4 Search Results for "Wang, Fan"


Document
Synthesizing Conjunctive Queries for Code Search

Authors: Chengpeng Wang, Peisen Yao, Wensheng Tang, Gang Fan, and Charles Zhang

Published in: LIPIcs, Volume 263, 37th European Conference on Object-Oriented Programming (ECOOP 2023)


Abstract
This paper presents Squid, a new conjunctive query synthesis algorithm for searching code with target patterns. Given positive and negative examples along with a natural language description, Squid analyzes the relations derived from the examples by a Datalog-based program analyzer and synthesizes a conjunctive query expressing the search intent. The synthesized query can be further used to search for desired grammatical constructs in the editor. To achieve high efficiency, we prune the huge search space by removing unnecessary relations and enumerating query candidates via refinement. We also introduce two quantitative metrics for query prioritization to select the queries from multiple candidates, yielding desired queries for code search. We have evaluated Squid on over thirty code search tasks. It is shown that Squid successfully synthesizes the conjunctive queries for all the tasks, taking only 2.56 seconds on average.

Cite as

Chengpeng Wang, Peisen Yao, Wensheng Tang, Gang Fan, and Charles Zhang. Synthesizing Conjunctive Queries for Code Search. In 37th European Conference on Object-Oriented Programming (ECOOP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 263, pp. 36:1-36:30, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


Copy BibTex To Clipboard

@InProceedings{wang_et_al:LIPIcs.ECOOP.2023.36,
  author =	{Wang, Chengpeng and Yao, Peisen and Tang, Wensheng and Fan, Gang and Zhang, Charles},
  title =	{{Synthesizing Conjunctive Queries for Code Search}},
  booktitle =	{37th European Conference on Object-Oriented Programming (ECOOP 2023)},
  pages =	{36:1--36:30},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-281-5},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{263},
  editor =	{Ali, Karim and Salvaneschi, Guido},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2023.36},
  URN =		{urn:nbn:de:0030-drops-182294},
  doi =		{10.4230/LIPIcs.ECOOP.2023.36},
  annote =	{Keywords: Query Synthesis, Multi-modal Program Synthesis, Code Search}
}
Document
GPU Computation of the Euler Characteristic Curve for Imaging Data

Authors: Fan Wang, Hubert Wagner, and Chao Chen

Published in: LIPIcs, Volume 224, 38th International Symposium on Computational Geometry (SoCG 2022)


Abstract
Persistent homology is perhaps the most popular and useful tool offered by topological data analysis - with point-cloud data being the most common setup. Its older cousin, the Euler characteristic curve (ECC) is less expressive - but far easier to compute. It is particularly suitable for analyzing imaging data, and is commonly used in fields ranging from astrophysics to biomedical image analysis. These fields are embracing GPU computations to handle increasingly large datasets. We therefore propose an optimized GPU implementation of ECC computation for 2D and 3D grayscale images. The goal of this paper is twofold. First, we offer a practical tool, illustrating its performance with thorough experimentation - but also explain its inherent shortcomings. Second, this simple algorithm serves as a perfect backdrop for highlighting basic GPU programming techniques that make our implementation so efficient - and some common pitfalls we avoided. This is intended as a step towards a wider usage of GPU programming in computational geometry and topology software. We find this is particularly important as geometric and topological tools are used in conjunction with modern, GPU-accelerated machine learning frameworks.

Cite as

Fan Wang, Hubert Wagner, and Chao Chen. GPU Computation of the Euler Characteristic Curve for Imaging Data. In 38th International Symposium on Computational Geometry (SoCG 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 224, pp. 64:1-64:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)


Copy BibTex To Clipboard

@InProceedings{wang_et_al:LIPIcs.SoCG.2022.64,
  author =	{Wang, Fan and Wagner, Hubert and Chen, Chao},
  title =	{{GPU Computation of the Euler Characteristic Curve for Imaging Data}},
  booktitle =	{38th International Symposium on Computational Geometry (SoCG 2022)},
  pages =	{64:1--64:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-227-3},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{224},
  editor =	{Goaoc, Xavier and Kerber, Michael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2022.64},
  URN =		{urn:nbn:de:0030-drops-160724},
  doi =		{10.4230/LIPIcs.SoCG.2022.64},
  annote =	{Keywords: topological data analysis, Euler characteristic, Euler characteristic curve, Betti curve, persistent homology, algorithms, parallel programming, algorithm engineering, GPU programming, imaging data}
}
Document
Making Rigorous Linear Programming Practical for Program Analysis

Authors: Tengbin Wang, Liqian Chen, Taoqing Chen, Guangsheng Fan, and Ji Wang

Published in: LIPIcs, Volume 210, 27th International Conference on Principles and Practice of Constraint Programming (CP 2021)


Abstract
Linear programming is a key technique for analysis and verification of numerical properties in programs, neural networks, etc. In particular, in program analysis based on abstract interpretation, many numerical abstract domains (such as Template Constraint Matrix, constraint-only polyhedra, etc.) are designed on top of linear programming. However, most state-of-the-art linear programming solvers use floating-point arithmetic in their implementations, leading to an approximate result that may be unsound. On the other hand, the solvers implemented using exact arithmetic are too costly. To this end, this paper focuses on advancing rigorous linear programming techniques based on floating-point arithmetic for building sound and efficient program analysis. Particularly, as a supplement to existing techniques, we present a novel rigorous linear programming technique based on Fourier-Mozkin elimination. On this basis, we implement a tool, namely, RlpSolver, combining our technique with existing techniques to lift effectiveness of rigorous linear programming in the scene of analysis and verification. Experimental results show that our technique is complementary to existing techniques, and their combination (RlpSolver) can achieve a better trade-off between cost and precision via heuristic rules.

