Making Automated Testing of Cloud Applications an Integral Component of PaaS

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A search is presented for the pair production of new heavy resonances, each decaying into a top quark (t) or antiquark and a gluon (g). The analysis uses data recorded with the CMS detector from proton-proton collisions at a center-of-mass energy of 13 TeV ...

Motivation Many machine learning (ML) models developed to classify phenotype from gene expression data provide interpretations for their decisions, with the aim of understanding biological processes. For many models, including neural networks, interpretati ...

This thesis focuses on understanding and improving the reasoning capabilities of neural networks. It develops theoretical results and empirical analyses to uncover reasoning potential and limitations, leveraging these insights to guide the design of improv ...

Neural networks have shown to be a powerful tool to represent the ground state of quantum many-body systems, including fermionic systems. However, efficiently integrating lattice symmetries into neural representations remains a significant challenge. In th ...

The evolution of airborne mapping witnesses the introduction of hybrid lidar-camera systems to enhance data collection, i.e. to obtain simultaneously high-density point-cloud and texture. Yet, the common adjustment of both optical data streams is a nontriv ...

Recent progress in computer vision has been driven by a simple yet transformative observation: the potential of 30-year-old neural networks can be unlocked by massively scaling their parameters and the amount of human-labeled training data. This breakthrou ...

The Dynamic Aperture is an important concept for the study of non-linear beam dynamics in a circular accelerator. The DA is defined as the extent of the phase-space region in which the particle's motion remains bounded over a finite number of turns. Such a ...

Hydropower plants play a crucial role in the power system facing ambitious renewable energy targets. Due to their inherent controllability, they are well suited to provide flexibility to the grid. However, an increased flexibility provision leads to a prol ...

Efficient sampling and approximation of Boltzmann distributions involving large sets of binary variables, or spins, are pivotal in diverse scientific fields even beyond physics. Recent advances in generative neural networks have significantly impacted this ...

Communication by rare, binary spikes is a key factor for the energy efficiency of biological brains. However, it is harder to train biologically-inspired spiking neural networks than artificial neural networks. This is puzzling given that theoretical resul ...

The substitution of portland clinker by supplementary cementitious materials (SCMs) is one of the main avenues for the further decarbonization of cement production. To secure future SCM supplies, generic and performance-related test methods for SCMs are re ...

Object detection is crucial in video analytics pipelines, but there is a need to optimize deep neural networks (DNNs)-based object detection for resource-constrained Internet of Things (IoT) devices. The computational constraints inherent to the IoT device ...

Steel beams with reduced beam section (RBS) are often used as part of prequalified connections in seismic regions. However, RBS connections are not as common in Europe. Reasons relate to easiness in fabrication, which requires on-site welding, and the seis ...

In this manuscript, we investigate the problem of how two-layer neural networks learn features from data, and improve over the kernel regime, after being trained with a single gradient descent step. Leveraging the insight from (Ba et al., 2022), we model t ...

The shear stiffness of headed stud connector is a critical parameter for the calculation of deflection and inter-facial shear force for steel-concrete composite structure. Thus, this study presented a promising data-driven model auto-tuning Deep Forest (AT ...

Machine learning models have been shown to leak sensitive information about their training datasets. Models are increasingly deployed on devices, raising concerns that white-box access to the model parameters increases the attack surface compared to black- ...

Establishing a reliable value for the axial pipe-soil interface resistance is critical for the design of offshore pipelines. Over the last few years, several independent research groups have modified existing methodologies or devised new testing setups to ...

How can we discern whether the covariance operator of a stochastic pro-cess is of reduced rank, and if so, what its precise rank is? And how can we do so at a given level of confidence? This question is central to a great deal of methods for functional dat ...

The need of full transposition of the current carrying elements (strands) in large cables is frequently retained as top design criterion for conductors operating in pulsed mode. However, when the transposition error, i.e., the inductance difference among t ...

When enough annotated training data is available, supervised deep-learning. algorithms excel at estimating human body pose and shape using a single camera. The effects of too little such data being available can be mitigated by using other information sour ...

Making Automated Testing of Cloud Applications an Integral Component of PaaS

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Search for Pair Production of Heavy Particles Decaying to a Top Quark and a Gluon in the Lepton Plus Jets Final State in Proton-proton Collisions at √s=13 Tev

A comparative analysis of gene expression profiling by statistical and machine learning approaches

Learning to Reason with Neural Networks: Principles and Methods

Spectroscopy of two-dimensional interacting lattice electrons using symmetry-aware neural backflow transformations

On the Perspectives of Image-to-Lidar Constraints in Dynamic Network Optimisation

Exploiting Representation Similarities in Self-Supervised Learning for Vision Tasks

Accelerating Dynamic Aperture Evaluation Using Deep Neural Networks

Structural impact of the start-up sequence on Pelton turbines lifetime: Analytical prediction and polynomial optimization

Sparse autoregressive neural networks for classical spin systems

High-performance deep spiking neural networks with 0.3 spikes per neuron

Interlaboratory Test Program to Determine the Precision of the R3 Test Method (ASTM C1897-20) for Measuring Reactivity of Supplementary Cementitious Materials

Using Physical Dynamics: Accurate and Real-Time Object Detection for High-Resolution Video Streaming on Internet of Things Devices

Full-scale testing of European steel beams with reduced beam section under reversed cyclic loading

Asymptotics of feature learning in two-layer networks after one gradient-step

Auto-tuning deep forest for shear stiffness prediction of headed stud connectors

Investigating the Effect of Misalignment on Membership Privacy in the White-box Setting

Drained Axial Pipe-Soil Resistance at Low Confinement Using Tilt Table and Direct Shear Tests

Testing For The Rank Of A Covariance Operator

Performance Comparison of Cables With Partial Transposition

Using Motion Cues to Supervise Single-Frame Body Pose and Shape Estimation in Low Data Regimes