Results 121 to 130 of about 965,256 (248)
Advanced Science, EarlyView.Deep‐learning‐based signal enhancement is an effective way to recover high‐resolution details from a low‐resolution chromatin contact map. However, due to computational challenges, existing methods commonly divide up the contact map into small patches and create artificial discontinuities at patch boundaries.
Qinyao Li +6 more
wiley +1 more source
Improved Prediction of Amyloid-β and Tau Burden Using Hippocampal Surface Multivariate Morphometry Statistics and Sparse Coding. [PDF]
J Alzheimers Dis, 2023 Wu J +10 more
europepmc +1 more source
Transformation invariant sparse coding
, 2011 Sparse coding is a well established principle for unsupervised learning. Traditionally, features are extracted in sparse coding in specific locations, however, often we would prefer invariant representation. This paper introduces a general transformation
Mikkel N. Schmidt +3 more
core +1 more source
Sustainable Materials Design With Multi‐Modal Artificial Intelligence
Advanced Science, EarlyView.Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu +8 more
wiley +1 more source
Super-Resolution and Feature Extraction for Ocean Bathymetric Maps Using Sparse Coding. [PDF]
Sensors (Basel), 2022 Yutani T +10 more
europepmc +1 more source
Advanced Science, EarlyView.This study develops an MMP‐9‐responsive protein release system that transforms resident cells into drug depots through in situ genetic engineering. Anchored to the membrane, the therapeutic protein TIMP1 is selectively released upon MMP‐9 cleavage.
Yuan Zhang +11 more
wiley +1 more source
On shift-invariant sparse coding
, 2004 The goals of this paper are: 1) the introduction of a shiftinvariant sparse coding model together with learning rules for this model; 2) the comparison of this model to the traditional sparse coding model; and 3) the analysis of some limitations of ...
Mike Davies +3 more
core +1 more source
Advanced Science, EarlyView.By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin +12 more
wiley +1 more source
The Impact of Sparse Coding on Memory Lifetimes in Simple and Complex Models of Synaptic Plasticity. [PDF]
Biol Cybern, 2022 Elliott T.
europepmc +1 more source
Early Retinal UCHL1 Dysregulation Coupled With Synaptic Loss Reflects Alzheimer's Disease Severity
Advanced Science, EarlyView.This study identifies synapse‐enriched deubiquitinase UCHL1 as an early Aβ‐responsive regulator of retinal synaptopathy in Alzheimer's disease. Retinal UCHL1 loss accompanies excitatory synapse degeneration, p75NTR activation, and neuroinflammation, and predicts Braak stage and cognitive decline. Aβ42 fibrils trigger synaptic and UCHL1 depletion before
Altan Rentsendorj +25 more
wiley +1 more source