125 Search Results for "Zhang, Charles"

Document
DOI: 10.4230/LIPIcs.SEA.2026.15
Computational Generation of Substrate-Specific Molecular Cages

Authors: Noé Demange, Yann Strozecki, and Sandrine Vial

Published in: LIPIcs, Volume 371, 24th International Symposium on Experimental Algorithms (SEA 2026)

Abstract
In this paper, we propose a method to build molecular cages designed to capture a specific substrate. We model a cage as a graph of atoms with coordinates in space, and several constraints on their edges (degree, length and angle). We use a simple method to place binding patterns which are able to interact with certain parts of the substrate. We then propose an algorithm which considers all possible ways of connecting these binding patterns and try to construct the smallest possible molecular paths realizing these connections. We investigate many variants of our method in order to obtain the most efficient algorithm, able to build cages of more than a hundred atoms.
Cite as

Noé Demange, Yann Strozecki, and Sandrine Vial. Computational Generation of Substrate-Specific Molecular Cages. In 24th International Symposium on Experimental Algorithms (SEA 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 371, pp. 15:1-15:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{demange_et_al:LIPIcs.SEA.2026.15,
  author =	{Demange, No\'{e} and Strozecki, Yann and Vial, Sandrine},
  title =	{{Computational Generation of Substrate-Specific Molecular Cages}},
  booktitle =	{24th International Symposium on Experimental Algorithms (SEA 2026)},
  pages =	{15:1--15:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-422-2},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{371},
  editor =	{Aum\"{u}ller, Martin and Finocchi, Irene},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2026.15},
  URN =		{urn:nbn:de:0030-drops-260191},
  doi =		{10.4230/LIPIcs.SEA.2026.15},
  annote =	{Keywords: Enumeration, Molecular Cage, Cheminformatics, Geometric Algorithms, Experimental Algorithms}
}
Document
DOI: 10.4230/LIPIcs.SWAT.2026.17
Incremental Strongly Connected Components with Predictions

Authors: Ronald Deng, Samuel McCauley, Aidin Niaparast, Helia Niaparast, Bennett Ptak, Shirel Quintanilla, Shikha Singh, and Nathan Vosburg

Published in: LIPIcs, Volume 370, 20th Scandinavian Symposium on Algorithm Theory (SWAT 2026)

Abstract
Algorithms with predictions is a growing area that aims to leverage machine-learned predictions to design faster beyond-worst-case algorithms. In this paper, we use this framework to design a learned data structure for the incremental strongly connected components (SCC) problem. In this problem, the n vertices of a graph are known a priori and the m directed edges arrive over time. The goal is to efficiently maintain the strongly connected components of the graph after each insert. Our algorithm receives a possibly erroneous prediction of the edge sequence and uses it to precompute partial solutions to support fast inserts. We show that our algorithm achieves nearly optimal bounds with good predictions and its performance smoothly degrades with the prediction error. We also implement our data structure and perform experiments on real datasets. Our empirical results show that the theory is predictive of practical runtime improvements.
Cite as

Ronald Deng, Samuel McCauley, Aidin Niaparast, Helia Niaparast, Bennett Ptak, Shirel Quintanilla, Shikha Singh, and Nathan Vosburg. Incremental Strongly Connected Components with Predictions. In 20th Scandinavian Symposium on Algorithm Theory (SWAT 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 370, pp. 17:1-17:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{deng_et_al:LIPIcs.SWAT.2026.17,
  author =	{Deng, Ronald and McCauley, Samuel and Niaparast, Aidin and Niaparast, Helia and Ptak, Bennett and Quintanilla, Shirel and Singh, Shikha and Vosburg, Nathan},
  title =	{{Incremental Strongly Connected Components with Predictions}},
  booktitle =	{20th Scandinavian Symposium on Algorithm Theory (SWAT 2026)},
  pages =	{17:1--17:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-421-5},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{370},
  editor =	{Fraigniaud, Pierre},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SWAT.2026.17},
  URN =		{urn:nbn:de:0030-drops-260530},
  doi =		{10.4230/LIPIcs.SWAT.2026.17},
  annote =	{Keywords: algorithms with predictions, learning augmented algorithms, incremental graph algorithms, strongly connected components, data structures}
}
Document
DOI: 10.4230/LIPIcs.SWAT.2026.14
Orthogonal Strip Partitioning of Polygons: Lattice-Theoretic Algorithms and Lower Bounds

Authors: Jaehoon Chung

Published in: LIPIcs, Volume 370, 20th Scandinavian Symposium on Algorithm Theory (SWAT 2026)

