A statistical shape model of the human second cervical vertebra

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Volume electron microscopy is the method of choice for the in situ interrogation of cellular ultrastructure at the nanometer scale, and with the increase in large raw image datasets generated, improving computational strategies for image segmentation and s ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

TimberSLAM (TSLAM) is an object-centered, tag-based visual self-localization and mapping (SLAM) system for monocular RGB cameras. It was specifically developed to support a robust and augmented reality pipeline for close-range, noisy, and cluttered fabrica ...

Recently, there has been an arms race of pose forecasting methods aimed at solving the spatio-temporal task of predicting a sequence of future 3D poses of a person given a sequence of past observed ones. However, the lack of unified benchmarks and limited ...

We consider the problem of comparing several samples of stochastic processes with respect to their second-order structure, and describing the main modes of variation in this second order structure, if present. These tasks can be seen as an Analysis of Vari ...

Neurodegenerative and neuroinflammatory disorders often involve complex pathophysiological mechanisms that are â to this date â only partially understood. A more comprehensive understanding of those microstructural processes and their characterization ...

Deep learning has revolutionized the field of computer vision, a success largely attributable to the growing size of models, datasets, and computational power. Simultaneously, a critical pain point arises as several computer vision applications are deploye ...

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

River networks play a crucial role in the global carbon cycle, as relevant sources of carbon dioxide (CO2) to the atmosphere. Advancements in high-frequency monitoring in aquatic environments have enabled measurement of dissolved CO2 concentration at tempo ...

Superconducting electronics provide us with cryogenic digital circuits that can rival established technologies in performance and energy consumption. Today, the lack of tools for the design of large-scale integrated superconducting circuits is a major obst ...

Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance. In this paper, we argue that such architectures offer an additional benefit: The convergence rate of the ...

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

Decision-making permeates every aspect of human and societal development, from individuals' daily choices to the complex decisions made by communities and institutions. Central to effective decision-making is the discipline of optimization, which seeks th ...

Data-driven approaches have been applied to reduce the cost of accurate computational studies on materials, by using only a small number of expensive reference electronic structure calculations for a representative subset of the materials space, and using ...

Infrared and Raman spectroscopies are ubiquitous techniques employed in many experimental laboratories, thanks to their fast and non-destructive nature able to capture materials' features as spectroscopic fingerprints. Nevertheless, these measurements freq ...

Pulmonary nodules and masses are crucial imaging features in lung cancer screening that require careful management in clinical diagnosis. Despite the success of deep learning-based medical image segmentation, the robust performance on various sizes of lesi ...

Multiparty homomorphic encryption (MHE) enables a group of parties to encrypt data in a way that (i) enables the evaluation of functions directly over its ciphertexts and (ii) enforces a joint cryptographic access-control over the underlying data. By exten ...

Prediction of ground motion triggered by earthquakes is a prime concern for both the seismology community and geotechnical earthquake engineering one. The subfield occupied with such a problem is termed site response analysis (SRA), its one-dimensional fla ...

Augmented reality in additive fabrication is predominantly applied to the manufacturing of structures from regularly shaped materials. Our work however embraces natural heterogeneity, by focusing on the assembly of irregularly shaped elements such as miner ...

The generation of 3D models depicting cities in the past holds great potential for documentation and educational purposes. However, it is often hindered by incomplete historical data and the specialized expertise required. To address these challenges, we p ...

A statistical shape model of the human second cervical vertebra

Chattez avec Graph Search

Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets

Aggregating Spatial and Photometric Context for Photometric Stereo

TSLAM: a tag-based object-centered monocular navigation system for augmented manual woodworking

Toward Reliable Human Pose Forecasting With Uncertainty

Transportation-based functional ANOVA and PCA for covariance operators

Development and clinical validation of computational imaging biomarkers for neurodegenerative diseases

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

Spectral Estimators for High-Dimensional Matrix Inference

Lotic-SIPCO2: Adaptation of an open-source CO2 sensor system and examination of associated emission uncertainties across a range of stream sizes and land uses

Technology-Aware Logic Synthesis for Superconducting Electronics

Enabling Uncertainty Estimation in Iterative Neural Networks

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Reliable data-driven decision-making through optimal transport

Efficient and insightful descriptors for representing molecular and material space

Automated all-functionals infrared and Raman spectra

Scale-Aware Test-Time Click Adaptation for Pulmonary Nodule and Mass Segmentation

Multiparty Homomorphic Encryption: from Theory to Practice

Data-driven 1D wave propagation for site response analysis

Projector-based augmented stacking framework for irregularly shaped objects

Machine-Learning-Enhanced Procedural Modeling for 4D Historical Cities Reconstruction