80 Search Results for "Chen, Fang"

Document
DOI: 10.4230/LIPIcs.SEA.2026.4
Integer Programming Models for the Median of a 0-1 String Set Under Levenshtein Distance

Authors: Claudio Arbib, Andrea D'Ascenzo, Oya E. Karaşan, and Andrea Pizzuti

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

Abstract
The Median String Problem calls for finding a string that minimizes the average distance from a given set of strings. Under the Levenshtein (or edit) metric, the problem is NP-hard even for binary strings. We devised two novel integer linear programming models for this case and tested them against the only formulation we are aware of in the literature. Our numerical experiments attest to the efficacy of the proposed approach.
Cite as

Claudio Arbib, Andrea D'Ascenzo, Oya E. Karaşan, and Andrea Pizzuti. Integer Programming Models for the Median of a 0-1 String Set Under Levenshtein Distance. In 24th International Symposium on Experimental Algorithms (SEA 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 371, pp. 4:1-4:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{arbib_et_al:LIPIcs.SEA.2026.4,
  author =	{Arbib, Claudio and D'Ascenzo, Andrea and Kara\c{s}an, Oya E. and Pizzuti, Andrea},
  title =	{{Integer Programming Models for the Median of a 0-1 String Set Under Levenshtein Distance}},
  booktitle =	{24th International Symposium on Experimental Algorithms (SEA 2026)},
  pages =	{4:1--4:15},
  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.2026.4},
  URN =		{urn:nbn:de:0030-drops-260081},
  doi =		{10.4230/LIPIcs.SEA.2026.4},
  annote =	{Keywords: Levenshtein Distance, Median String Problem, Integer Programming}
}
Document
DOI: 10.4230/LIPIcs.SEA.2026.25
Engineering Learned Heuristics to Improve Clustering for Multilevel Graph Partitioning

Authors: Simeon Schrape, Nikolai Maas, Kenneth Langedal, and Daniel Seemaier

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

Abstract
Balanced Graph Partitioning is a classical optimization problem where quality guarantees are computationally infeasible, and practical solvers therefore rely on manually engineered heuristics. Yet, the problem has also proven difficult for approaches that rely heavily on machine learning - especially since applications often need to partition graphs of huge scale in a short amount of time. Instead, we demonstrate how to achieve practical improvements with a more careful approach that uses machine learning to improve heuristic decisions within the state-of-the-art solver Mt-KaHyPar. We use a pre-trained neural network to predict a score for each edge, which then guides clustering decisions in the first phase of the partitioning (the coarsening). Combined with corresponding adjustments to the clustering algorithm and an efficient implementation of the neural network logic, we improve the overall solution quality while preserving the efficiency and scalability of the original algorithm. Our detailed evaluation on more than 180 graphs shows an average quality improvement of 2% on a class of graphs with beneficial properties, and unchanged quality on all remaining graphs. Moreover, our improvements generalize to a set of instances from the literature that are much larger than the graphs used during training.
Cite as

Simeon Schrape, Nikolai Maas, Kenneth Langedal, and Daniel Seemaier. Engineering Learned Heuristics to Improve Clustering for Multilevel Graph Partitioning. In 24th International Symposium on Experimental Algorithms (SEA 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 371, pp. 25:1-25:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{schrape_et_al:LIPIcs.SEA.2026.25,
  author =	{Schrape, Simeon and Maas, Nikolai and Langedal, Kenneth and Seemaier, Daniel},
  title =	{{Engineering Learned Heuristics to Improve Clustering for Multilevel Graph Partitioning}},
  booktitle =	{24th International Symposium on Experimental Algorithms (SEA 2026)},
  pages =	{25:1--25:21},
  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.2026.25},
  URN =		{urn:nbn:de:0030-drops-260295},
  doi =		{10.4230/LIPIcs.SEA.2026.25},
  annote =	{Keywords: Graph Partitioning, Graph Algorithms, Machine Learning, Neural Networks}
}
Document
DOI: 10.4230/OASIcs.AEiC.2026.1
Model-Agnostic Uncertainty-Aware Semantic Segmentation with Conformal Risk Guarantees for Scene Understanding

Authors: Bakary Badjie, José Cecílio, Nils-Jonathan Friedrich, Norman Seyffer, Georg Jäger, and António Casimiro

