Publications related to Unstructured data

DiffAirfoil: An Efficient Novel Airfoil Sampler Based on Latent Space Diffusion Model for Aerodynamic Shape Optimization

Surrogate-based optimization is widely used for aerodynamic shape optimization, and its effectiveness depends on representative sampling of the design space. However, traditional sampling methods are hard-pressed to effectively sample high-dimensional desi ...

2024

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

ECCE: Entity-centric Corpus Exploration Using Contextual Implicit Networks

Maud Ehrmann, Matteo Romanello, Andreas Oliver Spitz

In the Digital Age, the analysis and exploration of unstructured document collections is of central importance to members of investigative professions, whether they might be scholars, journalists, paralegals, or analysts. In many of their domains, entities ...

ACM Digital Press2022

High-dimensional Data Cubes

Christoph Koch, Sachin Basil John

This paper introduces an approach to supporting high-dimensional data cubes at interactive query speeds and moderate storage cost. The approach is based on binary(-domain) data cubes that are judiciously partially materialized; the missing information can ...

ASSOC COMPUTING MACHINERY2022

End-to-End Task-Oriented Dialog Modeling With Semi-Structured Knowledge Management

Antoine Bosselut, Silin Gao

Current task-oriented dialog (TOD) systems mostly manage structured knowledge (e.g. databases and tables) to guide the goal-oriented conversations. However, they fall short of handling dialogs which also involve unstructured knowledge (e.g. reviews and doc ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

High-dimensional Data Cubes

Christoph Koch, Sachin Basil John

This paper introduces an approach to supporting high-dimensional data cubes at interactive query speeds and moderate storage cost. The approach is based on binary(-domain) data cubes that are judiciously partially materialized; the missing information can ...

2022

Graph integration of structured, semistructured and unstructured data for data journalism

Angelos Christos Anadiotis, Jingmao You

Digital data is a gold mine for modern journalism. However, datasets which interest journalists are extremely heterogeneous, ranging from highly structured (relational databases), semi-structured (JSON, XML, HTML), graphs (e.g., RDF), and text. Journalists ...

PERGAMON-ELSEVIER SCIENCE LTD2022

Unsupervised Term Extraction for Highly Technical Domains

Diego Matteo Antognini

Term extraction is an information extraction task at the root of knowledge discovery platforms. Developing term extractors that are able to generalize across very diverse and potentially highly technical domains is challenging, as annotations for domains r ...

2022

Digitised Newspapers – A New Eldorado for Historians? Reflections on Tools, Methods and Epistemology

Maud Ehrmann, Frédéric Clavert

The application of digital technologies to historical newspapers has changed the research landscape historians were used to. An Eldorado? Despite undeniable advantages, the new digital affordance of historical newspapers also transforms research practices ...

De Gruyter2022

Textual Explanations and Critiques in Recommendation Systems

Diego Matteo Antognini

Artificial intelligence and machine learning algorithms have become ubiquitous. Although they offer a wide range of benefits, their adoption in decision-critical fields is limited by their lack of interpretability, particularly with textual data. Moreover, ...

EPFL2022

Conceptual frameworks in indicator‐based assessments of urban sustainability—An analysis based on 67 initiatives

Albert Merino, Pekka Jaakko Halla

Urban sustainability is today commonly considered to be a crucial determinant of global sustainable development, and indicator-based assessments represent a popular means of operationalizing this complex concept. An important aspect of these assessments co ...

2022

Efficient Simulation for Visual Comfort Evaluations

Marilyne Andersen, Jan Wienold, Stephen William Wasilewski

This paper provides a validation of a novel sampling, storage, and evaluation method named raytraverse that can quickly and accurately compute glare and visual comfort metrics including vertical illuminance, Daylight Glare Probability (DGP), and Unified Gl ...

2022

The role of convolutional neural networks in scanning probe microscopy: a review

Georg Fantner

Progress in computing capabilities has enhanced science in many ways. In recent years, various branches of machine learning have been the key facilitators in forging new paths, ranging from categorizing big data to instrumental control, from materials desi ...

BEILSTEIN-INSTITUT2021

Data Management Plans: the Importance of Data Management in the BIG-MAP Project[**]

Nicola Marzari, Giovanni Pizzi, Martin Uhrin, Ivano Eligio Castelli

Open access to research data is increasingly important for accelerating research. Grant authorities therefore request detailed plans for how data is managed in the projects they finance. We have recently developed such a plan for the EU H2020 BIG-MAP proje ...

WILEY-V C H VERLAG GMBH2021

Drought alters the biogeochemistry of boreal stream networks

Luis Gómez Gener

Drought is a global phenomenon, with widespread implications for freshwater ecosystems. While droughts receive much attention at lower latitudes, their effects on northern river networks remain unstudied. We combine a reach-scale manipulation experiment, o ...

Nature Research2020

Self-Supervised Prototypical Transfer Learning for Few-Shot Classification

Matthias Grossglauser, Arnout Jan J Devos, Carlos Roberto Medina Temme

Recent advances in transfer learning and few-shot learning largely rely on annotated data related to the goal task during (pre-)training. However, collecting sufficiently similar and annotated data is often infeasible. Building on advances in self-supervis ...

2020

Learning to Reduce Annotation Load

Ksenia Konyushkova

Modern machine learning methods and their applications in computer vision are known to crave for large amounts of training data to reach their full potential. Because training data is mostly obtained through humans who manually label samples, it induces a ...

EPFL2019

VO2 oscillators coupling for Neuromorphic Computation

Mihai Adrian Ionescu, Igor Stolichnov, Kirsten Emilie Moselund, Siegfried Karg, Elisabetta Corti

New computation schemes inspired by biological processes can outperform standard von-Neumann architectures in dealing with complex and unstructured tasks. As a new approach, systems of frequency-locked, coupled oscillators are investigated using the phase ...

IEEE2019

Data Science in Environmental Health Research

Christine Choirat

Purpose of ReviewData science is an exploding trans-disciplinary field that aims to harness the power of data to gain information or insights on researcher-defined topics of interest. In this paper, we review how data science can help advance environmental ...

2019

Practical Private Range Search in Depth

Odysseas Papapetrou, Ioannis Demertzis

We consider a data owner that outsources its dataset to an untrusted server. The owner wishes to enable the server to answer range queries on a single attribute, without compromising the privacy of the data and the queries. There are several schemes on “pr ...

2018