Unused CPU time on desktop computers could be put to good use, if distributed computing succeeds in capturing people's imagination. In their Perspective, Shirts and Pande describe existing distributed computing projects and recent efforts to overcome parallelization problems. This vast underused resource could raise biological and other scientific computation to fundamentally new predictive levels.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1126/science.290.5498.1903 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Optical single-shot readout of spin qubits in silicon.
Nat Commun
January 2025
TUM School of Natural Sciences, Department of Physics and Munich Center for Quantum Science and Technology (MCQST), Technical University of Munich, James-Franck-Str. 1, Garching, Germany.
Small registers of spin qubits in silicon can exhibit hour-long coherence times and exceeded error-correction thresholds. However, their connection to larger quantum processors is an outstanding challenge. To this end, spin qubits with optical interfaces offer key advantages: they can minimize the heat load and give access to modular quantum computing architectures that eliminate cross-talk and offer a large connectivity.
View Article and Find Full Text PDFGenome-Wide Exploration of Thiamin Pyrophosphate Riboswitches in Medically Relevant Fungi Reveals Diverse Distribution and Implications for Antimicrobial Drug Targeting.
ACS Omega
December 2024
Laboratory for Applied Genomics and Bioinnovations, Oswaldo Cruz Institute (IOC - FIOCRUZ), Rio de Janeiro 21040-900, Brazil.
The rising incidence of fungal infections coupled with limited treatment options underscores the urgent need for novel antifungal therapies. Riboswitches, particularly thiamin pyrophosphate (TPP) class, have emerged as promising antimicrobial targets. This study presents a comprehensive genome-wide analysis of TPP riboswitches in 156 medically relevant fungi utilizing advanced covariance models (CMs) tailored for fungal sequences.
View Article and Find Full Text PDFIdentifying and understanding the nonlinear behavior of memristive devices.
Sci Rep
December 2024
Chair of Applied Electrodynamics and Plasma Technology, Ruhr University Bochum, Universitätsstraße 150, 44780, Bochum, Germany.
Nonlinearity is a crucial characteristic for implementing hardware security primitives or neuromorphic computing systems. The main feature of all memristive devices is this nonlinear behavior observed in their current-voltage characteristics. To comprehend the nonlinear behavior, we have to understand the coexistence of resistive, capacitive, and inertia (virtual inductive) effects in these devices.
View Article and Find Full Text PDFIntegrated edge-to-exascale workflow for real-time steering in neutron scattering experiments.
Struct Dyn
November 2024
Second Target Station, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA.
We introduce a computational framework that integrates artificial intelligence (AI), machine learning, and high-performance computing to enable real-time steering of neutron scattering experiments using an edge-to-exascale workflow. Focusing on time-of-flight neutron event data at the Spallation Neutron Source, our approach combines temporal processing of four-dimensional neutron event data with predictive modeling for multidimensional crystallography. At the core of this workflow is the Temporal Fusion Transformer model, which provides voxel-level precision in predicting 3D neutron scattering patterns.
View Article and Find Full Text PDFIdentifying influential nodes in brain networks via self-supervised graph-transformer.
Comput Biol Med
December 2024
Center for Brain and Brain-Inspired Computing Research, School of Computer Science, Northwestern Polytechnical University, Xi'an, China. Electronic address:
Background: Studying influential nodes (I-nodes) in brain networks is of great significance in the field of brain imaging. Most existing studies consider brain connectivity hubs as I-nodes such as the regions of high centrality or rich-club organization. However, this approach relies heavily on prior knowledge from graph theory, which may overlook the intrinsic characteristics of the brain network, especially when its architecture is not fully understood.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!