University of Luxembourg

research

∙ 06/24/2019

Unifying machine learning and quantum chemistry -- a deep neural network for molecular wavefunctions

Machine learning advances chemistry and materials science by enabling la...

K. T. Schütt, et al. ∙

research

∙ 08/04/2018

Deep Learning Advances on Different 3D Data Representations: A Survey

3D data is a valuable asset in the field of computer vision as it provid...

Eman Ahmed, et al. ∙

research

∙ 11/02/2021

Probabilistic Deep Learning for Real-Time Large Deformation Simulations

For many novel applications, such as patient-specific computer-aided sur...

Saurabh Deshpande, et al. ∙

research

∙ 05/09/2019

Evaluating Model Testing and Model Checking for Finding Requirements Violations in Simulink Models

Matlab/Simulink is a development and simulation language that is widely ...

Shiva Nejati, et al. ∙

research

∙ 08/26/2016

Containers for portable, productive and performant scientific computing

Containers are an emerging technology that hold promise for improving pr...

Jack S. Hale, et al. ∙

research

∙ 11/13/2017

A Parallel Best-Response Algorithm with Exact Line Search for Nonconvex Sparsity-Regularized Rank Minimization

In this paper, we propose a convergent parallel best-response algorithm ...

Yang Yang, et al. ∙

research

∙ 09/09/2017

Identifying Irregular Power Usage by Turning Predictions into Holographic Spatial Visualizations

Power grids are critical infrastructure assets that face non-technical l...

Patrick Glauner, et al. ∙

research

∙ 01/30/2016

Deep Learning For Smile Recognition

Inspired by recent successes of deep learning in computer vision, we pro...

Patrick O. Glauner, et al. ∙

research

∙ 03/28/2017

Universal Reasoning, Rational Argumentation and Human-Machine Interaction

Classical higher-order logic, when utilized as a meta-logic in which var...

Christoph Benzmüller, et al. ∙

research

∙ 02/13/2017

Is Big Data Sufficient for a Reliable Detection of Non-Technical Losses?

Non-technical losses (NTL) occur during the distribution of electricity ...

Patrick Glauner, et al. ∙

research

∙ 01/18/2017

Reasoning in Non-Probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing as Examples

This article aims to achieve two goals: to show that probability is not ...

Tarek R. Besold, et al. ∙

research

∙ 11/21/2016

Interpreting Finite Automata for Sequential Data

Automaton models are often seen as interpretable models. Interpretabilit...

Christian Albert Hammerschmidt, et al. ∙

research

∙ 07/04/2016

Neighborhood Features Help Detecting Non-Technical Losses in Big Data Sets

Electricity theft is a major problem around the world in both developed ...

Patrick Glauner, et al. ∙

research

∙ 04/07/2016

Revising Incompletely Specified Convex Probabilistic Belief Bases

We propose a method for an agent to revise its incomplete probabilistic ...

Gavin Rens, et al. ∙

research

∙ 01/25/2017

Distributed methods for synchronization of orthogonal matrices over graphs

This paper addresses the problem of synchronizing orthogonal matrices ov...

Johan Thunberg, et al. ∙

research

∙ 08/29/2014

On computable abstractions (a conceptual introduction)

This paper introduces abstractions that are meaningful for computers and...

Alejandro Sánchez Guinea, et al. ∙

research

∙ 07/15/2014

Abduction and Dialogical Proof in Argumentation and Logic Programming

We develop a model of abduction in abstract argumentation, where changes...

Richard Booth, et al. ∙

research

∙ 10/30/2014

A Solution for Multi-Alignment by Transformation Synchronisation

The alignment of a set of objects by means of transformations plays an i...

Florian Bernard, et al. ∙

research

∙ 09/04/2009

Assessing the Impact of Informedness on a Consultant's Profit

We study the notion of informedness in a client-consultant setting. Usin...

Eugen Staab, et al. ∙

research

∙ 11/16/2017

Mechanizing Principia Logico-Metaphysica in Functional Type Theory

Principia Logico-Metaphysica proposes a foundational logical theory for ...

Daniel Kirchner, et al. ∙

research

∙ 12/16/2017

Statistical inference for Vasicek-type model driven by Hermite processes

Let (Z^q, H_t)_t ≥ 0 denote a Hermite process of order q ≥ 1 and self-si...

Ivan Nourdin, et al. ∙

research

∙ 06/02/2017

Reservoir Computing for Detection of Steady State in Performance Tests of Compressors

Fabrication of devices in industrial plants often includes undergoing qu...

Eric Aislan Antonelo, et al. ∙

research

∙ 08/11/2017

Dynamic controllers for column synchronization of rotation matrices: a QR-factorization approach

In the multi-agent systems setting, this paper addresses continuous-time...

Johan Thunberg, et al. ∙

research

∙ 01/17/2018

On the Reduction of Biases in Big Data Sets for the Detection of Irregular Power Usage

In machine learning, a bias occurs whenever training sets are not repres...

Patrick Glauner, et al. ∙

research

∙ 02/02/2018

From Clustering Supersequences to Entropy Minimizing Subsequences for Single and Double Deletions

A binary string transmitted via a memoryless i.i.d. deletion channel is ...

Arash Atashpendar, et al. ∙

research

∙ 02/05/2018

Abstractly Interpreting Argumentation Frameworks for Sharpening Extensions

Cycles of attacking arguments pose non-trivial issues in Dung style argu...

