arXiv subjects

Giacomo Parigi

Publications and source records attributed to Giacomo Parigi.

5 recordsLinked to original sources

arXiv2019

Biomolecular NMR at 1.2 GHz

The development of new superconducting ceramic materials, which maintain the superconductivity at very intense magnetic fields, has prompted the development of a new generation of highly homogeneous high field magnets that has trespassed the magnetic field attainable with the previous generation of instruments. But how can biomolecular NMR benefit from this? In this work, we review a few of the notable applications that, we expect, will be blooming thanks to this newly available technology.

physics.chem-ph↗

arXiv2019

Pseudocontact shifts and paramagnetic susceptibility in semiempirical and quantum chemistry theories

Pseudocontact shifts are traditionally described as a function of the anisotropy of the paramagnetic susceptibility tensor, according to the semiempirical theory mainly developed by Kurland and McGarvey (R.J. Kurland and B.R. McGarvey, J. Magn. Reson. 2, 286 (1970)). The paramagnetic susceptibility tensor is required to be symmetric. Applying point-dipole approximation to the quantum chemistry theory of hyperfine shift, pseudocontact shifts are found to scale with a non-symmetric tensor that differs by a factor g/ge from the paramagnetic susceptibility tensor derived within the semiempirical framework. We analyze the foundations of the Kurland-McGarvey pseudocontact shift expression and recall that it is inherently based on the Russell-Saunders (LS) coupling approximation for the spin-orbit coupling. We show that the difference between the semiempirical and quantum chemistry pseudocontact shift expressions arises directly from the different treatment of the orbital contribution to the hyperfine coupling.

physics.chem-ph↗

arXiv2015

A Multi-signal Variant for the GPU-based Parallelization of Growing Self-Organizing Networks

Among the many possible approaches for the parallelization of self-organizing networks, and in particular of growing self-organizing networks, perhaps the most common one is producing an optimized, parallel implementation of the standard sequential algorithms reported in the literature. In this paper we explore an alternative approach, based on a new algorithm variant specifically designed to match the features of the large-scale, fine-grained parallelism of GPUs, in which multiple input signals are processed at once. Comparative tests have been performed, using both parallel and sequential implementations of the new algorithm variant, in particular for a growing self-organizing network that reconstructs surfaces from point clouds. The experimental results show that this approach allows harnessing in a more effective way the intrinsic parallelism that the self-organizing networks algorithms seem intuitively to suggest, obtaining better performances even with networks of smaller size.

cs.DC↗

arXiv2015

Some Further Evidence about Magnification and Shape in Neural Gas

Neural gas (NG) is a robust vector quantization algorithm with a well-known mathematical model. According to this, the neural gas samples the underlying data distribution following a power law with a magnification exponent that depends on data dimensionality only. The effects of shape in the input data distribution, however, are not entirely covered by the NG model above, due to the technical difficulties involved. The experimental work described here shows that shape is indeed relevant in determining the overall NG behavior; in particular, some experiments reveal richer and complex behaviors induced by shape that cannot be explained by the power law alone. Although a more comprehensive analytical model remains to be defined, the evidence collected in these experiments suggests that the NG algorithm has an interesting potential for detecting complex shapes in noisy datasets.

cs.NE↗

arXiv2014

Gradient of the Objective Function for an Anisotropic Centroidal Voronoi Tessellation (CVT) - A revised, detailed derivation

In their recent article (2010), Levy and Liu introduced a generalization of Centroidal Voronoi Tessellation (CVT) - namely the Lp-CVT - that allows the computation of an anisotropic CVT over a sound mathematical framework. In this article a new objective function is defined, and both this function and its gradient are derived in closed-form for surfaces and volumes. This method opens a wide range of possibilities, also described in the paper, such as quad-dominant surface remeshing, hex-dominant volume meshing or fully-automated capturing of sharp features. However, in the same paper, the derivations of the gradient and of the new objective function are only partially expanded in the appendices, and some relevant requisites on the anisotropy field are left implicit. In order to better harness the possibilities described there, in this work the entire derivation process is made explicit. In the authors' opinion, this also helps understanding the working conditions of the method and its possible applications.

cs.CG↗