Zobrazeno 1 - 10
of 2 373
pro vyhledávání: '"P Gleich"'
We demonstrate a spatial hypergraph model that allows us to vary the amount of higher-order structure in the generated hypergraph. Specifically, we can vary from a model that is a pure pairwise graph into a model that is almost a pure hypergraph. We
Externí odkaz:
http://arxiv.org/abs/2410.12688
Autor:
Huang, Yufan, Gleich, David F.
Semidefinite programs (SDPs) and their solvers are powerful tools with many applications in machine learning and data science. Designing scalable SDP solvers is challenging because by standard the positive semidefinite decision variable is an $n \tim
Externí odkaz:
http://arxiv.org/abs/2406.10407
Autor:
Gleich, David F.
The Eckhart-Young theorem states that the best low-rank approximation of a matrix can be constructed from the leading singular values and vectors of the matrix. Here, we illustrate that the practical implications of this result crucially depend on th
Externí odkaz:
http://arxiv.org/abs/2402.18427
Network epidemic simulation holds the promise of enabling fine-grained understanding of epidemic behavior, beyond that which is possible with coarse-grained compartmental models. Key inputs to these epidemic simulations are the networks themselves. H
Externí odkaz:
http://arxiv.org/abs/2312.17351
Dense subgraph discovery is a fundamental primitive in graph and hypergraph analysis which among other applications has been used for real-time story detection on social media and improving access to data stores of social networking systems. We prese
Externí odkaz:
http://arxiv.org/abs/2310.13792
Autor:
Michelle Bonatti, Marcos Lana, Leonardo Medina, Paul Chevelev, Carla Baldivieso, Carla Erismann, Pia Gleich, Tatiana Rodriguez, Luca Eufemia, Teresa da Silva Rosa, Juliano Borba, Custodio Matavel, Sandro Schlindwein, Ray Ison, Klaus Eisenack, Jon Hellin, Grazia Pacillo, Vincent Vadez, Jérôme Bossuet, Aleksandra Dolinska, Stefan Sieber
Publikováno v:
Humanities & Social Sciences Communications, Vol 11, Iss 1, Pp 1-16 (2024)
Abstract Although social learning (SL) conceptualization and implementation are flourishing in sustainability sciences, and its non-rigid conceptual fluidity is regarded as an advantage, research must advance the understanding of SL phenomenon patter
Externí odkaz:
https://doaj.org/article/94d9edbcbdb044359c1f3761b2d9a6fc
We study a new connection between a technical measure called $\mu$-conductance that arises in the study of Markov chains for sampling convex bodies and the network community profile that characterizes size-resolved properties of clusters and communit
Externí odkaz:
http://arxiv.org/abs/2303.14550
Autor:
Huang, Yufan, Gleich, David F.
The $\mu$-conductance measure proposed by Lovasz and Simonovits is a size-specific conductance score that identifies the set with smallest conductance while disregarding those sets with volume smaller than a $\mu$ fraction of the whole graph. Using $
Externí odkaz:
http://arxiv.org/abs/2303.11452
Autor:
Ulrike Mütze, Alina Ottenberger, Florian Gleich, Esther M. Maier, Martin Lindner, Ralf A. Husain, Katja Palm, Skadi Beblo, Peter Freisinger, René Santer, Eva Thimm, Stephan vom Dahl, Natalie Weinhold, Karina Grohmann‐Held, Claudia Haase, Julia B. Hennermann, Alexandra Hörbe‐Blindt, Clemens Kamrath, Iris Marquardt, Thorsten Marquardt, Robert Behne, Dorothea Haas, Ute Spiekerkoetter, Georg F. Hoffmann, Sven F. Garbade, Sarah C. Grünert, Stefan Kölker
Publikováno v:
Annals of Clinical and Translational Neurology, Vol 11, Iss 4, Pp 883-898 (2024)
Abstract Objective This study aims to elucidate the long‐term benefit of newborn screening (NBS) for individuals with long‐chain 3‐hydroxy‐acyl‐CoA dehydrogenase (LCHAD) and mitochondrial trifunctional protein (MTP) deficiency, inherited me
Externí odkaz:
https://doaj.org/article/e200e6208cf9442cbf0392820af77732
Complex prediction models such as deep learning are the output from fitting machine learning, neural networks, or AI models to a set of training data. These are now standard tools in science. A key challenge with the current generation of models is t
Externí odkaz:
http://arxiv.org/abs/2207.14358