Ryuta Arisaka, et al. ∙

research

∙ 02/07/2018

CryptoRec: Secure Recommendations as a Service

Recommender systems rely on large datasets of historical data and entail...

Jun Wang, et al. ∙

research

∙ 02/14/2018

A co-located partitions strategy for parallel CFD-DEM couplings

In this work, a new partition-collocation strategy for the parallel exec...

Gabriele Pozzetti, et al. ∙

research

∙ 02/22/2018

A Polynomial Time Subsumption Algorithm for Nominal Safe ELO_ under Rational Closure

Description Logics (DLs) under Rational Closure (RC) is a well-known fra...

Giovanni Casini, et al. ∙

research

∙ 03/19/2018

Symbol-Level Precoding Design for Max-Min SINR in Multiuser MISO Broadcast Channels

In this paper, we address the symbol level precoding (SLP) design proble...

A. Haqiqatnejad, et al. ∙

research

∙ 03/20/2018

Advanced Signal Processing Techniques for Fixed and Mobile Satellite Communications

Enabling ultra fast systems has been widely investigated during recent d...

Pol Henarejos, et al. ∙

research

∙ 03/21/2018

Selecting Fault Revealing Mutants

Mutant selection refers to the problem of choosing, among a large number...

Thierry Titcheu Chekam, et al. ∙

research

∙ 03/23/2018

GreyCat: Efficient What-If Analytics for Data in Motion at Scale

Over the last few years, data analytics shifted from a descriptive era, ...

Thomas Hartmann, et al. ∙

research

∙ 04/03/2018

Symbol-Level Precoding Design Based on Distance Preserving Constructive Interference Regions

In this paper, we investigate the symbol-level precoding (SLP) design pr...

Alireza Haqiqatnejad, et al. ∙

research

∙ 04/05/2018

Composable, Unconditionally Secure Message Authentication without any Secret Key

We consider a setup in which the channel from Alice to Bob is less noisy...

Dimiter Ostrev, et al. ∙

research

∙ 05/07/2018

Lifting method for analyzing distributed synchronization on the unit sphere

This paper introduces a new lifting method for analyzing convergence of ...

Johan Thunberg, et al. ∙

research

∙ 05/07/2018

Robust Design of Power Minimizing Symbol-Level Precoder under Channel Uncertainty

In this paper, we investigate the downlink transmission of a multiuser m...

Alireza Haqiqatnejad, et al. ∙

research

∙ 05/07/2018

A Lifting method for analyzing distributed synchronization on the unit sphere

This paper introduces a new lifting method for analyzing convergence of ...

Johan Thunberg, et al. ∙

research

∙ 06/07/2018

Code Design for Non-Coherent Detection of Frame Headers in Precoded Satellite Systems

In this paper we propose a simple method for generating binary short-len...

Farbod Kayhan, et al. ∙

research

∙ 06/27/2018

Quantum-chemical insights from interpretable atomistic neural networks

With the rise of deep neural networks for quantum chemistry applications...

Kristof T. Schütt, et al. ∙

research

∙ 06/28/2018

Successive Convex Approximation Algorithms for Sparse Signal Estimation with Nonconvex Regularizations

In this paper, we propose a successive convex approximation framework fo...

Yang Yang, et al. ∙

research

∙ 07/31/2018

A parallel dual-grid multiscale approach to CFD-DEM couplings

In this work, a new parallel dual-grid multiscale approach for CFD-DEM c...

Gabriele Pozzetti, et al. ∙

research

∙ 07/30/2018

A Proof of Entropy Minimization for Outputs in Deletion Channels via Hidden Word Statistics

From the output produced by a memoryless deletion channel from a uniform...

Arash Atashpendar, et al. ∙

research

∙ 08/24/2018

Continuous time Gaussian process dynamical models in gene regulatory network inference

One of the focus areas of modern scientific research is to reveal myster...

Atte Aalto, et al. ∙

research

∙ 08/24/2018

The XDEM Multi-physics and Multi-scale Simulation Technology: Review on DEM-CFD Coupling, Methodology and Engineering Applications

The XDEM multi-physics and multi-scale simulation platform roots in the ...

Bernhard Peters, et al. ∙

research

∙ 09/28/2018

On Rational Entailment for Propositional Typicality Logic

Propositional Typicality Logic (PTL) is a recently proposed logic, obtai...

Richard Booth, et al. ∙

research

∙ 11/06/2018

A Bisimilarity Congruence for the Applied pi-Calculus Sufficiently Coarse to Verify Privacy Properties

This paper is the first thorough investigation into the coarsest notion ...

Ross Horne, et al. ∙

research

∙ 11/30/2018

Zest: Validity Fuzzing and Parametric Generators for Effective Random Testing

Programs expecting structured inputs often consist of both a syntactic a...

Rohan Padhye, et al. ∙

research

∙ 12/05/2018

Revisiting Deniability in Quantum Key Exchange via Covert Communication and Entanglement Distillation

We revisit the notion of deniability in quantum key exchange (QKE), a to...

Arash Atashpendar, et al. ∙

research

∙ 12/11/2018

Learning representations of molecules and materials with atomistic neural networks

Deep Learning has been shown to learn efficient representations for stru...

Kristof T. Schütt, et al. ∙

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