Results 81 to 90 of about 322,902 (281)

Effects of Instrumentation on Dental Microwear Textures: Reanalysis and Augmentation of an Early Hominin Sample [PDF]

, 2014
Dental microwear texture analysis has been refined to a methodology relying upon scanning confocal microscopy for its advantages of repeatability and standardized quantification.
Ragni, Anna Jacquelyn
core   +2 more sources

A Robust Adaptive One‐Sample‐Ahead Preview Super‐Twisting Sliding Mode Controller

International Journal of Adaptive Control and Signal Processing, EarlyView.
Block Diagram of the Robust Adaptive One‐Sample‐Ahead Preview Super‐Twisting Sliding Mode Controller. ABSTRACT This article introduces a discrete‐time robust adaptive one‐sample‐ahead preview super‐twisting sliding mode controller. A stability analysis of the controller by Lyapunov criteria is developed to demonstrate its robustness in handling both ...
Guilherme Vieira Hollweg, Paulo Jefferson Dias de Oliveira Evald, Shivam Chaturvedi, Hilton Abilio Gründling, Rodrigo Varella Tambara, Wencong Su   +5 more
wiley   +1 more source

On the Generalization Effects of Linear Transformations in Data Augmentation

, 2020
Data augmentation is a powerful technique to improve performance in applications such as image and text classification tasks. Yet, there is little rigorous understanding of why and how various augmentations work.
Ré, Christopher, Valiant, Gregory, Wu, Sen, Zhang, Hongyang R.   +3 more
core  

β‐Catenin/c‐Myc Axis Modulates Autophagy Response to Different Ammonia Concentrations

Advanced Biology, Volume 9, Issue 3, March 2025.
Ammonia, detoxified by the liver into urea and glutamine, impacts autophagy differently at varying levels. Low ammonia activates autophagy via c‐Myc and β‐catenin, while high levels suppress it. Using Huh7 cells and Spf‐ash mice, c‐Myc's role in cytoprotective autophagy is revealed, offering insights into hyperammonemia and potential therapeutic ...
S. Sergio, B. Spedicato, G. Corallo, A. Inguscio, M. Greco, D. Musarò, D. Vergara, A. F. Muro, G. De Sabbata, L. R. Soria, N. Brunetti Pierri, M. Maffia   +11 more
wiley   +1 more source

Counterexample-Guided Data Augmentation

, 2018
We present a novel framework for augmenting data sets for machine learning based on counterexamples. Counterexamples are misclassified examples that have important properties for retraining and improving the model. Key components of our framework include
Dreossi, Tommaso, Ghosh, Shromona, Keutzer, Kurt, Sangiovanni-Vincentelli, Alberto, Seshia, Sanjit A., Yue, Xiangyu   +5 more
core   +2 more sources

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source

Improving Photometric Redshift Estimates with Training Sample Augmentation

The Astrophysical Journal Letters
Large imaging surveys will rely on photometric redshifts (photo- z 's), which are typically estimated through machine-learning methods. Currently planned spectroscopic surveys will not be deep enough to produce a representative training sample for Legacy
Irene Moskowitz, Eric Gawiser, John Franklin Crenshaw, Brett H. Andrews, Alex I. Malz, Samuel Schmidt, The LSST Dark Energy Science Collaboration   +6 more
doaj   +1 more source

Parkinson’s Disease Detection Based on Spectrogram-Deep Convolutional Generative Adversarial Network Sample Augmentation

IEEE Access, 2020
As an essential biological feature of human beings, voiceprint is increasingly used in medical research and diagnosis, especially in identifying Parkinson's Disease (PD). This paper proposes a Spectrogram Deep Convolutional Generative Adversarial Network
Zhi-Jing Xu, Rong-Fei Wang, Juan Wang, Da-Hai Yu   +3 more
doaj   +1 more source

Simulating dysarthric speech for training data augmentation in clinical speech applications

, 2018
Training machine learning algorithms for speech applications requires large, labeled training data sets. This is problematic for clinical applications where obtaining such data is prohibitively expensive because of privacy concerns or lack of access.
Berisha, Visar, Jiao, Yishan, Liss, Julie, Tu, Ming   +3 more
core   +1 more source

A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions

Advanced Engineering Materials, EarlyView.
This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice, Krzysztof S. Stopka, Michael D. Sangid   +2 more
wiley   +1 more source