Abstract: We study the problem of minimum-number full-view area coverage in camera sensor networks, i.e., how to select the minimum number of camera sensors to guarantee the full-view coverage of a ...

The area of approximation algorithms is aimed at giving provable guarantees on the performance of heuristics for hard problems. The course will present general techniques (such as convex ...

ABSTRACT: Accurate land cover classification is essential for environmental monitoring, urban planning, and resource management. Conventional classifiers trained on raw spectral bands are often ...

This repository contains code for our SPAA paper "Theoretically Efficient Parallel Graph Algorithms Can Be Fast and Scalable" (SPAA'18). It includes implementations of the following parallel graph ...

Abstract: Online algorithms are crucial for real-time decision-making and adaptability across diverse fields, such as operations research, computer science, and combinatorics. These algorithms handle ...

To run the algorithms, you can either use cargo or use run.sh script. naive_method: Find the MVC of the graph using the naive method. use : cargo run -r --bin naive_search <file_name> ...

Feature attributions based on the Shapley value are popular for explaining machine learning models. However, their estimation is complex from both theoretical and computational standpoints. We ...

What is a Successive Approximation ADC? Successive Approximation ADC is the preferred choice for low-cost, medium to high-resolution applications. SAR ADC resolution typically ranges from 8-18 bits, ...

Despite decades of research, effective treatments for most cancers remain elusive. One reason is that different instances of cancer result from different combinations of multiple genetic mutations ...

Graph sparsification is the approximation of an arbitrary graph by a sparse graph. We explain what it means for one graph to be a spectral approximation of another and review the development of ...