Learning Smooth Pattern Transformation Manifolds

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Masonry aggregates, which emerged as layouts of cities and villages became denser, make up historical centres all over the world. In these aggregates, neighbouring structures may share structural walls that are joined at the interfaces by mortar or interlo ...

As data sizes continue to expand, the challenge of conducting meaningful analysis grows. Utilizing I/O (Input/Output) libraries, such as HDF5 (Hierarchical Data Format) and ADIOS2 (Adaptable IO System), facilitates the filtering of raw data, with prior res ...

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

The technological advancements of the past decades have allowed transforming an increasing part of our daily actions and decisions into storable data, leading to a radical change in the scale and scope of available data in relation to virtually any object ...

As large, data-driven artificial intelligence models become ubiquitous, guaranteeing high data quality is imperative for constructing models. Crowdsourcing, community sensing, and data filtering have long been the standard approaches to guaranteeing or imp ...

In this thesis we will present two results on global existence for nonlinear dispersive equations with data at or below the scaling regularity. In chapter 1 we take a probabilistic perspective to study the energy-critical nonlinear Schrödinger equation in ...

E. E. Floyd showed in 1973 that there exist only two nontrivial cobor-dism classes that contain manifolds with three cells, and that they lie in dimen-sions 10 and 5. We prove that there is an action of the cyclic group C2 on the 10-dimensional Floyd manif ...

This document contains all the data and the details of the analysis used in the manuscript titled " Control of Ge island coalescence for the formation of nanowires on silicon." https://doi.org/10.1039/D3NH00573A ...

The performance of modern object detectors drops when the test distribution differs from the training one. Most of the methods that address this focus on object appearance changes caused by, e.g., different illumination conditions, or gaps between syntheti ...

We propose a comparative study of three different methods aimed at optimizing existing groundwater monitoring networks. Monitoring piezometric heads in subsurface porous formations is crucial at regional scales to properly characterize the relevant subsurf ...

Encoding quantum information onto bosonic systems is a promising route to quantum error correc-tion. In a cat code, this encoding relies on the confinement of the dynamics of the system onto the two-dimensional manifold spanned by Schrodinger cats of oppos ...

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 ...

We propose a structured prediction approach for robot imitation learning from demonstrations. Among various tools for robot imitation learning, supervised learning has been observed to have a prominent role. Structured prediction is a form of supervised le ...

Numerous conductor designs for pulsed magnets based on High Temperature Superconductors (HTS), featuring stacks of tapes are currently being proposed. A major contribution to the AC losses is expected to be given by hysteresis losses. Several numerical mod ...

Deep learning has achieved remarkable success in various challenging tasks such as generating images from natural language or engaging in lengthy conversations with humans. The success in practice stems from the ability to successfully train massive neural ...

This data package supports the publication 'Complexity of crack front geometry enhances toughness of brittle solids' by Xinyue Wei, Chenzhuo Li, Cían McCarthy, and John M. Kolinski Nature physics (2024) - https://doi.org/10.1038/s41567-024-02435-x DOI: 1 ...

The advancement of data-driven decision making has become a key priority in political agendas and private sector strategies. However, the growing demand for data and the opacity of data-driven systems raise concerns about the threats to individual privacy ...

The decentralisation and unpredictability of new renewable energy sources require rethinking our energy system. Data-driven approaches, such as reinforcement learning (RL), have emerged as new control strategies for operating these systems, but they have n ...

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 ...

We determine the bounded cohomology of the group of homeomorphisms of certain low-dimensional manifolds. In particular, for the group of orientation-preserving homeomorphisms of the circle and of the closed 2-disc, it is isomorphic to the polynomial ring g ...

Learning Smooth Pattern Transformation Manifolds

Chattez avec Graph Search

Seismic Testing of Adjacent Interacting Masonry Structures – Analysis of a Blind Prediction

DAI: How Pre-computation Speeds up Data Analysis

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Discrete choice modeling in the era of big data

Extensions of Peer Prediction Incentive Mechanisms

Probabilistic and Deterministic Wellposedness for Low Regularity Dispersive Equations

Floyd's manifold is a conjugation space

Data set for control of Ge island coalescence for the formation of nanowires on silicon

Learning Transformations To Reduce the Geometric Shift in Object Detection

Data-driven statistical optimization of a groundwater monitoring network

Critical Schrodinger Cat Qubit

Orchestrating chromosome conformation capture analysis with Bioconductor

A structured prediction approach for robot imitation learning

Validation and Application of Hysteresis Loss Model for HTS Stacks and Conductors for Fusion Applications

A Theory of Finite-Width Neural Networks: Generalization, Scaling Laws, and the Loss Landscape

Dataset for "Complexity of crack front geometry enhances toughness of brittle solids"

On the fundamental limits of privacy-enhancing technologies

Reinforcement Learning for Joint Design and Control of Battery-PV Systems

Spectral Estimators for High-Dimensional Matrix Inference

Bounded and unbounded cohomology of homeomorphism and diffeomorphism groups