Published in: OASIcs, Volume 143, 30th Ada-Europe International Conference on Reliable Software Technologies (AEiC 2026)

Abstract
Accurate and reliable scene segmentation is a fundamental requirement for autonomous navigation systems operating in open and dynamic environments. As these systems increasingly rely on data-driven perception modules, their safety and operational robustness hinge on well-calibrated uncertainty estimates that can support explicit control of prediction errors through conformal calibration. Most existing uncertainty-aware segmentation approaches remain architecture-specific and are not evaluated under a common uncertainty-and-calibration protocol across distinct segmentation architectures and datasets. This work introduces a model-agnostic conformal segmentation pipeline that enables operationally meaningful, calibration-based error control in real-world deployments. The proposed framework treats segmentation networks as black boxes and operates on per-pixel class probabilities that are fine-tuned through evidential deep learning (EDL) to decompose aleatoric and epistemic uncertainties. We then apply pixel-wise, class-conditional split-conformal calibration to derive acceptance thresholds for user-defined target error rates. We instantiate the pipeline with DINOv2, Mask2Former, and SegFormer and evaluate it on a newly collected Lisbon street scene (LiSS) dataset; additional cross-dataset results on COCO, using a restricted set of safety-relevant classes, are reported in the appendix. Results show architecture- and class-dependent in-domain uncertainty-error alignment and indicate that dataset shift weakens uncertainty-based filtering and conformal risk control. This motivates continuous monitoring and recalibration as a practical requirement for trustworthy segmentation in safety-critical navigation.
Cite as

Bakary Badjie, José Cecílio, Nils-Jonathan Friedrich, Norman Seyffer, Georg Jäger, and António Casimiro. Model-Agnostic Uncertainty-Aware Semantic Segmentation with Conformal Risk Guarantees for Scene Understanding. In 30th Ada-Europe International Conference on Reliable Software Technologies (AEiC 2026). Open Access Series in Informatics (OASIcs), Volume 143, pp. 1:1-1:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{badjie_et_al:OASIcs.AEiC.2026.1,
  author =	{Badjie, Bakary and Cec{\'\i}lio, Jos\'{e} and Friedrich, Nils-Jonathan and Seyffer, Norman and J\"{a}ger, Georg and Casimiro, Ant\'{o}nio},
  title =	{{Model-Agnostic Uncertainty-Aware Semantic Segmentation with Conformal Risk Guarantees for Scene Understanding}},
  booktitle =	{30th Ada-Europe International Conference on Reliable Software Technologies (AEiC 2026)},
  pages =	{1:1--1:20},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-425-3},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{143},
  editor =	{Filieri, Antonio and Backeman, Peter},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.AEiC.2026.1},
  URN =		{urn:nbn:de:0030-drops-259199},
  doi =		{10.4230/OASIcs.AEiC.2026.1},
  annote =	{Keywords: semantic segmentation, uncertainty quantification, evidential deep learning, conformal prediction, risk control, selective prediction}
}
Document
DOI: 10.4230/OASIcs.AEiC.2026.4
HASCO: A Hybrid AI Simulation Compiler for Semantic Accident Reconstruction

Authors: Edin Jelačić, Rong Gu, Cristina Seceleanu, Ning Xiong, Peter Backeman, Tiberiu Seceleanu, Zhennan Fei, and Ali Nouri

Published in: OASIcs, Volume 143, 30th Ada-Europe International Conference on Reliable Software Technologies (AEiC 2026)

Abstract
The validation of Automated Driving Systems (ADSs) has shifted from distance-based metrics to Scenario-Based Testing (SBT). Large Language Models (LLMs) have emerged as powerful tools with potential for generating vehicular scenarios at scale. However, generative models, used for direct simulation synthesis, produce inadequate output, therefore necessitating a more structured compilation approach. In this regard, we present HASCO (Hybrid AI Simulation COmpiler), a system that translates natural-language driving scene specifications into executable simulation artifacts (XOSC/XODR files) for the esmini/OpenSCENARIO ecosystem. While LLMs excel at narrative parsing, we demonstrate that direct synthesis of simulation artifacts fails in the vast majority of cases due to hallucinated physics or schema violations. To resolve this, HASCO treats scenario creation as a compilation task rather than a generative one. The pipeline supports three compilation paths: direct synthesis, a Python intermediate (via scenariogeneration), and an ontology-guided path that grounds intent into an intermediate representation (IR) before compilation. We further evaluate a self-judging mechanism for automated repair. Across six operating modes evaluated on 40 real-world accident reports, the ontology-guided compiler and Python-based compiler achieve 95% and 90% executability rates, respectively (compared to 5% for direct synthesis). Additionally, we evaluate outputs on semantic fidelity, positioning HASCO as a robust tool for forensic scene reconstruction.
Cite as

