Results 51 to 60 of about 8,161,576 (257)
Network Anomaly Detection Using Federated Learning and Transfer Learning
, 2020 Since deep neural networks can learn data representation from training data automatically, deep learning methods are widely used in the network anomaly detection.
Jian Teng +9 more
core +1 more source
Science and Technology IndonesiaThis experiment was conducted to decide the impact of molasses and glycerol waste on upgraded methane production in anaerobic co-digestion with distillery wastewater.
Kiattisak Rattanadilok Na Phuket +3 more
doaj +1 more source
Design and analysis strategies for robust microbiome ageing research
FEBS Letters, EarlyView.The gut microbiome changes with age and associates with age‐related morbidity and mortality, establishing it as a potential biomarker and intervention target for ageing. Realising this potential requires methodological rigour, yet distinguishing biological signals from methodological artefacts remains challenging across cohorts. This review provides an
Mark Olenik +5 more
wiley +1 more source
Transferable learning on analog hardware
Science Advances, 2023 While analog neural network (NN) accelerators promise massive energy and time savings, an important challenge is to make them robust to static fabrication error. Present-day training methods for programmable photonic interferometer circuits, a leading analog NN platform, do not produce networks that perform well in the presence of static hardware ...
Sri Krishna Vadlamani +2 more
openaire +4 more sources
Reconstructing enzyme evolution by protein engineering
FEBS Letters, EarlyView.Natural enzyme evolution can be retraced by protein engineering methods such as directed evolution, rational design, and ancestral sequence reconstruction. These approaches reveal how enzymes emerged from ligand‐binding scaffolds, developed varying substrate preferences, formed oligomeric complexes, adapted to environmental changes, and evolved novel ...
Lukas Drexler +2 more
wiley +1 more source
A survey on heterogeneous transfer learning
Journal of Big Data, 2017 Transfer learning has been demonstrated to be effective for many real-world applications as it exploits knowledge present in labeled training data from a source domain to enhance a model’s performance in a target domain, which has little or no labeled ...
Oscar Day, Taghi M. Khoshgoftaar
doaj +1 more source
Transfer Learning for Radio Frequency Machine Learning: A Taxonomy and Survey
Sensors, 2022 Transfer learning is a pervasive technology in computer vision and natural language processing fields, yielding exponential performance improvements by leveraging prior knowledge gained from data with different distributions.
Lauren J. Wong, Alan J. Michaels
doaj +1 more source
Microbiome‐blood–brain barrier interactions in aging — mechanisms and therapeutic potential
FEBS Letters, EarlyView.Aging reshapes the gut microbiome (↓SCFA‐producing commensals; ↑pro‐inflammatory outputs), shifting circulating metabolites (↓SCFAs; ↑LPS, ↑TMAO, ↑PAA) that act at the BBB to increase nonspecific transcytosis, alter transport, and promote astrocyte reactivity, heightening brain vulnerability.
Daniel Cuervo‐Zanatta +3 more
wiley +1 more source
Climate Resilience and SustainabilityClimate change is threatening the resilience of smallholder agroecosystems in semi‐arid areas. Wetland agroecosystems provide critical life support and positive outcomes for people, nature and climate in semi‐arid areas.
Pascal Manyakaidze +2 more
doaj +1 more source
LTKT: Knowledge Tracing Based on Positive and Negative Learning Transfers
Tsinghua Science and TechnologyAs an essential part of educational psychology, the propagated influence among pedagogical concepts (i.e., learning transfer) is important for optimizing Knowledge Tracing (KT) tasks.
Jia Xu +5 more
doaj +1 more source