Anomalous high energy dispersion in photoemission spectra from insulating cuprates
, 2006 Angle resolved photoelectron spectroscopic measurements have been performed on an insulating cuprate Ca_2CuO_2Cl_2. High resolution data taken along the \Gamma to (pi,pi) cut show an additional dispersive feature that merges with the known dispersion of ...
A. Damascelli +8 more
core +1 more source
Physical mechanisms governing pattern fidelity in microscale offset printing [PDF]
, 2001 We have studied the offset printing of liquid polymers curable by exposure to ultraviolet light onto flat and unpatterned silicon and glass substrates.
Darhuber, Anton A. +2 more
core +2 more sources
Mapping the evolution of mitochondrial complex I through structural variation
FEBS Letters, EarlyView.Respiratory complex I (CI) is crucial for bioenergetic metabolism in many prokaryotes and eukaryotes. It is composed of a conserved set of core subunits and additional accessory subunits that vary depending on the organism. Here, we categorize CI subunits from available structures to map the evolution of CI across eukaryotes. Respiratory complex I (CI)
Dong‐Woo Shin +2 more
wiley +1 more source
Zero-Shot Recognition using Dual Visual-Semantic Mapping Paths
, 2017 Zero-shot recognition aims to accurately recognize objects of unseen classes by using a shared visual-semantic mapping between the image feature space and the semantic embedding space.
Hu, Huanhang +4 more
core +1 more source
FEBS Letters, EarlyView.Fluorescent probes allow dynamic visualization of phosphoinositides in living cells (left), whereas mass spectrometry provides high‐sensitivity, isomer‐resolved quantitation (right). Their synergistic use captures complementary aspects of lipid signaling. This review illustrates how these approaches reveal the spatiotemporal regulation and quantitative
Hiroaki Kajiho +3 more
wiley +1 more source
Feature Representation Analysis of Deep Convolutional Neural Network using Two-stage Feature Transfer -An Application for Diffuse Lung Disease Classification- [PDF]
, 2018 Transfer learning is a machine learning technique designed to improve generalization performance by using pre-trained parameters obtained from other learning tasks.
Kido, Shoji +3 more
core +2 more sources
A transfer-learning approach to feature extraction from cancer transcriptomes with deep autoencoders [PDF]
, 2019 Publicado en Lecture Notes in Computer Science.The diagnosis and prognosis of cancer are among the more challenging tasks that oncology medicine deals with.
A Bashiri +13 more
core +1 more source
Causal Feature Selection via Transfer Entropy
2024 International Joint Conference on Neural Networks (IJCNN)Machine learning algorithms are designed to capture complex relationships between features. In this context, the high dimensionality of data often results in poor model performance, with the risk of overfitting. Feature selection, the process of selecting a subset of relevant and non-redundant features, is, therefore, an essential step to mitigate ...
Bonetti, Paolo +2 more
openaire +2 more sources
An intracellular transporter mitigates the CO2‐induced decline in iron content in Arabidopsis shoots
FEBS Letters, EarlyView.This study identifies a gene encoding a transmembrane protein, MIC, which contributes to the reduction of shoot Fe content observed in plants under elevated CO2. MIC is a putative Fe transporter localized to the Golgi and endosomal compartments. Its post‐translational regulation in roots may represent a potential target for improving plant nutrition ...
Timothy Mozzanino +7 more
wiley +1 more source
Non-parametric style transfer: Correlation-aware exact distribution matching
Journal of King Saud University: Computer and Information Sciences, 2023 Early feed-forward neural methods of arbitrary image style transfer utilized the encoded feature map up to its second-order statistics, i.e., mean and variance (or covariance) of the encoded feature map.
Jeong-Sik Lee, Hyun-Chul Choi
doaj +1 more source