3 Search Results for "Lin, Ching-Chi"

Document
DOI: 10.4230/OASIcs.SpaceCHI.2025.29
Gaze Beyond Limits: Integrating Eye-Tracking and Augmented Reality for Next-Generation Spacesuit Interaction

Authors: Jiayu He, Yifan Li, Oliver R. Runswick, Peter D. Hodkinson, Jarle Steinberg, Felix Gorbatsevich, and Yang Gao

Published in: OASIcs, Volume 130, Advancing Human-Computer Interaction for Space Exploration (SpaceCHI 2025)

Abstract
Extravehicular activities (EVAs) are increasingly frequent in human spaceflight, particularly in spacecraft maintenance, scientific research, and planetary exploration. Spacesuits are essential for sustaining astronauts in the harsh environment of space, making their design a key factor in the success of EVA missions. The development of spacesuit technology has traditionally been driven by highly engineered solutions focused on life support, mission adaptability and operational efficiency. Modern spacesuits prioritize maintaining optimal internal temperature, humidity and pressure, as well as withstanding extreme temperature fluctuations and providing robust protection against micrometeoroid impacts and space debris. However, their bulkiness and rigidity impose significant physical strain on astronauts, reducing mobility and dexterity, particularly in tasks requiring fine motor control. The restricted field of view further complicates situational awareness, increasing the cognitive load during high-precision operations. While traditional spacesuits support basic EVA tasks, future space exploration shifting toward long-duration lunar and Martian surface missions demand more adaptive, intelligent, and astronaut-centric designs to overcome current constraints. To explore a next-generation spacesuit, this paper proposed an in-process eye-tracking embedded Augmented Reality (AR) Spacesuit System to enhance astronaut-environment interactions. By leveraging Segment-Anything Models (SAM) and Vision-Language Models (VLMs), we demonstrate a four-step approach to enable top-down gaze detection to minimize erroneous fixation data, gaze-based segmentation of objects of interest, real-time contextual assistance via AR overlays and hands-free operation within the spacesuit. This approach enhances real-time situational awareness and improves EVA task efficiency. We conclude with an exploration of the AR Helmet System’s potential in revolutionizing human-space interaction paradigms for future long-duration deep-space missions and discuss the further optimization of eye-tracking interactions using VLMs to predict astronaut intent and highlight relevant objects preemptively.
Cite as

Jiayu He, Yifan Li, Oliver R. Runswick, Peter D. Hodkinson, Jarle Steinberg, Felix Gorbatsevich, and Yang Gao. Gaze Beyond Limits: Integrating Eye-Tracking and Augmented Reality for Next-Generation Spacesuit Interaction. In Advancing Human-Computer Interaction for Space Exploration (SpaceCHI 2025). Open Access Series in Informatics (OASIcs), Volume 130, pp. 29:1-29:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{he_et_al:OASIcs.SpaceCHI.2025.29,
  author =	{He, Jiayu and Li, Yifan and Runswick, Oliver R. and Hodkinson, Peter D. and Steinberg, Jarle and Gorbatsevich, Felix and Gao, Yang},
  title =	{{Gaze Beyond Limits: Integrating Eye-Tracking and Augmented Reality for Next-Generation Spacesuit Interaction}},
  booktitle =	{Advancing Human-Computer Interaction for Space Exploration (SpaceCHI 2025)},
  pages =	{29:1--29:15},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-384-3},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{130},
  editor =	{Bensch, Leonie and Nilsson, Tommy and Nisser, Martin and Pataranutaporn, Pat and Schmidt, Albrecht and Sumini, Valentina},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SpaceCHI.2025.29},
  URN =		{urn:nbn:de:0030-drops-240197},
  doi =		{10.4230/OASIcs.SpaceCHI.2025.29},
  annote =	{Keywords: Augmented Reality (AR), Eye-Tracking, Cognitive Load/Workload, Segment Anything Model (SAM), Visual Language Models (VLMs)}
}
Document
Position
DOI: 10.4230/TGDK.1.1.5
Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities

Authors: Jiaoyan Chen, Hang Dong, Janna Hastings, Ernesto Jiménez-Ruiz, Vanessa López, Pierre Monnin, Catia Pesquita, Petr Škoda, and Valentina Tamma