Edin Jelačić, Rong Gu, Cristina Seceleanu, Ning Xiong, Peter Backeman, Tiberiu Seceleanu, Zhennan Fei, and Ali Nouri. HASCO: A Hybrid AI Simulation Compiler for Semantic Accident Reconstruction. In 30th Ada-Europe International Conference on Reliable Software Technologies (AEiC 2026). Open Access Series in Informatics (OASIcs), Volume 143, pp. 4:1-4:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{jelacic_et_al:OASIcs.AEiC.2026.4,
  author =	{Jela\v{c}i\'{c}, Edin and Gu, Rong and Seceleanu, Cristina and Xiong, Ning and Backeman, Peter and Seceleanu, Tiberiu and Fei, Zhennan and Nouri, Ali},
  title =	{{HASCO: A Hybrid AI Simulation Compiler for Semantic Accident Reconstruction}},
  booktitle =	{30th Ada-Europe International Conference on Reliable Software Technologies (AEiC 2026)},
  pages =	{4:1--4:22},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-425-3},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{143},
  editor =	{Filieri, Antonio and Backeman, Peter},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.AEiC.2026.4},
  URN =		{urn:nbn:de:0030-drops-259220},
  doi =		{10.4230/OASIcs.AEiC.2026.4},
  annote =	{Keywords: Autonomous Driving, OpenSCENARIO, Large Language Models, Scenario Generation, Semantic Reconstruction}
}
Document
DOI: 10.4230/LIPIcs.FORC.2026.10
Packing Compact Subgraphs with Applications to Districting

Authors: Ho-Lin Chen, Po-Yu Chou, Prathamesh Dharangutte, Jie Gao, Shang-En Huang, and Fang-Yi Yu

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

Abstract
Packing disjoint subgraphs in a given graph is a fundamental problem with many applications. Motivated by political districting, we focus on connected subgraphs that are compact (e.g., having constant radius from a single center vertex) and that satisfy additional composition requirements, such as a minimum population/weight threshold or balanced weight types (e.g., political affiliations). We aim to maximize coverage by disjoint districts that meet these requirements. In this work, we present new results that substantially improve the previously known bounds on balanced star districts for planar and minor-free graphs [Prathamesh Dharangutte et al., 2025]. In particular, we improve the approximation factor from O(log n) to O(1) for packing balanced star districts using the exact same algorithm, but with a refined analysis. We also extend the results beyond planar graphs to minor-free graphs and an even broader family of graphs of bounded expansion. Additionally, we obtain an O(1) approximation for packing radius-k districts (with a constant k) in planar and apex-minor-free graphs. In order to get a (1+ε) approximation on the max coverage, we show that this can be achieved if we allow a slight relaxation of the balancedness parameters (by a factor that can be made arbitrarily close to 1), for bounded radius-k districts on planar and apex-minor-free graphs. We show that all of these results can also be obtained if we enforce a minimum weight threshold for each district as the composition requirement, rather than balancedness. We present various results on hardness and hardness of approximation for this variant, by graph and district types.
Cite as

Ho-Lin Chen, Po-Yu Chou, Prathamesh Dharangutte, Jie Gao, Shang-En Huang, and Fang-Yi Yu. Packing Compact Subgraphs with Applications to Districting. In 7th Symposium on Foundations of Responsible Computing (FORC 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 368, pp. 10:1-10:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{chen_et_al:LIPIcs.FORC.2026.10,
  author =	{Chen, Ho-Lin and Chou, Po-Yu and Dharangutte, Prathamesh and Gao, Jie and Huang, Shang-En and Yu, Fang-Yi},
  title =	{{Packing Compact Subgraphs with Applications to Districting}},
  booktitle =	{7th Symposium on Foundations of Responsible Computing (FORC 2026)},
  pages =	{10:1--10:25},
  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.2026.10},
  URN =		{urn:nbn:de:0030-drops-259820},
  doi =		{10.4230/LIPIcs.FORC.2026.10},
  annote =	{Keywords: Approximation algorithms, algorithmic fairness}
}
Document
DOI: 10.4230/LIPIcs.FORC.2026.6
Escaping the Subprime Trap in Algorithmic Lending

