arXiv subjects

Nathan Nguyen

Publications and source records attributed to Nathan Nguyen.

4 recordsLinked to original sources

arXiv2026

Emergent Workload Inequality in Collective Excavation

Living collectives and artificial swarms frequently employ a division of labor, wherein individuals take on different tasks or perform different amounts of work. However, the mechanisms used by collectives to divide labor remain poorly understood. Here, we study how workload inequality arises in collectives by monitoring excavation in Solenopsis invicta fire ants, whose coordination in constrained environments makes them an attractive system for studying division of labor. We vary group size (between 2 and 25 ants) and track digging activity to create Lorenz curves and corresponding Gini coefficients, which represent relative workload inequality. We find that that workload becomes more unequal as group size increases: the number of "active" ants scales with the square root of the group size. We implement a cellular automata (CA) model in which agents regulate their activity based on local crowding in the tunnel. The CA reproduces experimental Gini coefficients over a wide range of parameters and group sizes, indicating that local decisions emergently account for the scaling of workload inequality. An analytic rate equation model recovers the square root scaling with the assumption that individuals exit the tunnel at a rate which scales quadratically with the group size. Power law scalings in workload distribution have been observed in other systems, including social and natural sciences; however, these laws are primarily observational. Here, we provide a mechanistic explanation for the emergent workload scaling patterns in constrained biological collectives, offering insight into organization in both natural and future task capable engineered collectives and swarms.

physics.bio-ph↗

arXiv2025

Enhancing Ultrasound Molecular Imaging: Toward Real-Time RPCA-Based Filtering to Differentiate Bound and Free Microbubbles

Ultrasound molecular imaging (UMI) is an advanced imaging modality that shows promise in detecting cancer at early stages. It uses microbubbles as contrast agents, which are functionalized to bind to cancer biomarkers overexpressed on endothelial cells. A major challenge in UMI is isolating bound microbubble signal, which represents the molecular imaging signal, from that of free-floating microbubbles, which is considered background noise. In this work, we propose a fast GPU-based method using robust principal component analysis (RPCA) to distinguish bound microbubbles from free-floating ones. We explore the method using simulations and measure the accuracy using the Dice coefficient and RMS error as functions of the number of frames used in RPCA reconstruction. Experiments using stationary and flowing microbubbles in tissue-mimicking phantoms were used to validate the method. Additionally, the method was applied to data from ten transgenic mouse models of breast cancer development, injected with B7-H3-targeted microbubbles, and two mice injected with non-targeted microbubbles. The results showed that RPCA using 20 frames achieved a Dice score of 0.95 and a computation time of 0.2 seconds, indicating that 20 frames is potentially suitable for real-time implementation. On in vivo data, RPCA using 20 frames achieved a Dice score of 0.82 with DTE, indicating good agreement between the two, given the limitations of each method.

physics.med-ph↗

arXiv2024

Novel Approach for Predicting the Air Quality Index of Megacities through Attention-Enhanced Deep Multitask Spatiotemporal Learning

Air pollution remains one of the most formidable environmental threats to human health globally, particularly in urban areas, contributing to nearly 7 million premature deaths annually. Megacities, defined as cities with populations exceeding 10 million, are frequent hotspots of severe pollution, experiencing numerous weeks of dangerously poor air quality due to the concentration of harmful pollutants. In addition, the complex interplay of factors makes accurate air quality predictions incredibly challenging, and prediction models often struggle to capture these intricate dynamics. To address these challenges, this paper proposes an attention-enhanced deep multitask spatiotemporal machine learning model based on long-short-term memory networks for long-term air quality monitoring and prediction. The model demonstrates robust performance in predicting the levels of major pollutants such as sulfur dioxide and carbon monoxide, effectively capturing complex trends and fluctuations. The proposed model provides actionable information for policymakers, enabling informed decision making to improve urban air quality.

cs.LG↗

arXiv2023

HyPFuzz: Formal-Assisted Processor Fuzzing

Recent research has shown that hardware fuzzers can effectively detect security vulnerabilities in modern processors. However, existing hardware fuzzers do not fuzz well the hard-to-reach design spaces. Consequently, these fuzzers cannot effectively fuzz security-critical control- and data-flow logic in the processors, hence missing security vulnerabilities. To tackle this challenge, we present HyPFuzz, a hybrid fuzzer that leverages formal verification tools to help fuzz the hard-to-reach part of the processors. To increase the effectiveness of HyPFuzz, we perform optimizations in time and space. First, we develop a scheduling strategy to prevent under- or over-utilization of the capabilities of formal tools and fuzzers. Second, we develop heuristic strategies to select points in the design space for the formal tool to target. We evaluate HyPFuzz on five widely-used open-source processors. HyPFuzz detected all the vulnerabilities detected by the most recent processor fuzzer and found three new vulnerabilities that were missed by previous extensive fuzzing and formal verification. This led to two new common vulnerabilities and exposures (CVE) entries. HyPFuzz also achieves 11.68$\times$ faster coverage than the most recent processor fuzzer.

cs.CR↗