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Finding cycles in directed graphs enables important applications in various domains such as finance, biology, chemistry, and network science. However, as the size of graph datasets continues to grow, it becomes increasingly difficult to discover cycles wit ...

Cycles are one of the fundamental subgraph patterns and being able to enumerate them in graphs enables important applications in a wide variety of fields, including finance, biology, chemistry, and network science. However, to enable cycle enumeration in r ...

Modern machine learning (ML) models are capable of impressive performances. However, their prowess is not due only to the improvements in their architecture and training algorithms but also to a drastic increase in computational power used to train them.|S ...

Two prominent categories for achieving coordinated multirobot displacement are flocking and navigation in formation. Both categories have their own body of literature and characteristics, including their respective advantages and disadvantages. While typic ...

Design exploration is a creative process that consists of the incremental generation of design candidates. Supported by digital means or not, the process handles the ill-structured nature of design and allows creativity to flourish through diversity of des ...

Modern data analytical workloads often need to run queries over a large number of tables. An optimal query plan for such queries is crucial for being able to run these queries within acceptable time bounds. However, with queries involving many tables, find ...

Understanding epidemic propagation in large networks is an important but challenging task, especially since we usually lack information, and the information that we have is often counter-intuitive. An illustrative example is the dependence of the final siz ...

This work aims to study the effects of wind uncertainties in civil engineering structural design. Optimising the design of a structure for safety or operability without factoring in these uncertainties can result in a design that is not robust to these per ...

Cross-validation is commonly used to select the recommendation algorithms that will generalize best on yet unknown data. Yet, in many situations the available dataset used for cross-validation is scarce and the selected algorithm might not be the best suit ...

Weight sharing has become a de facto standard in neural architecture search because it enables the search to be done on commodity hardware. However, recent works have empirically shown a ranking disorder between the performance of stand-alone architectures ...

Machine intelligence greatly impacts almost all domains of our societies. It is profoundly changing the field of mechanical engineering with new technical possibilities and processes. The education of future engineers also needs to adapt in terms of techni ...

The Quantum Fisher Information matrix (QFIM) is a central metric in promising algorithms, such as Quantum Natural Gradient Descent and Variational Quantum Imaginary Time Evolution. Computing the full QFIM for a model with d parameters, however, is computat ...

Quantification of mobility is the key to monitor the progression of mobility disorders as well as the effect of an intervention. Inertial measurement units (IMUs) with dedicated algorithms can quantify postural transitions and gait as the two key aspects o ...

Many important problems in contemporary machine learning involve solving highly non- convex problems in sampling, optimization, or games. The absence of convexity poses significant challenges to convergence analysis of most training algorithms, and in some ...

Large dataflow designs are a result of behavioral specification of modern complex digital systems and/or a result of unfolding and transforming looped and branched programs. Since deep-submicron silicon technology provides large amounts of available resour ...

As demands for computing have been continuously increasing, various solutions have been proposed. Some approaches deal with improving algorithms and software programs, mainly through the tuning of advanced heuristics and learning methods. Some other tackle ...

In this article, the design of a quality control network is considered in an entire supply chain, in the context of an international food and beverage company, denoted as ABC. For each possible control point of the network, two decisions have to be made: ( ...

Nonconvex minimax problems appear frequently in emerging machine learning applications, such as generative adversarial networks and adversarial learning. Simple algorithms such as the gradient descent ascent (GDA) are the common practice for solving these ...

This project presents a pre-State Estimation method for the detection of Bad Data (BD) in Phasor Measurement Units (PMUs) using correlation analysis and a Neural Network Classifier. Presented in this paper is the algorithm design, the steps for generating ...

In this thesis, we propose new algorithms to solve inverse problems in the context of biomedical images. Due to ill-posedness, solving these problems require some prior knowledge of the statistics of the underlying images. The traditional algorithms, in th ...

Squall: Scalable Real-time Analytics

Chattez avec Graph Search

Fast Parallel Algorithms for Enumeration of Simple, Temporal, and Hop-Constrained Cycles

Fast Parallel Algorithms for Enumeration of Simple, Temporal, and Hop-constrained Cycles

Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search

Hybrid Flock - Formation Control Algorithms

Policy-based Exploration of Equilibrium Representations (PEER): A topology grammar for generative conceptual structural design

Efficient Massively Parallel Join Optimization for Large Queries

The Role of Adaptivity in Source Identification with Time Queries

Multi-Level Monte Carlo Methods for Uncertainty Quantification and Risk-Averse Optimisation

The Imitation Game: Algorithm Selection by Exploiting Black-Box Recommenders

Landmark Regularization: Ranking Guided Super-Net Training in Neural Architecture Search

Automated Design: A Journey Across Modelling, Optimization, and Education

Simultaneous Perturbation Stochastic Approximation of the Quantum Fisher Information

Toward an objective assessment of mobility in clinical and daily activity settings

Convergence without Convexity: Sampling, Optimization, and Games

Pipeline Synthesis and Optimization from Branched Feedback Dataflow Programs

Roundtable Panel Discussion at DAC 2019: Evolutionary Computing or Heuristic Forever?

Design of a quality control network for an international food company

Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems

Detection of Bad PMU Data using Machine Learning Techniques

From Classical to Unsupervised-Deep-Learning Methods for Solving Inverse Problems in Imaging