Network Design via Core Detouring for Problems Without a Core
Graph Chatbot
Chat with Graph Search
Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.
DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.
We analyze several florae (collections of plant species populating specific areas) in different geographic and climatic regions. For every list of species we produce a taxonomic classification tree and we consider its statistical properties. We find that r ...
This article reviews known results and contains new ones concerning the power spectra of large classes of signals and random fields driven by an underlying point process, such as spatial shot noises (with random impulse response and arbitrary basic station ...
We present a simple randomized algorithmic framework for connected facility location problems. The basic idea is as follows: We run a black-box approximation algorithm for the unconnected facility location problem, randomly sample the clients, and open the ...
In this paper, we present an acoustic direction-of-arrival (DOA) tracking system to track multiple maneuvering targets using a state space approach. The system consists of three blocks: beamformer, random sampling, and particle filter. The beamformer block ...
This paper presents a probabilistic algorithm for segmenting text embedded in video based on Monte Carlo sampling. The algorithm approximates the posterior of segmentation thresholds of video text by a set of weighted samples, referred to as particles. The ...
This paper presents a probabilistic algorithm for segmenting text embedded in video based on Monte Carlo sampling. The algorithm approximates the posterior of segmentation thresholds of video text by a set of weighted samples, referred to as particles. The ...
This paper presents a probabilistic algorithm for segmenting and recognizing text embedded in video sequences based on adaptive thresholding using a Bayes filtering method. The algorithm approximates the posterior distribution of segmentation thresholds of ...
Recent research advocates applying sampling to accelerate microarchitecture simulation. Simple random sampling offers accurate performance estimates (with a high quantifiable confidence) by taking a large number (e.g., 10,000) of short performance measurem ...
This paper presents a probabilistic algorithm for segmenting and recognizing text embedded in video sequences based on adaptive thresholding using a Bayes filtering method. The algorithm approximates the posterior distribution of segmentation thresholds of ...
This paper addresses the issue of segmentation and recognition of text embedded in video sequences from their associated text image sequence extracted by a text detection module. To this end, we propose a probabilistic algorithm based on Bayesian adaptive ...
