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pro vyhledávání: '"P Carriere"'
The generalized persistence diagram (GPD) is a natural extension of the classical persistence barcode to the setting of multi-parameter persistence and beyond. The GPD is defined as an integer-valued function whose domain is the set of intervals in t
Externí odkaz:
http://arxiv.org/abs/2412.05900
Autor:
Scisciò, M., Petringa, G., Zhu, Z., Rodrigues, M. R. D., Alonzo, M., Andreoli, P. L., Filippi, F., Consoli, Fe., Huault, M., Raffestin, D., Molloy, D., Larreur, H., Singappuli, D., Carriere, T., Verona, C., Nicolai, P., McNamee, A., Ehret, M., Filippov, E., Lera, R., Pérez-Hernández, J. A., Agarwal, S., Krupka, M., Singh, S., Istokskaia, V., Lattuada, D., La Cognata, M., Guardo, G. L., Palmerini, S., Rapisarda, G., Batani, K., Cipriani, M., Cristofari, G., Di Ferdinando, E., Di Giorgio, G., De Angelis, R., Giulietti, D., Xu, J., Volpe, L., Rodríguez-Frías, M. D., Giuffrida, L., Margarone, D., Batani, D., Cirrone, G. A. P., Bonasera, A., Consoli, Fa.
Driving the nuclear fusion reaction p+11B -> 3 alpha + 8.7 MeV in laboratory conditions, by interaction between high-power laser pulses and matter, has become a popular field of research, due to numerous applications that it can potentially allow: an
Externí odkaz:
http://arxiv.org/abs/2411.04577
We introduce algorithms for robustly computing intrinsic coordinates on point clouds. Our approach relies on generating many candidate coordinates by subsampling the data and varying hyperparameters of the embedding algorithm (e.g., manifold learning
Externí odkaz:
http://arxiv.org/abs/2408.01379
Publikováno v:
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:43986-44011, 2024
Real-valued functions on geometric data -- such as node attributes on a graph -- can be optimized using descriptors from persistent homology, allowing the user to incorporate topological terms in the loss function. When optimizing a single real-value
Externí odkaz:
http://arxiv.org/abs/2406.07224
Topological Data Analysis (TDA) provides a pipeline to extract quantitative topological descriptors from structured objects. This enables the definition of topological loss functions, which assert to what extent a given object exhibits some topologic
Externí odkaz:
http://arxiv.org/abs/2405.18820
Unsupervised data representation and visualization using tools from topology is an active and growing field of Topological Data Analysis (TDA) and data science. Its most prominent line of work is based on the so-called Mapper graph, which is a combin
Externí odkaz:
http://arxiv.org/abs/2402.12854
Autor:
Gu, Qi-Lao, Liu, Tie, Li, Pak Shing, Shen, Zhi-Qiang, Liu, Xunchuan, Liu, Junhao, Lu, Xing, Montillaud, Julien, Jiao, Sihan, Juvela, Mika, Rawlings, Mark G., Zhang, Qizhou, Koch, Patrick, Ristorcelli, Isabelle, Carriere, Jean-Sébastien, Eden, David, Ren, Zhiyuan, Tatematsu, Ken'ichi, Hirano, Naomi, Luo, Qiu-yi, Mai, Xiaofeng, Issac, Namitha
We observe the magnetic field morphology towards a nearby star-forming filamentary cloud, G202.3+2.5, by the JCMT/POL-2 850 {\mu}m thermal dust polarization observation with an angular resolution of 14.4" (~0.053 pc). The average magnetic field orien
Externí odkaz:
http://arxiv.org/abs/2401.05079
Autor:
Rodrigues, M. R. D., Bonasera, A., Scisciò, M., Pérez-Hernández, J. A., Ehret, M., Filippi, F., Andreoli, P. L., Huault, M., Larreur, H., Singappuli, D., Molloy, D., Raffestin, D., Alonzo, M., Rapisarda, G. G., Lattuada, D., Guardo, G. L., Verona, C., Consoli, Fe., Petringa, G., McNamee, A., La Cognata, M., Palmerini, S., Carriere, T., Cipriani, M., Di Giorgio, G., Cristofari, G., De Angelis, R., Cirrone, G. A. P., Margarone, D., Giuffrida, L., Batani, D., Nicolai, P., Batani, K., Lera, R., Volpe, L., Giulietti, D., Agarwal, S., Krupka, M., Singh, S., Consoli, Fa.
Laser technologies improved after the understanding of the Chirped Pulse Amplification (CPA) which allows energetic laser beams to be compressed to tens of femtosecond (fs) pulse durations and focused to few $\mu$m. Protons of tens of MeV can be acce
Externí odkaz:
http://arxiv.org/abs/2312.09145
A Framework for Fast and Stable Representations of Multiparameter Persistent Homology Decompositions
Topological data analysis (TDA) is an area of data science that focuses on using invariants from algebraic topology to provide multiscale shape descriptors for geometric data sets such as point clouds. One of the most important such descriptors is {\
Externí odkaz:
http://arxiv.org/abs/2306.11170
Persistent homology (PH) provides topological descriptors for geometric data, such as weighted graphs, which are interpretable, stable to perturbations, and invariant under, e.g., relabeling. Most applications of PH focus on the one-parameter case --
Externí odkaz:
http://arxiv.org/abs/2306.03801