Zobrazeno 1 - 10
of 22 411
pro vyhledávání: '"Frías, A."'
This work addresses the problem of optimally steering the state covariance of a linear stochastic system from an initial to a target, subject to hybrid transitions. The nonlinear and discontinuous jump dynamics complicate the control design for hybri
Externí odkaz:
http://arxiv.org/abs/2410.13222
Soft robots are known for their ability to perform tasks with great adaptability, enabled by their distributed, non-uniform stiffness and actuation. Bending is the most fundamental motion for soft robot design, but creating robust, and easy-to-fabric
Externí odkaz:
http://arxiv.org/abs/2410.13003
Autor:
Jones, Michael W. M., Flannery, David T., Hurowitz, Joel A., Tice, Mike T., Schrank, Christoph E., Allwood, Abigail C., Tosca, Nicholas J., Catling, David C., VanBommel, Scott J., Knight, Abigail L., Ganly, Briana, Siebach, Kirsten L., Benison, Kathleen C., Broz, Adrian P., Zorzano, Maria-Paz, Heirwegh, Chris M., Orenstein, Brendan J., Clark, Benton C., Sinclair, Kimberly P., Shumway, Andrew O., Wade, Lawrence A., Davidoff, Scott, Nemere, Peter, Wright, Austin P., Galvin, Adrian E., Randazzo, Nicholas, Martinez-Frias, Jesus, ONeil, Lauren P.
Late-stage Ca-sulfate-filled fractures are common on Mars. Notably, the Shenandoah formation in the western edge of Jezero crater preserves a variety of Ca-sulfate minerals in the fine-grained siliciclastic rocks explored by the Perseverance rover. H
Externí odkaz:
http://arxiv.org/abs/2410.05615
Autor:
Moreno-Frías, M. A., Rosales, J. C.
The main aim of this work is to introduce and justify the study of semi-covarities. A {\it semi-covariety} is a non-empty family $\mathcal{F}$ of numerical semigroups such that it is closed under finite intersections, has a minimum, $\min(\mathcal{F}
Externí odkaz:
http://arxiv.org/abs/2407.18984
Autor:
Hodge, Jacqueline A., da Cunha, Elisabete, Kendrew, Sarah, Li, Juno, Smail, Ian, Westoby, Bethany A., Nayak, Omnarayani, Swinbank, Mark, Chen, Chian-Chou, Walter, Fabian, van der Werf, Paul, Cracraft, Misty, Battisti, Andrew, Brandt, Willian N., Rivera, Gabriela Calistro, Chapman, Scott C., Cox, Pierre, Dannerbauer, Helmut, Decarli, Roberto, Castillo, Marta Frias, Greve, Thomas R., Knudsen, Kirsten K., Leslie, Sarah, Menten, Karl M., Rybak, Matus, Schinnerer, Eva, Wardlow, Julie L., Weiss, Axel
We present JWST NIRCam imaging targeting 13 $z\sim3$ infrared-luminous ($L_{\rm IR}\sim5\times10^{12}L_{\odot}$) galaxies from the ALESS survey with uniquely deep, high-resolution (0.08$''$$-$0.16$''$) ALMA 870$\mu$m imaging. The 2.0$-$4.4$\mu$m (obs
Externí odkaz:
http://arxiv.org/abs/2407.15846
Autor:
Project, CTA-LST, Abe, K., Abe, S., Abhishek, A., Acero, F., Aguasca-Cabot, A., Agudo, I., Crespo, N. Alvarez, Antonelli, L. A., Aramo, C., Arbet-Engels, A., Arcaro, C., Artero, M., Asano, K., Aubert, P., Baktash, A., Bamba, A., Larriva, A. Baquero, Baroncelli, L., de Almeida, U. Barres, Barrio, J. A., Batkovic, I., Baxter, J., González, J. Becerra, Bernardini, E., Medrano, J. Bernete, Berti, A., Bhattacharjee, P., Bigongiari, C., Bissaldi, E., Blanch, O., Bonnoli, G., Bordas, P., Brunelli, G., Bulgarelli, A., Burelli, I., Burmistrov, L., Buscemi, M., Cardillo, M., Caroff, S., Carosi, A., Carrasco, M. S., Cassol, F., Castrejón, N., Cauz, D., Cerasole, D., Ceribella, G., Chai, Y., Cheng, K., Chiavassa, A., Chikawa, M., Chon, G., Chytka, L., Cicciari, G. M., Cifuentes, A., Contreras, J. L., Cortina, J., Costantini, H., Da Vela, P., Dalchenko, M., Dazzi, F., De Angelis, A., de Lavergne, M. de Bony, De Lotto, B., de Menezes, R., Del Peral, L., Delgado, C., Mengual, J. Delgado, della Volpe, D., Dellaiera, M., Di Piano, A., Di Pierro, F., Di Tria, R., Di