CMM Modeling

Applications of noisy quantum devices

Event Date: Jan 11, 2019 in CMM Modeling, Seminars

Abstract: Near-term applications of early quantum devices rely on accurate estimates of expectation values to become relevant. Decoherence and gate errors lead to wrong estimates. This problem was, at least in theory, remedied with the advent of quantum error correction. However, the overhead that is needed to implement a fully fault-tolerant gate set with current codes and current devices seems prohibitively large. In turn, steady progress is made in improving the quality of the quantum hardware and we believe that we can build machines in the near term that cannot be emulated by a...

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Minicuro: Sarnak Conjecture: a link between ergodic theory and number theory

Event Date: Jan 14, 2019 in CMM Modeling, Seminars

Abstract:   In these series of talks we will look how several conjecture in number theory have a translation in ergodic theory and how applications go from one theory to the other. We focus mostly in the mechanisms allowing to pass from one theory to the other. No pre-requisists in number theory are needed.   Date: 10:30 AM, Monday 14, Wednesday 16, Thursday 17, January 2019.

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Functional inequalities, flows, symmetry and spectral estimates

Event Date: Dec 07, 2018 in CMM Modeling, Seminars

ABSTRACT:   In this talk I will review recent result about how the use of linear and nonlinear flows has been key to prove functional inequalities and qualitative properties for their extremal functions. I will also explain how from these inequalities and their best constants, optimal spectral estimates can be obtained for Schrödinger operators. This is a topic which is at the crossroads of nonlinear analysis and probability, with implications in differential geometry and potential applications in modelling in physics and...

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Particles-based simulations and GPU computing for soft matter science and computer graphics applications

Event Date: Oct 25, 2018 in CMM Modeling, Seminars

Summary:   Particle-based simulation codes used in soft matter science aim at represent the interactions that occur in colloidal suspensions at nanoscale level. These suspensions are a mixture of solid particles of diameter between 100 and 1000 nanometers and a solvent (usually water), all interacting with each other. The main goal when implementing these codes on the GPU is to accelerate the “particle neighbour searching” phase, which is the bottleneck in most lagrangian-based simulations. I will present recent results obtained for Brownian Dynamics, where the solvent is...

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“Deep Variational Transfer: Transfer Learning through Semi-supervised Deep Generative Models”

Event Date: Jun 14, 2018 in CMM Modeling, Seminars

Abstract: “In real-world applications, it is expensive and time-consuming to obtain labeled examples. In such cases, knowledge transfer from related domains, where labels are abundant, could greatly reduce the need for extensive labeling efforts. In this scenario, transfer and multi-task learning come in hand. In this paper, we propose Deep Variational Transfer (DVT), a variational autoencoder that transfers knowledge across domains using a shared latent Gaussian mixture model. More in details, we align all supervised examples of the same class into the same Gaussian Mixture component,...

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“Opportunities for collaboration with the School of Mathematics & Statistics at University College Dublin, Ireland”

Event Date: Mar 22, 2016 in CMM Modeling, Seminars

ABSTRACT: We will present an overview of the research topics and graduate programs available at the UCD School of Mathematics & Statistics, Ireland’s number one according to QS World Rankings in both Mathematics and Statistics & Operations Research. We are at the forefront of collaborative research in computational science and data analytics. Active international collaborations include high-performance computing across disciplines (ranging from black hole binaries to molecular dynamics to fluid mechanics to cryptography and financial mathematics) and the application of statistical...

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