Results 181 to 190 of about 549,786 (309)
Algebraic structures emerge from the self-supervised learning of natural sounds [PDF]
Pierre Orhan +2 more
openalex +1 more source
General schematic of the approach. Abstract Conventional Silver/Silver Chloride (Ag/AgCl) electrodes remain the clinical standard for electrophysiological monitoring but are hindered by poor skin conformity, mechanical rigidity, and signal degradation, particularly under motion or sweat.
Nazmi Alsaafeen +11 more
wiley +1 more source
ViewMix: Augmentation for Robust Representation in Self-Supervised Learning [PDF]
Arjon Das, Xin Zhong
openalex +1 more source
This work develops dynamically softening polyacrylamide hydrogels for time‐resolved imaging during continuous mechanical transitions. The study revealed that mechanotransduction is biphasic; YAP/TAZ inactivation is driven by early loss of the nucleocytoskeletal continuum connecting subnuclear adhesions, F‐actin, and the nuclear envelope, coupled with ...
Alessandro Gandin +12 more
wiley +1 more source
Towards Multi-Brain Decoding in Autism: A Self-Supervised Learning Approach. [PDF]
Ranjabaran G +3 more
europepmc +1 more source
Machine Learning Discovery of Record‐Low Lattice Thermal Conductivity in Double Perovskites
A deep learning interatomic potential is introduced to predict forces for computing phonon properties and thermal transport behavior in double perovskites. Screening 9,709 compounds identifies 1,597 stable materials, and Boltzmann transport calculations including both three and four‐phonon scattering suggests a record‐low lattice thermal conductivity ...
Md Zaibul Anam +3 more
wiley +1 more source
Intelligent identification of medical and veterinary intracellular protozoa by using self-supervised learning. [PDF]
Kittichai V +5 more
europepmc +1 more source
From Differentiable Reasoning to Self-supervised Embodied Active Learning
Ruslan Salakhutdinov
openalex +1 more source
S3RL: Enhancing Spatial Single‐Cell Transcriptomics With Separable Representation Learning
Separable Spatial Representation Learning (S3RL) is introduced to enhance the reconstruction of spatial transcriptomic landscapes by disentangling spatial structure and gene expression semantics. By integrating multimodal inputs with graph‐based representation learning and hyperspherical prototype modeling, S3RL enables high‐fidelity spatial domain ...
Laiyi Fu +6 more
wiley +1 more source

