Unsupervised Electrofacies Clustering Based on Parameterization of Petrophysical Properties: A Dynamic Programming Approach

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Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization

Volkan Cevher, Alp Yurtsever, Maria-Luiza Vladarean

We propose a stochastic conditional gradient method (CGM) for minimizing convex finitesum objectives formed as a sum of smooth and non-smooth terms. Existing CGM variants for this template either suffer from slow convergence rates, or require carefully inc ...

2022

Physical characterization of fault rocks within the Opalinus Clay formation

Marie Estelle Solange Violay, Luis Felipe Orellana Espinoza, Christophe Nussbaum

Near-surface disposal of radioactive waste in shales is a promising option to safeguard the population and environment. However, natural faults intersecting these geological formations can potentially affect the long-term isolation of the repositories. Thi ...

Nature Research2022

Discriminative clustering with representation learning with any ratio of labeled to unlabeled data

We present a discriminative clustering approach in which the feature representation can be learned from data and moreover leverage labeled data. Representation learning can give a similarity-based clustering method the ability to automatically adapt to an ...

2022

Deep MinCut: Learning Node Embeddings by Detecting Communities

Quoc Viet Hung Nguyen, Thành Tâm Nguyên, Chi Thang Duong, Trung-Dung Hoang

We present Deep MinCut (DMC), an unsupervised approach to learn node embeddings for graph -structured data. It derives node representations based on their membership in communities. As such, the embeddings directly provide insights into the graph structure ...

ELSEVIER SCI LTD2022

Zero-Knowledge IOPs with Linear-Time Prover and Polylogarithmic-Time Verifier

Alessandro Chiesa, Siqi Liu

Interactive oracle proofs (IOPs) are a multi-round generalization of probabilistically checkable proofs that play a fundamental role in the construction of efficient cryptographic proofs. ...

SPRINGER INTERNATIONAL PUBLISHING AG2022

Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap

Florent Gérard Krzakala, Lenka Zdeborová, Luca Pesce, Bruno Loureiro, Bruno Loureiro

A simple model to study subspace clustering is the high-dimensional k -Gaussian mixture model where the cluster means are sparse vectors. Here we provide an exact asymptotic characterization of the statistically optimal reconstruction error in this model i ...

2022

Latent Space Slicing for Enhanced Entropy Modeling in Learning-Based Point Cloud Geometry Compression

Touradj Ebrahimi, Davi Nachtigall Lazzarotto

The growing adoption of point clouds as an imaging modality has stimulated the search for efficient solutions for compression. Learning-based algorithms have been reporting increasingly better performance and are drawing the attention from the research com ...

IEEE2022

Hydration and mixture design of calcined clay blended cements: review by the RILEM TC 282-CCL

Karen Scrivener, Franco Alberto Zunino Sommariva, Mohsen Ben Haha, François Henri Avet, Anuj Parashar, Yuvaraj Dhandapani, Sreejith Krishnan

The RILEM technical committee 282-CCL: Calcined Clays as Supplementary Cementitious Materials, investigates all the aspects related to calcined clays, from clay exploration and characterization to calcination process, hydration reactions and concrete prope ...

SPRINGER2022

Combining Model Driven and Data Driven Approaches for Inverse Problems in Parameter Estimation and Image Reconstruction: From Modelling to Validation

Thomas Yu

Machine learning has become the state of the art for the solution of the diverse inverse problems arising from computer vision and medical imaging, e.g. denoising, super-resolution, de-blurring, reconstruction from scanner data, quantitative magnetic reson ...

EPFL2022

Linearization and Identification of Multiple-Attractor Dynamical Systems through Laplacian Eigenmaps

Aude Billard, Bernardo Fichera

Dynamical Systems (DS) are fundamental to the modeling and understanding time evolving phenomena, and have application in physics, biology and control. As determining an analytical description of the dynamics is often difficult, data-driven approaches are ...

Microtome Publishing2022

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Combining Model Driven and Data Driven Approaches for Inverse Problems in Parameter Estimation and Image Reconstruction: From Modelling to Validation

Thomas Yu

EPFL2022

Zero-Knowledge IOPs with Linear-Time Prover and Polylogarithmic-Time Verifier

Alessandro Chiesa, Siqi Liu

Interactive oracle proofs (IOPs) are a multi-round generalization of probabilistically checkable proofs that play a fundamental role in the construction of efficient cryptographic proofs. ...

SPRINGER INTERNATIONAL PUBLISHING AG2022