@article{9827,
  abstract     = {The Nearest neighbour search (NNS) is a fundamental problem in many application domains dealing with multidimensional data. In a concurrent setting, where dynamic modifications are allowed, a linearizable implementation of the NNS is highly desirable.This paper introduces the LockFree-kD-tree (LFkD-tree ): a lock-free concurrent kD-tree, which implements an abstract data type (ADT) that provides the operations Add, Remove, Contains, and NNS. Our implementation is linearizable. The operations in the LFkD-tree use single-word read and compare-and-swap (Image 1 ) atomic primitives, which are readily supported on available multi-core processors. We experimentally evaluate the LFkD-tree using several benchmarks comprising real-world and synthetic datasets. The experiments show that the presented design is scalable and achieves significant speed-up compared to the implementations of an existing sequential kD-tree and a recently proposed multidimensional indexing structure, PH-tree.},
  author       = {Chatterjee, Bapi and Walulya, Ivan and Tsigas, Philippas},
  issn         = {0304-3975},
  journal      = {Theoretical Computer Science},
  keywords     = {Concurrent data structure, kD-tree, Nearest neighbor search, Similarity search, Lock-free, Linearizability},
  pages        = {27--48},
  publisher    = {Elsevier},
  title        = {{Concurrent linearizable nearest neighbour search in LockFree-kD-tree}},
  doi          = {10.1016/j.tcs.2021.06.041},
  volume       = {886},
  year         = {2021},
}

@misc{5549,
  abstract     = {This repository contains the experimental part of the CAV 2015 publication Counterexample Explanation by Learning Small Strategies in Markov Decision Processes.
We extended the probabilistic model checker PRISM to represent strategies of Markov Decision Processes as Decision Trees.
The archive contains a java executable version of the extended tool (prism_dectree.jar) together with a few examples of the PRISM benchmark library.
To execute the program, please have a look at the README.txt, which provides instructions and further information on the archive.
The archive contains scripts that (if run often enough) reproduces the data presented in the publication.},
  author       = {Fellner, Andreas},
  keywords     = {Markov Decision Process, Decision Tree, Probabilistic Verification, Counterexample Explanation},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{Experimental part of CAV 2015 publication: Counterexample Explanation by Learning Small Strategies in Markov Decision Processes}},
  doi          = {10.15479/AT:ISTA:28},
  year         = {2015},
}