25 papers · ranked by Valyu relevance
Adams Wei Yu, Hao Su, Li Fei-Fei
Using sparse-inducing norms to learn robust models has received increasing attention from many fields for its attractive properties. Projection-based methods have been widely applied to learning tasks constrained by such norms. As a key building block of these methods, an efficient operator for Euclidean projection…
Qiao-Li Dong, Dan Jiang
In this article, we first introduce two simultaneous projection algorithms for solving the split equality problem by using a new choice of the stepsize, and then propose two semi-alternating projection algorithms. The weak convergence of the proposed algorithms is analyzed under standard conditions. As applications, we…
Ismail Melik Turker, Isa Yildirim
The per-cell quantities are the base coefficients , the per-slice shifts , and the normalization coefficient . The base coefficients are the values of from ((170-3)$22$) evaluated on the first slice () for each detector cell. The shifts are the per-voxel changes in along the slice axis, constant across slices for each…
Dimitry Tegunov
Fourier-space projection operations are central to electron microscopy single-particle analysis and electron tomography algorithms. Machine learning methods require differentiable implementations for end-to-end model training, but PyTorch’s built-in operations are too slow for practical use. This paper introduces…
Thomas F. Kirk, Martin S. Craig, Michael A. Chappell
Projection of volumetric data onto the cortical surface is an important precursor to performing surface-based analysis. Numerous projection methods have been reported in the literature, many of which make assumptions which tie them to use with specific modalities, notably blood oxygenation level dependent (BOLD)…
Takuto Hirukawa, Marco Visentini-Scarzanella, Hiroshi Kawasaki, Ryo Furukawa + 1 more
'Ryo Furukawa' 'Shinsaku Hiura'] Abstract. We propose a new system to visualize depth-dependent patterns and images on solid objects with complex geometry using multiple projectors. The system, despite consisting of conventional passive LCD projectors, is able to project different images and patterns depending on the…
Mahmoud Nabil
—Random Projection is a foundational research topic that connects a bunch of machine learning algorithms under a similar mathematical basis. It is used to reduce the dimensionality of the dataset by projecting the data points efficiently to a smaller dimensions while preserving the original relative distance between…
Authors not listed
High-level quantum mechanical (QM) simulations provide accurate electronic information of chemical systems but scale unfavourably with system size, making calculations of applied systems challenging. Hierarchical quantum mechanics in quantum mechanics embedding (QM/QM) addresses this issue by localising the highly…
Danny Salem, Anuradha Surendra, Graeme SV McDowell, Miroslava Čuperlović-Culf
Unsupervised data projection for the determination of trends in the data, visualization of multidimensional data in a reduced dimension space or feature space reduction through combination of data is a major step in data mining. Methods such as Principal Component Analysis or t-Distribution Stochastic Neighbor…
Mohammad Majid al-Rifaie, Tim Blackwell
This paper extends particle aggregate reconstruction technique (PART), a reconstruction algorithm for binary tomography based on the movement of particles. PART supposes that pixel values are particles, and that particles diffuse through the image, staying together in regions of uniform pixel value known as aggregates.…
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…
Ruochen Zhao, Ruonan Wang, Yang Gao, Xiaolin Ning + 3 more
'Francesco Amato' 'Raissa Schiavoni'] A class of algorithms based on subspace projection is widely used in the denoising of magnetoencephalography (MEG) signals. Setting the dimension of the interference (external) subspace matrix of these algorithms is the key to balancing the denoising effect and the degree of signal…
Jorge Barrios, O. P. Ferreira, Sándor Németh
By using Moreau's decomposition theorem for projecting onto cones, the problem of projecting onto a simplicial cone is reduced to finding the unique solution of a nonsmooth system of equations. It is shown that Picard's method applied to the system of equations associated to the problem of projecting onto a simplicial…
Andrew Lee, Harlin Lee, José A. Perea, Nikolas Schonsheck + 1 more
