Publications related to Prescriptive analytics

Quantitative convergence rates for scaling limit of SPDEs with transport noise

We consider on the torus the scaling limit of stochastic 2D (inviscid) fluid dynamics equations with transport noise to deterministic viscous equations. Quantitative estimates on the convergence rates are provided by combining analytic and probabilistic ar ...

Academic Press Inc Elsevier Science2024

Electromagnetic Radiation of Implantable Antennas

Mingxiang Gao

In the development of implantable bioelectronics, the establishment of efficient wireless RF links between implants and external nodes is crucial, providing substantial contributions to the advancement of medical diagnosis, therapies, and basic science. Im ...

EPFL2024

Analytical Engines With Context-Rich Processing: Towards Efficient Next-Generation Analytics

Anastasia Ailamaki, Viktor Sanca

As modern data pipelines continue to collect, produce, and store a variety of data formats, extracting and combining value from traditional and context-rich sources such as strings, text, video, audio, and logs becomes a manual process where such formats a ...

2023

Blue Brain Nexus: An open, secure, scalable system for knowledge graph management and data-driven science

Henry Markram, Sean Lewis Hill, Samuel Claude Kerrien, Carolina Johanna Elisabeth Lindqvist, Alejandra Garcia Rojas Martinez, Huanxiang Lu, Mohameth François Sy, Anna-Kristin Kaufmann, Jonathan Raël Lurie, Henry Genet, Pierre-Alexandre Fonta, Alexander Désiré Ulbrich, Michaël Fernand Paul Dupont, Silvia Rosario Jimenez Tejeda, Bogdan Roman, Ian Lavriushev, Didac Montero Mendez, Wojciech Adam Wajerowicz, Kenneth William Pirman, Julien Antonin Machon, Dhanesh Neela Mana, Natalia Stafeeva

Modern data-driven science often consists of iterative cycles of data discovery, acquisition, preparation, analysis, model building and validation leading to knowledge discovery as well as dissemination at scale. The unique challenges of building and simul ...

2023

Pixels: An Efficient Column Store for Cloud Data Lakes

Anastasia Ailamaki, Haoqiong Bian

To benefit from the cloud’s higher elasticity and price-efficiency, most modern data-lake engines support S3-like cloud object storage (COS) services as their optional or preferred underlying storage. Meanwhile, the widespread column stores, such as Parque ...

IEEE2022

Analytical prediction of the final error due to noise in a ϕ-OTDR system

Luc Thévenaz, Malak Mohamed Hossameldeen Omar Mohamed Galal, Yuting Yang, Li Zhang, Suneetha Sebastian

An analytical expression to estimate the uncertainty on the frequency shift in ϕ-OTDR systems is established and confirmed by actual data. ...

Optica Publishing Group2022

Microscopic interplay of temperature and disorder of a one-dimensional elastic interface

Elisabeth Agoritsas, Thierry Giamarchi

Elastic interfaces display scale-invariant geometrical fluctuations at sufficiently large lengthscales. Their asymptotic static roughness then follows a power-law behavior, whose associated exponent provides a robust signature of the universality class to ...

2022

An Integrated Approach to Learning Analytics

Denis Gillet, Juan Carlos Farah, Sandy Ingram

Despite the rise in services generating learning analytics, there is a lack of standard models and guidelines for data integration and aggregation to inform the design choices of applications supporting learning analytics. We propose a bottom-up, user-driv ...

2021

An End-to-End Data Pipeline for Managing Learning Analytics

Denis Gillet, Juan Carlos Farah, Sandy Ingram, Joana Catarina Soares Machado

Despite the importance of learning analytics in digital education, there is limited support for researchers in education to generate, access, and share experimental data while complying with ethical and privacy legislation. We propose a set of related tool ...

2021

Understanding Computational Costs of Cellular-Level Brain Tissue Simulations Through Analytical Performance Models

Felix Schürmann, Francesco Cremonesi

Computational modeling and simulation have become essential tools in the quest to better understand the brain's makeup and to decipher the causal interrelations of its components. The breadth of biochemical and biophysical processes and structures in the b ...

HUMANA PRESS INC2020

Negative Capacitance Double-Gate Junctionless FETs: A Charge-Based Modeling Investigation of Swing, Overdrive and Short Channel Effect

Mihai Adrian Ionescu, Jean-Michel Sallese, Farzan Jazaeri, Amin Rassekh

In this article, an analytical predictive model of the negative capacitance (NC) effect in symmetric long channel double-gate junctionless transistor is proposed based on a charge-based model. In particular, we have investigated the effect of the thickness ...

2020

Analytics for learning design: A layered framework and tools

Maria Jesus Rodriguez Triana

The field of learning design studies how to support teachers in devising suitable activities for their students to learn. The field of learning analytics explores how data about students' interactions can be used to increase the understanding of learning e ...

2019

Using Educational Data From Teaching And Learning To Inform Teachers’ Reflective Educational Design In Inquiry-Based STEM Education

Denis Gillet, Maria Jesus Rodriguez Triana

Science, Technology, Engineering and Mathematics (STEM) education is recognized as a top priority for school education worldwide and Inquiry-based teaching and learning is identified as one of the most dominant approaches. To effectively engage individual ...

2019

Rock You like a Hurricane: Taming Skew in Large Scale Analytics

Willy Zwaenepoel, Nicolas Schiper, Laurent Bindschaedler, Jasmina Malicevic, Ashvin Goel

Current cluster computing frameworks suffer from load imbalance and limited parallelism due to skewed data distributions, processing times, and machine speeds. We observe that the underlying cause for these issues in current systems is that they partition ...

2018

Analysis of the vertical displacement of energy pile groups

Lyesse Laloui, Alessandro Francesco Rotta Loria

Over the last fifty years, the interaction factor method has been widely used to address the vertical displacement and the increased deformation of conventional pile groups subjected to mechanical loads when group effects and interactions occur among the p ...

2018

Editorial

Maria Jesus Rodriguez Triana

Learning is a complex, mostly invisible process that happens across spaces, occurring in the physical world but also increasingly in virtual worlds or web-based spaces. In order to explore what happens in such blended learning experience, there is a need ...

CEUR2017

ReCache: Reactive Caching for Fast Analytics over Heterogeneous Data

Anastasia Ailamaki, Manolis Karpathiotakis, Tahir Azim

As data continues to be generated at exponentially growing rates in heterogeneous formats, fast analytics to extract meaningful information is becoming increasingly important. Systems widely use in-memory caching as one of their primary techniques to speed ...

2017

Improved Analytical Model of Hyperelastic Magnetic Membrane

Yves Perriard, Yoan René Cyrille Civet, Louis Antoine Masson, Guillaume Paul-André Burnand, Cécile Gisèle Aimée Jeannine Cuchet

This paper proposes an analysis of multi-factor fabrications of magnetic membranes. In order to acquire some knowledge about the influence of fabrication factors, an analysis is performed using a Design of Experiment (DoE) methodology. The latter is applie ...

2017

Stealing Machine Learning Models via Prediction APIs

Florian Tramèr, Fan Zhang, Ari Juels

Machine learning (ML) models may be deemed confidential due to their sensitive training data, commercial value, or use in security applications. Increasingly often, confidential ML models are being deployed with publicly accessible query interfaces. ML-as- ...

Usenix Assoc2016

Toward timely, predictable and cost-effective data analytics

Renata Borovica-Gajic

Modern industrial, government, and academic organizations are collecting massive amounts of data at an unprecedented scale and pace. The ability to perform timely, predictable and cost-effective analytical processing of such large data sets in order to ext ...

EPFL2016