Zobrazeno 1 - 10
of 42 626
pro vyhledávání: '"A, Angelini"'
We compute the MU-based syntomic cohomologies, mod $(p,v_1,\cdots,v_{n+1})$, of all $\mathbb{E}_1$-MU-algebra forms of connective Morava K-theory k(n). As qualitative consequences, we deduce the Lichtenbaum--Quillen conjecture, telescope conjecture,
Externí odkaz:
http://arxiv.org/abs/2410.07048
We provide a unifying framework for studying variants of topological Hochschild homology such as Real topological Hochschild homology. Associated to a crossed simplicial group $\Delta G$, a category that generalizes Connes' cyclic category, we introd
Externí odkaz:
http://arxiv.org/abs/2409.18187
Autor:
Angelini, Daniele, Garcin, Matthieu
The Fractional Stochastic Regularity Model (FSRM) is an extension of Black-Scholes model describing the multifractal nature of prices. It is based on a multifractional process with a random Hurst exponent $H_t$, driven by a fractional Ornstein-Uhlenb
Externí odkaz:
http://arxiv.org/abs/2409.07159
Autor:
Angelini, Patrizio, Biedl, Therese, Chimani, Markus, Cornelsen, Sabine, Da Lozzo, Giordano, Hong, Seok-Hee, Liotta, Giuseppe, Patrignani, Maurizio, Pupyrev, Sergey, Rutter, Ignaz, Wolff, Alexander
Not every directed acyclic graph (DAG) whose underlying undirected graph is planar admits an upward planar drawing. We are interested in pushing the notion of upward drawings beyond planarity by considering upward $k$-planar drawings of DAGs in which
Externí odkaz:
http://arxiv.org/abs/2409.01475
Quantum Sensing is a rapidly growing branch of research within the area of quantum science and technology offering key resources, beyond classical ones, with potential for commercialisation of novel (quantum) sensors. The exploitation of quantum reso
Externí odkaz:
http://arxiv.org/abs/2409.00833
Autor:
Franco, Silvia, Severini, Leonardo, Buratti, Elena, Tavagnacco, Letizia, Sennato, Simona, Missori, Mauro, Ruzicka, Barbara, Mazzuca, Claudia, Zaccarelli, Emanuela, Angelini, Roberta
Gellan gum has gained significant attention due to its versatility in multiple applications in the form of hydrogels and microgels. A thorough understanding of the rheological behaviour of these systems is crucial both for fundamental research and to
Externí odkaz:
http://arxiv.org/abs/2408.17247
Deep learning has revolutionized medical image segmentation, but it relies heavily on high-quality annotations. The time, cost and expertise required to label images at the pixel-level for each new task has slowed down widespread adoption of the para
Externí odkaz:
http://arxiv.org/abs/2407.20395
This paper introduces a novel approach that combines unsupervised active contour models with deep learning for robust and adaptive image segmentation. Indeed, traditional active contours, provide a flexible framework for contour evolution and learnin
Externí odkaz:
http://arxiv.org/abs/2407.10696
Autor:
Keshavarzi, Ali, Angelini, Elsa
The lack of large annotated datasets in medical imaging is an intrinsic burden for supervised Deep Learning (DL) segmentation models. Few-shot learning approaches are cost-effective solutions to transfer pre-trained models using only limited annotate
Externí odkaz:
http://arxiv.org/abs/2407.04507
The major difference between percolation and other phase transition models is the absence of an Hamiltonian and of a partition function. For this reason it is not straightforward to identify the corresponding field theory to be used as starting point
Externí odkaz:
http://arxiv.org/abs/2407.00338