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Room-temperature, pulsed-operation lasing of 8.5  μm-emitting InP-based quantum cascade lasers (QCLs), with low threshold-current density and watt-level output power, is demonstrated from structures grown on (001) GaAs substrates by metal-organic chemical vapor deposition. Prior to growing the laser structure, which contains a 35-stage In 0.53 Ga 0.47 As/In 0.52 Al 0.48 As lattice-matched active-core region, a ∼2  μm-thick nearly fully relaxed InP buffer with strained 1.6 nm-thick InAs quantum-dot-like dislocation-filter layers was grown. A smooth terminal buffer-layer surface, with roughness as low as 0.4 nm on a 10 × 10  μm 2 scale, was obtained, while the estimated threading-dislocation density was in the mid-range × 10 8  cm −2 . A series of measurements, on lasers grown on InP metamorphic buffer layers (MBLs) and on native InP substrates, were performed for understanding the impact of the buffer-layer's surface roughness, residual strain, and threading-dislocation density on unipolar devices such as QCLs. As-cleaved devices, grown on InP MBLs, were fabricated as 25  μm × 3 mm deep-etched ridge guides with lateral current injection. The results are pulsed maximum output power of 1.95 W/facet and a low threshold-current density of 1.86 kA/cm 2 at 293 K. These values are comparable to those obtained from devices grown on InP: 2.09 W/facet and 2.42 kA/cm 2 . This demonstrates the relative insensitivity of the device-performance metrics on high residual threading-dislocation density, and high-performance InP-based QCLs emitting near 8  μm can be achieved on lattice-mismatched substrates. 

Crowdsourcing markets provide workers with a centralized place to find paid work. What may not be obvious at first glance is that, in addition to the work they do for pay, crowd workers also have to shoulder a variety of unpaid invisible labor in these markets, which ultimately reduces workers' hourly wages. Invisible labor includes finding good tasks, messaging requesters, or managing payments. However, we currently know little about how much time crowd workers actually spend on invisible labor or how much it costs them economically. To ensure a fair and equitable future for crowd work, we need to be certain that workers are being paid fairly for ALL of the work they do. In this paper, we conduct a field study to quantify the invisible labor in crowd work. We build a plugin to record the amount of time that 100 workers on Amazon Mechanical Turk dedicate to invisible labor while completing 40,903 tasks. If we ignore the time workers spent on invisible labor, workers' median hourly wage was $3.76. But, we estimated that crowd workers in our study spent 33% of their time daily on invisible labor, dropping their median hourly wage to $2.83. We found that the invisible labor differentially impacts workers depending on their skill level and workers' demographics. The invisible labor category that took the most time and that was also the most common revolved around workers having to manage their payments. The second most time-consuming invisible labor category involved hyper-vigilance, where workers vigilantly watched over requesters' profiles for newly posted work or vigilantly searched for labor. We hope that through our paper, the invisible labor in crowdsourcing becomes more visible, and our results help to reveal the larger implications of the continuing invisibility of labor in crowdsourcing. 

In the minimum constraint removal problem, we are given a set of overlapping geometric objects as obstacles in the plane, and we want to find the minimum number of obstacles that must be removed to reach a target point t from the source point s by an obstacle-free path. The problem is known to be intractable and no sub-linear approximations are known even for simple obstacles such as rectangles and disks. The main result of our paper is an approximation framework that gives an O(√nα(n))-approximation for polygonal obstacles, where α(n) denotes the inverse Ackermann’s function. For pseudodisks and rectilinear polygons, the same technique achieves an O(√n)-approximation. The technique also gives O (√n)-approximation for the minimum color path problem in graphs. We also present some inapproximability results for the geometric constraint removal problem. 

We investigate dynamic versions of geometric set cover and hitting set where points and ranges may be inserted or deleted, and we want to efficiently maintain an (approximately) optimal solution for the current problem instance. While their static versions have been extensively studied in the past, surprisingly little is known about dynamic geometric set cover and hitting set. For instance, even for the most basic case of one-dimensional interval set cover and hitting set, no nontrivial results were known. The main contribution of our paper are two frameworks that lead to efficient data structures. 

We study the problem of finding shortest paths in the plane among h convex obstacles, where the path is allowed to pass through (violate) up to k obstacles, for 𝑘≤ℎ. Equivalently, the problem is to find shortest paths that become obstacle-free if k obstacles are removed from the input. Given a fixed source point s, we show how to construct a map, called a shortest k-path map, so that all destinations in the same region of the map have the same combinatorial shortest path passing through at most k obstacles. We prove a tight bound of 𝛩(𝑘𝑛) on the size of this map, and show that it can be computed in 𝑂(𝑘2𝑛log𝑛) time, where n is the total number of obstacle vertices. 

Given a set of points P and axis-aligned rectangles R in the plane, a point p ∈ P is called exposed if it lies outside all rectangles in R. In the max-exposure problem, given an integer parameter k, we want to delete k rectangles from R so as to maximize the number of exposed points. We show that the problem is NP-hard and assuming plausible complexity conjectures is also hard to approximate even when rectangles in R are translates of two fixed rectangles. However, if R only consists of translates of a single rectangle, we present a polynomial-time approximation scheme. For general rectangle range space, we present a simple O(k) bicriteria approximation algorithm; that is by deleting O(k2) rectangles, we can expose at least Ω(1/k) of the optimal number of points.