Venere, L., Díaz, C., Dominik, R. M., Prester, D. Dominis, Donini, A., Dorner, D., Doro, M., Eisenberger, L., Elsässer, D., Emery, G., Escudero, J., Ramazani, V. Fallah, Ferrarotto, F., Fiasson, A., Foffano, L., Coromina, L. Freixas, Fröse, S., Fukazawa, Y., López, R. Garcia, Gasbarra, C., Gasparrini, D., Gavira, L., Geyer, D., Paiva, J. Giesbrecht, Giglietto, N., Giordano, F., Gliwny, P., Godinovic, N., Grau, R., Green, D., Green, J., Gunji, S., Günther, P., Hackfeld, J., Hadasch, D., Hahn, A., Hassan, T., Hayashi, K., Heckmann, L., Heller, M., Llorente, J. Herrera, Hirotani, K., Hoffmann, D., Horns, D., Houles, J., Hrabovsky, M., Hrupec, D., Hui, D., Iarlori, M., Imazawa, R., Inada, T., Inome, Y., Ioka, K., Iori, M., Martinez, I. Jimenez, Quiles, J. Jiménez, Jurysek, J., Kagaya, M., Karas, V., Katagiri, H., Kataoka, J., Kerszberg, D., Kobayashi, Y., Kohri, K., Kong, A., Kubo, H., Kushida, J., Lainez, M., Lamanna, G., Lamastra, A., Lemoigne, L., Linhoff, M., Longo, F., López-Coto, R., López-Moya, M., López-Oramas, A., Loporchio, S., Lorini, A., Bahilo, J. Lozano, Luque-Escamilla, P. L., Majumdar, P., Makariev, M., Mallamaci, M., Mandat, D., Manganaro, M., Manicò, G., Mannheim, K., Marchesi, S., Mariotti, M., Marquez, P., Marsella, G., Martí, J., Martinez, O., Martínez, G., Martínez, M., Mas-Aguilar, A., Maurin, G., Mazin, D., Guillen, E. Mestre, Micanovic, S., Miceli, D., Miener, T., Miranda, J. M., Mirzoyan, R., Mizuno, T., Gonzalez, M. Molero, Molina, E., Montaruli, T., Moralejo, A., Morcuende, D., Morselli, A., Moya, V., Muraishi, H., Nagataki, S., Nakamori, T., Neronov, A., Nickel, L., Rosillo, M. Nievas, Nikolic, L., Nishijima, K., Noda, K., Nosek, D., Novotny, V., Nozaki, S., Ohishi, M., Ohtani, Y., Oka, T., Okumura, A., Orito, R., Otero-Santos, J., Ottanelli, P., Owen, E., Palatiello, M., Paneque, D., Pantaleo, F. R., Paoletti, R., Paredes, J. M., Pech, M., Pecimotika, M., Peresano, M., Pfeiffle, F., Pietropaolo, E., Pihet, M., Pirola, G., Plard, C., Podobnik, F., Pons, E., Prandini, E., Priyadarshi, C., Prouza, M., Rando, R., Rhode, W., Ribó, M., Righi, C., Rizi, V., Fernandez, G. Rodriguez, Frías, M. D. Rodríguez, Saito, T., Sakurai, S., Sanchez, D. A., Sano, H., Šarić, T., Sato, Y., Saturni, F. G., Savchenko, V., Schiavone, F., Schleicher, B., Schmuckermaier, F., Schubert, J. L., Schussler, F., Schweizer, T., Arroyo, M. Seglar, Siegert, T., Silvia, R., Sitarek, J., Sliusar, V., Strišković, J., Strzys, M., Suda, Y., Tajima, H., Takahashi, H., Takahashi, M., Takata, J., Takeishi, R., Tam, P. H. T., Tanaka, S. J., Tateishi, D., Tavernier, T., Temnikov, P., Terada, Y., Terauchi, K., Terzic, T., Teshima, M., Tluczykont, M., Tokanai, F., Torres, D. F., Travnicek, P., Truzzi, S., Tutone, A., Vacula, M., Vallania, P., van Scherpenberg, J., Acosta, M. Vázquez, Verna, G., Viale, I., Vigliano, A., Vigorito, C. F., Visentin, E., Vitale, V., Voitsekhovskyi, V., Voutsinas, G., Vovk, I., Vuillaume, T., Walter, R., Wan, L., Will, M., Yamamoto, T., Yamazaki, R., Yeung, P. K. H., Yoshida, T., Yoshikoshi, T., Zhang, W., Zywucka, N.
Context: There are currently three pulsars firmly detected by imaging atmospheric Cherenkov telescopes (IACTs), two of them reaching TeV energies, challenging models of very-high-energy (VHE) emission in pulsars. More precise observations are needed
Externí odkaz:
http://arxiv.org/abs/2407.02343
We develop the fundamentals of a new theory of convex geometry -- which we call "broken line convex geometry". This is a theory of convexity where the ambient space is the rational tropicalization of a cluster variety, as opposed to an ambient vector
Externí odkaz:
http://arxiv.org/abs/2407.02427
Graph contrastive learning (GCL) has recently emerged as a new concept which allows for capitalizing on the strengths of graph neural networks (GNNs) to learn rich representations in a wide variety of applications which involve abundant unlabeled inf
Externí odkaz:
http://arxiv.org/abs/2406.17251
Autor:
Wu, Jiahui, Frias-Martinez, Vanessa
Deep learning architectures enhanced with human mobility data have been shown to improve the accuracy of short-term crime prediction models trained with historical crime data. However, human mobility data may be scarce in some regions, negatively imp
Externí odkaz:
http://arxiv.org/abs/2406.06645
Autor:
Wu, Jiahui, Frias-Martinez, Vanessa
Deep learning crime predictive tools use past crime data and additional behavioral datasets to forecast future crimes. Nevertheless, these tools have been shown to suffer from unfair predictions across minority racial and ethnic groups. Current appro
Externí odkaz:
http://arxiv.org/abs/2406.04382