Results 171 to 180 of about 603,409 (311)
Molecular doping of conjugated polymers is fundamentally constrained by thermodynamic phase behavior. This Perspective reframes doping efficiency and stability in terms of miscibility limits, binodals, and solvus boundaries, highlighting the role of effective interaction parameters and charge transfer.
Somayeh Kashani +10 more
wiley +1 more source
Controlling the protein corona formation onto carbon nanomaterials (CNMs) enhances their functionalities as platforms for cancer theranostics. Here, we reviewed the effects of the intrinsic and acquired properties of CNMs on protein corona formation, the consequent biological and toxicological outcomes, and the strategies to reshape corona formation ...
Yajuan Zou +5 more
wiley +1 more source
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj +8 more
wiley +1 more source
Leaftronics: Bio‐Fractal Scaffolds From Leaf Venation for Low‐Waste Electronics
“Leaftronics” transforms naturally evolved leaf venation into quasi‐fractal scaffolds for sustainable electronics. Polymer‐infiltrated leaf skeletons can be used to fabricate ultra‐smooth, reflow‐ and thin‐film‐compatible decomposable substrates, while making the same lignocellulose networks conducting results in flexible transparent electrodes.
Rakesh Rajendran Nair +3 more
wiley +1 more source
Less is More : Towards parsimonious multi-task models using structured sparsity
Model sparsification in deep learning promotes simpler, more interpretable models with fewer parameters. This not only reduces the model’s memory footprint and computational needs but also shortens inference time.
Saini, Rajkumar +3 more
core +1 more source
Bayesian Multi-Task Reinforcement Learning. [PDF]
We consider the problem of multi-task reinforcement learning where the learner is provided with a set of tasks, for which only a small number of samples can be generated for any given policy. As the number of samples may not be enough to learn an accurate evaluation of the policy, it would be necessary to identify classes of tasks with similar ...
Lazaric, Alessandro +1 more
openaire +1 more source
Local rademacher complexity-based learning guarantees for multi-task learning
We show a Talagrand-type concentration inequality for Multi-Task Learning (MTL), with which we establish sharp excess risk bounds for MTL in terms of the Local Rademacher Complexity (LRC).
Anagnostopoulos, Georgios C. +4 more
core
AI–Guided 4D Printing of Carnivorous Plants–Inspired Microneedles for Accelerated Wound Healing
This work presents an artificial intelligence (AI)‐guided 4D‐printed microneedle platform inspired by carnivorous plants for wound healing. A thermo‐responsive shape memory polymer enables body temperature–triggered self‐coiling for autonomous wound closure.
Hyun Lee +21 more
wiley +1 more source
Measuring the Hall Effect in Hysteretic Materials
The authors highlight common pitfalls in measuring the Hall effect: in hysteretic magnets, improper data processing can create signals that look exotic but are not real. This Perspective explains the origin of these artifacts and presents practical measurement strategies that help researchers identify reliable Hall responses in complex magnetic ...
Jaime M. Moya +6 more
wiley +1 more source

