616 research outputs found
Rounding Algorithms for a Geometric Embedding of Minimum Multiway Cut
The multiway-cut problem is, given a weighted graph and k >= 2 terminal
nodes, to find a minimum-weight set of edges whose removal separates all the
terminals. The problem is NP-hard, and even NP-hard to approximate within
1+delta for some small delta > 0.
Calinescu, Karloff, and Rabani (1998) gave an algorithm with performance
guarantee 3/2-1/k, based on a geometric relaxation of the problem. In this
paper, we give improved randomized rounding schemes for their relaxation,
yielding a 12/11-approximation algorithm for k=3 and a 1.3438-approximation
algorithm in general.
Our approach hinges on the observation that the problem of designing a
randomized rounding scheme for a geometric relaxation is itself a linear
programming problem. The paper explores computational solutions to this
problem, and gives a proof that for a general class of geometric relaxations,
there are always randomized rounding schemes that match the integrality gap.Comment: Conference version in ACM Symposium on Theory of Computing (1999). To
appear in Mathematics of Operations Researc
Huckleberry Finn
https://digitalcommons.library.umaine.edu/mmb-vp/1620/thumbnail.jp
Make Research Data Public? -- Not Always so Simple: A Dialogue for Statisticians and Science Editors
Putting data into the public domain is not the same thing as making those
data accessible for intelligent analysis. A distinguished group of editors and
experts who were already engaged in one way or another with the issues inherent
in making research data public came together with statisticians to initiate a
dialogue about policies and practicalities of requiring published research to
be accompanied by publication of the research data. This dialogue carried
beyond the broad issues of the advisability, the intellectual integrity, the
scientific exigencies to the relevance of these issues to statistics as a
discipline and the relevance of statistics, from inference to modeling to data
exploration, to science and social science policies on these issues.Comment: Published in at http://dx.doi.org/10.1214/10-STS320 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
How should institutions help clinicians to practise greener anaesthesia : first-order and second-order responsibilities to practice sustainably
There is a need for all industries, including healthcare, to reduce their greenhouse gas emissions. In anaesthetic practice, this not only requires a reduction in resource use and waste, but also a shift away from inhaled anaesthetic gases and towards alternatives with a lower carbon footprint. As inhalational anaesthesia produces greenhouse gas emissions at the point of use, achieving sustainable anaesthetic practice involves individual practitioner behaviour change. However, changing the practice of healthcare professionals raises potential ethical issues. The purpose of this paper is twofold. First, we discuss what moral duties anaesthetic practitioners have when it comes to practices that impact the environment. We argue that behaviour change among practitioners to align with certain moral responsibilities must be supplemented with an account of institutional duties to support this. In other words, we argue that institutions and those in power have second-order responsibilities to ensure that practitioners can fulfil their first-order responsibilities to practice more sustainably. The second goal of the paper is to consider not just the nature of second-order responsibilities but the content. We assess four different ways that second-order responsibilities might be fulfilled within healthcare systems: removing certain anaesthetic agents, seeking consensus, education and methods from behavioural economics. We argue that, while each of these are a necessary part of the picture, some interventions like nudges have considerable advantages
HyperNetX: A Python package for modeling complex network data as hypergraphs
HyperNetX (HNX) is an open source Python library for the analysis and
visualization of complex network data modeled as hypergraphs. Initially
released in 2019, HNX facilitates exploratory data analysis of complex networks
using algebraic topology, combinatorics, and generalized hypergraph and graph
theoretical methods on structured data inputs. With its 2023 release, the
library supports attaching metadata, numerical and categorical, to nodes
(vertices) and hyperedges, as well as to node-hyperedge pairings (incidences).
HNX has a customizable Matplotlib-based visualization module as well as
HypernetX-Widget, its JavaScript addon for interactive exploration and
visualization of hypergraphs within Jupyter Notebooks. Both packages are
available on GitHub and PyPI. With a growing community of users and
collaborators, HNX has become a preeminent tool for hypergraph analysis.Comment: 3 pages, 2 figure
Hypergraph Topological Features for Autoencoder-Based Intrusion Detection for Cybersecurity Data
In this position paper, we argue that when hypergraphs are used to capture
multi-way local relations of data, their resulting topological features
describe global behaviour. Consequently, these features capture complex
correlations that can then serve as high fidelity inputs to autoencoder-driven
anomaly detection pipelines. We propose two such potential pipelines for
cybersecurity data, one that uses an autoencoder directly to determine network
intrusions, and one that de-noises input data for a persistent homology system,
PHANTOM. We provide heuristic justification for the use of the methods
described therein for an intrusion detection pipeline for cyber data. We
conclude by showing a small example over synthetic cyber attack data
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