombocytopenic purpura (iTTP) is characterized by a severe ADAMTS13 deficiency due to the presence of anti-ADAMTS13 auto-antibodies, with subsequent accumulation of circulating ultra-large von Willebrand factor (VWF) multimers. The role of endothelial cell activation as a trigger of the disease has been suggested in animal models but remains to be demonstrated in humans. We prospectively obtained plasma from the first plasma exchange of 25 patients during iTTP acute phase. iTTP but not control plasma, induced a rapid VWF release and P-selectin exposure on the surface of dermal human micro-vascular endothelial cell (HMVEC-d), associated with angiopoietin-2 and endothelin-1 secretion, consistent with Weibel-Palade bodies exocytosis. Calcium (Ca2+) blockade significantly decreased VWF release, whereas iTTP plasma induced a rapid and sustained Ca2+ flux in HMVEC-d which correlated in retrospect, with disease severity and survival in 62 iTTP patients. F(ab)’2 fragments purified from the immunoglobulin G fraction of iTTP plasma mainly induced endothelial cell activation with additional minor roles for circulating free heme and nucleosomes, but not for complement. Furthermore, two anti-ADAMTS13 monoclonal antibodies purified from iTTP patients’ B cells, but not serum from hereditary TTP, induced endothelial Ca2+ flux associated with Weibel-Palade bodies exocytosis in vitro, whereas inhibition of endothelial ADAMTS13 expression using small intering RNA, significantly decreased the stimulating effects of iTTP immunoglobulin G. In conclusion, Ca2+-mediated endothelial cell activation constitutes a “second hit” of iTTP, is correlated with the severity of the disease and may constitute a possible therapeutic target.

Published
2022
Full Text
View/download PDF

34. Effect of anakinra on mortality in patients with COVID-19: a systematic review and patient-level meta-analysis

Author

Kyriazopoulou, Evdoxia, Huet, Thomas, Cavalli, Giulio, Gori, Andrea, Kyprianou, Miltiades, Pickkers, Peter, Eugen-Olsen, Jesper, Clerici, Mario, Veas, Francisco, Chatellier, Gilles, Kaplanski, Gilles, Netea, Mihai G., Pontali, Emanuele, Gattorno, Marco, Cauchois, Raphael, Kooistra, Emma, Kox, Matthijs, Bandera, Alessandra, Beaussier, Hélène, Mangioni, Davide, Dagna, Lorenzo, van der Meer, Jos W.M., Giamarellos-Bourboulis, Evangelos J., Hayem, Gilles, Volpi, Stefano, Sormani, Maria Pia, Signori, Alessio, Bozzi, Giorgio, Minoia, Francesca, Aliberti, Stefano, Grasselli, Giacomo, Alagna, Laura, Lombardi, Andrea, Ungaro, Riccardo, Agostoni, Carlo, Blasi, Francesco, Costantino, Giorgio, Fracanzani, Anna Ludovica, Montano, Nicola, Peyvandi, Flora, Sottocorno, Marcello, Muscatello, Antonio, Filocamo, Giovanni, Papadopoulos, Antonios, Mouktaroudi, Maria, Karakike, Eleni, Saridaki, Maria, Gkavogianni, Theologia, Katrini, Konstantina, Vechlidis, Nikolaos, Avgoustou, Christina, Chalvatzis, Stamatios, Marantos, Theodoros, Damoulari, Christina, Damoraki, Georgia, Ktena, Sofia, Tsilika, Maria, Koufargyris, Panagiotis, Karageorgos, Athanasios, Droggiti, Dionysia-Irene, Koliakou, Aikaterini, Poulakou, Garyfallia, Tsiakos, Konstantinos, Myrodia, Dimitra-Melia, Gravvani, Areti, Trontzas, Ioannis P., Syrigos, Konstantinos, Kalomenidis, Ioannis, Kranidioti, Eleftheria, Panagopoulos, Periklis, Petrakis, Vasileios, Metallidis, Simeon, Loli, Georgia, Tsachouridou, Olga, Dalekos, George N., Gatselis, Nikolaos, Stefos, Aggelos, Georgiadou, Sarah, Lygoura, Vassiliki, Milionis, Haralampos, Kosmidou, Maria, Papanikolaou, Ilias C., Akinosoglou, Karolina, Giannitsioti, Efthymia, Chrysos, Georgios, Mavroudis, Panagiotis, Sidiropoulou, Chrysanthi, Adamis, Georgios, Fragkou, Archontoula, Rapti, Aggeliki, Alexiou, Zoi, Symbardi, Styliani, Masgala, Aikaterini, Kostaki, Konstantina, Kostis, Evangelos, Samarkos, Michael, Bakakos, Petros, Tzavara, Vassiliki, Dimakou, Katerina, Tzatzagou, Glykeria, Chini, Maria, Kotsis, Vasileios, Tsoukalas, George, Bliziotis, Ioannis, Doumas, Michael, Argyraki, Aikaterini, Kainis, Ilias, Fantoni, Massimo, Cingolani, Antonella, Angheben, Andrea, Cardellino, Chiara Simona, Castelli, Francesco, Serino, Francesco Saverio, Nicastri, Emanuele, Ippolito, Giuseppe, Bassetti, Matteo, Selmi, Carlo, Netea, Mihai G, van der Meer, Jos W M, and Giamarellos-Bourboulis, Evangelos J

