scholarly journals Graph Minors and Parameterized Algorithm Design

2012 ◽  
pp. 228-256 ◽  
Author(s):  
Dimitrios M. Thilikos
Keyword(s):  
Algorithm Design ◽  
Parameterized Algorithm ◽  
Graph Minors

Algorithmic Puzzles

2011 ◽  
Author(s):  
Anany Levitin ◽  
Maria Levitin
Keyword(s):  
Problem Solving ◽  
Computer Science ◽  
Algorithm Design ◽  
Divide And Conquer ◽  
Algorithmic Problem ◽  
Design Strategies ◽  
Job Interviews ◽  
Skill Levels ◽  
Problem Solving Skills ◽  
Solution Methods

While many think of algorithms as specific to computer science, at its core algorithmic thinking is defined by the use of analytical logic to solve problems. This logic extends far beyond the realm of computer science and into the wide and entertaining world of puzzles. In Algorithmic Puzzles, Anany and Maria Levitin use many classic brainteasers as well as newer examples from job interviews with major corporations to show readers how to apply analytical thinking to solve puzzles requiring well-defined procedures. The book's unique collection of puzzles is supplemented with carefully developed tutorials on algorithm design strategies and analysis techniques intended to walk the reader step-by-step through the various approaches to algorithmic problem solving. Mastery of these strategies--exhaustive search, backtracking, and divide-and-conquer, among others--will aid the reader in solving not only the puzzles contained in this book, but also others encountered in interviews, puzzle collections, and throughout everyday life. Each of the 150 puzzles contains hints and solutions, along with commentary on the puzzle's origins and solution methods. The only book of its kind, Algorithmic Puzzles houses puzzles for all skill levels. Readers with only middle school mathematics will develop their algorithmic problem-solving skills through puzzles at the elementary level, while seasoned puzzle solvers will enjoy the challenge of thinking through more difficult puzzles.

Value-Sensitive Algorithm Design

2018 ◽  
Vol 2 (CSCW) ◽  
pp. 1-23 ◽  
Author(s):  
Haiyi Zhu ◽  
Bowen Yu ◽  
Aaron Halfaker ◽  
Loren Terveen
Keyword(s):  
Algorithm Design

scholarly journals Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem

2021 ◽  
pp. 089443932110122
Author(s):  
Dennis Assenmacher ◽  
Derek Weber ◽  
Mike Preuss ◽  
André Calero Valdez ◽  
Alison Bradshaw ◽  
...  
Keyword(s):  
Social Media ◽  
Data Sharing ◽  
Algorithm Design ◽  
Computational Social Science ◽  
Evaluation Framework ◽  
Social Media Analytics ◽  
Data Sets ◽  
The Public ◽  
Research Areas ◽  
Media Data

Computational social science uses computational and statistical methods in order to evaluate social interaction. The public availability of data sets is thus a necessary precondition for reliable and replicable research. These data allow researchers to benchmark the computational methods they develop, test the generalizability of their findings, and build confidence in their results. When social media data are concerned, data sharing is often restricted for legal or privacy reasons, which makes the comparison of methods and the replicability of research results infeasible. Social media analytics research, consequently, faces an integrity crisis. How is it possible to create trust in computational or statistical analyses, when they cannot be validated by third parties? In this work, we explore this well-known, yet little discussed, problem for social media analytics. We investigate how this problem can be solved by looking at related computational research areas. Moreover, we propose and implement a prototype to address the problem in the form of a new evaluation framework that enables the comparison of algorithms without the need to exchange data directly, while maintaining flexibility for the algorithm design.

FFBV Algorithm Design for Soft Synchronization FMCW Radar

2021 ◽  
Vol 70 ◽  
pp. 1-11
Author(s):  
Ping Li ◽  
Tao Wang ◽  
Rui Wang ◽  
Yanzan Sun ◽  
Yating Wu ◽  
...  
Keyword(s):  
Algorithm Design ◽  
Fmcw Radar

scholarly journals Dynamic Modeling and Anti-Disturbing Control of an Electromagnetic MEMS Torsional Micromirror Considering External Vibrations in Vehicular LiDAR

Micromachines ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 69
Author(s):  
Yong Hua ◽  
Shuangyuan Wang ◽  
Bingchu Li ◽  
Guozhen Bai ◽  
Pengju Zhang
Keyword(s):  
Dynamic Model ◽  
Optical Switching ◽  
Control Method ◽  
Sliding Mode ◽  
Algorithm Design ◽  
Coupling Model ◽  
Micro Electro Mechanical Systems ◽  
Mems Technology ◽  
Torsional Micromirror ◽  
Dynamic Coupling Model

Micromirrors based on micro-electro-mechanical systems (MEMS) technology are widely employed in different areas, such as optical switching and medical scan imaging. As the key component of MEMS LiDAR, electromagnetic MEMS torsional micromirrors have the advantages of small size, a simple structure, and low energy consumption. However, MEMS micromirrors face severe disturbances due to vehicular vibrations in realistic use situations. The paper deals with the precise motion control of MEMS micromirrors, considering external vibration. A dynamic model of MEMS micromirrors, considering the coupling between vibration and torsion, is proposed. The coefficients in the dynamic model were identified using the experimental method. A feedforward sliding mode control method (FSMC) is proposed in this paper. By establishing the dynamic coupling model of electromagnetic MEMS torsional micromirrors, the proposed FSMC is evaluated considering external vibrations, and compared with conventional proportion-integral-derivative (PID) controls in terms of robustness and accuracy. The simulation experiment results indicate that the FSMC controller has certain advantages over a PID controller. This paper revealed the coupling dynamic of MEMS micromirrors, which could be used for a dynamic analysis and a control algorithm design for MEMS micromirrors.

scholarly journals Energy-aware very fast decision tree

2021 ◽  
Author(s):  
Eva García-Martín ◽  
Niklas Lavesson ◽  
Håkan Grahn ◽  
Emiliano Casalicchio ◽  
Veselka Boeva
Keyword(s):  
Energy Consumption ◽  
Decision Tree ◽  
Concept Drift ◽  
Algorithm Design ◽  
Energy Aware ◽  
Battery Capacity ◽  
Very Fast Decision Tree ◽  
Public Datasets ◽  
Additional Constraints ◽  
Fast Decision

AbstractRecently machine learning researchers are designing algorithms that can run in embedded and mobile devices, which introduces additional constraints compared to traditional algorithm design approaches. One of these constraints is energy consumption, which directly translates to battery capacity for these devices. Streaming algorithms, such as the Very Fast Decision Tree (VFDT), are designed to run in such devices due to their high velocity and low memory requirements. However, they have not been designed with an energy efficiency focus. This paper addresses this challenge by presenting the nmin adaptation method, which reduces the energy consumption of the VFDT algorithm with only minor effects on accuracy. nmin adaptation allows the algorithm to grow faster in those branches where there is more confidence to create a split, and delays the split on the less confident branches. This removes unnecessary computations related to checking for splits but maintains similar levels of accuracy. We have conducted extensive experiments on 29 public datasets, showing that the VFDT with nmin adaptation consumes up to 31% less energy than the original VFDT, and up to 96% less energy than the CVFDT (VFDT adapted for concept drift scenarios), trading off up to 1.7 percent of accuracy.

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