arXiv subjects

Andrew Kennedy

Publications and source records attributed to Andrew Kennedy.

3 recordsLinked to original sources

arXiv2026

Closing the gap: A two-site MLC matching study for Varian TrueBeam Linear Accelerators

Purpose: To characterise and harmonise multileaf collimator (MLC) gap calibrations across four beam-matched TrueBeams and assess a common planning-system model. Methods: Four Millennium 120 TrueBeams were studied: TS1 (installed 2016), TS5 (2021), TS6 and TS7 (2024). Gap calibration was assessed by RayStation sweeping-gap measurement, feeler gauge, the EPID-based Stakitt Fence test, and a leaf-resolved Machine Performance Check (MPC). TS1 and TS5 were adjusted in service mode, TS5 also for MLC centreline offset. Ten clinical VMAT plans, and six acquired earlier on an unadjusted reference machine, were measured with an ArcCHECK 1220 against doses computed at 0.053 and 0.065 cm x-offsets in RayStation. Results: The 2024 machines had wider baseline gaps. All four methods registered the TS1 and TS5 changes in the same direction and within 0.060 mm of predictions. After adjustment TS1 and TS5 lay within the TS6/TS7 sweeping-gap and Stakitt ranges but read wider on the feeler gauge. A conventional picket-fence test on TS5 registered no change. Raising the RayStation x-offset parameter increased mean local 3%/2 mm pass rates from 92.3% to 94.8% on TS1 and 93.2% to 95.6% on TS5; all 24 plan-criterion pairs improved on the unadjusted machine. Conclusions: Mechanical, EPID, and sweeping gap MLC gap measurements responded consistently to the MLC adjustment, moving TS1 and TS5 towards the local reference range. Every global 3%/2 mm ArcCHECK measurement exceeded the TG-218 95% limit.

physics.med-ph↗

arXiv2026

What's DAT? Three Case Studies of Measuring Software Development Productivity at Meta With Diff Authoring Time

This paper introduces Diff Authoring Time (DAT), a powerful, yet conceptually simple approach to measuring software development productivity that enables rigorous experimentation. DAT is a time based metric, which assess how long engineers take to develop changes, using a privacy-aware telemetry system integrated with version control, the IDE, and the OS. We validate DAT through observational studies, surveys, visualizations, and descriptive statistics. At Meta, DAT has powered experiments and case studies on more than 20 projects. Here, we highlight (1) an experiment on introducing mock types (a 14% DAT improvement), (2) the development of automatic memoization in the React compiler (33% improvement), and (3) an estimate of thousands of DAT hours saved annually through code sharing (> 50% improvement). DAT offers a precise, yet high-coverage measure for development productivity, aiding business decisions. It enhances development efficiency by aligning the internal development workflow with the experiment-driven culture of external product development. On the research front, DAT has enabled us to perform rigorous experimentation on long-standing software engineering questions such as "do types make development more efficient?"

cs.SE↗

arXiv2023

Dynamic Hand Gesture-Featured Human Motor Adaptation in Tool Delivery using Voice Recognition

Human-robot collaboration has benefited users with higher efficiency towards interactive tasks. Nevertheless, most collaborative schemes rely on complicated human-machine interfaces, which might lack the requisite intuitiveness compared with natural limb control. We also expect to understand human intent with low training data requirements. In response to these challenges, this paper introduces an innovative human-robot collaborative framework that seamlessly integrates hand gesture and dynamic movement recognition, voice recognition, and a switchable control adaptation strategy. These modules provide a user-friendly approach that enables the robot to deliver the tools as per user need, especially when the user is working with both hands. Therefore, users can focus on their task execution without additional training in the use of human-machine interfaces, while the robot interprets their intuitive gestures. The proposed multimodal interaction framework is executed in the UR5e robot platform equipped with a RealSense D435i camera, and the effectiveness is assessed through a soldering circuit board task. The experiment results have demonstrated superior performance in hand gesture recognition, where the static hand gesture recognition module achieves an accuracy of 94.3\%, while the dynamic motion recognition module reaches 97.6\% accuracy. Compared with human solo manipulation, the proposed approach facilitates higher efficiency tool delivery, without significantly distracting from human intents.

cs.RO↗