Carnegie Mellon University

Illegal opioids create challenges for public health and safety. There is imperfect understanding of when use of illegally manufactured opioids increased. This paper examined data on self-reported year of first heroin use in the United States. Secondary analysis of general population survey data. United States. 1 708 720 across 38 surveys from 1979 to 2023. The U.S. National Survey on Drug Use and Health and its predecessors ask respondents about their year of first use for various substances. In any single survey, few report initiating heroin in a specific year, but combining multiple surveys improves precision, enabling the plotting of time trends. Those who initiate can be broken down by age and by whether they report having misused pain relievers before they first used heroin. Self-reported heroin initiation appears to have been rare before the late 1960s, although data for those years are sparse. It rose sharply to a peak in 1972, fell by almost half, and remained stable from 1978 to 1994. It rose by about 75% in the late 1990s and a further 85% by the early 2010s. The proportion reporting misuse of prescription opioids before first using heroin increased from about one-third before 1990 to one-half by 2000 and 80% by 2010. The proportion who were over age 40 at the time of first heroin use increased from nearly 0 before 1990 to 10% in 2012 and about 40% in 2020-2021. In the United States, heroin initiation that is self-reported to have occurred after 2000 differs in magnitude and character from that from the late 1970s through mid-1990s. Changes began before dates commonly associated with restrictions on opioid prescribing. This appears consistent with a view that "trading down" from prescription opioid misuse to consumption of illegally manufactured opioids did not only occur after implementation of policies to reduce opioid prescribing.

In 2024, Major League Baseball released new bat tracking data, reporting swing-by-swing bat speed and swing length measured at the point of contact. While exciting, the data present challenges for their interpretation. The timing of the batter’s swing relative to the pitch determines the point of measurement relative to the full swing path. The relationship between swing metrics and swing outcomes is confounded by the batter’s pitch recognition. We introduce a framework for interpreting bat tracking data in which we first estimate the batter’s intention conditional on ball-strike count and pitch location using a Bayesian hierarchical skew-normal model with random intercept and random slopes for batter. This yields batter-specific effects of count on swing metrics, which we leverage via instrumental variables regression to estimate causal effects of bat speed and swing length on contact and power outcomes. Finally, we evaluate the tradeoff between contact and power due to bat speed by modeling a plate appearance as a Markov chain. We conclude that batters can reduce their strikeout rate by reducing bat speed as strikes increase, but the tradeoff in reduced power approximately counteracts the benefit to the average batter.

The Proceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS) focuses on the measurement and performance evaluation of computer systems and operates in close collaboration with the ACM Special Interest Group SIGMETRICS. All papers in this issue of POMACS will be presented at the ACM SIGMETRICS 2026 conference on June 8-12, 2026 in Ann Arbor, Michigan, USA. The papers in this issue were selected during the Fall submission round by the 113 members of the ACM SIGMETRICS 2026 program committee via a rigorous review process. Each paper was conditionally accepted (and shepherded), or allowed to be resubmitted to one of the subsequent three SIGMETRICS deadlines, or rejected (with resubmission allowed only after a year). Of the 149 papers submitted, 25 papers were accepted by the program committee members (including two revised papers from the previous rounds out of four resubmissions). Each submission received between three and five reviews. Although most of the papers were decided in the online discussion phase, borderline cases were discussed during the online program committee meeting. There were two types of submission this year: regular papers and operational systems track -- the latter being intended to report measurement studies on currently deployed systems -- and the vast majority of the papers were regular papers (144 out of 149). Among these regular papers, each submission could indicate one or two possible tracks and roughly 36% of the submissions indicated Measurement & Applied Modeling, 33% Theory, 46% Systems, and 20% Learning. All papers went through the same review process. Many individuals contributed to the success of this POMACS issue. We thank the authors, who submitted their best work to SIGMETRICS/POMACS. We also thank the program committee members who provided constructive feedback in their reviews to authors and participated in the online discussions and program committee meeting. In addition, we thank several external reviewers who provided their expert opinions on specific submissions that required additional input. Finally, we are also grateful to the ACM SIGMETRICS Chair, Mor Harchol-Balter, to the SIGMETRICS Organization Committee, and to the SIGMETRICS Executive Committee for their ongoing efforts and initiatives for creating an exciting program for ACM SIGMETRICS 2026.

Abstract Research Summary Many new employees leave their firms before realizing the returns to experience. One reason is that they cannot see how performance evolves with tenure. We study whether making performance trajectories visible improves retention and firm performance. In a randomized controlled trial at a multinational spa chain in China, workers received twice‐weekly information for 28 weeks about the performance path of a high‐performing senior coworker. The intervention reduces new‐worker attrition by 11–12% and increases revenue by 15% in stores with more new workers. These effects are largely driven by reduced stress and improved mental health, as the information lowers their beliefs about how well senior coworkers performed early in their careers. By contrast, showing only the current performance of a similar‐tenure peer has no detectable effect. Managerial Summary In many firms, new employees leave before realizing the returns to experience because they lack information about how performance evolves with tenure. We examine whether making senior workers’ performance trajectories visible improves retention and firm performance. In a randomized controlled trial involving over 7,000 workers at a multinational spa chain in China, employees received twice‐weekly information for 28 weeks about the performance trajectory of a high‐performing senior coworker. The intervention reduces new‐worker attrition by 11–12% and increases revenue by 15% in stores with more new workers. These effects are driven by reduced stress and improved mental health, as the information lowers beliefs about senior coworkers’ early‐career performance. Overall, our findings show that making senior workers’ performance trajectories visible can mitigate social comparison costs within firms.

Abstract Virtual Reality (VR) applications aim to create an immersive virtual world, which demands a high level of visual realism. The analytical material models commonly used in VR often fall short of reproducing complex real‐world appearances. Recently, neural materials have emerged as a promising alternative, offering a compact yet effective representation of real‐world materials. Deploying neural materials on low‐power mobile VR devices poses significant challenges due to the computational complexity of neural networks and the high display resolution and frame rate requirements of VR devices (commonly 72+ frames per second). We address these challenges by leveraging texture‐space shading with spatiotemporal computation amortization, driven by a compact, coarse‐to‐fine neural material model of extremely low capacity. Thanks to our distillation training scheme, our compact neural materials achieve visual quality comparable to NeuMIP [KMX*21] at a much lower cost. Our method reaches over 90 FPS on a mobile VR device (Meta Quest 3) even under multiple light sources.