Authors: Adam Bouyamourn and Alexander Williams Tolbert

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

Abstract
Disparities in lending to minority applicants persist even as algorithmic lending finds widespread adoption. We study the role of risk-management constraints, specifically Value-at-Risk and Expected Shortfall, in inducing inequality in loan approval decisions, even among applicants who are equally creditworthy. We contribute an analysis of 431,551 loan applications recorded under the Home Mortgage Disclosure Act, illustrating that disparities in data quality are associated with higher rates of loan denial and higher interest rate spreads for Black borrowers. We develop a formal model in which a mainstream bank (low-interest) is more sensitive to variance risk than a subprime bank (high-interest). If the mainstream bank has an inflated prior belief about the variance of the minority group, it may deny that group credit indefinitely, never learning the true risk of lending to that group, while the subprime lender serves this population at higher rates. We call this "The Subprime Trap": an equilibrium in which minority borrowers can borrow only from high-cost lenders, even when they are as creditworthy as majority applicants. We show that a finite subsidy can help minority groups escape the trap by covering enough of the mainstream bank’s downside so that it can afford to lend to, and thereby learn the true risk of lending to, the minority group. Once the mainstream bank has observed sufficiently many loans, its beliefs converge to the true underlying risk, and competition drives down the interest rates of subprime loans.
Cite as

Adam Bouyamourn and Alexander Williams Tolbert. Escaping the Subprime Trap in Algorithmic Lending. In 7th Symposium on Foundations of Responsible Computing (FORC 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 368, pp. 6:1-6:27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{bouyamourn_et_al:LIPIcs.FORC.2026.6,
  author =	{Bouyamourn, Adam and Tolbert, Alexander Williams},
  title =	{{Escaping the Subprime Trap in Algorithmic Lending}},
  booktitle =	{7th Symposium on Foundations of Responsible Computing (FORC 2026)},
  pages =	{6:1--6:27},
  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.2026.6},
  URN =		{urn:nbn:de:0030-drops-259777},
  doi =		{10.4230/LIPIcs.FORC.2026.6},
  annote =	{Keywords: Algorithmic fairness, algorithmic lending, Risk management, Value-at-Risk, Algorithmic Philosophy}
}
Document
DOI: 10.4230/LIPIcs.FORC.2026.15
Incentivizing High-Quality Content in Online Recommender Systems

Authors: Xinyan Hu, Meena Jagadeesan, Michael I. Jordan, and Jacob Steinhardt

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

Abstract
In content recommender systems such as TikTok and YouTube, the platform’s recommendation algorithm shapes content producer incentives. Many platforms employ online learning, which generates intertemporal incentives, since content produced today affects recommendations of future content. We study the game between producers and analyze the content created at equilibrium. We prove that standard online learning algorithms, such as Hedge and EXP3, unfortunately incentivize producers to create low-quality content, where producers' effort approaches zero in the long run for typical learning rate schedules. Motivated by this negative result, we design learning algorithms that incentivize producers to invest high effort and achieve high user welfare. At a conceptual level, our work illustrates the unintended impact that a platform’s learning algorithm can have on content quality and introduces algorithmic approaches to mitigating these effects.
Cite as

Xinyan Hu, Meena Jagadeesan, Michael I. Jordan, and Jacob Steinhardt. Incentivizing High-Quality Content in Online Recommender Systems. In 7th Symposium on Foundations of Responsible Computing (FORC 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 368, pp. 15:1-15:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{hu_et_al:LIPIcs.FORC.2026.15,
  author =	{Hu, Xinyan and Jagadeesan, Meena and Jordan, Michael I. and Steinhardt, Jacob},
  title =	{{Incentivizing High-Quality Content in Online Recommender Systems}},
  booktitle =	{7th Symposium on Foundations of Responsible Computing (FORC 2026)},
  pages =	{15:1--15:18},
  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.2026.15},
  URN =		{urn:nbn:de:0030-drops-259887},
  doi =		{10.4230/LIPIcs.FORC.2026.15},
  annote =	{Keywords: recommender systems, content quality, producer incentives, online learning, algorithmic game theory, Stackelberg games}
}
Document
DOI: 10.4230/LIPIcs.SoCG.2026.78
Mapping Chemical Space: Topological Data Analysis of Chemical Latent Space with Mapper