Abstract
We study a variant of a polygon partition problem, introduced by Chung, Iwama, Liao, and Ahn [ISAAC'25]. Given orthogonal unit vectors 𝐮,𝐯 ∈ ℝ² and a polygon P with n vertices, we partition P into connected pieces by cuts parallel to 𝐯 such that each resulting subpolygon has width at most one in direction 𝐮. We consider the value version, which asks for the minimum number of strips, and the reporting version, which outputs a compact encoding of the cuts in an optimal strip partition. We give efficient algorithms and lower bounds for both versions on three classes of polygons of increasing generality: convex, simple, and self-overlapping. For convex polygons, we solve the value version in O(log n) time and the reporting version in O(h log (1 + n/h)) time, where h is the width of P in direction 𝐮. We prove matching lower bounds in the decision-tree model, showing that the reporting algorithm is input-sensitive optimal with respect to h. For simple polygons, we present O(n log n)-time, O(n)-space algorithms for both versions and prove an Ω(n) lower bound. For self-overlapping polygons, we extend the approach for simple polygons to obtain O(n log n)-time, O(n)-space algorithms for both versions, and we prove a matching Ω(n log n) lower bound in the algebraic computation-tree model via a reduction from the δ-closeness problem. Our approach relies on a lattice-theoretic formulation of the problem. We represent strip partitions as antichains of intervals in the Clarke-Cormack-Burkowski lattice, originally developed for minimal-interval semantics in information retrieval. Within this lattice framework, we design a dynamic programming algorithm that uses the lattice operations of meet and join. To the best of our knowledge, this is the first geometric application of the Clarke-Cormack-Burkowski lattice.
Cite as

Jaehoon Chung. Orthogonal Strip Partitioning of Polygons: Lattice-Theoretic Algorithms and Lower Bounds. In 20th Scandinavian Symposium on Algorithm Theory (SWAT 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 370, pp. 14:1-14:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{chung:LIPIcs.SWAT.2026.14,
  author =	{Chung, Jaehoon},
  title =	{{Orthogonal Strip Partitioning of Polygons: Lattice-Theoretic Algorithms and Lower Bounds}},
  booktitle =	{20th Scandinavian Symposium on Algorithm Theory (SWAT 2026)},
  pages =	{14:1--14:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-421-5},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{370},
  editor =	{Fraigniaud, Pierre},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SWAT.2026.14},
  URN =		{urn:nbn:de:0030-drops-260506},
  doi =		{10.4230/LIPIcs.SWAT.2026.14},
  annote =	{Keywords: Polygon partitioning, Strip partition, Lattice, Self-overlapping curves}
}
Document
DOI: 10.4230/LIPIcs.FORC.2026.2
Learning Rate Scheduling with Matrix Factorization for Private Training

Authors: Nikita P. Kalinin and Joel Daniel Andersson

Published in: LIPIcs, Volume 368, 7th Symposium on Foundations of Responsible Computing (FORC 2026)

Abstract
We study differentially private model training with stochastic gradient descent under learning rate scheduling and correlated noise. Although correlated noise, in particular via matrix factorizations, has been shown to improve accuracy, prior theoretical work focused primarily on the prefix-sum workload. That workload assumes a constant learning rate, whereas in practice learning rate schedules are widely used to accelerate training and improve convergence. We close this gap by deriving general upper and lower bounds for a broad class of learning rate schedules in both single- and multi-epoch settings. Building on these results, we propose a learning-rate-aware factorization that achieves improvements over prefix-sum factorizations under both MaxSE and MeanSE error metrics. Our theoretical analysis yields memory-efficient constructions suitable for practical deployment, and experiments on CIFAR-10 and IMDB datasets confirm that schedule-aware factorizations improve accuracy in private training.
Cite as

Nikita P. Kalinin and Joel Daniel Andersson. Learning Rate Scheduling with Matrix Factorization for Private Training. In 7th Symposium on Foundations of Responsible Computing (FORC 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 368, pp. 2:1-2:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{kalinin_et_al:LIPIcs.FORC.2026.2,
  author =	{Kalinin, Nikita P. and Andersson, Joel Daniel},
  title =	{{Learning Rate Scheduling with Matrix Factorization for Private Training}},
  booktitle =	{7th Symposium on Foundations of Responsible Computing (FORC 2026)},
  pages =	{2:1--2:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-419-2},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{368},
  editor =	{Lin, Huijia (Rachel)},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2026.2},
  URN =		{urn:nbn:de:0030-drops-259738},
  doi =		{10.4230/LIPIcs.FORC.2026.2},
  annote =	{Keywords: differential privacy, machine learning, matrix factorization}
}
Document
DOI: 10.4230/LIPIcs.FORC.2026.7
When to Ask a Question: Understanding Communication Strategies in Generative AI Tools