Cite as

Tengbin Wang, Liqian Chen, Taoqing Chen, Guangsheng Fan, and Ji Wang. Making Rigorous Linear Programming Practical for Program Analysis. In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 57:1-57:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)


Copy BibTex To Clipboard

@InProceedings{wang_et_al:LIPIcs.CP.2021.57,
  author =	{Wang, Tengbin and Chen, Liqian and Chen, Taoqing and Fan, Guangsheng and Wang, Ji},
  title =	{{Making Rigorous Linear Programming Practical for Program Analysis}},
  booktitle =	{27th International Conference on Principles and Practice of Constraint Programming (CP 2021)},
  pages =	{57:1--57:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-211-2},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{210},
  editor =	{Michel, Laurent D.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2021.57},
  URN =		{urn:nbn:de:0030-drops-153486},
  doi =		{10.4230/LIPIcs.CP.2021.57},
  annote =	{Keywords: Linear programming, rigorous linear programming, abstract interpretation, program analysis, Fourier-Mozkin elimination}
}
Document
Topological Analysis of Scalar Fields with Outliers

Authors: Mickaël Buchet, Frédéric Chazal, Tamal K. Dey, Fengtao Fan, Steve Y. Oudot, and Yusu Wang

Published in: LIPIcs, Volume 34, 31st International Symposium on Computational Geometry (SoCG 2015)


Abstract
Given a real-valued function f defined over a manifold M embedded in R^d, we are interested in recovering structural information about f from the sole information of its values on a finite sample P. Existing methods provide approximation to the persistence diagram of f when geometric noise and functional noise are bounded. However, they fail in the presence of aberrant values, also called outliers, both in theory and practice. We propose a new algorithm that deals with outliers. We handle aberrant functional values with a method inspired from the k-nearest neighbors regression and the local median filtering, while the geometric outliers are handled using the distance to a measure. Combined with topological results on nested filtrations, our algorithm performs robust topological analysis of scalar fields in a wider range of noise models than handled by current methods. We provide theoretical guarantees and experimental results on the quality of our approximation of the sampled scalar field.

Cite as

Mickaël Buchet, Frédéric Chazal, Tamal K. Dey, Fengtao Fan, Steve Y. Oudot, and Yusu Wang. Topological Analysis of Scalar Fields with Outliers. In 31st International Symposium on Computational Geometry (SoCG 2015). Leibniz International Proceedings in Informatics (LIPIcs), Volume 34, pp. 827-841, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2015)


Copy BibTex To Clipboard

@InProceedings{buchet_et_al:LIPIcs.SOCG.2015.827,
  author =	{Buchet, Micka\"{e}l and Chazal, Fr\'{e}d\'{e}ric and Dey, Tamal K. and Fan, Fengtao and Oudot, Steve Y. and Wang, Yusu},
  title =	{{Topological Analysis of Scalar Fields with Outliers}},
  booktitle =	{31st International Symposium on Computational Geometry (SoCG 2015)},
  pages =	{827--841},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-83-5},
  ISSN =	{1868-8969},
  year =	{2015},
  volume =	{34},
  editor =	{Arge, Lars and Pach, J\'{a}nos},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.SOCG.2015.827},
  URN =		{urn:nbn:de:0030-drops-51052},
  doi =		{10.4230/LIPIcs.SOCG.2015.827},
  annote =	{Keywords: Persistent Homology, Topological Data Analysis, Scalar Field Analysis, Nested Rips Filtration, Distance to a Measure}
}
  • Refine by Author
  • 1 Buchet, Mickaël
  • 1 Chazal, Frédéric
  • 1 Chen, Chao
  • 1 Chen, Liqian
  • 1 Chen, Taoqing
  • Show More...

  • Refine by Classification
  • 1 Human-centered computing → User interface programming
  • 1 Mathematics of computing → Combinatorial algorithms
  • 1 Software and its engineering → Automatic programming
  • 1 Software and its engineering → Formal methods
  • 1 Theory of computation → Computational geometry
  • Show More...

  • Refine by Keyword
  • 1 Betti curve
  • 1 Code Search
  • 1 Distance to a Measure
  • 1 Euler characteristic
  • 1 Euler characteristic curve
  • Show More...

  • Refine by Type
  • 4 document

  • Refine by Publication Year
  • 1 2015
  • 1 2021
  • 1 2022
  • 1 2023

Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail