arXiv subjects

Chanuka Algama

Publications and source records attributed to Chanuka Algama.

3 recordsLinked to original sources

arXiv2026

SF-LIFE: A Large-Scale Simulated Movement Dataset for the San Francisco Bay Area

We introduce SF-LIFE, a large-scale simulated movement dataset designed to accelerate research in transportation, mobility, and machine learning. The dataset contains 3,024,000,000,000 location records capturing complete, noise-free, multi-modality trajectories of 500,000 simulated agents observed at a 1Hz frequency navigating the San Francisco Bay Area network over a 70-day period. The data captures (1) needs-driven daily agendas of individual agents generated by an agent-based simulation of human patterns of life and (2) detailed kinematic trajectories moving agents across the OpenStreetMap representation of San Francisco using data from 40+ transit agencies across 9 counties. SF-LIFE provides unprecedented scale and detail as trajectories are based on real transit infrastructure using San Francisco General Transit Feed Specification (GTFS) data, having agent movements across multiple modalities, including bus, rail, bike, automobile, and walking. For this high-fidelity simulated representation of San Francisco, we provide (1) the full trajectory data annotated with transportation mode labels, (2) reduced-size versions of the trajectory data with reduced temporal frequency, (3) agent activity information describing the causal activity why an agent visits a place, (4) agent demographic data, and (5) the underlying OSM road network and building data. As the first dataset of its scale and level of detail, SF-LIFE overcomes the privacy, noise, and completeness limitations inherent in real-world tracking data, providing a robust and ethically sourced resource for research in transit optimization, human mobility analysis, and urban computing.

physics.soc-ph↗

arXiv2024

Robust Optical Flow Computation: A Higher-Order Differential Approach

In the domain of computer vision, optical flow stands as a cornerstone for unraveling dynamic visual scenes. However, the challenge of accurately estimating optical flow under conditions of large nonlinear motion patterns remains an open question. The image flow constraint is vulnerable to substantial displacements, and rapid spatial transformations. Inaccurate approximations inherent in numerical differentiation techniques can further amplify such intricacies. In response, this research proposes an innovative algorithm for optical flow computation, utilizing the higher precision of second-order Taylor series approximation within the differential estimation framework. By embracing this mathematical underpinning, the research seeks to extract more information about the behavior of the function under complex real-world scenarios and estimate the motion of areas with a lack of texture. An impressive showcase of the algorithm's capabilities emerges through its performance on renowned optical flow benchmarks such as KITTI (2015) and Middlebury. The average endpoint error (AEE), which computes the Euclidian distance between the calculated flow field and the ground truth flow field, stands notably diminished, validating the effectiveness of the algorithm in handling complex motion patterns.

cs.CV↗

arXiv2024

A Review on Message Complexity of the Algorithms for Clock Synchronization in Distributed Systems

In this work, we present an extensive analysis of clock synchronization algorithms, with a specific focus on message complexity. We begin by introducing fundamental concepts in clock synchronization, such as the Byzantine generals problem and specific concepts like clock accuracy, precision, skew, offset, timestamping, and clock drift estimation. Describing the concept of logical clocks, their implementation in distributed systems is discussed, highlighting their significance and various approaches. The paper then examines four prominent clock synchronization algorithms: Lamport's Algorithm, Ricart-Agrawala Algorithm, Vector Clocks Algorithm, and Christian's Algorithm. Special attention is given to the analysis of message complexity, providing insights into the efficiency of each algorithm. Finally, we compare the message complexities of the discussed algorithms.

cs.DC↗