Henry Lin

Henry Lin, Frans Schalekamp

We show NP-hardness of the minimum latency scheduling (MLS) problem under the physical model of wireless networking. In this model a transmission is received successfully if the Signal to Interference-plus-Noise Ratio (SINR), is above a given threshold. In the minimum latency scheduling problem, the goal is to assign a time slot and power level to each transmission, so that all the messages are received successfully, and the number of distinct times slots is minimized. Despite its seeming simplicity and several previous hardness results for various settings of the minimum latency scheduling problem, it has remained an open question whether or not the minimum latency scheduling problem is NP-hard, when the nodes are placed in the Euclidean plane and arbitrary power levels can be chosen for the transmissions. We resolve this open question for all path loss exponent values $α\geq 3$.

Yonas Atinafu, Henry Lin, Robin Cohen

In this paper, we present a novel black-box online controller that uses only end-to-end measurements over short segments, without internal instrumentation, and hill climbing to maximize goodput, defined as the throughput of requests that satisfy the service-level objective. We provide empirical evidence that this design is well-founded. Using this advance in LLM serving as a concrete example, we then discuss the importance of integrating system performance and sustainability metrics into Factsheets for organizations adopting AI systems.

Zainul Abi Din, Hari Venugopalan, Henry Lin et al.

App builders commonly use security challenges, a form of step-up authentication, to add security to their apps. However, the ethical implications of this type of architecture has not been studied previously. In this paper, we present a large-scale measurement study of running an existing anti-fraud security challenge, Boxer, in real apps running on mobile devices. We find that although Boxer does work well overall, it is unable to scan effectively on devices that run its machine learning models at less than one frame per second (FPS), blocking users who use inexpensive devices. With the insights from our study, we design Daredevil, anew anti-fraud system for scanning payment cards that work swell across the broad range of performance characteristics and hardware configurations found on modern mobile devices. Daredevil reduces the number of devices that run at less than one FPS by an order of magnitude compared to Boxer, providing a more equitable system for fighting fraud. In total, we collect data from 5,085,444 real devices spread across 496 real apps running production software and interacting with real users.