Zobrazeno 1 - 10
of 11 168
pro vyhledávání: '"P, Robins"'
The long computational time and large memory requirements for computing Vietoris Rips persistent homology from point clouds remains a significant deterrent to its application to big data. This paper aims to reduce the memory footprint of these comput
Externí odkaz:
http://arxiv.org/abs/2412.07805
Sequential learning involves learning tasks in a sequence, and proves challenging for most neural networks. Biological neural networks regularly conquer the sequential learning challenge and are even capable of transferring knowledge both forward and
Externí odkaz:
http://arxiv.org/abs/2409.15729
Autor:
Ilten, Nathan, Robins, Sharon
We study locally trivial deformations of toric varieties from a combinatorial point of view. For any fan $\Sigma$, we construct a deformation functor $\mathrm{Def}_\Sigma$ by considering \v{C}ech zero-cochains on certain simplicial complexes. We show
Externí odkaz:
http://arxiv.org/abs/2409.02824
A method for the automatic classification of the orbits of magnetic field lines into topologically distinct classes using the Vietoris-Rips persistent homology is presented. The input to the method is the Poincare map orbits of field lines and the ou
Externí odkaz:
http://arxiv.org/abs/2408.09298
We study the action of the Hecke operators $U_n$ on the space $\mathcal R$ of rational functions in one variable, over $\mathbb C$. The main goal is to give a complete classification of the eigenfunctions of $U_n$. We accomplish this by introducing c
Externí odkaz:
http://arxiv.org/abs/2406.15744
Autor:
Qin, Chuhao, Robins, Alexander, Lillywhite-Roake, Callum, Pearce, Adam, Mehta, Hritik, James, Scott, Wong, Tsz Ho, Pournaras, Evangelos
Distributed sensing by cooperative drone swarms is crucial for several Smart City applications, such as traffic monitoring and disaster response. Using an indoor lab with inexpensive drones, a testbed supports complex and ambitious studies on these s
Externí odkaz:
http://arxiv.org/abs/2406.10916
Autor:
Robins, Andey, Borowczak, Mike
The von-Neumann architecture has a bottleneck which limits the speed at which data can be made available for computation. To combat this problem, novel paradigms for computing are being developed. One such paradigm, known as in-memory computing, inte
Externí odkaz:
http://arxiv.org/abs/2406.09677
Autor:
Eijnden, J. van den, Robins, D., Sharma, R., Sánchez-Fernández, C., Russell, T. D., Degenaar, N., Miller-Jones, J. C. A., Maccarone, T.
The Rapid Burster is a unique neutron star low-mass X-ray binary system, showing both thermonuclear Type-I and accretion-driven Type-II X-ray bursts. Recent studies have demonstrated how coordinated observations of X-ray and radio variability can con
Externí odkaz:
http://arxiv.org/abs/2405.19827
The Dense Associative Memory generalizes the Hopfield network by allowing for sharper interaction functions. This increases the capacity of the network as an autoassociative memory as nearby learned attractors will not interfere with one another. How
Externí odkaz:
http://arxiv.org/abs/2407.08742
We discuss prototype formation in the Hopfield network. Typically, Hebbian learning with highly correlated states leads to degraded memory performance. We show this type of learning can lead to prototype formation, where unlearned states emerge as re
Externí odkaz:
http://arxiv.org/abs/2407.03342