LSH At Large - Distributed KNN Search in High Dimensions

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As it has become easier and cheaper to collect big datasets in the last few decades, designing efficient and low-cost algorithms for these datasets has attracted unprecedented attention. However, in most applications, even storing datasets as acquired has ...

Traffic congestion constitutes one of the most frequent, yet challenging, problems to address in the urban space. Caused by the concentration of population, whose mobility needs surpass the serving capacity of urban networks, congestion cannot be resolved ...

This dissertation introduces traffic forecasting methods for different network configurations and data availability. Chapter 2 focuses on single freeway cases. Although its topology is simple, the non-linearity of traffic features makes this prediction sti ...

The safety of existing bridges may be assessed using data from monitoring. The fatigue damage induced during observation period is extrapolated to the total service duration of a structure, and the reserve capacity is estimated. However, due to the randomn ...

Forecasting pedestrians' future motions is essential for autonomous driving systems to safely navigate in urban areas. However, existing prediction algorithms often overly rely on past observed trajectories and tend to fail around abrupt dynamic changes, s ...

Machine learning (ML) solutions are nowadays distributed, according to the so-called server/worker architecture. One server holds the model parameters while several workers train the model. Clearly, such architecture is prone to various types of component ...

Production quality and process efficiency are the two main drivers that lead any industrial strategy. To ensure product quality, a duality historically existed between two approaches, namely batch sampling and systematic sampling. In batch sampling, the ba ...

Testing mutual independence among several random vectors of arbitrary dimensions is a challenging problem in Statistics, and it has gained considerable interest in recent years. In this article, we propose some nonparametric tests based on different notion ...

Deep learning algorithms are responsible for a technological revolution in a variety oftasks including image recognition or Go playing. Yet, why they work is not understood.Ultimately, they manage to classify data lying in high dimension – a feat generical ...

The proliferation of phasor measurement units (PMUs) presents new challenges in archiving and processing large amounts of synchrophasor data which necessitates advanced data compression methods. This paper proposes a singular value decomposition (SVD)-base ...

Modeling urban traffic on the level of network is a wide research area oriented to the development of ITS. In this thesis properties of models based on MFD (Macroscopic Fundamental Diagram) are studied. The idea behind MFD is to say that the state of the t ...

Our work addresses long-term motion context issues for predicting future frames. To predict the future precisely, it is required to capture which long-term motion context (e.g., walking or running) the input motion (e.g., leg movement) belongs to. The bott ...

LSH At Large - Distributed KNN Search in High Dimensions

Chattez avec Graph Search

Sketches, metrics and fast algorithms

Large-scale traffic signal control and multimodal network design

Multivariate time series forecasting for freeway networks

Quantification of influence of monitoring duration on measured traffic action effects on fatigue of RC deck slabs of road bridges

Pedestrian Stop and Go Forecasting with Hybrid Feature Fusion

Genuinely distributed Byzantine machine learning

Virtual metrology applied to milling process

Tests of mutual independence among several random vectors using univariate and multivariate ranks of nearest neighbours

Landscape and training regimes in deep learning

A Real-time Synchrophasor Data Compression Method Using Singular Value Decomposition

The influence of trip length distribution on urban traffic in network-level models

Video Prediction Recalling Long-term Motion Context via Memory Alignment Learning