A range of behavioral and contextual factors, including eating and drinking behavior, mood, social context, and other daily activities, can significantly impact an individual's quality of life and overall well-being. Therefore, inferring everyday life aspe ...
A central task in high-level synthesis is scheduling: the allocation of operations to clock cycles. The classic approach to scheduling is static, in which each operation is mapped to a clock cycle at compile-time, but recent years have seen the emergence o ...
Buildings significantly impact urban energy consumption. Mobile passive solutions, such as manual solar protections, can mitigate heat gains, but their effectiveness depends on occupants’ decisions. Analyzing occupant adjustments of manual systems is chall ...
Scheduling in datacenters is an important, yet challenging problem. Datacenters are composed of a large number, typically tens of thousands, of commodity computers running a variety of data-parallel jobs. The role of the scheduler is to assign cluster reso ...
The mammalian brain, one of the most fascinating systems in nature, is a complex biological structure that has kept scientists busy for over a century. Many of the brain's mysteries have been unraveled due to the enormous efforts of the scientific communit ...
Randomization is a fundamental tool used in many theoretical and practical areas of computer science. We study here the role of randomization in the area of submodular function maximization. In this area, most algorithms are randomized, and in almost all c ...
Bias-resistant public randomness is a critical component in many (distributed) protocols. Existing solutions do not scale to hundreds or thousands of participants, as is needed in many decentralized systems. We propose two large-scale distributed protocols ...
Modern computing systems are based on multi-processor systems, i.e. multiple cores on the same chip. Hard real-time systems are required to perform particular tasks within certain amount of time; failure to do so characterises an unaccepted behavior. Hard ...
We study the homogenization problem for the system of equations of dynamics of a mixture of liquid crystals with random structure. We consider a simplified form of the Ericksen-Leslie equations for an incompressible medium with inhomogeneous density with r ...
We study bias arising as a result of nonlinear transformations of random variables in random or mixed effects models and its effect on inference in group-level studies or in meta-analysis. The findings are illustrated on the example of overdispersed binomi ...
This thesis is devoted to the derivation of error estimates for partial differential equations with random input data, with a focus on a posteriori error estimates which are the basis for adaptive strategies. Such procedures aim at obtaining an approximati ...
We develop a canonical framework for the study of the problem of registration of multiple point processes subjected to warping, known as the problem of separation of amplitude and phase variation. The amplitude variation of a real random function {Y(x) : x ...
In recent years, persistent homology (PH) has been successfully applied to real-world data in many different settings. Despite significant computational advances, PH algorithms do not yet scale to large datasets preventing interesting applications. One app ...
Hyperdimensional (HD) computing is a novel approach to machine learning inspired by neuroscience, which uses vectors in a hyper-dimensional space to represent data and models. This approach has gained significant interest in recent years with applications ...
We analyze the recent Multi-index Stochastic Collocation (MISC) method for computing statistics of the solution of a partial differential equation (PDE) with random data, where the random coefficient is parametrized by means of a countable sequence of term ...