Published in: TGDK, Volume 1, Issue 1 (2023): Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge, Volume 1, Issue 1

Abstract
The term life sciences refers to the disciplines that study living organisms and life processes, and include chemistry, biology, medicine, and a range of other related disciplines. Research efforts in life sciences are heavily data-driven, as they produce and consume vast amounts of scientific data, much of which is intrinsically relational and graph-structured. The volume of data and the complexity of scientific concepts and relations referred to therein promote the application of advanced knowledge-driven technologies for managing and interpreting data, with the ultimate aim to advance scientific discovery. In this survey and position paper, we discuss recent developments and advances in the use of graph-based technologies in life sciences and set out a vision for how these technologies will impact these fields into the future. We focus on three broad topics: the construction and management of Knowledge Graphs (KGs), the use of KGs and associated technologies in the discovery of new knowledge, and the use of KGs in artificial intelligence applications to support explanations (explainable AI). We select a few exemplary use cases for each topic, discuss the challenges and open research questions within these topics, and conclude with a perspective and outlook that summarizes the overarching challenges and their potential solutions as a guide for future research.
Cite as

Jiaoyan Chen, Hang Dong, Janna Hastings, Ernesto Jiménez-Ruiz, Vanessa López, Pierre Monnin, Catia Pesquita, Petr Škoda, and Valentina Tamma. Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 5:1-5:33, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{chen_et_al:TGDK.1.1.5,
  author =	{Chen, Jiaoyan and Dong, Hang and Hastings, Janna and Jim\'{e}nez-Ruiz, Ernesto and L\'{o}pez, Vanessa and Monnin, Pierre and Pesquita, Catia and \v{S}koda, Petr and Tamma, Valentina},
  title =	{{Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{5:1--5:33},
  year =	{2023},
  volume =	{1},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.5},
  URN =		{urn:nbn:de:0030-drops-194791},
  doi =		{10.4230/TGDK.1.1.5},
  annote =	{Keywords: Knowledge graphs, Life science, Knowledge discovery, Explainable AI}
}
Document
DOI: 10.4230/LIPIcs.ISAAC.2017.64
Tight Approximation for Partial Vertex Cover with Hard Capacities

Authors: Jia-Yau Shiau, Mong-Jen Kao, Ching-Chi Lin, and D. T. Lee

Published in: LIPIcs, Volume 92, 28th International Symposium on Algorithms and Computation (ISAAC 2017)

Abstract
We consider the partial vertex cover problem with hard capacity constraints (Partial VC-HC) on hypergraphs. In this problem we are given a hypergraph G=(V,E) with a maximum edge size f and a covering requirement R. Each edge is associated with a demand, and each vertex is associated with a capacity and an (integral) available multiplicity. The objective is to compute a minimum vertex multiset such that at least R units of demand from the edges are covered by the capacities of the vertices in the multiset and the multiplicity of each vertex does not exceed its available multiplicity. In this paper we present an f-approximation for this problem, improving over a previous result of (2f+2)(1+epsilon) by Cheung et al to the tight extent possible. Our new ingredient of this work is a generalized analysis on the extreme points of the natural LP, developed from previous works, and a strengthened LP lower-bound obtained for the optimal solutions.
Cite as

Jia-Yau Shiau, Mong-Jen Kao, Ching-Chi Lin, and D. T. Lee. Tight Approximation for Partial Vertex Cover with Hard Capacities. In 28th International Symposium on Algorithms and Computation (ISAAC 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 92, pp. 64:1-64:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@InProceedings{shiau_et_al:LIPIcs.ISAAC.2017.64,
  author =	{Shiau, Jia-Yau and Kao, Mong-Jen and Lin, Ching-Chi and Lee, D. T.},
  title =	{{Tight Approximation for Partial Vertex Cover with Hard Capacities}},
  booktitle =	{28th International Symposium on Algorithms and Computation (ISAAC 2017)},
  pages =	{64:1--64:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-054-5},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{92},
  editor =	{Okamoto, Yoshio and Tokuyama, Takeshi},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ISAAC.2017.64},
  URN =		{urn:nbn:de:0030-drops-82694},
  doi =		{10.4230/LIPIcs.ISAAC.2017.64},
  annote =	{Keywords: Approximation Algorithm, Capacitated Vertex Cover, Hard Capacities}
}
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