5,462 research outputs found
The domination number and the least -eigenvalue
A vertex set of a graph is said to be a dominating set if every
vertex of is adjacent to at least a vertex in , and the
domination number (, for short) is the minimum cardinality
of all dominating sets of . For a graph, the least -eigenvalue is the
least eigenvalue of its signless Laplacian matrix. In this paper, for a
nonbipartite graph with both order and domination number , we show
that , and show that it contains a unicyclic spanning subgraph
with the same domination number . By investigating the relation between
the domination number and the least -eigenvalue of a graph, we minimize the
least -eigenvalue among all the nonbipartite graphs with given domination
number.Comment: 13 pages, 3 figure
Optimization Coaching for JavaScript
The performance of dynamic object-oriented programming languages such as JavaScript depends heavily on highly optimizing just-in-time compilers. Such compilers, like all compilers, can silently fall back to generating conservative, low-performance code during optimization. As a result, programmers may inadvertently cause performance issues on users\u27 systems by making seemingly inoffensive changes to programs. This paper shows how to solve the problem of silent optimization failures. It specifically explains how to create a so-called optimization coach for an object-oriented just-in-time-compiled programming language. The development and evaluation build on the SpiderMonkey JavaScript engine, but the results should generalize to a variety of similar platforms
Optimization Coaching for JavaScript (Artifact)
This artifact is based on our prototype optimization coach for the SpiderMonkey (https://developer.mozilla.org/en-US/docs/Mozilla/Projects/SpiderMonkey) JavaScript engine. An optimization coach is a performance tool that aims to provide programmers with insight into how their compiler optimizes their programs and to help them better harness the optimization process. It does so by reporting optimization near misses, i.e., reports of optimizations that the compiler did not apply, but could apply if the program were to be modified slightly.
This artifact provides the necessary environment, programs and data to repeat our experiments, and to allow readers to run our tool on JavaScript programs of their choic
Monolithic shape-programmable dielectric liquid crystal elastomer actuators
Macroscale robotic systems have demonstrated great capabilities of high
speed, precise, and agile functions. However, the ability of soft robots to
perform complex tasks, especially in centimeter and millimeter scale, remains
limited due to the unavailability of fast, energy-efficient soft actuators that
can programmably change shape. Here, we combine desirable characteristics from
two distinct active materials: fast and efficient actuation from dielectric
elastomers and facile shape programmability from liquid crystal elastomers into
a single shape changing electrical actuator. Uniaxially aligned monoliths
achieve strain rates over 120%/s with energy conversion efficiency of 20% while
moving loads over 700 times the actuator weight. The combined actuator
technology offers unprecedented opportunities towards miniaturization with
precision, efficiency, and more degrees of freedom for applications in soft
robotics and beyond
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