Authors: Dhruv Meduri, Chuan-Shen Hu, Cong Shen, Kelin Xia, and Bei Wang

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

Abstract
The vast chemical space, encompassing virtually innumerable molecules and materials, presents both immense opportunities and significant challenges. The design and discovery of novel drugs and functional materials may be viewed as a search within this space; however, the sheer scale of potential candidates renders exhaustive exploration infeasible. To address this, we introduce Chemical Mapper, a framework that integrates topological data analysis with deep learning to enable the visual exploration and analysis of chemical latent spaces. At its core, Chemical Mapper employs mapper, a widely used tool in topological data analysis, to investigate the organizational principles of chemical latent spaces defined by molecular representations learned by geometric deep learning models. In doing so, Chemical Mapper not only highlights groups of molecular representations but also uncovers the relationships among them through linkages and branching structures. Our results show that Chemical Mapper reveals intrinsic patterns associated with molecular scaffolds, functional groups, and chemical properties, as well as the structural and functional evolutions of the molecules.
Cite as

Dhruv Meduri, Chuan-Shen Hu, Cong Shen, Kelin Xia, and Bei Wang. Mapping Chemical Space: Topological Data Analysis of Chemical Latent Space with Mapper. In 42nd International Symposium on Computational Geometry (SoCG 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 367, pp. 78:1-78:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{meduri_et_al:LIPIcs.SoCG.2026.78,
  author =	{Meduri, Dhruv and Hu, Chuan-Shen and Shen, Cong and Xia, Kelin and Wang, Bei},
  title =	{{Mapping Chemical Space: Topological Data Analysis of Chemical Latent Space with Mapper}},
  booktitle =	{42nd International Symposium on Computational Geometry (SoCG 2026)},
  pages =	{78:1--78:20},
  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.2026.78},
  URN =		{urn:nbn:de:0030-drops-258854},
  doi =		{10.4230/LIPIcs.SoCG.2026.78},
  annote =	{Keywords: Practice of computational topology, topological data analysis, applications in chemistry, mapper algorithm, high-dimensional data analysis, chemical spaces, geometric deep learning, latent space geometry}
}
Document
DOI: 10.4230/OASIcs.PARMA-DITAM.2026.5
LAMINA: An MLIR-Based Translation Library for Heterogeneous Quantum-Classical Compilation

Authors: Marco De Pascale, Mario Hernández Vera, Jorge Echavarria, Muhammad Nufail Farooqi, Martin Schulz, and Laura Schulz

Published in: OASIcs, Volume 141, 17th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures and 15th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2026)

Abstract
Quantum computing is increasingly integrated into High-Performance Computing (HPC) environments, where quantum processors act as specialized accelerators within hybrid workflows. The Munich Quantum Software Stack (MQSS) - a unified compilation and runtime framework for hybrid quantum–classical computing - provides the foundation for this integration. However, the growing heterogeneity of applications demands more flexible compilation tools. This work introduces an MultiLevel Intermediate Representation (MLIR)-based translation library that extends MQSS by enabling the conversion of CUDA-Quantum (CUDA-Q) (quake) dialects into machine learning–oriented MLIR representations compatible with modern compiler ecosystems. Leveraging MLIR’s dialect-driven design, the library enables hardware-agnostic transformations, device-specific optimizations, and seamless integration with MQSS components. The proposed approach bridges quantum compilation and contemporary machine learning frameworks, facilitating GPU-accelerated circuit simulation, hybrid quantum–classical workflows, and heterogeneous execution, thereby advancing a unified compiler infrastructure for quantum computing.
Cite as