Authors: Charlotte Park, Kate Donahue, and Manish Raghavan

Published in: LIPIcs, Volume 368, 7th Symposium on Foundations of Responsible Computing (FORC 2026)

Abstract
Generative AI models differ from traditional machine learning tools in that they allow users to provide as much or as little information as they choose in their inputs. This flexibility often leads users to omit certain details, relying on the models to infer and fill in under-specified information based on distributional knowledge of user preferences. Such inferences may privilege majority viewpoints and disadvantage users with atypical preferences, raising concerns about fairness. Unlike more traditional recommender systems, LLMs can explicitly solicit more information from users through natural language. However, while directly eliciting user preferences could increase personalization and mitigate inequality, excessive querying places a burden on users who value efficiency. We develop a stylized model of user-LLM interaction and develop an objective that captures tradeoff between user burden and preference representation. Building on the observation that individual preferences are often correlated, we analyze how AI systems should balance inference and elicitation, characterizing the optimal amount of information to solicit before content generation. Ultimately, we show that information elicitation can mitigate the systematic biases of preference inference, enabling the design of generative tools that better incorporate diverse user perspectives while maintaining efficiency. We complement this theoretical analysis with an empirical evaluation illustrating the model’s predictions and exploring their practical implications.
Cite as

Charlotte Park, Kate Donahue, and Manish Raghavan. When to Ask a Question: Understanding Communication Strategies in Generative AI Tools. In 7th Symposium on Foundations of Responsible Computing (FORC 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 368, pp. 7:1-7:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{park_et_al:LIPIcs.FORC.2026.7,
  author =	{Park, Charlotte and Donahue, Kate and Raghavan, Manish},
  title =	{{When to Ask a Question: Understanding Communication Strategies in Generative AI Tools}},
  booktitle =	{7th Symposium on Foundations of Responsible Computing (FORC 2026)},
  pages =	{7:1--7:25},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-419-2},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{368},
  editor =	{Lin, Huijia (Rachel)},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2026.7},
  URN =		{urn:nbn:de:0030-drops-259782},
  doi =		{10.4230/LIPIcs.FORC.2026.7},
  annote =	{Keywords: human-AI interaction, user modeling, personalization}
}
Document
DOI: 10.4230/LIPIcs.SoCG.2026.26
Finding a Fair Scoring Function for Top-k Selection: From Hardness to Practice

Authors: Guangya Cai

Published in: LIPIcs, Volume 367, 42nd International Symposium on Computational Geometry (SoCG 2026)

Abstract
We study the problem of finding a fair linear scoring function over (numerical) attributes for top-k selection, ensuring fairness through a proportional representation constraint on the protected group. Existing algorithms do not scale efficiently, particularly in higher dimensions. Our hardness analysis shows that in more than two dimensions, no algorithm is likely to scale efficiently with respect to dataset size, and the computational complexity is likely to grow rapidly with dimensionality. However, the hardness results also provide key insights guiding algorithm design, leading to our two-pronged solution: (1) For small k, our analysis reveals a gap in the hardness barrier. By addressing various engineering challenges, including achieving efficient parallelism, we turn this potential of efficiency into an optimized geometry-based algorithm delivering substantial performance gains. (2) For large k, where the hardness is robust, we employ a practically efficient optimization-based algorithm which, despite being theoretically worse, achieves superior real-world performance. Experimental evaluations on real-world datasets then explore scenarios where worst-case behavior does not manifest, identifying areas critical to practical performance. Our solution achieves speedups of up to several orders of magnitude compared to the state of the art, an efficiency made possible through a tight integration of hardness analysis, algorithm design, practical engineering, and empirical evaluation.
Cite as

Guangya Cai. Finding a Fair Scoring Function for Top-k Selection: From Hardness to Practice. In 42nd International Symposium on Computational Geometry (SoCG 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 367, pp. 26:1-26:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{cai:LIPIcs.SoCG.2026.26,
  author =	{Cai, Guangya},
  title =	{{Finding a Fair Scoring Function for Top-k Selection: From Hardness to Practice}},
  booktitle =	{42nd International Symposium on Computational Geometry (SoCG 2026)},
  pages =	{26:1--26:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-418-5},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{367},
  editor =	{Ahn, Hee-Kap and Hoffmann, Michael and Nayyeri, Amir},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2026.26},
  URN =		{urn:nbn:de:0030-drops-258320},
  doi =		{10.4230/LIPIcs.SoCG.2026.26},
  annote =	{Keywords: Fairness, Top-k, Integration}
}
Document
DOI: 10.4230/LIPIcs.SoCG.2026.37
A Free Lunch: Manifolds of Positive Reach Can Be Smoothed Without Decreasing the Reach