Published
2021
Full Text
View/download PDF

36. Successful heart transplantation for COVID‐19‐associated post‐infectious fulminant myocarditis

Author

Baptiste Gaudriot, Alexandre Mansour, Vincent Thibault, Mathieu Lederlin, Aurélie Cauchois, Bernard Lelong, James T. Ross, Guillaume Leurent, Jean‐Marc Tadié, Matthieu Revest, Jean‐Philippe Verhoye, Erwan Flecher, and Nicolas Nesseler

Subjects
Fulminant myocarditis, COVID‐19, Cardiogenic shock, Extracorporeal membrane oxygenation, Heart transplantation, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Abstract Various clinical presentations of the 2019 coronavirus disease (COVID‐19) have been described, including post‐infectious acute and fulminant myocarditis. Here, we describe the case of a young patient admitted for COVID‐19‐associated post‐infectious fulminant myocarditis. Despite optimal pharmacologic management, haemodynamic status worsened requiring support by veno‐arterial extracorporeal membrane oxygenation. Emergent heart transplantation was required at Day 11 given the absence of cardiac function improvement. The diagnosis of post‐infectious COVID‐19‐associated myocarditis was made from both pathologic examination of the explanted heart and positive SARS‐CoV‐2 serology.

Published
2021
Full Text
View/download PDF

40. Avdoralimab (Anti-C5aR1 mAb) Versus Placebo in Patients With Severe COVID-19: Results From a Randomized Controlled Trial (FOR COVID Elimination [FORCE])*

