2,790 research outputs found
Intrinsic energy conversion mechanism via telescopic extension and retraction of concentric carbon nanotubes
The conversion of other forms of energy into mechanical work through the
geometrical extension and retraction of nanomaterials has a wide variety of
potential applications, including for mimicking biomotors. Here, using
molecular dynamic simulations, we demonstrate that there exists an intrinsic
energy conversion mechanism between thermal energy and mechanical work in the
telescopic motions of double-walled carbon nanotubes (DWCNTs). A DWCNT can
inherently convert heat into mechanical work in its telescopic extension
process, while convert mechanical energy into heat in its telescopic retraction
process. These two processes are thermodynamically reversible. The underlying
mechanism for this reversibility is that the entropy changes with the
telescopic overlapping length of concentric individual tubes. We find also that
the entropy effect enlarges with the decreasing intertube space of DWCNTs. As a
result, the spontaneously telescopic motion of a condensed DWCNT can be
switched to extrusion by rising the system temperature above a critical value.
These findings are important for fundamentally understanding the mechanical
behavior of concentric nanotubes, and may have general implications in the
application of DWCNTs as linear motors in nanodevices
Label Embedding by Johnson-Lindenstrauss Matrices
We present a simple and scalable framework for extreme multiclass
classification based on Johnson-Lindenstrauss matrices (JLMs). Using the
columns of a JLM to embed the labels, a -class classification problem is
transformed into a regression problem with \cO(\log C) output dimension. We
derive an excess risk bound, revealing a tradeoff between computational
efficiency and prediction accuracy, and further show that under the Massart
noise condition, the penalty for dimension reduction vanishes. Our approach is
easily parallelizable, and experimental results demonstrate its effectiveness
and scalability in large-scale applications
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