Authors: Hana Dal Poz Kouřimská, André Lieutier, and Mathijs Wintraecken

Published in: LIPIcs, Volume 367, 42nd International Symposium on Computational Geometry (SoCG 2026)

Abstract
Assumptions on the reach are crucial for ensuring the correctness of many geometric and topological algorithms, including triangulation, manifold reconstruction and learning, homotopy reconstruction, and methods for estimating curvature or reach. However, these assumptions are often coupled with the requirement that the manifold be smooth, typically at least C². In this paper, we prove that any manifold with positive reach can be approximated arbitrarily well by a C^∞ manifold without significantly reducing the reach. More precisely, given a manifold with reach R, we construct a manifold that is ε-close to it in the C¹ sense (both the manifold and its tangent spaces are close), and has reach at least R-ε. The proof employs techniques from differential topology - partitions of unity and smoothing using convolution kernels. This result implies that nearly all theorems established for C² or manifolds with a certain reach naturally extend to manifolds with the same reach, even if they are not C², for free!
Cite as

Hana Dal Poz Kouřimská, André Lieutier, and Mathijs Wintraecken. A Free Lunch: Manifolds of Positive Reach Can Be Smoothed Without Decreasing the Reach. In 42nd International Symposium on Computational Geometry (SoCG 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 367, pp. 37:1-37:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{dalpozkourimska_et_al:LIPIcs.SoCG.2026.37,
  author =	{Dal Poz Kou\v{r}imsk\'{a}, Hana and Lieutier, Andr\'{e} and Wintraecken, Mathijs},
  title =	{{A Free Lunch: Manifolds of Positive Reach Can Be Smoothed Without Decreasing the Reach}},
  booktitle =	{42nd International Symposium on Computational Geometry (SoCG 2026)},
  pages =	{37:1--37:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-418-5},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{367},
  editor =	{Ahn, Hee-Kap and Hoffmann, Michael and Nayyeri, Amir},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2026.37},
  URN =		{urn:nbn:de:0030-drops-258434},
  doi =		{10.4230/LIPIcs.SoCG.2026.37},
  annote =	{Keywords: Reach, Manifolds, Smoothing, Differentiability, Differential topology}
}
Document
DOI: 10.4230/LIPIcs.SoCG.2026.71
Approximating Euclidean Shallow-Light Trees

Authors: Hung Le, Shay Solomon, Cuong Than, Csaba D. Tóth, and Tianyi Zhang

Published in: LIPIcs, Volume 367, 42nd International Symposium on Computational Geometry (SoCG 2026)

Abstract
For a weighted graph G = (V, E, w) and a designated source vertex s ∈ V, a spanning tree that simultaneously approximates a shortest-path tree w.r.t. source s and a minimum spanning tree is called a shallow-light tree (SLT). Specifically, an (α, β)-SLT of G w.r.t. s ∈ V is a spanning tree of G with root-stretch α (preserving all distances between s and all other vertices up to a factor of α) and lightness β (its weight is at most β times the weight of a minimum spanning tree of G). It was shown in the early 1990s that (1) for any graph, any source, and any ε > 0, there is a (1 + ε, O(1/ε))-SLT, and (2) there exist graphs for which β = Ω(1/ε) for any (1+ε,β)-SLT. The focus of this work is on SLTs in low-dimensional Euclidean spaces, which are of special interest for some applications of SLTs, in geometric network optimization problems. The aforementioned existential lower bound applies to Euclidean plane, as well. It was shown more than a decade ago that (1) by using Steiner points, one can reduce the lightness bound from O(1/ε) to O(√{1/ε}), and (2) there exist point sets in the plane for which β = Ω(√{1/ε}) for any Steiner (1+ε,β)-SLT. These tight existential bounds for the Euclidean case yield approximation factors of O(1/ε) and O(√{1/ε}) on the minimum weight of any non-Steiner and Steiner tree with root-stretch 1+ε, respectively. Despite the large body of work on SLTs, the basic question of whether a better approximation algorithm exists was left untouched to date, and this holds in any graph family. This paper makes a first nontrivial step towards resolving this question by presenting two bicriteria approximation algorithms. For any ε > 0, a set P of n points in constant-dimensional Euclidean space and a source s ∈ P, our first (respectively, second) algorithm returns, in O(n log n ⋅ polylog(ε^{-1})) time, a non-Steiner (resp., Steiner) tree with root-stretch 1+O(ε log ε^{-1}) and weight at most O(opt_ε ⋅ log² ε^{-1}) (resp., O(opt_ε ⋅ log ε^{-1})), where opt_ε denotes the minimum weight of a non-Steiner (resp., Steiner) tree with root-stretch 1+ε.
Cite as

