Publications associées à Interactive semantic fisheye views for information workspaces

Orchestrating chromosome conformation capture analysis with Bioconductor

Genome-wide chromatin conformation capture assays provide formidable insights into the spatial organization of genomes. However, due to the complexity of the data structure, their integration in multi-omics workflows remains challenging. We present data st ...

Nature Research2024

The JPEG AI Standard: Providing Efficient Human and Machine Visual Data Consumption

Touradj Ebrahimi

The Joint Photographic Experts Group (JPEG) AI learning-based image coding system is an ongoing joint standardization effort between International Organization for Standardization (ISO), International Electrotechnical Commission (IEC), and International Te ...

IEEE COMPUTER SOC2023

impresso Text Reuse at Scale. An interface for the exploration of text reuse data in semantically enriched historical newspapers

Maud Ehrmann, Matteo Romanello

Text Reuse reveals meaningful reiterations of text in large corpora. Humanities researchers use text reuse to study, e.g., the posterior reception of influential texts or to reveal evolving publication practices of historical media. This research is often ...

2023

From scattered sources to comprehensive technology landscape : A recommendation-based retrieval approach

Karl Aberer, Chi Thang Duong

Mapping the technology landscape is crucial for market actors to take informed investment decisions. However, given the large amount of data on the Web and its subsequent information overload, manually retrieving information is a seemingly ineffective and ...

ELSEVIER2023

Efficient and Effective Multi-Modal Queries Through Heterogeneous Network Embedding

Karl Aberer, Quoc Viet Hung Nguyen, Thành Tâm Nguyên, Chi Thang Duong, Thanh Trung Huynh

The heterogeneity of today's Web sources requires information retrieval (IR) systems to handle multi-modal queries. Such queries define a user's information needs by different data modalities, such as keywords, hashtags, user profiles, and other media. Rec ...

IEEE COMPUTER SOC2022

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

Visual Scene Understanding for Transportation: From Detecting Objects To Relationships

George Adaimi

Transportation, which deals with moving people and goods around, has a clear impact on the economic development of our society and our well-being. Traditionally, transportation was studied and analyzed using expensive sensors, such as induction loops, that ...

EPFL2022

Recent advances in trajectory inference from single-cell omics data

Bart Deplancke, Wouter Saelens

Trajectory inference methods have emerged as a novel class of single-cell bioinformatics tools to study cellular dynamics at unprecedented resolution. Initial development focused on adapting methods based on clustering or graph traversal, but recent advanc ...

ELSEVIER2021

Development of the RAPTOR suite of codes towards real-time reconstruction of JET discharges

Federico Alberto Alfredo Felici

The RAPTOR suite of codes combines real-time model-based predictions of the plasma state with the available diagnostic measurements. Following the work on TCV and ASDEX-Upgrade, this paper presents the implementation of the RAPTOR suite for JET. This suite ...

2021

Radial Scope: A New Visualization Tool for Structure-Data Relationships

Communication of data associated with chemical structures in a concise fashion can be challenging. Here, we describe an alternative to the classic substrate table that integrates numerical data with struttural changes. These radial-scopeplots facilitate th ...

2020

The SONATA data format for efficient description of large-scale network models

James Gonzalo King, Juan Bautista Hernando Vieites, Eilif Benjamin Muller, Jean-Denis Georges Emile Courcol, Werner Alfons Hilda Van Geit, Michael Emiel Gevaert, Judit Planas Carbonell, Adrien Michel Achille Devresse

Increasing availability of comprehensive experimental datasets and of high-performance computing resources are driving rapid growth in scale, complexity, and biological realism of computational models in neuroscience. To support construction and simulation ...

2020

Multi-scale sequential network for semantic text segmentation and localization

Jean-Marc Odobez, Olivier Canévet, Michael Villamizar

We present a novel method for semantic text document analysis which in addition to localizing text it labels the text in user-defined semantic categories. More precisely, it consists of a fully-convolutional and sequential network that we apply to the part ...

2020

Comparison of MRI-based automated segmentation methods and functional neurosurgery targeting with direct visualization of the Ventro-intermediate thalamic nucleus at 7T

Jean-Philippe Thiran, Meritxell Bach Cuadra, Elena Najdenovska, José Pedro Rebelo Ferreira Marques, João Pedro Forjaco Jorge, Daniel Gallichan, Constantin Tuleasca

The ventro-intermediate nucleus (Vim), as part of the motor thalamic nuclei, is a commonly used target in functional stereotactic neurosurgery for treatment of drug-resistant tremor. As it cannot be directly visualized on routinely used magnetic resonance ...

Nature Research2019

Point cloud quality evaluation: Towards a definition for test conditions

Touradj Ebrahimi, Rafael Xavier Meriade Duarte, Evangelos Alexiou

Recently stakeholders in the area of multimedia representation and transmission have been looking at plenoptic technologies to improve immersive experience. Among these technologies, point clouds denote a volumetric information representation format with i ...

2019

Evaluating and Interpreting Deep Convolutional Neural Networks via Non-negative Matrix Factorization

Edo Collins

With ever greater computational resources and more accessible software, deep neural networks have become ubiquitous across industry and academia. Their remarkable ability to generalize to new samples defies the conventional view, which holds that complex, ...

EPFL2019

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

The Fifteenth Data Release of the Sloan Digital Sky Surveys: First Release of MaNGA-derived Quantities, Data Visualization Tools, and Stellar Library

Guillaume Blanc, Johan Comparat, Amy Jones, Yuguang Chen, Anand Stéphane Raichoor, John Wilson, Meng Yang, David Schlegel, Cheng Li, Zheng Zheng, Jiayi Sun

Twenty years have passed since first light for the Sloan Digital Sky Survey (SDSS). Here, we release data taken by the fourth phase of SDSS (SDSS-IV) across its first three years of operation (2014 July-2017 July). This is the third data release for SDSS-I ...

2019

A new clustering and visualization method to evaluate urban heat energy planning scenarios

Jean-Louis Scartezzini, Silvia Coccolo, Dasaraden Mauree, Sara Torabi Moghadam

Spatial visualization is a very useful tool to help decision-makers in the urban planning process, i) to define future energy transition pathways, ii) to implement energy efficiency strategies and iii) to integrate renewable energy technologies in the cont ...

2019

Visbrain: A Multi-Purpose GPU-Accelerated Open-Source Suite for Multimodal Brain Data Visualization

Raphaël Vallat, Christian O'Reilly

We present Visbrain, a Python open-source package that offers a comprehensive visualization suite for neuroimaging and electrophysiological brain data. Visbrain consists of two levels of abstraction: (1) objects which represent highly configurable neuroori ...

2019

Learning to Find Unpaired Cross-Spectral Correspondences

Seungryong Kim

We present a deep architecture and learning framework for establishing correspondences across cross-spectral visible and infrared images in an unpaired setting. To overcome the unpaired cross-spectral data problem, we design the unified image translation a ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019