Results 61 to 70 of about 2,307,608 (312)

Mapping the evolution of mitochondrial complex I through structural variation

open access: yesFEBS 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

Incorporating Active Learning into Machine Learning Techniques for Sensory Evaluation of Food

open access: yesInternational Journal of Computational Intelligence Systems, 2020
The sensory evaluation of food quality using a machine learning approach provides a means of measuring the quality of food products. Thus, this type of evaluation may assist in improving the composition of foods and encouraging the development of new ...
Nhat-Vinh Lu   +3 more
doaj   +1 more source

Onception: Active Learning with Expert Advice for Real World Machine Translation

open access: yesComputational Linguistics, 2023
Active learning can play an important role in low-resource settings (i.e., where annotated data is scarce), by selecting which instances may be more worthy to annotate.
Vânia Mendonça   +3 more
doaj   +1 more source

Active Learning for Dialogue Act Classification [PDF]

open access: yes, 2011
Active learning techniques were employed for classification of dialogue acts over two dialogue corpora, the English human-human Switchboard corpus and the Spanish human-machine Dihana corpus.
Gambäck, Björn   +2 more
core   +1 more source

Machine-Learned Potentials by Active Learning from Organic Crystal Structure Prediction Landscapes

open access: yesThe Journal of Physical Chemistry A, 2023
A primary challenge in organic molecular crystal structure prediction (CSP) is accurately ranking the energies of potential structures. While high-level solid-state density functional theory (DFT) methods allow for mostly reliable discrimination of the low energy structures, their high computational cost is problematic because of the need to evaluate ...
Patrick W. V. Butler   +2 more
openaire   +4 more sources

Gut microbiome and aging—A dynamic interplay of microbes, metabolites, and the immune system

open access: yesFEBS Letters, EarlyView.
Age‐dependent shifts in microbial communities engender shifts in microbial metabolite profiles. These in turn drive shifts in barrier surface permeability of the gut and brain and induce immune activation. When paired with preexisting age‐related chronic inflammation this increases the risk of neuroinflammation and neurodegenerative diseases.
Aaron Mehl, Eran Blacher
wiley   +1 more source

Towards high entropy alloy with enhanced strength and ductility using domain knowledge constrained active learning

open access: yesMaterials & Design, 2022
The simultaneous optimization of competing properties is a challenge of machine learning based materials design. We proposed a domain knowledge constrained active learning loop for the design of high entropy alloys with optimized strength and ductility ...
Hongchao Li   +5 more
doaj   +1 more source

A Cognitive Science Based Machine Learning Architecture [PDF]

open access: yes, 2006
In an attempt to illustrate the application of cognitive science principles to hard AI problems in machine learning we propose the LIDA technology, a cognitive science based architecture capable of more human-like learning. A LIDA based software agent or
Baars, Bernard J.   +3 more
core   +1 more source

Constraining the Parameters of High-Dimensional Models with Active Learning

open access: yes, 2019
Constraining the parameters of physical models with $>5-10$ parameters is a widespread problem in fields like particle physics and astronomy. The generation of data to explore this parameter space often requires large amounts of computational resources ...
Caron, Sascha   +3 more
core   +1 more source

Active Learning for Interactive Neural Machine Translation of Data Streams [PDF]

open access: yesConference on Computational Natural Language Learning, 2018
We study the application of active learning techniques to the translation of unbounded data streams via interactive neural machine translation. The main idea is to select, from an unbounded stream of source sentences, those worth to be supervised by a ...
Álvaro Peris, F. Casacuberta
semanticscholar   +1 more source

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