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pro vyhledávání: '"Moghadam P"'
Akademický článek
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Autor:
Delfan, Niloufar, Moghadam, Pardis Ketabi, Khoshnevisan, Mohammad, Chagahi, Mehdi Hosseini, Hatami, Behzad, Asgharzadeh, Melika, Zali, Mohammadreza, Moshiri, Behzad, Moghaddam, Amin Momeni, Khalafi, Mohammad Amin, Dehnad, Khosrow
Non-alcoholic fatty liver disease (NAFLD) is one of the most widespread liver disorders on a global scale, posing a significant threat of progressing to more severe conditions like nonalcoholic steatohepatitis (NASH), liver fibrosis, cirrhosis, and h
Externí odkaz:
http://arxiv.org/abs/2412.04884
Autor:
Aharonian, F., Benkhali, F. Ait, Aschersleben, J., Ashkar, H., Backes, M., Martins, V. Barbosa, Batzofin, R., Becherini, Y., Berge, D., Bernlöhr, K., Bi, B., Böttcher, M., Boisson, C., Bolmont, J., de Lavergne, M. de Bony, Borowska, J., Bouyahiaoui, M., Brose, R., Brown, A., Brun, F., Bruno, B., Bulik, T., Burger-Scheidlin, C., Bylund, T., Casanova, S., Celic, J., Cerruti, M., Chand, T., Chandra, S., Chen, A., Chibueze, J., Chibueze, O., Collins, T., Cotter, G., Mbarubucyeye, J. Damascene, Devin, J., Djuvsland, J., Dmytriiev, A., Egberts, K., Einecke, S., Ernenwein, J. -P., Fegan, S., Feijen, K., Fontaine, G., Funk, S., Gabici, S., Gallant, Y. A., Glicenstein, J. F., Glombitza, J., Grolleron, G., Heß, B., Hofmann, W., Holch, T. L., Holler, M., Horns, D., Huang, Zhiqiu, Jamrozy, M., Jankowsky, F., Joshi, V., Jung-Richardt, I., Kasai, E., Katarzynski, K., Kerszberg, D., Khatoon, R., Khelifi, B., Kluzniak, W., Komin, Nu., Kosack, K., Kostunin, D., Kundu, A., Lang, R. G., Stum, S. Le, Leitl, F., Lemiere, A., Lemoine-Goumard, M., Lenain, J. -P., Leuschner, F., Luashvili, A., Mackey, J., Malyshev, D., Marandon, V., Marinos, P., Marti-Devesa, G., Marx, R., Meyer, M., Mitchell, A., Moderski, R., Moghadam, M. O., Mohrmann, L., Montanari, A., Moulin, E., de Naurois, M., Niemiec, J., Ohm, S., Olivera-Nieto, L., Wilhelmi, E. de Ona, Ostrowski, M., Panny, S., Panter, M., Parsons, D., Pensec, U., Peron, G., Pühlhofer, G., Punch, M., Quirrenbach, A., Ravikularaman, S., Regeard, M., Reimer, A., Reimer, O., Reis, I., Ren, H., Reville, B., Rieger, F., Rowell, G., Rudak, B., Ruiz-Velasco, E., Sahakian, V., Salzmann, H., Santangelo, A., Sasaki, M., Schäfer, J., Schüssler, F., Schutte, H. M., Shapopi, J. N. S., Sharma, A., Sol, H., Spencer, S., Stawarz, L., Steinmassl, S., Steppa, C., Suzuki, H., Takahashi, T., Tanaka, T., Taylor, A. M., Terrier, R., Tsirou, M., van Eldik, C., Vecchi, M., Venter, C., Vink, J., Wach, T., Wagner, S. J., Wierzcholska, A., Zacharias, M., Zdziarski, A. A., Zech, A., Zywucka, N.
Publikováno v:
Phys. Rev. Lett. 133, 221001 (2024)
Owing to their rapid cooling rate and hence loss-limited propagation distance, cosmic-ray electrons and positrons (CRe) at very high energies probe local cosmic-ray accelerators and provide constraints on exotic production mechanisms such as annihila
Externí odkaz:
http://arxiv.org/abs/2411.08189
Node classification with Graph Neural Networks (GNN) under a fixed set of labels is well known in contrast to Graph Few-Shot Class Incremental Learning (GFSCIL), which involves learning a GNN classifier as graph nodes and classes growing over time sp
Externí odkaz:
http://arxiv.org/abs/2411.06634
Autor:
MAGIC Collaboration, Abe, H., Abe, S., Acciari, V. A., Agudo, I., Aniello, T., Ansoldi, S., Antonelli, L. A., Engels, A. Arbet, Arcaro, C., Artero, M., Asano, K., Baack, D., Babić, A., Baquero, A., de Almeida, U. Barres, Batković, I., Baxter, J., Bernardini, E., Bernardos, M., Bernete, J., Berti, A., Bigongiari, C., Biland, A., Blanch, O., Bonnoli, G., Bošnjak, Ž., Burelli, I., Busetto, G., Campoy-Ordaz, A., Carosi, A., Carosi, R., Carretero-Castrillo, M., Castro-Tirado, A. J., Chai, Y., Cifuentes, A., Cikota, S., Colombo, E., Contreras, J. L., Cortina, J., Covino, S., D'Amico, G., D'Elia, V., Da Vela, P., Dazzi, F., De Angelis, A., De Lotto, B., Del Popolo, A., Delfino, M., Delgado, J., Mendez, C. Delgado, Depaoli, D., Di Pierro, F., Di