Results 121 to 130 of about 623,991 (302)

Factored sparse inverse covariance matrices [PDF]

2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100), 2002
Most HMM-based speech recognition systems use Gaussian mixtures as observation probability density functions. An important goal in all such systems is to improve parsimony. One method is to adjust the type of covariance matrices used. In this work, factored sparse inverse covariance matrices are introduced.
openaire   +2 more sources

In Situ X‐Ray Tomography and Acoustic Emission Monitoring of Damage Evolution in C/C‐SiC Composites Fabricated by Liquid Silicon Infiltration

Advanced Science, EarlyView.
This study investigates how the internal structure of fiber‐reinforced ceramic composites affects their resistance to damage. By combining 3D X‐ray imaging with acoustic emission monitoring during mechanical testing, it reveals how silicon distribution influences crack formation.
Yang Chen, Renato S. M. Almeida, Stefan Flauder, Guillaume Couégnat, Kamen Tushtev, Jürgen Horvath, Peter Wriggers, Kurosch Rezwan   +7 more
wiley   +1 more source

Estimation of Large-Dimensional Covariance Matrices via Second-Order Stein-Type Regularization. [PDF]

Entropy (Basel), 2022
Zhang B, Huang H, Chen J.
europepmc   +1 more source

Microscale Mapping of Fiber Strain and Damage in Composite Wrinkled Laminates Using Computed Tomography Assisted Wide‐Angle X‐Ray Scattering

Advanced Science, EarlyView.
This study combines full‐field tomography with diffraction mapping to quantify radial (ε002$\varepsilon _{002}$) and axial (ε100$\varepsilon _{100}$) lattice strain in wrinkled carbon‐fiber specimens for the first time. Radial microstrain gradients (−14.5 µεMPa$\varepsilon \mathrm{MPa}$−1) are found to signal damage‐prone zones ahead of failure, which ...
Hoang Minh Luong, James Trevarthen, Igor Dolbnya, Wadwan Singhapong, Nicholas Pilato, Richard Butler, Jiraphant Srisuriyachot, Alexander J. G. Lunt   +7 more
wiley   +1 more source

Constrained Covariance Matrices With a Biologically Realistic Structure: Comparison of Methods for Generating High-Dimensional Gaussian Graphical Models

Frontiers in Applied Mathematics and Statistics, 2019
High-dimensional data from molecular biology possess an intricate correlation structure that is imposed by the molecular interactions between genes and their products forming various different types of gene networks.
Frank Emmert-Streib, Frank Emmert-Streib, Shailesh Tripathi, Shailesh Tripathi, Matthias Dehmer, Matthias Dehmer, Matthias Dehmer   +6 more
doaj   +1 more source

Principal regression for high dimensional covariance matrices. [PDF]

Electron J Stat, 2021
Zhao Y, Caffo B, Luo X, Alzheimer’s Disease Neuroimaging Initiative.   +3 more
europepmc   +1 more source

Selenium Nanoparticles Selectively Target KRAS G13D to Inhibit Colorectal Cancer

Advanced Science, EarlyView.
The mechanisms of SeNPs therapy in cancer treatment, encompass three parallel actions: (1) seleno‐amino acids, key metabolites, upregulate GPX2 expression, thereby inhibiting tumor metastasis via the GPX2‐HIF1α‐VEGF signaling pathway; (2) selenite (SeO32−), an inorganic metabolite, forms hydrogen bonds with amino acid residues 13–17 of the KRAS G13D ...
Xiaoting Liu, Chong Wu, Shiyao Song, Li Ma, Shaoqing Huang, Yuting He, Qinghai Li, Yanzhou Chang, Dan Jiang, Yong Mei, Junchao Cai, Zhenshuang Du, Tianfeng Chen, Weiling He   +13 more
wiley   +1 more source

The spectrum of covariance matrices of randomly connected recurrent neuronal networks with linear dynamics. [PDF]

PLoS Comput Biol, 2022
Hu Y, Sompolinsky H.
europepmc   +1 more source

A Global Methane Observation System to Reduce Uncertainty for Anthropogenic and Natural Sources and Sinks for Detecting and Attributing Climate Feedbacks

Advanced Science, EarlyView.
Conceptual illustration of a Global Ecosystem Methane Observing System (GEM‐OS) integrating satellites, aircraft, atmospheric networks, and ecosystem measurements to quantify methane emissions from anthropogenic and natural sources. The multi‐scale observing framework improves source attribution, reduces uncertainty in regional methane budgets, and ...
P. Ciais, S. Peng, J. Chang, F. Li, Q. Zhu, K. Yuan, G. Hugelius, H. Li, Y. Cai, F. Chevallier, K. Tibrewal, E. A. Kort, K. Arndt, J. Watts, B. Buma, N. Besic, P. I. Palmer, H. Cadillo‐Quiroz, E. Euskirchen, M. J. Gondwe, A. Hoyt, R. Jackson, S. Malone, D. Monteverde, S. Natali, M. Ramonet, C. Rey‐Sanchez, L. B. Sagang, E. A. G. Schuur, R. Vargas, R. Varner, Z. Zhang, B. Poulter   +32 more
wiley   +1 more source

Riemannian Covariance Enhancement for Robust Direct Position Determination Under Intermittent Interference

IEEE Open Journal of Signal Processing
Direct position determination (DPD) localizes a source directly from received array data, but its performance can degrade under model mismatch. In this work, we consider DPD in the presence of intermittent interfering sources, which lead to covariance ...
Yehonatan-Itay Segman, Joseph S. Picard, Ronen Talmon   +2 more
doaj   +1 more source