Results 71 to 80 of about 332,886 (299)
Online feedforward parameter learning with robustness to set-point variations [PDF]
, 2023 High-tech motion system development is driven by increasingly accurate and fast positioning requirements. Feedforward compensation together with high bandwidth feedback control are essential to achieve these ever tightening performance demands.
Heemels, W.P.M.H. +2 more
core +1 more source
Tree boosting for learning EFT parameters
Computer Physics Communications, 2022 23 pages, 3 figures.
Suman Chatterjee +4 more
openaire +2 more sources
Rapid screening of staphylokinase protein variants using an unpurified cell‐free expression system
FEBS Open Bio, EarlyView.An unpurified cell‐free protein synthesis (CFPS) platform enables rapid functional screening of staphylokinase variants. Direct plasminogen‐activation assays performed in microplate format provide real‐time activity readouts, allowing rapid identification and ranking of variants with improved or reduced fibrinolytic activity without protein ...
Maria Tomková +3 more
wiley +1 more source
Supervised Machine Learning for Refractive Index Structure Parameter Modeling
Quantum Beam Science, 2023 The Hellenic Naval Academy (HNA) reports the latest results from a medium-range, near-maritime, free-space laser-communications-testing facility, between the lighthouse of Psitalia Island and the academy’s laboratory building. The FSO link is established
Antonios Lionis +5 more
doaj +1 more source
FEBS Open Bio, EarlyView.Activation of the mitochondrial protein OXR1 increases pSyn129 αSynuclein aggregation by lowering ATP levels and altering mitochondrial membrane potential, particularly in response to MSA‐derived fibrils. In contrast, ablation of the ER protein EMC4 enhances autophagic flux and lysosomal clearance, broadly reducing α‐synuclein aggregates.
Sandesh Neupane +11 more
wiley +1 more source
Parameter learning in general equilibrium: The asset pricing implications [PDF]
, 2015 Parameter learning strongly amplifies the impact of macro shocks on marginal utility when the representative agent has a preference for early resolution of uncertainty.
Pierre Collin-Dufresne +6 more
core +1 more source
Learning to Reason in 13 Parameters
CoRRRecent research has shown that language models can learn to \textit{reason}, often via reinforcement learning. Some work even trains low-rank parameterizations for reasoning, but conventional LoRA cannot scale below the model dimension. We question whether even rank=1 LoRA is necessary for learning to reason and propose TinyLoRA, a method for scaling ...
John X. Morris +3 more
openaire +2 more sources
FEBS Open Bio, EarlyView.Bioscience students were asked for their opinions on the value and teaching of skills. 204 responded that teamwork, time management and study skills are necessary to reach University, that scientific writing, research, laboratory and presentation skills are taught effectively during their studies, while other skills are gained inherently through study ...
Janella Borrell, Susan Crennell
wiley +1 more source
Directed evolution of enzymes at the crossroads of tradition and innovation
FEBS Open Bio, EarlyView.An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova +2 more
wiley +1 more source
Modeling and parameter learning method for the Hammerstein–Wiener model with disturbance
Measurement + Control, 2020 In this paper, a novel modeling and parameter learning method for the Hammerstein–Wiener model with disturbance is proposed, and the Hammerstein–Wiener model is implemented to approximate complex nonlinear industrial processes.
Feng Li +4 more
doaj +1 more source