Results 101 to 110 of about 424,943 (312)

PFML: Self-Supervised Learning of Time-Series Data Without Representation Collapse

IEEE Access
Self-supervised learning (SSL) is a data-driven learning approach that utilizes the innate structure of the data to guide the learning process.
Einari Vaaras, Manu Airaksinen, Okko Rasanen   +2 more
doaj   +1 more source

Label-Efficient Time Series Representation Learning: A Review

IEEE Transactions on Artificial Intelligence
Label-efficient time series representation learning, which aims to learn effective representations with limited labeled data, is crucial for deploying deep learning models in real-world applications. To address the scarcity of labeled time series data, various strategies, e.g., transfer learning, self-supervised learning, and semi-supervised learning ...
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, Xiaoli Li   +5 more
openaire   +2 more sources

Genetic attenuation of ALDH1A1 increases metastatic potential and aggressiveness in colorectal cancer

Molecular Oncology, EarlyView.
Aldehyde dehydrogenase 1A1 (ALDH1A1) is a cancer stem cell marker in several malignancies. We established a novel epithelial cell line from rectal adenocarcinoma with unique overexpression of this enzyme. Genetic attenuation of ALDH1A1 led to increased invasive capacity and metastatic potential, the inhibition of proliferation activity, and ultimately ...
Martina Poturnajova, Zuzana Kozovska, Ondrej Pos, Kristina Pavlov, Sachin Gulati, Peter Makovicky, Kristina Jakic, Monika Burikova, Eva Sedlackova, Barbora Svitkova, Silvia Tyciakova, Vojtech Bystry, Nicolas Blavet, Boris Tichy, Matej Hrnciar, Jaroslav Budis, Miroslav Tomas, Peter Dubovan, Georgina Kolnikova, Veronika Repaska, Nikoleta Mojzesova, Eva Zomborska, Daniel Pindak, Michal Mego, Tomas Szemes, Miroslava Matuskova   +25 more
wiley   +1 more source

Characteristic Representation of Stock Time Series Data Based on Trend Extreme Points of K-line Combinations

Journal of Computing and Information Technology
The study of stock time series has been an important area of research in economics, finance, and management. Time series feature representation serves as the primary approach for studying time series.
Lei Han, Xuedong Gao, Haining Yang
doaj   +1 more source

Unlabeled Sentinel 2 time series dataset (training, T30TYQ): Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series

, 2023
Iris Dumeur, Silvia Valero, Jordi Inglada   +2 more
openalex   +2 more sources

Deep Time Series Representation Learning

This work explores advances in end-to-end time series forecasting within the context of energy systems using deep learning techniques to learn feature representations. With an exponential increase in data availability and the increasing need for accurate prediction for renewable energy integration and power grid management, this work aims to enhance ...
openaire   +1 more source

RIPK4 function interferes with melanoma cell adhesion and metastasis

Molecular Oncology, EarlyView.
RIPK4 promotes melanoma growth and spread. RIPK4 levels increase as skin lesions progress to melanoma. CRISPR/Cas9‐mediated deletion of RIPK4 causes melanoma cells to form less compact spheroids, reduces their migratory and invasive abilities and limits tumour growth and dissemination in mouse models.
Norbert Wronski, Sławomir Lasota, Ewelina Madej, Anna A. Brożyna, Małgorzata Szczygieł, Agnieszka Harazin‐Lechowska, Jan Czerbniak, Janusz Rys, Jaroslaw Czyz, Agnieszka Wolnicka‐Glubisz   +9 more
wiley   +1 more source

Geometrical versus time-series representation of data in quantum control learning [PDF]

, 2020
M Ostaszewski, J A Miszczak, P Sadowski
openalex   +1 more source

COMP–PMEPA1 axis promotes epithelial‐to‐mesenchymal transition in breast cancer cells

Molecular Oncology, EarlyView.
This study reveals that cartilage oligomeric matrix protein (COMP) promotes epithelial‐to‐mesenchymal transition (EMT) in breast cancer. We identify PMEPA1 (protein TMEPAI) as a novel COMP‐binding partner that mediates EMT via binding to the TSP domains of COMP, establishing the COMP–PMEPA1 axis as a key EMT driver in breast cancer.
Konstantinos S. Papadakos, Gilar Gorji‐bahri, Lejla Gradjan, Julia Zajac, Kacper Gil, Yvonne Thokozile Nduku, Anna M. Blom   +6 more
wiley   +1 more source

Learning Visibility Attention Graph Representation for Time Series Forecasting [PDF]

, 2023
Shengzhong Mao, Xiao‐Jun Zeng
openalex   +1 more source