Marco De Pascale, Mario Hernández Vera, Jorge Echavarria, Muhammad Nufail Farooqi, Martin Schulz, and Laura Schulz. LAMINA: An MLIR-Based Translation Library for Heterogeneous Quantum-Classical Compilation. In 17th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures and 15th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2026). Open Access Series in Informatics (OASIcs), Volume 141, pp. 5:1-5:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{depascale_et_al:OASIcs.PARMA-DITAM.2026.5,
  author =	{De Pascale, Marco and Hern\'{a}ndez Vera, Mario and Echavarria, Jorge and Nufail Farooqi, Muhammad and Schulz, Martin and Schulz, Laura},
  title =	{{LAMINA: An MLIR-Based Translation Library for Heterogeneous Quantum-Classical Compilation}},
  booktitle =	{17th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures and 15th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2026)},
  pages =	{5:1--5:13},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-416-1},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{141},
  editor =	{Baroffio, Davide and Busia, Paola and Denisov, Lev and Shukla, Nitin},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.PARMA-DITAM.2026.5},
  URN =		{urn:nbn:de:0030-drops-256720},
  doi =		{10.4230/OASIcs.PARMA-DITAM.2026.5},
  annote =	{Keywords: HPCQC, MLIR, Quantum Computing, Heterogeneous Computing}
}
Document
DOI: 10.4230/OASIcs.NINeS.2026.7
What Obstructed Skies Teach Us About Satellite Internet

Authors: Bhaskar Kataria, Hammas Bin Tanveer, Rishab Nithyanand, and Rachee Singh

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

Abstract
Low Earth Orbit satellite networks can extend Internet connectivity to remote areas where traditional broadband infrastructure is unavailable. However physical obstructions, e.g., dense forest cover, can interfere with satellite communication by blocking the user terminal’s line of sight to the satellite constellation. Unfortunately the impact of such obstructions on the connectivity of user terminals is not well studied. We bridge this gap by conducting an experimental study of how physical obstructions influence satellite network connectivity. Through controlled experiments using a purpose-built hardware testbed, we quantify the performance degradation caused by physical obstructions to user terminals. Our results show that obstructions increase round-trip latency by an average of 4% and packet loss by 0.3%. Obstructions cause user terminals to connect to a different satellite than the unobstructed terminal approximately 15% of the time. We find evidence of a previously undocumented adaptive mechanism we call responsive routing, where the satellite network switches obstructed terminals to alternative satellites within the standard 15-second interval between typical handovers. Our data is publicly available as supplementary material to this article.
Cite as

Bhaskar Kataria, Hammas Bin Tanveer, Rishab Nithyanand, and Rachee Singh. What Obstructed Skies Teach Us About Satellite Internet. In 1st New Ideas in Networked Systems (NINeS 2026). Open Access Series in Informatics (OASIcs), Volume 139, pp. 7:1-7:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{kataria_et_al:OASIcs.NINeS.2026.7,
  author =	{Kataria, Bhaskar and Tanveer, Hammas Bin and Nithyanand, Rishab and Singh, Rachee},
  title =	{{What Obstructed Skies Teach Us About Satellite Internet}},
  booktitle =	{1st New Ideas in Networked Systems (NINeS 2026)},
  pages =	{7:1--7:25},
  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.2026.7},
  URN =		{urn:nbn:de:0030-drops-255923},
  doi =		{10.4230/OASIcs.NINeS.2026.7},
  annote =	{Keywords: Satellite Internet, Starlink, Network Measurement, LEO Satellites, Obstructions}
}
Document
DOI: 10.4230/OASIcs.NINeS.2026.13
In-Kernel Aggregation and Broadcast Acceleration for Distributed Communication

Authors: Jianchang Su, Yifan Zhang, and Wei Zhang

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

Abstract
Broadcasting and aggregation dominate the communication overhead in distributed systems, from machine learning training to data analytics. Current acceleration approaches require specialized hardware (RDMA) or dedicated resources (DPDK), limiting their deployment in commodity clouds. However, we present a counter-intuitive alternative: rather than bypassing the kernel, we move operations into it using eBPF. While this imposes severe constraints including no floating-point, limited memory, and stateless execution, we show these restrictions paradoxically drive innovative protocol designs that yield unexpected benefits. We introduce AggBox, which implements broadcast and aggregation operations entirely within eBPF’s constrained environment. Our key innovations include stateless group acknowledgments for reliability, edge quantization for floating-point aggregation using only integer arithmetic, and tail-call chains that create virtual memory beyond eBPF’s 512-byte stack limit. These designs emerge from and exploit the constraints rather than fighting them. AggBox achieves remarkable performance on commodity hardware: 84.5% reduction in broadcast latency, 43× speedup for MapReduce workloads, and 56.1% faster ML gradient aggregation, all without specialized NICs or dedicated cores. Beyond performance, our work demonstrates that constrained environments can drive fundamental innovation in protocol design, offering insights for future resource-limited and verified systems.
Cite as

