The domination number and the least QQ-eigenvalue

A vertex set $D$ of a graph $G$ is said to be a dominating set if every vertex of $V(G)\setminus D$ is adjacent to at least a vertex in $D$, and the domination number $\gamma(G)$ ($\gamma$, for short) is the minimum cardinality of all dominating sets of $G$. For a graph, the least $Q$-eigenvalue is the least eigenvalue of its signless Laplacian matrix. In this paper, for a nonbipartite graph with both order $n$ and domination number $\gamma$, we show that $n\geq 3\gamma-1$, and show that it contains a unicyclic spanning subgraph with the same domination number $\gamma$. By investigating the relation between the domination number and the least $Q$-eigenvalue of a graph, we minimize the least $Q$-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