A review: machine learning for combinatorial optimization problems in energy areas
Combinatorial optimization problems (COPs) are a class of NP-hard problems with great
practical significance. Traditional approaches for COPs suffer from high computational time …
practical significance. Traditional approaches for COPs suffer from high computational time …
ARC–MOF: a diverse database of metal-organic frameworks with DFT-derived partial atomic charges and descriptors for machine learning
Metal–organic frameworks (MOFs) are a class of crystalline materials composed of metal
nodes or clusters connected via semi-rigid organic linkers. Owing to their high-surface area …
nodes or clusters connected via semi-rigid organic linkers. Owing to their high-surface area …
Human AI collaboration for unsupervised categorization of live surgical feedback
Formative verbal feedback during live surgery is essential for adjusting trainee behavior and
accelerating skill acquisition. Despite its importance, understanding optimal feedback is …
accelerating skill acquisition. Despite its importance, understanding optimal feedback is …
Parallel cover trees and their applications
The cover tree is the canonical data structure that efficiently maintains a dynamic set of
points on a metric space and supports nearest and k-nearest neighbor searches. For most …
points on a metric space and supports nearest and k-nearest neighbor searches. For most …
Efficient density-peaks clustering algorithms on static and dynamic data in euclidean space
Clustering multi-dimensional points is a fundamental task in many fields, and density-based
clustering supports many applications because it can discover clusters of arbitrary shapes …
clustering supports many applications because it can discover clusters of arbitrary shapes …
High-Performance and Flexible Parallel Algorithms for Semisort and Related Problems
Semisort is a fundamental algorithmic primitive widely used in the design and analysis of
efficient parallel algorithms. It takes input as an array of records and a function extracting a …
efficient parallel algorithms. It takes input as an array of records and a function extracting a …
Geograph: A framework for graph processing on geometric data
In many applications of graph processing, the input data is often generated from an
underlying geometric point data set. However, existing high-performance graph processing …
underlying geometric point data set. However, existing high-performance graph processing …
Parchain: A framework for parallel hierarchical agglomerative clustering using nearest-neighbor chain
This paper studies the hierarchical clustering problem, where the goal is to produce a
dendrogram that represents clusters at varying scales of a data set. We propose the …
dendrogram that represents clusters at varying scales of a data set. We propose the …
[書籍][B] A new compressed cover tree for k-nearest neighbour search and the stable-under-noise mergegram of a point cloud
Y Elkin - 2022 - search.proquest.com
The analysis of data sets mathematically representable as finite metric spaces plays a
significant role in every scientific study. In this thesis we focus on constructing new effective …
significant role in every scientific study. In this thesis we focus on constructing new effective …
Pargeo: A library for parallel computational geometry
Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel
Programming: ParGeo: a library for parallel Page 1 POSTER: ParGeo: A Library for Parallel …
Programming: ParGeo: a library for parallel Page 1 POSTER: ParGeo: A Library for Parallel …