Jianchang Su, Yifan Zhang, and Wei Zhang. In-Kernel Aggregation and Broadcast Acceleration for Distributed Communication. In 1st New Ideas in Networked Systems (NINeS 2026). Open Access Series in Informatics (OASIcs), Volume 139, pp. 13:1-13:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{su_et_al:OASIcs.NINeS.2026.13,
  author =	{Su, Jianchang and Zhang, Yifan and Zhang, Wei},
  title =	{{In-Kernel Aggregation and Broadcast Acceleration for Distributed Communication}},
  booktitle =	{1st New Ideas in Networked Systems (NINeS 2026)},
  pages =	{13:1--13:23},
  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.2026.13},
  URN =		{urn:nbn:de:0030-drops-255981},
  doi =		{10.4230/OASIcs.NINeS.2026.13},
  annote =	{Keywords: eBPF, distributed communication, broadcast, aggregation, in-kernel processing, XDP}
}
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}
}
Document
DOI: 10.4230/OASIcs.NINeS.2026.24
SwiftQueue: Optimizing Low-Latency Applications with Swift Packet Queuing

Authors: Siddhant Ray, Xi Jiang, Jack Luo, Nick Feamster, and Junchen Jiang

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

Abstract
Low Latency, Low Loss, and Scalable Throughput (L4S), as an emerging router-queue management technique, has seen steady deployment in the industry. An L4S-enabled router assigns each packet to the queue based on the packet header marking. Currently, L4S employs per-flow queue selection, i.e., all packets of a flow are marked the same way and thus use the same queues, even though each packet is marked separately. However, this may hurt tail latency and latency-sensitive applications because transient congestion and queue buildups may only affect a fraction of packets in a flow. We present SwiftQueue, a new L4S queue-selection strategy in which a sender uses a novel per-packet latency predictor to pinpoint which packets likely have latency spikes or drops. The insight is that many packet-level latency variations result from complex interactions among recent packets at shared router queues. Yet, these intricate packet-level latency patterns are hard to learn efficiently by traditional models. Instead, SwiftQueue uses a custom Transformer, which is well-studied for its expressiveness on sequential patterns, to predict the next packet’s latency based on the latencies of recently received ACKs. Based on the predicted latency of each outgoing packet, SwiftQueue’s sender dynamically marks the L4S packet header to assign packets to potentially different queues, even within the same flow. Using real network traces, we show that SwiftQueue is 45-65% more accurate in predicting latency and its variations than state-of-art methods. Based on its latency prediction, SwiftQueue reduces the tail latency for L4S-enabled flows by 36-45%, compared with the existing L4S queue-selection method.
Cite as

Siddhant Ray, Xi Jiang, Jack Luo, Nick Feamster, and Junchen Jiang. SwiftQueue: Optimizing Low-Latency Applications with Swift Packet Queuing. In 1st New Ideas in Networked Systems (NINeS 2026). Open Access Series in Informatics (OASIcs), Volume 139, pp. 24:1-24:29, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{ray_et_al:OASIcs.NINeS.2026.24,
  author =	{Ray, Siddhant and Jiang, Xi and Luo, Jack and Feamster, Nick and Jiang, Junchen},
  title =	{{SwiftQueue: Optimizing Low-Latency Applications with Swift Packet Queuing}},
  booktitle =	{1st New Ideas in Networked Systems (NINeS 2026)},
  pages =	{24:1--24:29},
  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.2026.24},
  URN =		{urn:nbn:de:0030-drops-256093},
  doi =		{10.4230/OASIcs.NINeS.2026.24},
  annote =	{Keywords: Latency prediction, L4S Queue Management}
}
Document
DOI: 10.4230/OASIcs.NINeS.2026.27
Making Congestion Control Algorithms Insensitive to Underlying Propagation Delays