Author

Carvelli, Julien, Meziani, Ferhat, Dellamonica, Jean, Cordier, Pierre-Yves, Allardet-Servent, Jerome, Fraisse, Megan, Velly, Lionel, Barbar, Saber Davide, Lehingue, Samuel, Guervilly, Christophe, Desgrouas, Maxime, Camou, Fabrice, Piperoglou, Christelle, Vely, Frederic, Demaria, Olivier, Karakunnel, Joyson, Fares, Joanna, Batista, Luciana, Rotolo, Federico, Viotti, Julien, Boyer-Chammard, Agnes, Lacombe, Karine, Le Dault, Erwan, Carles, Michel, Schleinitz, Nicolas, Vivier, Eric, Schleinitz, Nicolas, Carvelli, Julien, Gainnier, Marc, Bourenne, Jérémy, Bichon, Amandine, Le Saux, Audrey, Bouzana, Fouad, Tilmont, Antoine, Cauchois, Emi, Coularet, Charlotte, Bruder, Nicolas, Velly, Lionel, Ebbo, Mikael, Veit, Véronique, Jean, Estelle, Simeone, Pierre, Blasco, Valéry, Vely, Frédéric, Piperoglou, Christelle, Coutard, Bruno, Pastorino, Boris, Villaroel, Maria Saba, Garrido-Pradalie, Emilie, Amichi, Kahéna, Larosa, Aurélie, Blondelon, Aurélie, Inal, Imane, Amichi, Kahéna, Dhorne, Jean, Durieux, Frédérique, Brunet, Julie, Cohen, Anita, Deluca, Bénédicte, Malkoun, Richard, Dellamonica, Jean, Buscot, Matthieu, Saccheri, Clément, Carles, Michel, Demonchy, Elisa, Cua, Eric, Chirio, David, Courjon, Johan, Risso, Karine, Rigault, Marie-Christine, Gazoppi, Loïc, Salas, Virginie, Bouskila, Nadège, Touitou, Irit, Breaud, Sophie, Boughdiri, Nihed, Marrane, Guillaume, Meziani, Ferhat, Merdji, Hamid, Helms, Julie, Monier, Alexandra, Demiselle, Julien, Jandeaux, Louise-Marie, Studer, Antoine, Allam, Hayat, Thiebaut, Léonie, Hutt-Clauss, Anne, Le Dault, Erwan, Cordier, Pierre-Yves, Savini, Hélène, Clerc, Axelle, Spadoni, Sophie, Javelle, Emilie, Clerc, Axelle, Chouaki-Benmansour, Nassima, Le Garlantezec, Patrick, Le Tohic, Sarah, Allardet-Servent, Jérôme, Benarous, Lucas, Madjarian, Corinne, Aouadenne-Mesbah, Assia, Rognon, Amélie, Fraisse, Megan, Plantefeve, Gaétan, Benrezzak, Nasro, Dubief, Emeline, Chauvel, Olivia, Jamet, Charlotte, Davide Barbar, Saber, Ambert, Audrey, Lloret, Sophie, Elotmani, Loubna, Dubois, Grégory, Meyrieux, Séverine, Barthelemi, Laurie, Lehingue, Samuel, Poulet, Antoine, Bezirganyan, Kristina, Asselate, Belkacem, Provitolo, Vincent, Lacombe, Karine, Bollens, Diane, Letaillandier, Cyrielle, Tran, Christian, Sebire, Manuela, Lamarque, Julie, Deguenel-Nguyen, Anne, Desgrouas, Maxime, Jacquier, Sophie, Muller, Grégoire, Bretagnol, Anne, Mathonnet, Armelle, Benzekri, Dalila, Barbier, François, Mai-Anh, Nay, Runge, Isabelle, Kamel, Toufik, Muller, Lucie, Tellec, Sophie, Guervilly, Christophe, Papazian, Laurent, Forel, Jean-Marie, Sanz, Céline, Pinglis, Camille, Valera, Sabine, Colombini, Nathalie, Camou, Fabrice, Mourissoux, Gaelle, Guisset, Olivier, Issa, Nahéma, Pedenon-Peyrichout, Delphine, Delaune, Jean, Langlade, Claire, Pedeboscq, Stéphane, Lescure, Xavier, Isernia, Valentina, Bachelard, Antoine, Fleurot, Odile, Le Gac, Sylvie, Da Conceicao, Olivia, Julia, Zelie, Chalal, Lynda, Oualit, Lynda, Kramer, Laura, Le Grand, Jennifer, Poissy, Julien, Nseir, Saadalla, Mariller, Laure, Delcoutre, Claire, Brice, Sylvie, Lelievre, Jean-Daniel, Gallien, Sébastien, Cotellon, Christine, Vindrios, William, Melica, Giovanna, Natella, Pierre-André, Bourhis, Marion, Thiemele, Alaki, De Nailly, Delphine Lefebvre, Malvy, Denis, Desclaux, Arnaud, Ducours, Mailys, Perreau, Pauline, Boyer, Alexandre, Clouzeau, Benjamin, Bui, Hoang-Nam, Bourgoin, Nicolas, Kabala, Anna, Ghezzoul, Bellabes, Servant, Vincent, and Djabarouti, Sarah

Published
2022
Full Text
View/download PDF

41. The impact of a closed-loop thalamocortical model on the spatiotemporal dynamics of cortical and thalamic traveling waves

Author

Sayak Bhattacharya, Matthieu B. L. Cauchois, Pablo A. Iglesias, and Zhe Sage Chen