Hung Le, Shay Solomon, Cuong Than, Csaba D. Tóth, and Tianyi Zhang. Approximating Euclidean Shallow-Light Trees. In 42nd International Symposium on Computational Geometry (SoCG 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 367, pp. 71:1-71:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{le_et_al:LIPIcs.SoCG.2026.71,
  author =	{Le, Hung and Solomon, Shay and Than, Cuong and T\'{o}th, Csaba D. and Zhang, Tianyi},
  title =	{{Approximating Euclidean Shallow-Light Trees}},
  booktitle =	{42nd International Symposium on Computational Geometry (SoCG 2026)},
  pages =	{71:1--71:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-418-5},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{367},
  editor =	{Ahn, Hee-Kap and Hoffmann, Michael and Nayyeri, Amir},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2026.71},
  URN =		{urn:nbn:de:0030-drops-258789},
  doi =		{10.4230/LIPIcs.SoCG.2026.71},
  annote =	{Keywords: geometric network design, optimization, shallow-light tree, Steiner point}
}
Document
DOI: 10.4230/LIPIcs.SoCG.2026.74
Manifolds of Positive Reach, Differentiability, Tangent Variation, and Attaining the Reach

Authors: André Lieutier and Mathijs Wintraecken

Published in: LIPIcs, Volume 367, 42nd International Symposium on Computational Geometry (SoCG 2026)

Abstract
This paper contains three main results. Firstly, we give an elementary proof of the following statement: Let ℳ be a topological manifold without boundary embedded in R^d. If ℳ has positive reach, then ℳ can locally be written as the graph of a C^{1,1} function from the tangent space to the normal space. Conversely if ℳ can locally be written as the graph of a C^{1,1} function from the tangent space to the normal space, then ℳ has positive reach. The result was hinted at by Federer when he introduced the reach, and proved by Lytchak. Lytchak’s proof relies heavily on CAT(k)-theory. The proof presented here uses only basic results on homology. Secondly, we give optimal Lipschitz-constants for the derivative, in other words we give an optimal bound for the angle between tangent spaces in term of the distance between the points. We stress that Lytchak did not provide any bound, let alone an optimal one, making his proof, although interesting from a mathematical perspective, ineffectual in an algorithmic setting. To provide precise and optimal bounds on the angle between tangent spaces, we formally introduce the local reach for sets of positive reach, based on Aamari et al.’s discussion for C² manifolds. We prove that the local reach of a manifold is completely characterized by the variation of tangent spaces. This improves earlier results, that were either suboptimal or assumed that the manifold was C². Thirdly, we show that the value of the reach is equals minimum of the local reach of the set and a global bottleneck for any set. This generalizes a result by Aamari et al. which explains how the reach is attained for C² manifolds.
Cite as

André Lieutier and Mathijs Wintraecken. Manifolds of Positive Reach, Differentiability, Tangent Variation, and Attaining the Reach. In 42nd International Symposium on Computational Geometry (SoCG 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 367, pp. 74:1-74:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{lieutier_et_al:LIPIcs.SoCG.2026.74,
  author =	{Lieutier, Andr\'{e} and Wintraecken, Mathijs},
  title =	{{Manifolds of Positive Reach, Differentiability, Tangent Variation, and Attaining the Reach}},
  booktitle =	{42nd International Symposium on Computational Geometry (SoCG 2026)},
  pages =	{74:1--74:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-418-5},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{367},
  editor =	{Ahn, Hee-Kap and Hoffmann, Michael and Nayyeri, Amir},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2026.74},
  URN =		{urn:nbn:de:0030-drops-258812},
  doi =		{10.4230/LIPIcs.SoCG.2026.74},
  annote =	{Keywords: Reach, Manifolds, Differentiability class, Lipschitz continuity, Tangent space}
}
Document
Research
DOI: 10.4230/TGDK.4.1.1
On the Computational Cost of Knowledge Graph Embeddings

Authors: Victor Charpenay, Mansour Zoubeirou A Mayaki, and Antoine Zimmermann

Published in: TGDK, Volume 4, Issue 1 (2026). Transactions on Graph Data and Knowledge, Volume 4, Issue 1