Authors: Cyrus Illick, Michael Roger, Vishal Misra, and Dan Rubenstein

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

Abstract
Underlying propagation delays, which significantly impact flow Round-Trip-Times (RTTs), fundamentally shape how congestion control algorithms (CCAs) allocate bandwidth among competing flows. Traditionally, lower RTTs were desirable, as a flow rate would decrease as RTT increased under conventional CCAs such as Reno and Cubic. More recently, algorithms like BBR reversed this behavior, favoring flows with larger RTTs. In modern heterogeneous networks, where competing flows may employ different CCAs and serve diverse application workloads, notions of fairness become ambiguous and RTT’s role further complexifies addressing an already very challenging problem. We offer a simple but initially counter-intuitive remedy to address how all CCAs can remove this inherent sensitivity to propagation delay: have all flows "emulate" the same underlying propagation delay. Unsurprisingly, this idea is often met with a lot of resistance, since it is in many ways counter to many fundamental tenets of networking and congestion control. In this paper, we try to make the case that while the idea is not a silver bullet on its own, it can greatly simplify design and predictability across existing and future congestion control protocols going forward.
Cite as

Cyrus Illick, Michael Roger, Vishal Misra, and Dan Rubenstein. Making Congestion Control Algorithms Insensitive to Underlying Propagation Delays. In 1st New Ideas in Networked Systems (NINeS 2026). Open Access Series in Informatics (OASIcs), Volume 139, pp. 27:1-27:26, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{illick_et_al:OASIcs.NINeS.2026.27,
  author =	{Illick, Cyrus and Roger, Michael and Misra, Vishal and Rubenstein, Dan},
  title =	{{Making Congestion Control Algorithms Insensitive to Underlying Propagation Delays}},
  booktitle =	{1st New Ideas in Networked Systems (NINeS 2026)},
  pages =	{27:1--27:26},
  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.2026.27},
  URN =		{urn:nbn:de:0030-drops-256122},
  doi =		{10.4230/OASIcs.NINeS.2026.27},
  annote =	{Keywords: Congestion Control, Propagation Delay}
}
Document
DOI: 10.4230/OASIcs.NINeS.2026.22
There Is More to Internet Invariants Than Meets the Eye

Authors: Chris Misa, Walter Willinger, Ramakrishnan Durairajan, and Reza Rejaie

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

Abstract
A rich body of literature assembled over the last 30 years shows that traffic traversing wide-area Internet links is consistent with self-similar (temporal) scaling behavior and that sets of observed addresses have multifractal (spatial) scaling behavior. In view of this empirical evidence, these behaviors cannot be viewed as mere mathematical curiosities but should justifiably be called invariants of measured Internet traffic (Internet invariants, for short). At the same time, it is fair to say that the early architects of the Internet were largely unaware of these properties and certainly did not intend to design a network so that the traffic traversing its links would exhibit self-similar scaling in time or multifractal scaling in the IP address space. In this paper, we resolve this apparent disconnect between architectural intentions and observed behaviors by applying a three-part framework that leverages, at its core, the perspective of Highly Optimized Tolerance (HOT). In particular, we take inspiration from studies on the origins of (temporal) self-similarity in measured Internet traffic but focus on a fundamentally new approach to understanding multifractal (spatial) scaling behavior. Specifically, we examine whether this invariant can be viewed as a visible hallmark of underlying but largely unknown robust design efforts, and explore a reverse-engineering approach to determine the concrete nature of the constrained optimization problems that these robust designs solve. Based on the insights gained from such reverse-engineering efforts, we speculate on the benefits of future efforts at forward-engineering - systematically leveraging the identified robust designs in order to provide scientifically sound intellectual foundations and practical principles for designing future networked systems.
Cite as

Chris Misa, Walter Willinger, Ramakrishnan Durairajan, and Reza Rejaie. There Is More to Internet Invariants Than Meets the Eye. In 1st New Ideas in Networked Systems (NINeS 2026). Open Access Series in Informatics (OASIcs), Volume 139, pp. 22:1-22:26, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{misa_et_al:OASIcs.NINeS.2026.22,
  author =	{Misa, Chris and Willinger, Walter and Durairajan, Ramakrishnan and Rejaie, Reza},
  title =	{{There Is More to Internet Invariants Than Meets the Eye}},
  booktitle =	{1st New Ideas in Networked Systems (NINeS 2026)},
  pages =	{22:1--22:26},
  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.2026.22},
  URN =		{urn:nbn:de:0030-drops-256077},
  doi =		{10.4230/OASIcs.NINeS.2026.22},
  annote =	{Keywords: Internet traffic, self-similarity, multifractal scaling, reverse-engineering}
}
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