21 papers · ranked by Valyu relevance
Liu, Xinyu, Li, Bei + 14 more
High-order numerical methods enhance Transformer performance in tasks like NLP and CV, but introduce a performance-efficiency trade-off due to increased computational overhead. Our analysis reveals that conventional efficiency techniques, such as distillation, can be detrimental to the performance of these models…
Yibo Chen, Jeffrey Gao, Marius Petruc, Richard D. Hammer + 2 more
ChatGPT has demonstrated its potential as a surrogate knowledge graph. Trained on extensive data sources, including open-access publications, peer-reviewed research articles and biomedical websites, ChatGPT extracted information on gene relationships and biological pathways. However, a major challenge is model…
Ali Punjani, Haowei Zhang, David J. Fleet
Single particle cryo-EM is a powerful method for studying proteins and other biological macromolecules. Many of these molecules comprise regions with varying structural properties including disorder, flexibility, and partial occupancy. These traits make computational 3D reconstruction from 2D images challenging.…
Xieting Chu, Sriram Vishwanath, Vijay Ganesh
Symbolic regression (SR) seeks closed-form mathematical expressions that fit observed data. Neural SR methods amortize the search by training an encoder to map observations directly to expressions in a single pass, but this amortized inference leaves a residual amortization gap between its one-shot prediction and the…
Xin-Yu Wang, Ting-Zhu Huang, Liang-Jian Deng, Li Zeng
One method of solving the single-image super-resolution problem is to use Heaviside functions. This has been done previously by making a binary classification of image components as “smooth” and “non-smooth”, describing these with approximated Heaviside functions (AHFs), and iteration including l1 regularization. We…
Michael Patzer, Christian W. Lehmann, A. Fitch
This work presents a new iterative refinement method, comparable to Hirshfeld atom refinement, using the Hansen-Coppens multipole model charge density description to obtain accurate atomic coordinates and atomic displacements based on CRYSTAL17 periodic boundary calculations. The refinement, performed using the Python…
Arjen J. Jakobi, Matthias Wilmanns, Carsten Sachse
Atomic models based on high-resolution density maps are the ultimate result of the cryo-EM structure determination process. Current cryo-EM model refinement procedures work with the experimental density map that remains constant throughout the process. Here, we introduce a general procedure that iteratively improves…
Thomas C. Terwilliger
A procedure for iterative model-building, statistical density modification and refinement at moderate resolution (up to about 2.8 Å) is described.
Lim Heo, Collin Arbour, Michael Feig
Protein structures provide valuable information for understanding biological processes. Protein structures can be determined by experimental methods such as X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, or cryogenic electron microscopy. As an alternative, in silico methods can be used to predict…
Minh Pham, Yakun Yuan, Arjun Rana, Stanley Osher + 1 more
Tomography has made a revolutionary impact on the physical, biological and medical sciences. The mathematical foundation of tomography is to reconstruct a three-dimensional (3D) object from a set of two-dimensional (2D) projections. As the number of projections that can be measured from a sample is usually limited by…
Kailash Ramlaul, Colin M. Palmer, Christopher H. S. Aylett
Single particle analysis of cryo-EM images enables macromolecular structure determination at resolutions approaching the atomic scale. Experimental images are extremely noisy, however, and during iterative refinement it is possible to stably incorporate noise into the reconstructed density. Such “over-fitting” can lead…
Siddharth Mittal, Michael Woletz, David Linhardt, Christian Windischberger
Population receptive field (pRF) mapping is a fundamental technique for understanding retinotopic organization of the human visual system. Since its introduction in 2008, however, its scalability has been severely hindered by the computational bottleneck of iterative parameter refinement. Current state-of-the-art…
Cesare Bracco, Carlotta Giannelli, Francesco Patrizi, Alessandra Sestini
'Alessandra Sestini'] We present new fault jump estimates to guide local refinement in surface approximation schemes with adaptive spline constructions. The proposed approach is based on the idea that, since discontinuities in the data should naturally correspond to sharp variations in the reconstructed surface, the…
Luca Blum, Mohamed Elgendi, Carlo Menon
This paper studied the effects of applying the Box-Cox transformation for classification tasks. Different optimization strategies were evaluated, and the results were promising on four synthetic datasets and two real-world datasets. A consistent improvement in accuracy was demonstrated using a grid exploration with…
Samuel Lippl, Benjamin Peters, Nikolaus Kriegeskorte, Xiao Luo
Recent work has suggested that feedforward residual neural networks (ResNets) approximate iterative recurrent computations. Iterative computations are useful in many domains, so they might provide good solutions for neural networks to learn. However, principled methods for measuring and manipulating iterative…
Yuhao Huang, David L. Chopp
In this paper we propose an improved fast iterative method to solve the Eikonal equation, which can be implemented in parallel. We improve the fast iterative method for Eikonal equation in two novel ways, in the value update and in the error correction. The new value update is very similar to the fast iterative method…
Raian Noufel Lefgoum
In this paper we study a new approach in optimization that aims to search a large domain D where a given function takes large, small or specific values via an iterative optimization algorithm based on the gradient. We show that the objective function used is not directly optimizable, however, we use a trick to…
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…
Jürgen Köfinger, Gerhard Hummer
The proper balancing of information from experiment and theory is a long-standing problem in the analysis of noisy and incomplete data. Viewed as a Pareto optimization problem, improved agreement with the experimental data comes at the expense of growing inconsistencies with the theoretical reference model. Here, we…
Jürgen Köfinger, Gerhard Hummer
The proper balancing of information from experiment and theory is a long-standing problem in the analysis of noisy and incomplete data. Viewed as a Pareto optimization problem, improved agreement with the experimental data comes at the expense of growing inconsistencies with the theoretical reference model. Here, we…
Authors not listed
Quantum mechanics/molecular mechanics (QM/MM) simulations are crucial for understanding enzymatic reactions, but their accuracy depends heavily on the quantum-mechanical method used. Semiempirical methods offer computational efficiency but often struggle with accuracy in complex systems. This work presents a novel…