Results 181 to 190 of about 33,950 (290)

Asymptotic theory of in-context learning by linear attention. [PDF]

Proc Natl Acad Sci U S A
Lu YM, Letey M, Zavatone-Veth JA, Maiti A, Pehlevan C.   +4 more
europepmc   +1 more source

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

New insights into multicollinearity in the Cox proportional hazard models: the Kibria-Lukman estimator and its application. [PDF]

J Appl Stat
Seifollahi S, Algamal ZY, Arashi M.
europepmc   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

A new Liu-type estimator in a mixed Poisson regression model. [PDF]

Sci Rep
Alqasem OA, Hammad AT, El-Raouf MMA, Yousuf AM, Gemeay AM.   +4 more
europepmc   +1 more source

Automated Bacterial Identification and Morphological Feature Analysis in Low‐Dose Cryo‐EM Using YOLOv11

Advanced Intelligent Discovery, EarlyView.
AI‐based tools enable rapid characterization of bacterial ultrastructure in low‐dose cryogenic transmission electron microscopy. The envelope thickness tool quantifies membrane thickness and anisotropy. The flagella module analyzes filament morphology and detects cell‐flagella contacts.
Sita Sirisha Madugula, Lynnicia N. Massenburg, Spenser R. Brown, Amber N. Bible, Chanda R. Harris, Lance X. Zhang, Kiara Parker, Scott T. Retterer, Jennifer L. Morrell‐Falvey, Rama K. Vasudevan, Alexis N. Williams   +10 more
wiley   +1 more source

Safety versus performance: How multi-objective learning reduces barriers to market entry. [PDF]

Proc Natl Acad Sci U S A
Jagadeesan M, Jordan MI, Steinhardt J.
europepmc   +1 more source

Multi‐Property Machine Learning Models to Accelerate the Transition Toward Bio‐Based Emulsion Polymers

Advanced Intelligent Discovery, EarlyView.
A machine learning framework simultaneously predicts four critical properties of monomers for emulsion polymerization: propagation rate constant, reactivity ratios, glass transition temperature, and water solubility. These tools can be used to systematically identify viable bio‐based monomer pairs as replacements for conventional formulations, with ...
Kiarash Farajzadehahary, Nicholas Ballard   +1 more
wiley   +1 more source

Some simulations of age-period-cohort analysis applying Bayesian regularization: Conditions for using random walk model. [PDF]

PLoS One
Matsumoto Y.
europepmc   +1 more source

Self‐Sensing Artificial‐Muscle‐Empowered Humanlike Perception, Interaction, and Positioning

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
The proposed self‐sensorized artificial muscle (SSAM) can sense its length change as small as 0.01 mm via a seamlessly integrated multi‐segment induction coil. The SSAM provides accurate length information regardless of its loadings, driving pressure, or muscle design, adequate for robust data‐driven feedback control.
Houping Wu, Chenchen Li, Yufeng Wang, Zhengyan Wang, Yulian Peng, Zhipeng Wei, Hongbo Wang   +6 more
wiley   +1 more source