Abstract
Over a decade, numerous Knowledge Graph Embedding (KGE) models have been designed and evaluated on reference datasets, always with increasing performance. In this paper, we re-evaluate these models with respect to their computational efficiency during training, by estimating the computational cost of the procedure expressed in floating-point operations. We design a cost model based on analytical expressions and apply it on a collection of 20 KGE models, representative of the state-of-the-art. We show that dimensionality or parameter efficiency, used in the literature to compare models with each other, are not suitable to evaluate the true cost of models. Through fixed-budget experiments, a novel approach to evaluate KGE models based on cost estimates, we re-assess the relative performance of model families compared to the state-of-the-art. Bilinear models such as ComplEx underperform with a low computational budget while hyperbolic linear models appear to offer no particular benefit compared to simpler Euclidian models, especially the MuRE model. Neural models, such as ConvE or CompGCN, achieve reasonable performance in the literature but their high computational cost appears unnecessary when compared with other models. The trade-off between efficiency and expressivity of both linear and neural models is to be further explored.
Cite as

Victor Charpenay, Mansour Zoubeirou A Mayaki, and Antoine Zimmermann. On the Computational Cost of Knowledge Graph Embeddings. In Transactions on Graph Data and Knowledge (TGDK), Volume 4, Issue 1, pp. 1:1-1:30, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@Article{charpenay_et_al:TGDK.4.1.1,
  author =	{Charpenay, Victor and Zoubeirou A Mayaki, Mansour and Zimmermann, Antoine},
  title =	{{On the Computational Cost of Knowledge Graph Embeddings}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{1:1--1:30},
  ISSN =	{2942-7517},
  year =	{2026},
  volume =	{4},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.4.1.1},
  URN =		{urn:nbn:de:0030-drops-256863},
  doi =		{10.4230/TGDK.4.1.1},
  annote =	{Keywords: Knowledge Graph Embedding, Parameter Efficiency, Computational Budget, Green AI}
}
Document
DOI: 10.4230/OASIcs.NINeS.2026.5
OrbitalBrain: A Distributed Framework for Training ML Models in Space

Authors: Om Chabra, Chenning Li, Kevin Hsieh, Santiago Segarra, Behnaz Arzani, Peder Olsen, and Ranveer Chandra

Published in: OASIcs, Volume 139, 1st New Ideas in Networked Systems (NINeS 2026)

Abstract
Earth observation nanosatellites capture high-resolution photos of the Earth in near real-time. These images increasingly support ML applications that are critical for safety and response, such as forest fire and flood detection. However, the downlink bandwidth is limited, resulting in days or weeks of delay from image capture to training. In this work, we propose OrbitalBrain, an efficient in-space distributed ML training framework that leverages limited and predictable satellite compute, bandwidth, and power to intelligently balance data transfer, model aggregation, and local training. Our evaluations demonstrate that OrbitalBrain achieves 1.52×-12.4× speedup in time-to-accuracy while always reaching a higher final model accuracy compared to state-of-the-art ground-based or federated learning baselines. Furthermore, our approach is complementary to satellite imagery capturing and downloading, enhancing the overall efficiency of satellite-based applications.
Cite as

Om Chabra, Chenning Li, Kevin Hsieh, Santiago Segarra, Behnaz Arzani, Peder Olsen, and Ranveer Chandra. OrbitalBrain: A Distributed Framework for Training ML Models in Space. In 1st New Ideas in Networked Systems (NINeS 2026). Open Access Series in Informatics (OASIcs), Volume 139, pp. 5:1-5:32, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{chabra_et_al:OASIcs.NINeS.2026.5,
  author =	{Chabra, Om and Li, Chenning and Hsieh, Kevin and Segarra, Santiago and Arzani, Behnaz and Olsen, Peder and Chandra, Ranveer},
  title =	{{OrbitalBrain: A Distributed Framework for Training ML Models in Space}},
  booktitle =	{1st New Ideas in Networked Systems (NINeS 2026)},
  pages =	{5:1--5:32},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-414-7},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{139},
  editor =	{Argyraki, Katerina and Panda, Aurojit},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NINeS.2026.5},
  URN =		{urn:nbn:de:0030-drops-255907},
  doi =		{10.4230/OASIcs.NINeS.2026.5},
  annote =	{Keywords: Satellite networks, Distributed machine learning, Federated learning, Earth observation, In-orbit computing}
}
Document
DOI: 10.4230/OASIcs.NINeS.2026.10
Scalable Routing in a City-Scale Wi-Fi Network for Disaster Recovery

Authors: Ziqian Liu, Om Chabra, James Lynch, Aaron Martin, Chenning Li, and Hari Balakrishnan

Published in: OASIcs, Volume 139, 1st New Ideas in Networked Systems (NINeS 2026)