'Madeleine Weinstein'] - Abstract. Many real-world datasets live on high-dimensional Stiefel and Grassmannian manifolds, Vk(R N ) and Gr(k, R N ) respectively, and benefit from projection onto lower-dimensional Stiefel (respectively, Grassmannian) manifolds. In this work, we propose an algorithm called Principal…
Emil Dumic, Anamaria Bjelopera, Andreas Nüchter, Steve Vanlanduit
In this paper we will present a new dynamic point cloud compression based on different projection types and bit depth, combined with the surface reconstruction algorithm and video compression for obtained geometry and texture maps. Texture maps have been compressed after creating Voronoi diagrams. Used video…
Mariusz Pleszczyński, Robertas Damaševičius
Computer tomography has a wide field of applicability; however, most of its applications assume that the data, obtained from the scans of the examined object, satisfy the expectations regarding their amount and quality. Unfortunately, sometimes such expected data cannot be achieved. Then we deal with the incomplete set…
Kenneth Lange
The current paper proposes and tests algorithms for finding the diameter of a compact convex set and the farthest point in the set to another point. For these two nonconvex problems, I construct Frank-Wolfe and projected gradient ascent algorithms. Although these algorithms are guaranteed to go uphill, they can become…
Anthony Fan, Justin Cassidy, Richard W. Carthew, Sascha Hilgenfeldt
Confocal microscopy has been experimentally proven for decades to provide high-quality images for biological research. Its unique property of blocking out-of-focus light enables 3D rendering from planar stacks and visualization of internal features. However, visualizing 3D data on a flat display is not intuitive, and…
Maria Júlia R. Aguiar, Tiago da Rocha Alves, Leonardo M. Honório, Ivo C. S. Junior + 2 more
'Ivo C. S. Junior' 'Vinícius F. Vidal' 'Junliang Xing'] The image stitching process is based on the alignment and composition of multiple images that represent parts of a 3D scene. The automatic construction of panoramas from multiple digital images is a technique of great importance, finding applications in different…
Christina Humer, Rachel Nicholls, Henry Heberle, Moritz Heckmann + 7 more
Chemical reaction optimization (RO) is an iterative process that results in large and high-dimensional datasets. Current tools only allow for limited analysis and understanding of parameter spaces, making it hard for scientists to review or follow changes throughout the process. With the recent emergence of using…
Xianghai Sheng, Lee Thompson, Hrant Hratchian
This work evaluates the quality of exchange coupling constant and spin crossover gap calculations using density functional theory corrected by the Approximate Projection model. Results show that improvements using the Approximate Projection model range from modest to significant. This study demonstrates that, at least…
Alejandro Omar Blenkmann, Sabine Liliana Leske, Anaïs Llorens, Jack J. Lin + 8 more
Precise electrode localization is important for maximizing the utility of intracranial EEG data. Electrodes are typically localized from post-implantation CT artifacts, but algorithms can fail due to low signal-to-noise ratio, unrelated artifacts, or high-density electrode arrays. Minimizing these errors usually…
Andy Jiang, Zachary Glick, David Poole, Justin Turney + 2 more
Here, we present an efficient, open-source formulation for coupled-cluster theory through perturbative triples with domain-based local pair natural orbitals [DLPNO-CCSD(T)]. Similar to the implementation of the DLPNO-CCSD(T) method found in the ORCA package, the most expensive integral generation and contraction steps…
Xianghai Sheng, Lee Thompson, Hrant Hratchian
This work evaluates the quality of exchange coupling constant and spin crossover gap calculations using density functional theory corrected by the Approximate Projection model. Results show that improvements using the Approximate Projection model range from modest to significant. This study demonstrates that, at least…
Eric Hermes, Khachik Sargsyan, Habib Najm, Judit Zádor
We present a new algorithm for the optimization of molecular structures to saddle points on the potential energy surface using a redundant internal coordinate system. This algorithm automates the procedure of defining the internal coordinate system, including the handling of linear bending angles, e.g. through the…