2,740 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
Minimum Entangling Power is Close to Its Maximum
Given a quantum gate acting on a bipartite quantum system, its maximum
(average, minimum) entangling power is the maximum (average, minimum)
entanglement generation with respect to certain entanglement measure when the
inputs are restricted to be product states. In this paper, we mainly focus on
the 'weakest' one, i.e., the minimum entangling power, among all these
entangling powers. We show that, by choosing von Neumann entropy of reduced
density operator or Schmidt rank as entanglement measure, even the 'weakest'
entangling power is generically very close to its maximal possible entanglement
generation. In other words, maximum, average and minimum entangling powers are
generically close. We then study minimum entangling power with respect to other
Lipschitiz-continuous entanglement measures and generalize our results to
multipartite quantum systems.
As a straightforward application, a random quantum gate will almost surely be
an intrinsically fault-tolerant entangling device that will always transform
every low-entangled state to near-maximally entangled state.Comment: 26 pages, subsection III.A.2 revised, authors list updated, comments
are welcom
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