Venere, L., Prester, D. Dominis, Donini, A., Dorner, D., Doro, M., Elsaesser, D., Emery, G., Escudero, J., Fariña, L., Fattorini, A., Foffano, L., Font, L., Fukami, S., Fukazawa, Y., López, R. J. García, Gasparyan, S., Gaug, M., Paiva, J. G. Giesbrecht, Giglietto, N., Giordano, F., Gliwny, P., Grau, R., Green, J. G., Hadasch, D., Hahn, A., Heckmann, L., Herrera, J., Hrupec, D., Hütten, M., Imazawa, R., Inada, T., Iotov, R., Ishio, K., Martínez, I. Jiménez, Jormanainen, J., Kerszberg, D., Kluge, G. W., Kobayashi, Y., Kubo, H., Kushida, J., Lezáun, M. Láinez, Lamastra, A., Leone, F., Lindfors, E., Linhoff, L., Lombardi, S., Longo, F., López-Moya, M., López-Oramas, A., Loporchio, S., Lorini, A., Fraga, B. Machado de Oliveira, Majumdar, P., Makariev, M., Maneva, G., Mang, N., Manganaro, M., Mangano, S., Mannheim, K., Mariotti, M., Martínez, M., Mas-Aguilar, A., Mazin, D., Menchiari, S., Mender, S., Mićanović, S., Miceli, D., Miranda, J. M., Mirzoyan, R., Molina, E., Mondal, H. A., Morcuende, D., Nanci, C., Neustroev, V., Nigro, C., Nishijima, K., Ekoume, T. Njoh, Noda, K., Nozaki, S., Ohtani, Y., Otero-Santos, J., Paiano, S., Palatiello, M., Paneque, D., Paoletti, R., Paredes, J. M., Pavletić, L., Persic, M., Pihet, M., Pirola, G., Podobnik, F., Moroni, P. G. Prada, Prandini, E., Principe, G., Priyadarshi, C., Rhode, W., Ribó, M., Rico, J., Righi, C., Sahakyan, N., Saito, T., Satalecka, K., Saturni, F. G., Schleicher, B., Schmidt, K., Schmuckermaier, F., Schubert, J. L., Schweizer, T., Sitarek, J., Spolon, A., Stamerra, A., Strišković, J., Strom, D., Suda, Y., Surić, T., Suutarinen, S., Tajima, H., Takahashi, M., Takeishi, R., Tavecchio, F., Temnikov, P., Terzić, T., Teshima, M., Tosti, L., Truzzi, S., Ubach, S., van Scherpenberg, J., Ventura, S., Verguilov, V., Viale, I., Vigorito, C. F., Vitale, V., Walter, R., Yamamoto, T., Collaborators, Benkhali, F. Ait, Becherini, Y., Bi, B., Böttcher, M., Bolmont, J., Brown, A., Bulik, T., Casanova, S., Chand, T., Chandra, S., Chibueze, J., Chibueze, O., Egberts, K., Einecke, S., Ernenwein, J. -P., Fontaine, G., Gabici, S., Goswami, P., Holler, M., Jamrozy, M., Joshi, V., Kasai, E., Katarzyński, K., Khatoon, R., Khélifi, B., Kluzniak, W., Kosack, K., Stum, S. Le, Lemière, A., Marx, R., Moderski, R., Moghadam, M. O., de Naurois, M., Niemiec, J., O'Brien, P., Ostrowski, M., Peron, G., Pita, S., Pühlhofer, G., Quirrenbach, A., Rudak, B., Sahakian, V., Sanchez, D. A., Santangelo, A., Sasaki, M., Schutte, H. M., Seglar-Arroyo, M., Shapopi, J. N. S., Steenkamp, R., Steppa, C., Suzuki, H., Tanaka, T., Tluczykont, M., Venter, C., Wagner, S. J., Wierzcholska, A., Zdziarski, A. A., Żywucka, N., Collaboration, Fermi-LAT, González, J. Becerra, Ciprini, S., Venters, T. M., collaborators, MWL, D'Ammando, F., Esteban-Gutiérrez, A., Ramazani, V. Fallah, Filippenko, A. V., Hovatta, T., Jermak, H., Jorstad, S., Kiehlmann, S., Lähteenmäki, A., Larionov, V. M., Larionova, E., Marscher, A. P., Morozova, D., Max-Moerbeck, W., Readhead, A. C. S., Reeves, R., Steele, I. A., Tornikoski, M., Verrecchia, F., Xiao, H., Zheng, W.
OT 081 is a well-known, luminous blazar that is remarkably variable in many energy bands. We present the first broadband study of the source which includes very-high-energy (VHE, $E>$100\,GeV) $\gamma$-ray data taken by the MAGIC and H.E.S.S. imaging
Externí odkaz:
http://arxiv.org/abs/2410.22557
Earth observation data have shown promise in predicting species richness of vascular plants ($\alpha$-diversity), but extending this approach to large spatial scales is challenging because geographically distant regions may exhibit different composit
Externí odkaz:
http://arxiv.org/abs/2410.19256
Autor:
Hausler, Stephen, Moghadam, Peyman
In this work we propose a novel joint training method for Visual Place Recognition (VPR), which simultaneously learns a global descriptor and a pair classifier for re-ranking. The pair classifier can predict whether a given pair of images are from th
Externí odkaz:
http://arxiv.org/abs/2410.06614
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
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K zobrazení výsledku je třeba se přihlásit.
Lifelong imitation learning for manipulation tasks poses significant challenges due to distribution shifts that occur in incremental learning steps. Existing methods often focus on unsupervised skill discovery to construct an ever-growing skill libra
Externí odkaz:
http://arxiv.org/abs/2410.00064
Autor:
Rahman, Saimunur, Moghadam, Peyman
This paper presents a novel approach to learn compact channel correlation representation for LiDAR place recognition, called C3R, aimed at reducing the computational burden and dimensionality associated with traditional covariance pooling methods for
Externí odkaz:
http://arxiv.org/abs/2409.15919