A practical comparison of two K-Means clustering algorithms

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On PTAS for planar graph problems

Approximation algorithms for a class of planar graph problems, including planar independent set, planar vertex cover and planar dominating set, were intensively studied. The current upper bound on the running time of the polynomial time approximation schemes (PTAS) for these planar graph problems is of 2O(1/∈ )nO(1). Here we study the lower bound on the running time of the PTAS for these planar graph problems. We prove that there is no PTAS of time 2=(√(1/∈ )nO(1) for planar independent set, planar vertex cover and planar dominating set unless an unlikely collapse occurs in parameterized complexity theory. For the gap between our lower bound and the current known upper bound, we speci cally show that to further improve the upper bound on the running time of the PTAS for planar vertex cover, we can concentrate on planar vertex cover on pla- nar graphs of degree bounded by three.4th IFIP International Conference on Theoretical Computer ScienceRed de Universidades con Carreras en Informática (RedUNCI

Parameterized complexity and polynomial-time approximation schemes

According to the theory of NPcompleteness, many problems that have important realworld applications are NPhard. This excludes the possibility of solving them in polynomial time unless P=NP. A number of approaches have been proposed in dealing with NPhard problems, among them are approximation algorithms and parameterized algorithms. The study of approximation algorithms tries to find good enough solutions instead of optimal solutions in polynomial time, while parameterized algorithms try to give exact solutions when a natural parameter is small. In this thesis, we study the structural properties of parameterized computation and approximation algorithms for NP optimization problems. In particular, we investigate the relationship between parameterized complexity and polynomialtime approximation scheme (PTAS) for NP optimization problems. We give nice characterizations for two important subclasses in PTAS: Fully Polynomial Time Approximation Scheme (FPTAS) and Effcient Polynomial Time Approximation Scheme (EPTAS), using the theory of parameterized complexity. Our characterization of the class FPTAS has its advantages over the former characterizations, and our characterization of EPTAS is the first systematic investigation of this new but important approximation class. We develop new techniques to derive strong computational lower bounds for certain parameterized problems based on the theory of parameterized complexity. For example, we prove that unless an unlikely collapse occurs in parameterized complexity theory, the clique problem could not be solved in time O(f (k)no(k)) for any function f . This lower bound matches the upper bound of the trivial algorithm that simply enumerates and checks all subsets of k vertices in the given graph of n vertices. We then extend our techniques to derive computational lower bounds for PTAS and EPTAS algorithms of NP optimization problems. We prove that certain NP optimization problems with known PTAS algorithms have no PTAS algorithms of running time O(f (1/Epsilon)no(1/Epsilon)) for any function f . Therefore, for these NP optimization problems, although theoretically they can be approximated in polynomial time to an arbitrarily small error bound Epsilon, they have no practically effective approximation algorithms for small error bound Epsilon. To our knowledge, this is the first time such lower bound results have been derived for PTAS algorithms. This seems to open a new direction for the study of computational lower bounds on the approximability of NP optimization problems

Effects of bolt slippage on the wind induced responses of transmission tower line system

The wind induced responses of transmission tower line system are studied by finite element method. Firstly, a slip model considering eccentricity and bolt joint slippage in diagonal bracings, tower legs and tower head is built by ANSYS. The slip model has a more accurate result compared with conventional models. Secondly, the finite element models of tower line systems are established and the wind speed time histories are simulated using MATLAB. Finally, the wind induced responses of different tower line systems are studied. The results of a single tower and the tower line systems are compared to study the effects of tower-line coupling effects and bolt slippage on wind induced responses of transmission tower line systems

Team Building Without Boundaries

Team building can be challenging when participants are from the same discipline or sub-discipline, but needs special attention when participants use a different vocabulary and have different cultural views on what constitutes viable problems and solutions. Essential to No Boundary Thinking (NBT) teams is proper formulation of the problem to be solved, and a basic tenant is that the NBT team must come together with diverse perspectives to decide the problem before solutions can be considered. Given that participants come with different views on problem formulation and solution, it is important to consider a robust process for team formation and maintenance. This takes extra effort and time, but scholars studying teams of experts with diverse training have found that they are better positioned to be successful in solving even deep and difficult problems especially if they have learned to work well with each other. At this workshop we will discuss principles that scholars who have worked in NBT teams have discovered as effective. We will then engage with the workshop participants to consider discuss these principles and brainstorm to consider other approaches

NBT (No-Boundary Thinking): Needed To Attend To Ethical Implications Of Data And AI

In this era of Big Data and AI, expertise in multiple aspects of data, computing, and the domains of application is needed. This calls for teams of experts with different training and perspectives. Because data analysis can have serious ethical implications, it is important that these teams are well and deeply integrated. No-Boundary Thinking (NBT) teams can provide support for team formation and maintenance, thereby attending to the many dimensions of the ethics of data and analysis. In this NBT workshop session, we discuss the ethical concerns that arise from the use of data and AI, and the implications for team building; and provide and brainstorm suggestions for ethical data enabled science and AI

On PTAS for planar graph problems

Approximation algorithms for a class of planar graph problems, including planar independent set, planar vertex cover and planar dominating set, were intensively studied. The current upper bound on the running time of the polynomial time approximation schemes (PTAS) for these planar graph problems is of 2O(1/∈ )nO(1). Here we study the lower bound on the running time of the PTAS for these planar graph problems. We prove that there is no PTAS of time 2=(√(1/∈ )nO(1) for planar independent set, planar vertex cover and planar dominating set unless an unlikely collapse occurs in parameterized complexity theory. For the gap between our lower bound and the current known upper bound, we speci cally show that to further improve the upper bound on the running time of the PTAS for planar vertex cover, we can concentrate on planar vertex cover on pla- nar graphs of degree bounded by three.4th IFIP International Conference on Theoretical Computer ScienceRed de Universidades con Carreras en Informática (RedUNCI

Highly accurate model for prediction of lung nodule malignancy with CT scans

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://bioinformatics.astate.edu/NoduleX