Abstract
This paper presents CityMesh, a city-scale decentralized mesh network designed for disaster recovery and emergency scenarios. When wide-area Internet connectivity is unavailable or severely degraded, CityMesh leverages both static access points and mobile devices equipped with Wi-Fi to provide intra-city connectivity and reach opportunistic gateways to the Internet (e.g., via satellite links). The main contribution of this paper is a scalable routing protocol that supports millions of devices, addressing a long-standing limitation of wireless mesh and mobile ad hoc networks. Unlike prior approaches, CityMesh exploits rich building-location and building-geometry data from widely available city maps to guide route computation, improving packet delivery while significantly reducing transmission overhead. Simulation results from 70 cities show that CityMesh improves packet delivery rates by 88% over WEAVE (a state-of-the-art geographic routing protocol). A campus-scale deployment of 300 Wi-Fi devices across 31 buildings shows the practical deployability of CityMesh. These results demonstrate the promise of map-aware routing as a foundation for scalable, resilient city-wide Wi-Fi networks.
Cite as

Ziqian Liu, Om Chabra, James Lynch, Aaron Martin, Chenning Li, and Hari Balakrishnan. Scalable Routing in a City-Scale Wi-Fi Network for Disaster Recovery. In 1st New Ideas in Networked Systems (NINeS 2026). Open Access Series in Informatics (OASIcs), Volume 139, pp. 10:1-10:31, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{liu_et_al:OASIcs.NINeS.2026.10,
  author =	{Liu, Ziqian and Chabra, Om and Lynch, James and Martin, Aaron and Li, Chenning and Balakrishnan, Hari},
  title =	{{Scalable Routing in a City-Scale Wi-Fi Network for Disaster Recovery}},
  booktitle =	{1st New Ideas in Networked Systems (NINeS 2026)},
  pages =	{10:1--10:31},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-414-7},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{139},
  editor =	{Argyraki, Katerina and Panda, Aurojit},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NINeS.2026.10},
  URN =		{urn:nbn:de:0030-drops-255954},
  doi =		{10.4230/OASIcs.NINeS.2026.10},
  annote =	{Keywords: mesh networking, disaster recovery, geographic routing, scalability, Wi-Fi}
}
Document
DOI: 10.4230/OASIcs.NINeS.2026.11
Stealthy Low Earth Orbit Satellite-To-Ground Quantum Communication

Authors: Guanqun Song and Ting Zhu

Published in: OASIcs, Volume 139, 1st New Ideas in Networked Systems (NINeS 2026)

Abstract
Quantum key distribution (QKD) leveraging satellites holds promise for global-scale secure communication. However, its practical deployment is threatened by the inherent predictability of satellite orbits, which exposes quantum channels to targeted eavesdropping attacks, compromising the physical-layer security guarantees of QKD. Through security analysis, we demonstrate that such attacks can drastically increase the quantum bit error rate (QBER) from 4.7% to 27.5%, effectively disrupting secure key generation. To address this fundamental vulnerability, we introduce a novel defense framework that integrates two strategies: (1) Stealthy Deployment, which obfuscates quantum satellites within massive LEO constellations to drastically increase an adversary’s search space, and (2) Dynamic Re-routing, which is an adaptive countermeasure that re-establishes QKD sessions via alternative paths upon eavesdropping detection. Evaluated through large-scale simulations incorporating real-world satellite data, our framework demonstrates up to a 90% improvement in key generation rate under active attack, ensuring robust and resilient satellite-based QKD without modifications to the underlying quantum hardware.
Cite as

Guanqun Song and Ting Zhu. Stealthy Low Earth Orbit Satellite-To-Ground Quantum Communication. In 1st New Ideas in Networked Systems (NINeS 2026). Open Access Series in Informatics (OASIcs), Volume 139, pp. 11:1-11:26, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{song_et_al:OASIcs.NINeS.2026.11,
  author =	{Song, Guanqun and Zhu, Ting},
  title =	{{Stealthy Low Earth Orbit Satellite-To-Ground Quantum Communication}},
  booktitle =	{1st New Ideas in Networked Systems (NINeS 2026)},
  pages =	{11:1--11:26},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-414-7},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{139},
  editor =	{Argyraki, Katerina and Panda, Aurojit},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NINeS.2026.11},
  URN =		{urn:nbn:de:0030-drops-255963},
  doi =		{10.4230/OASIcs.NINeS.2026.11},
  annote =	{Keywords: LEO satellites, QKD, quantum communication}
}
Document
DOI: 10.4230/OASIcs.NINeS.2026.15
BISCAY: Practical Radio KPI Driven Congestion Control for Mobile Networks

Authors: Jon Larrea, Tanya Shreedhar, Atte Niemi, Adel Sefiane, and Mahesh K. Marina

Published in: OASIcs, Volume 139, 1st New Ideas in Networked Systems (NINeS 2026)

