Deep learning assisted image transmission in multimode fibers

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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 ...

The atomic configuration of phases and their interfaces is fundamental to materials design and engineering. Here, we unveil a transition metal oxide interface, whose formation is driven by energetic influences─epitaxial tensile strain versus oxygen octahed ...

Whole-cell biophotovoltaic (BPV) devices leverage photoautotrophic microorganisms like cyanobacteria to convert sunlight and water into bioelectricity. They are capable of growing on sequestered carbon dioxide (CO2), eliminating the need to add exogenous o ...

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 ...

The oxidation behavior of stoichiometric Ti0.12Al0.21B0.67 coatings is investigated by scanning transmission electron microscopy (STEM) after oxidizing for 1, 4 and 8 h at 700 degrees C and at 800 and 900 degrees C. In the as deposited state, a similar to ...

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 ...

Machine learning and artificial intelligence methods have achieved remarkable success, matching and even surpassing human capabilities in various complex tasks. However, many demonstrations have generally neglected a critical part of the intelligence that ...

With the advances in clean technologies, the role of fuel cells and batteries is becoming increasingly vital. Proton exchange membrane fuel cells are known to be the best candidates for low-temperature and portable operations, such as hydrogen cars. The ef ...

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 ...

Pacinian corpuscles are among the most sensitive mechanoreceptors found in vertebrates, and they are tuned to vibrations in the highest perceptible frequency range (100 to 2000 Hz). One of their anatomical hallmarks is the onion-like cell layers surroundin ...

We present a finite elements-neural network approach for the numerical approximation of parametric partial differential equations. The algorithm generates training data from finite element simulations, and uses a data -driven (supervised) feedforward neura ...

Energy dispersive x-ray (EDX) spectrum imaging yields compositional information with a spatial resolution down to the atomic level. However, experimental limitations often produce extremely sparse and noisy EDX spectra. Under such conditions, every detecte ...

In recent years, the emphasis on computational thinking (CT) has intensified as an effect of accelerated digitalisation. While most researchers are concentrating on defining CT and developing tools for its instruction and assessment, we focus on the charac ...

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

Machine learning algorithms such as Convolutional Neural Networks (CNNs) are characterized by high robustness towards quantization, supporting small-bitwidth fixed-point arithmetic at inference time with little to no degradation in accuracy. In turn, small ...

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

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-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

The morphology and distribution of nanoscale structures, such as catalytic active nanoparticles and quantum dots on surfaces, have a significant impact on their function. Thus, the capability of monitoring these properties during manufacturing and operatio ...

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 ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

Ultrafast ultrasound imaging achieves high frame rates but results in lower image quality compared to focused acquisitions. Convolutional neural networks (CNNs) have emerged as effective tools for reducing diffraction artifacts and enhancing the quality of ...

Deep learning assisted image transmission in multimode fibers

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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

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Learning to Reason with Neural Networks: Principles and Methods

Passivating oxidation behavior of Ti0.12Al0.21B0.67 coatings investigated by scanning transmission electron microscopy and chemical environment dependent density functional theory simulations

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

Sequential Reasoning with Socially Caused Beliefs

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