Results 81 to 90 of about 1,601 (294)
AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley +1 more source
Machine‐Learning‐Assisted Onset‐Time Determination in Transient Luminescence Thermometry
Artificial neural networks enable autonomous extraction of onset times from transient heating curves in luminescence thermometry. Using Ln3+‐doped upconverting nanoparticles as luminescent thermometers, we combine experimental transients with physically motivated synthetic curves to enhance data diversity and improve generalization.
David J. Sousa +3 more
wiley +1 more source
AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia +2 more
wiley +1 more source
In Tropic of Orange (1997), Karen Tei Yamashita builds an expansive narrative on the premise that the Tropic of Cancer shifts mysteriously from its actual latitude, barely north of Mazatlán, México, to that of L.A.’s latitude: from 23 ...
Nathan Dwight Frank
doaj +1 more source
Multimodal Learning with Rashomon Analysis for Battery Discharge Capacity Prediction
Multimodal fusion integrates composition, crystal‐structure, and radial‐distribution descriptors to predict battery discharge capacity. Rashomon analysis across near‐optimal models reveals that explanatory variation is structured rather than arbitrary, separating stable mechanistic signals from model‐contingent attributions and providing a more ...
Jue Gong +4 more
wiley +1 more source
The Knowledge Argument, Diaphanousness, Representationalism
This chapter develops a representationalist view about perceptual experience and defends its application to the knowledge argument. This view is based partly on the idea that perceptual experience is diaphanous - in other words, that accessing the nature
Jackson, Frank, Frank Jackson
core +1 more source
Untangling the Knot of Intentionality: Between Directedness, Reference, and Content
DOI: http://doi.org/10.26333/sts.xxxiii1.06 The notion of “intentionality” is much invoked in various foundational theories of meaning, being very often equated with “meaning”, “content” and “reference”.
Pierre Steiner
doaj
Capacitism and the False Dichotomy [PDF]
IntroductionSusanna Schellenberg, over a decade, developed the idea of capacitism in order to explain the perceptual experience. Capacitism declares that perception is constituted by employing perceptual capacities, i.e.
Faraz Attar
doaj +1 more source
EMG‐Driven Telemetry and Inference System for Fish: Pose Reconstruction and Flow Sensing
This work introduces an electromyography (EMG)‐driven telemetry framework that reconstructs body pose and infers hydrodynamic conditions in freely swimming fish. A custom 16‐channel archival system records intramuscular EMG, enabling deep‐learning models to decode joint kinematics, classify flow regimes, and reveal channel‐efficient sensing strategies.
Rahdar Hussain Afridi +7 more
wiley +1 more source
Strong Representationalism and Centred Content
I argue that strong representationalism, the view that for a perceptual experience to have a certain phenomenal character just is for it to have a certain representational content (perhaps represented in the right sort of way), encounters two problems ...
Brogaard, Berit
core +2 more sources

