Publications associées à HD-Index: Pushing the Scalability-Accuracy Boundary for Approximate kNN Search in High-Dimensional Spaces

Publications associées (29)

Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks

Alfio Quarteroni, Francesco Regazzoni, Stefano Pagani

Predicting the evolution of systems with spatio-temporal dynamics in response to external stimuli is essential for scientific progress. Traditional equations-based approaches leverage first principles through the numerical approximation of differential equ ...

Nature Portfolio2024

Long plasma duration operation analyses with an international multi-machine (tokamaks and stellarators) database

Stefano Coda, Jeffrey Huang

Combined high-fusion performance and long-pulse operation is one of the key integration challenges for fusion energy development in magnetic devices. Addressing these challenges requires an integrated vision of physics and engineering aspects with the purp ...

IOP Publishing Ltd2024

Snowfall deposition in mountainous terrain: a statistical downscaling scheme from high-resolution model data on simulated topographies

Michael Lehning

One of the primary causes of non-uniform snowfall deposition on the ground in mountainous regions is the preferential deposition of snow, which results from the interaction of near-surface winds with topography and snow particles. However, producing high-r ...

Frontiers Media Sa2024

Towards Trustworthy Deep Learning for Image Reconstruction

Alexis Marie Frederic Goujon

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

EPFL2024

GELEX: Generative AI-Hybrid System for Example-Based Learning

Aybars Yazici, Tanja Christina Käser Jacober, Paola Mejia Domenzain, Jibril Albachir Frej

Traditional example-based learning methods are often limited by static, expert-created content. Hence, they face challenges in scalability, engagement, and effectiveness, as some learners might struggle to relate to the examples or find them relevant. To a ...

Association for Computing Machinery2024

Determining an optimum quantity of interleaved instruction streams of defined coroutines

Anastasia Ailamaki, Georgios Psaropoulos

A method for optimizing memory access for database operations is provided. The method may include performing a first database operation by at least executing a first instruction stream associated with the first database operation. The first database opera ...

2023

Aggregation and Exploration of High-Dimensional Data Using the Sudokube Data Cube Engine

Zhekai Jiang, Christoph Koch, Sachin Basil John, Peter Lindner

We present Sudokube, a novel system that supports interactive speed querying on high-dimensional data using partially materialized data cubes. Given a storage budget, it judiciously chooses what projections to precompute and materialize during cube constru ...

Association for Computing Machinery2023

Efficient Concurrent Analytical Query Processing using Data and Workload-conscious Sharing

Panagiotis Sioulas

Analytical workloads are evolving as the number of users surges and applications that submit queries in batches become popular. However, traditional analytical databases that optimize-then-execute each query individually struggle to provide timely response ...

EPFL2023

Interactive-time Exploration, Querying, and Analysis of Large High-dimensional Datasets

Sachin Basil John

In the current era of big data, aggregation queries on high-dimensional datasets are frequently utilized to uncover hidden patterns, trends, and correlations critical for effective business decision-making. Data cubes facilitate such queries by employing p ...

EPFL2023

AiiDA-defects: an automated and fully reproducible workflow for the complete characterization of defect chemistry in functional materials

Nicola Marzari, Sokseiha Muy, Conrad Johnston

Functional materials that enable many technological applications in our everyday lives owe their unique properties to defects that are carefully engineered and incorporated into these materials during processing. However, optimizing and characterizing thes ...

IOP Publishing Ltd2023

AIDRES: A database for the decarbonisation of the heavy industry in Europe

François Maréchal, Luc Girardin, Daniel Alexander Florez Orrego, Ivan Daniel Kantor, Shivom Sharma, Julia Granacher, Meire Ellen Gorete Ribeiro Domingos, Rafael Amorim Leandro De Castro Amoedo, Marie Jane Francine Jones, Yi Zhao

The AIDRES database aims to support the long-term objective of a fully integrated industrial strategy in the EU 27, providing a service to the European Commission and a catalogue for industries to understand the effectiveness, efficiency and cost of potent ...

Universidad de Las Palmas de Gran Canaria2023

NMR Crystallography in the Big Data Era: New Methods and Applications Powered by Machine Learning

Manuel Cordova

Structure determination of materials is key to understanding their physical properties. While single-crystal X-ray diffraction is the gold standard for structures displaying long-range order, many materials of interest are polycrystalline and/or disordered ...

EPFL2023

Robust Collaborative Learning with Linear Gradient Overhead

Rachid Guerraoui, Nirupam Gupta, Rafaël Benjamin Pinot, Le Nguyen Hoang, Sadegh Farhadkhani, John Stephan

Collaborative learning algorithms, such as distributed SGD (or D-SGD), are prone to faulty machines that may deviate from their prescribed algorithm because of software or hardware bugs, poisoned data or malicious behaviors. While many solutions have been ...

PLMR2023

Sparse Autoencoders for Speech Modeling and Recognition

Selen Hande Kabil

Speech recognition-based applications upon the advancements in artificial intelligence play an essential role to transform most aspects of modern life. However, speech recognition in real-life conditions (e.g., in the presence of overlapping speech, varyin ...

EPFL2023

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

Characterization and prediction of peptide structures on inorganic surfaces

Dmitrii Maksimov

Interfaces between peptides and metallic surfaces are the subject of great interest for possible use in technological and medicinal applications, mainly since organic systems present an extensive range of functionalities, are abundant, cheap, and exhibit l ...

EPFL2022

Adaptive table placement in NUMA architectures

Anastasia Ailamaki, Iraklis Psaroudakis

Techniques and solutions are provided for performing adaptive database table placement in a non-uniform memory access (NUMA) architecture. The adaptive table placement can occur in response to changing workloads on the NUMA nodes. For example, if a particu ...

2022

HyperLogLog: Exponentially Bad in Adversarial Settings

Mathilde Aliénor Raynal

Computing the count of distinct elements in large data sets is a common task but naive approaches are memory-expensive. The HyperLogLog (HLL) algorithm (Flajolet et al., 2007) estimates a data set's cardinality while using significantly less memory than a ...

IEEE COMPUTER SOC2022

Understanding JET-C quiescent phases with edge harmonic magnetohydrodynamic activity and comparison with behaviour under ITER-like wall conditioning

Jonathan Graves, Wilfred Anthony Cooper, Daniele Brunetti, Alberto Mariani, Andreas Kleiner, Guillermo Bustos Ramirez

An analysis of edge localised mode-free (quiescent) H-mode discharges exhibiting edge harmonic magnetoydrodynamic activity in the JET-carbon wall machine is presented. It is observed that the otherwise quiescent pulses with multiple-n harmonic oscillations ...

IOP Publishing Ltd2022

On Generalizations of Some Distance Based Classifiers for HDLSS Data

Soham Sarkar

In high dimension, low sample size (HDLSS) settings, classifiers based on Euclidean distances like the nearest neighbor classifier and the average distance classifier perform quite poorly if differences between locations of the underlying populations get m ...

Microtome Publishing2022