Abstract
Mobile application performance is often bottlenecked by cellular links with rapid bandwidth fluctuations. We show that radio KPIs from the device chipset can precisely and promptly measure available cellular bandwidth. Building on this, we propose Biscay, a practical KPI-driven congestion control for mobile networks. Biscay leverages OpenDiag, an in-kernel, real-time KPI extractor we introduce along with a KPI-based bandwidth estimator to adjust the congestion window, utilizing available bandwidth while minimizing delay. We implement Biscay and OpenDiag on unrooted Android 5G phones. Across trace-driven emulations and real-world 4G/5G experiments, Biscay outperforms state-of-the-art CCAs (e.g., BBR, CUBIC), typically reducing average and tail delay by >90% while matching or improving throughput. These gains stem from OpenDiag’s 100× finer on-device KPI granularity than existing alternatives like MobileInsight.
Cite as

Jon Larrea, Tanya Shreedhar, Atte Niemi, Adel Sefiane, and Mahesh K. Marina. BISCAY: Practical Radio KPI Driven Congestion Control for Mobile Networks. In 1st New Ideas in Networked Systems (NINeS 2026). Open Access Series in Informatics (OASIcs), Volume 139, pp. 15:1-15:32, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{larrea_et_al:OASIcs.NINeS.2026.15,
  author =	{Larrea, Jon and Shreedhar, Tanya and Niemi, Atte and Sefiane, Adel and Marina, Mahesh K.},
  title =	{{BISCAY: Practical Radio KPI Driven Congestion Control for Mobile Networks}},
  booktitle =	{1st New Ideas in Networked Systems (NINeS 2026)},
  pages =	{15:1--15:32},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-414-7},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{139},
  editor =	{Argyraki, Katerina and Panda, Aurojit},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NINeS.2026.15},
  URN =		{urn:nbn:de:0030-drops-256002},
  doi =		{10.4230/OASIcs.NINeS.2026.15},
  annote =	{Keywords: Cellular Networks, Congestion Control, LTE/5G}
}
Document
DOI: 10.4230/OASIcs.NINeS.2026.18
TURBO: Utility-Aware Bandwidth Allocation for Cloud-Augmented Autonomous Control

Authors: Peter Schafhalter, Alexander Krentsel, Hongbo Wei, Joseph E. Gonzalez, Sylvia Ratnasamy, Scott Shenker, and Ion Stoica

Published in: OASIcs, Volume 139, 1st New Ideas in Networked Systems (NINeS 2026)

Abstract
Autonomous driving system progress has been driven by improvements in machine learning (ML) models, whose computational demands now exceed what edge devices alone can provide. The cloud offers abundant compute, but the network has long been treated as an unreliable bottleneck rather than a co-equal part of the autonomous vehicle control loop. We argue that this separation is no longer tenable: safety-critical autonomy requires co-design of control, models, and network resource allocation itself. We introduce TURBO, a cloud-augmented control framework that addresses this challenge, formulating bandwidth allocation and control pipeline configuration across both the car and cloud as a joint optimization problem. TURBO maximizes benefit to the car while guaranteeing safety in the face of highly variable network conditions. We implement TURBO and evaluate it in both simulation and real-world deployment, showing it can improve average accuracy by up to 15.6%pt over existing on-vehicle-only pipelines. Our code is made available at www.github.com/NetSys/turbo.
Cite as

Peter Schafhalter, Alexander Krentsel, Hongbo Wei, Joseph E. Gonzalez, Sylvia Ratnasamy, Scott Shenker, and Ion Stoica. TURBO: Utility-Aware Bandwidth Allocation for Cloud-Augmented Autonomous Control. In 1st New Ideas in Networked Systems (NINeS 2026). Open Access Series in Informatics (OASIcs), Volume 139, pp. 18:1-18:34, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{schafhalter_et_al:OASIcs.NINeS.2026.18,
  author =	{Schafhalter, Peter and Krentsel, Alexander and Wei, Hongbo and Gonzalez, Joseph E. and Ratnasamy, Sylvia and Shenker, Scott and Stoica, Ion},
  title =	{{TURBO: Utility-Aware Bandwidth Allocation for Cloud-Augmented Autonomous Control}},
  booktitle =	{1st New Ideas in Networked Systems (NINeS 2026)},
  pages =	{18:1--18:34},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-414-7},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{139},
  editor =	{Argyraki, Katerina and Panda, Aurojit},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NINeS.2026.18},
  URN =		{urn:nbn:de:0030-drops-256039},
  doi =		{10.4230/OASIcs.NINeS.2026.18},
  annote =	{Keywords: autonomous vehicles, bandwidth allocation, cloud computing, edge computing, machine learning}
}
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