2 Search Results for "Wang, Mien Brabeeba"

Document
DOI: 10.4230/LIPIcs.SAND.2026.12
Nanobot Algorithms for Treatment of Diffuse Cancer

Authors: Noble C. Harasha and Nancy Lynch

Published in: LIPIcs, Volume 373, 5th Symposium on Algorithmic Foundations of Dynamic Networks (SAND 2026)

Abstract
We consider the problem of a swarm of nanobots detecting and treating human cancer that is diffuse, that is, dispersed in a region with multiple separate cancer sites in need of treatment. We present a mathematical model of nanobots and their colloidal environment that is inspired by actual chemotactic nanoparticles, involving agents noisily following chemical gradients (both attractively and repellently, depending on the chemical). We present three incrementally sophisticated algorithms that describe additional chemical payloads that agents carry onboard, beyond the cancer-treating drug K, as well as the rules for when agents drop their payloads: Algorithm KM, in which agents simply ascend naturally existing chemical M signals that surround cancer sites; Algorithm KMA, in which agents themselves amplify these natural signals by dropping chemical A payloads upon reaching a site; and Algorithm KMAR, in which agents choose to either amplify the signal by dropping chemical A or counteract/reduce the signal by dropping chemical R, according to the current unsatisfied demand of the site. We present simulation results for all of the algorithms, across a set of distinct cancer arrangements, that track both the achieved treatment success as well as the time/duration of the treatment. KM has generally successful treatment unless the natural M-signals are weak, in which case the treatment progresses too slowly. KMA demonstrates a significant speedup in treatment time (over KM), but also a drop in success except for the most concentrated cancer patterns. KMAR has relatively optimal performance across all types of cancer patterns, demonstrating robustness and adaptability in its mechanisms for nanobot coordination.
Cite as

Noble C. Harasha and Nancy Lynch. Nanobot Algorithms for Treatment of Diffuse Cancer. In 5th Symposium on Algorithmic Foundations of Dynamic Networks (SAND 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 373, pp. 12:1-12:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

Copy BibTex To Clipboard

@InProceedings{harasha_et_al:LIPIcs.SAND.2026.12,
  author =	{Harasha, Noble C. and Lynch, Nancy},
  title =	{{Nanobot Algorithms for Treatment of Diffuse Cancer}},
  booktitle =	{5th Symposium on Algorithmic Foundations of Dynamic Networks (SAND 2026)},
  pages =	{12:1--12:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-427-7},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{373},
  editor =	{Mertzios, George B. and Richa, Andr\'{e}a W.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAND.2026.12},
  URN =		{urn:nbn:de:0030-drops-262469},
  doi =		{10.4230/LIPIcs.SAND.2026.12},
  annote =	{Keywords: Nanobots, biological modeling, distributed algorithms, agent-based models, random walks, cancer detection, cancer treatment}
}
Document
Brief Announcement
DOI: 10.4230/LIPIcs.DISC.2019.48
Brief Announcement: Integrating Temporal Information to Spatial Information in a Neural Circuit

Authors: Nancy Lynch and Mien Brabeeba Wang

Published in: LIPIcs, Volume 146, 33rd International Symposium on Distributed Computing (DISC 2019)

Abstract
In this paper, we consider networks of deterministic spiking neurons, firing synchronously at discrete times. We consider the problem of translating temporal information into spatial information in such networks, an important task that is carried out by actual brains. Specifically, we define two problems: "First Consecutive Spikes Counting" and "Total Spikes Counting", which model temporal-coding and rate-coding aspects of temporal-to-spatial translation respectively. Assuming an upper bound of T on the length of the temporal input signal, we design two networks that solve two problems, each using O(log T) neurons and terminating in time T+1. We also prove that these bounds are tight.
Cite as

Nancy Lynch and Mien Brabeeba Wang. Brief Announcement: Integrating Temporal Information to Spatial Information in a Neural Circuit. In 33rd International Symposium on Distributed Computing (DISC 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 146, pp. 48:1-48:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

Copy BibTex To Clipboard

@InProceedings{lynch_et_al:LIPIcs.DISC.2019.48,
  author =	{Lynch, Nancy and Wang, Mien Brabeeba},
  title =	{{Brief Announcement: Integrating Temporal Information to Spatial Information in a Neural Circuit}},
  booktitle =	{33rd International Symposium on Distributed Computing (DISC 2019)},
  pages =	{48:1--48:3},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-126-9},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{146},
  editor =	{Suomela, Jukka},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.DISC.2019.48},
  URN =		{urn:nbn:de:0030-drops-113551},
  doi =		{10.4230/LIPIcs.DISC.2019.48},
  annote =	{Keywords: Spiking Neural Network, Distributed Algorithm, Biological Networks}
}
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