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Fairness and Explainability in Clustering Problems

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Shrinking FPGA Static Power via Machine Learning-Based Power Gating and Enhanced Routing

Mirjana Stojilovic, Hossein Asadi, Zeinab Seifoori

Despite FPGAs rapidly evolving to support the requirements of the most demanding emerging applications, their high static power consumption, concentrated within the routing resources, still presents a major hurdle for low-power applications. Augmenting the ...

2021

Metric dimension of critical Galton–Watson trees and linear preferential attachment trees

Gergely Odor

The metric dimension of a graph G is the minimal size of a subset R of vertices of G that, upon reporting their graph distance from a distinguished (source) vertex v⋆, enable unique identification of the source vertex v⋆ among all possible vertices of G. I ...

2021

Plunge and rebound of a taxi market through COVID-19 lockdown: Lessons learned from Shenzhen, China

Kenan Zhang

This paper traces the plunge and rebound of the taxi market in Shenzhen, China through the COVID-19 lockdown. A four-week taxi GPS trajectory data set is collected in the first quarter of 2020, which covers the period of lockdown and phased reopening in th ...

2021

An instance of colorful k-center consists of points in a metric space that are colored red or blue, along with an integer k and a coverage requirement for each color. The goal is to find the smallest radius ρ such that there exist balls of radius ρ around ...

Springer2020

On Perfect Clustering of High Dimension, Low Sample Size Data

Soham Sarkar

Popular clustering algorithms based on usual distance functions (e.g., the Euclidean distance) often suffer in high dimension, low sample size (HDLSS) situations, where concentration of pairwise distances and violation of neighborhood structure have advers ...

IEEE COMPUTER SOC2020

Modeling and Analyzing Inquiry Strategies in Open-Ended Learning Environments

Modeling and predicting student learning in computer-based environments often relies solely on sequences of accuracy data. Previous research suggests that it does not only matter what we learn, but also how we learn. The detection and analysis of learning ...

2020

The (h, k)-Server Problem on Bounded Depth Trees

Marek Elias

We study the k-server problem in the resource augmentation setting, i.e., when the performance of the online algorithm with k servers is compared to the offline optimal solution with h

ASSOC COMPUTING MACHINERY2019

A method for improving ideas selection in crowdsourcing

Gianluigi Viscusi

This paper presents an early-stage application of the design science research (DSR) method to obtain a new idea selection approach, which uses clustering to filter ideas while taking into account the seeker’s goals and the learning dynamics. Most of previo ...

2019

Quantitative estimate on singularities in isoperimetric clusters

Maria Colombo

We prove a quantitative estimate on the number of certain singularities in almost minimizing clusters. In particular, we consider the singular points belonging to the lowest stratum of the Federer-Almgren stratification (namely, where each tangent cone doe ...

2019

The Speed Submission to DIHARD II: Contributions & Lessons Learned

Sébastien Marcel, Nicholas Evans

This paper describes the speaker diarization systems developed for the Second DIHARD Speech Diarization Challenge (DIHARD II) by the Speed team. Besides describing the system, which considerably outperformed the challenge baselines, we also focus on the le ...

Idiap2019