FISSION: Practical Algorithms for Computing Minimum Balanced Node Separators

Hypokalemia is a common electrolyte disturbance, especially in hospitalized patients. It can have various causes, including endocrine ones. Sometimes, hypokalemia requires urgent medical attention. The aim of this review is to present updated information regarding: (1) the definition and prevalence of hypokalemia, (2) the physiology of potassium homeostasis, (3) the various causes leading to hypokalemia, (4) the diagnostic steps for the assessment of hypokalemia and (5) the appropriate treatment of hypokalemia depending on the cause. Practical algorithms for the optimal diagnostic, treatment and follow-up strategy are presented, while an individualized approach is emphasized.

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Computing Constrained Shortest-Paths at Scale

Operations Research ◽

10.1287/opre.2021.2166 ◽

2021 ◽

Author(s):

Alberto Vera ◽

Siddhartha Banerjee ◽

Samitha Samaranayake

Keyword(s):

Shortest Paths ◽

Data Sets ◽

Network Clustering ◽

Real World Data ◽

Scalable Algorithm ◽

Theoretical Contribution ◽

Practical Algorithms ◽

Transportation Service ◽

Constrained Shortest Paths

Motivated by the needs of modern transportation service platforms, we study the problem of computing constrained shortest paths (CSP) at scale via preprocessing techniques. Our work makes two contributions in this regard: 1) We propose a scalable algorithm for CSP queries and show how its performance can be parametrized in terms of a new network primitive, the constrained highway dimension. This development extends recent work that established the highway dimension as the appropriate primitive for characterizing the performance of unconstrained shortest-path (SP) algorithms. Our main theoretical contribution is deriving conditions relating the two notions, thereby providing a characterization of networks where CSP and SP queries are of comparable hardness. 2) We develop practical algorithms for scalable CSP computation, augmenting our theory with additional network clustering heuristics. We evaluate these algorithms on real-world data sets to validate our theoretical findings. Our techniques are orders of magnitude faster than existing approaches while requiring only limited additional storage and preprocessing.

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Practical Algorithms for Pattern Based Linear Regression

Discovery Science - Lecture Notes in Computer Science ◽

10.1007/11563983_6 ◽

2005 ◽

pp. 44-56 ◽

Cited By ~ 1

Author(s):

Hideo Bannai ◽

Kohei Hatano ◽

Shunsuke Inenaga ◽

Masayuki Takeda

Keyword(s):

Linear Regression ◽

Practical Algorithms

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A Facility Location Model for Air Pollution Detection

Mathematical Problems in Engineering ◽

10.1155/2018/1683249 ◽

2018 ◽

Vol 2018 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

G. Lancia ◽

F. Rinaldi ◽

P. Serafini

Keyword(s):

Genetic Algorithm ◽

Air Pollution ◽

Air Quality ◽

Mathematical Models ◽

Facility Location ◽

Large City ◽

Location Model ◽

Practical Algorithms ◽

Pollution Detection ◽

The City

We describe mathematical models and practical algorithms for a problem concerned with monitoring the air pollution in a large city. We have worked on this problem within a project for assessing the air quality in the city of Rome by placing a certain number of sensors on some of the city buses. We cast the problem as a facility location model. By reducing the large number of data variables and constraints, we were able to solve to optimality the resulting MILP model within minutes. Furthermore, we designed a genetic algorithm whose solutions were on average very close to the optimal ones. In our computational experiments we studied the placement of sensors on 187 candidate bus routes. We considered the coverage provided by 10 up to 60 sensors.

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Practical Algorithms for Multi-Stage Voting Rules with Parallel Universes Tiebreaking

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33012189 ◽

2019 ◽

Vol 33 ◽

pp. 2189-2196 ◽

Cited By ~ 1

Author(s):

Jun Wang ◽

Sujoy Sikdar ◽

Tyler Shepherd ◽

Zhibing Zhao ◽

Chunheng Jiang ◽

...

Keyword(s):

Machine Learning ◽

Decision Making ◽

Group Decision Making ◽

Group Decision ◽

Search Algorithms ◽

Voting Rules ◽

Practical Algorithms ◽

Multi Stage ◽

Parallel Universes ◽

Np Complete

STV and ranked pairs (RP) are two well-studied voting rules for group decision-making. They proceed in multiple rounds, and are affected by how ties are broken in each round. However, the literature is surprisingly vague about how ties should be broken. We propose the first algorithms for computing the set of alternatives that are winners under some tiebreaking mechanism under STV and RP, which is also known as parallel-universes tiebreaking (PUT). Unfortunately, PUT-winners are NP-complete to compute under STV and RP, and standard search algorithms from AI do not apply. We propose multiple DFS-based algorithms along with pruning strategies, heuristics, sampling and machine learning to prioritize search direction to significantly improve the performance. We also propose novel ILP formulations for PUT-winners under STV and RP, respectively. Experiments on synthetic and realworld data show that our algorithms are overall faster than ILP.

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