Subjects
Medicine, Science
Abstract

Abstract Propagation of activity in spatially structured neuronal networks has been observed in awake, anesthetized, and sleeping brains. How these wave patterns emerge and organize across brain structures, and how network connectivity affects spatiotemporal neural activity remains unclear. Here, we develop a computational model of a two-dimensional thalamocortical network, which gives rise to emergent traveling waves similar to those observed experimentally. We illustrate how spontaneous and evoked oscillatory activity in space and time emerge using a closed-loop thalamocortical architecture, sustaining smooth waves in the cortex and staggered waves in the thalamus. We further show that intracortical and thalamocortical network connectivity, cortical excitation/inhibition balance, and thalamocortical or corticothalamic delay can independently or jointly change the spatiotemporal patterns (radial, planar and rotating waves) and characteristics (speed, direction, and frequency) of cortical and thalamic traveling waves. Computer simulations predict that increased thalamic inhibition induces slower cortical frequencies and that enhanced cortical excitation increases traveling wave speed and frequency. Overall, our results provide insight into the genesis and sustainability of thalamocortical spatiotemporal patterns, showing how simple synaptic alterations cause varied spontaneous and evoked wave patterns. Our model and simulations highlight the need for spatially spread neural recordings to uncover critical circuit mechanisms for brain functions.

Published
2021
Full Text
View/download PDF

44. Salivary Alpha Amylase Bronchial Measure for Early Aspiration Pneumonia Diagnosis in Patients Treated With Therapeutic Hypothermia After Out-of-hospital Cardiac Arrest

Author

Anis Moussali, Emi Cauchois, Julien Carvelli, Sami Hraeich, Fouad Bouzana, Audrey Lesaux, Mohamed Boucekine, Amandine Bichon, Marc Gainnier, Julien Fromonot, and Jeremy Bourenne

Subjects
cardiac arrest, alpha-amylase, aspiration pneumonia, acute lung injury, antibiotics, Medicine (General), R5-920
Abstract

BackgroundAspiration pneumonia is the most common respiratory complication following out-of-hospital cardiac arrests (OHCA). Alpha-amylase (α-amylase) in pulmonary secretions is a biomarker of interest in detecting inhalation. The main goal of this study is to evaluate the performance of bronchoalveolar levels of α-amylase in early diagnosis of aspiration pneumonia, in patients admitted to intensive care unit (ICU) after OHCA.MethodsThis is a prospective single-center trial, led during 5 years (July 2015 to September 2020). We included patients admitted to ICU after OHCA. A protected specimen bronchial brushing and a mini-bronchoalveolar lavage (mini-BAL) were collected during the first 6 h after admission. Dosage of bronchial α-amylase and standard bacterial analysis were performed. Investigators confirmed pneumonia diagnosis using clinical, radiological, and microbiological criteria. Every patient underwent targeted temperature management.Results88 patients were included. The 34% (30 patients) developed aspiration pneumonia within 5 days following admission. The 55% (17) of pneumonias occurred during the first 48 h. The 57% of the patients received a prophylactic antibiotic treatment on their admission day. ICU mortality was 50%. Median value of bronchial α-amylase did not differ whether patients had aspiration pneumonia (15 [0–94]) or not (3 [0–61], p = 0,157). Values were significantly different concerning early-onset pneumonia (within 48 h) [19 (7–297) vs. 3 (0–82), p = 0,047]. If one or more microorganisms were detected in the initial mini-BAL, median value of α-amylase was significantly higher [25 (2–230)] than in sterile cultures (2 [0–43], p = 0,007). With an 8.5 IU/L cut-point, sensitivity and specificity of α-amylase value for predicting aspiration pneumonia during the first 2 days were respectively 74 and 62%. True positive and negative rates were respectively 44 and 86%. The area under the ROC curve was 0,654 (CI 95%; 0,524–0,785). Mechanical ventilation duration, length of ICU stay, and mortality were similar in both groups.ConclusionIn our study, dosage of bronchial α-amylase was not useful in predicting aspiration pneumonia within the first 5 days after ICU admission for OHCA. Performance in predicting early-onset pneumonia was moderate.

Published
2022
Full Text
View/download PDF

45. Mortality, cardiac and cerebral damages reduction by IL-1 inhibition in a murine model of TTP

Author

Muller, Romain, primary, Cauchois, Raphael, additional, Lagarde, Marie, additional, Roffino, Sandrine, additional, Genovesio, Cecile, additional, Fernandez, Samantha, additional, Hache, Guillaume, additional, Guillet, Benjamin, additional, Kara, Yeter, additional, Marlinge, Marion, additional, Lenting, Peter J, additional, Poullin, Pascale, additional, Dignat-George, Françoise, additional, Tellier, Edwige, additional, and Kaplanski, Gilles, additional

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results