23 papers · ranked by Valyu relevance
Luigi Tamè, Raffaele Tucciarelli, Renata Sadibolova, Martin I. Sereno + 1 more
Psychophysical experiments have demonstrated large and highly systematic perceptual distortions of tactile space. We investigated the neural basis of tactile space by analyzing activity patterns induced by tactile stimulation of nine points on a 3 × 3 square grid on the hand dorsum using functional magnetic resonance…
Leonard Sasse, Casey Paquola, Juergen Dukart, Felix Hoffstaedter + 2 more
Functional connectivity (FC) gradients derived from fMRI provide valuable insights into individual differences in brain organisation, yet aligning these gradients across individuals poses challenges for meaningful group comparisons. Procrustes alignment is often employed to standardize gradients, but the choice of the…
Sai S. Reddy, Luis G. Jaimes, Onur Toker, Sergio Toral Marín
Accurate localization is a critical requirement for autonomous vehicle (AV) navigation, particularly in environments where GPS signals are unreliable or unavailable. A wide range of LiDAR-based point cloud similarity metrics have been proposed for high-definition (HD) map localization, but systematic comparisons of…
Leonard Sasse, Casey Paquola, Juergen Dukart, Felix Hoffstaedter + 2 more
Functional connectivity (FC) gradients provide valuable insights into individual differences in brain organization, yet aligning these gradients across individuals poses challenges. Procrustes alignment is often employed to standardize gradients across multiple subjects, but the choice of the number of gradients used…
Lucy E. Roberts, Marco Camaiti, Anjali Goswami
Recent advances in shape quantification techniques have revolutionised the field of evolutionary morphology by providing a time-efficient alternative to well-established methods such as geometric morphometrics. In particular, landmark-free methods are at the forefront of new developments in shape analysis, but concerns…
Maria Evangelia Vlachou, Elizabeth Thomas, Jean Blouin
In this paper, we address the problem of quantifying similarity between planar 2D shapes, which is relevant to studies of internal representations in cognitive, developmental, and neurological research. We designed a set of test shapes arranged along a visually defined perceptual similarity gradient and used them to…
Angela Andreella, Livio Finos
The Procrustes-based perturbation model (Goodall in J R Stat Soc Ser B Methodol 53(2):285-321, 1991) allows minimization of the Frobenius distance between matrices by similarity transformation. However, it suffers from non-identifiability, critical interpretation of the transformed matrices, and inapplicability in…
Alexandre Bleuzé, Jérémie Mattout, Marco Congedo
Statistical variability of electroencephalography (EEG) between subjects and between sessions is a common problem faced in the field of Brain-Computer Interface (BCI). Such variability prevents the usage of pre-trained machine learning models and requires the use of a calibration for every new session. This paper…
Thomas Bazeille, Elizabeth DuPre, Jean-Baptiste Poline, Bertrand Thirion
Inter-individual variability in the functional organization of the brain presents a major obstacle to identifying generalizable neural coding principles. Functional alignment—a class of methods that matches subjects’ neural signals based on their functional similarity—is a promising strategy for addressing this…
Eric Cramer, Tamara Lopez-Vidal, Jeanette Johnson, Vania Wang + 7 more
Longitudinal imaging of 3D cell cultures like tumor organoids and spheroids offers crucial insights into cancer progression and treatment. However, spatial displacement during time-course imaging, caused by matrix detachment or experimental artifacts, can confound analyses. Existing computational methods struggle to…
Angela Andreella, Livio Finos
The Procrustes-based perturbation model (Goodall, 1991) allows minimization of the Frobenius distance between matrices by similarity transformation. However, it suffers from nonidentifiability, critical interpretation of the transformed matrices, and inapplicability in highdimensional data. We provide an extension of…
Angela Andreella, Livio Finos, Martin A. Lindquist
Functional alignment between subjects is an important assumption of functional magnetic resonance imaging (fMRI) group-level analysis. However, it is often violated in practice, even after alignment to a standard anatomical template. Hyperalignment, based on sequential Procrustes orthogonal transformations, has been…
Michael Greenacre, Marina Martínez-Álvaro, Agustín Blasco
Microbiome and omics datasets are, by their intrinsic biological nature, of high dimensionality, characterized by counts of large numbers of components (microbial genes, operational taxonomic units, RNA transcripts, etc…). These data are generally regarded as compositional since the total number of counts identified…
E. Adrian Henle, Nickolas Gantzler, Praveen K. Thallapally, Xiaoli Z. Fern + 1 more
PoreMatMod.jl is a free, open-source, user-friendly, and documented Julia package for modifying crystal structure models of porous materials such as metal-organic frameworks (MOFs). PoreMatMod.jl functions as a find-and-replace algorithm on crystal structures by leveraging (i) Ullmann's algorithm to search for…
Alma Eguizabal, Peter J. Schreier, Jürgen Schmidt
—Statistical shape models are a useful tool in image processing and computer vision. A Procrustres registration of the contours of the same shape is typically perform to align the training samples to learn the statistical shape model. A Procrustes registration between two contours with known correspondences is…
M. Hou, M. J. Fagan, Borja Esteve-Altava
As a common feature, bilateral symmetry of biological forms is ubiquitous, but in fact rarely exact. In a setting of analytic geometry, bilateral symmetry is defined with respect to a point, line or plane, and the well-known notions of fluctuating asymmetry, directional asymmetry and antisymmetry are recast. A…
Vladimiros Sterzentsenko, Alexandros Doumanoglou, Spyridon Thermos, Nikolaos Zioulis + 2 more
'Nikolaos Zioulis' 'Dimitrios Zarpalas' 'Daras Petros'] With the advent of consumer grade depth sensors, low-cost volumetric capture systems are easier to deploy. Their wider adoption though depends on their usability and by extension on the practicality of spatially aligning multiple sensors. Most existing alignment…
Congzhou M Sha
There exist elegant methods of aligning point clouds in R 3 . Unfortunately, these methods fail to generalize to the case of Minkowski space, as we will show. Instead, we propose two solutions to the following problem: given inertial reference frames A and B, and given (possibly noisy) measurements of a set of…
Ashutosh Singandhupe, Sanket Lokhande, Hung Manh La
—Point cloud registration is a fundamental problem in computer vision and robotics, involving the alignment of 3D point sets captured from varying viewpoints using depth sensors such as LiDAR or structured light. In modern robotic systems, especially those focused on mapping, it is essential to merge multiple views of…
Andrew J. Hanson
Quaternion methods for obtaining solutions to the problem of finding global rotations that optimally align pairs of corresponding lists of 3D spatial and/or orientation data are critically studied. The existence of multiple literatures and historical contexts is pointed out, and the algebraic solutions of the…
Authors not listed
In molecular machine learning, the choice of the representation of molecules can have a significant impact on model performance. However, understanding the root causes of these performance differences often proves challenging. One promising approach to explore model behavior is representational alignment, which…
Yael Harpaz, Yoel Shkolnisky
A common task in cryo-electron microscopy (cryo-EM) data processing is to compare three-dimensional density maps of macromolecules. In this paper, we propose an algorithm for aligning three-dimensional density maps that exploits common lines between projection images of the maps. The algorithm is fully automatic and…
Authors not listed
Machine learning models are increasingly applied to heterogeneous materials datasets spanning different synthesis routes, measurement protocols, and structural classes. Although multi-task and representation-learning approaches are commonly used to improve predictive performance, the latent representations learned by…