Data-driven control in atomic force microscopy using a genetic algorithm

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Deep neural networks (DNNs) are receiving increasing attention in wind power forecasting due to their ability to effectively capture complex patterns in wind data. However, their forecast errors are severely limited by the local optimal weight issue in opt ...

Low-dimensional materials exhibiting stable magnetic ordering are interesting from a fundamental point of view as well as for future application in information technologies. Metalorganic magnets, created by linking metal atoms with specific organic molecul ...

We demonstrate the reversible control of interactions between a local molecular spin, hosted within an iron phthalocyanine (FePc) molecule, and the conduction electrons of a supporting Au(111) surface. Using the tip of a scanning tunneling microscope, we d ...

Established techniques that enable robots to learn from demonstrations are based on learning a stable dynamical system (DS). To increase the robots' resilience to perturbations during tasks that involve static obstacle avoidance, we propose incorporating b ...

The increasing reliance on numerical methods for controlling dynamical systems and training machine learning models underscores the need to devise algorithms that dependably and efficiently navigate complex optimization landscapes. Classical gradient desce ...

Recently, the diffusion moving-average (D-MA) scheme has been proposed as a way to combat noisy links over adaptive networks. However, the current theoretical results focus on networks with mean-square error costs where the optimal local solution agrees wi ...

We study the plasmon modes of gold nanorods (as short as similar to 100 nm) on a nonmetallic conductive substrate using scanning tunneling microscope-induced light emission (STM-LE) with a nonplasmonic tungsten tip at room temperature in high vacuum (10-7 ...

In this paper, the CONFIG algorithm, a simple and provably efficient constrained global optimization algorithm, is applied to optimize the closed-loop control performance of an unknown system with unmodeled constraints. Existing Gaussian process based clos ...

This work aims to study the effects of wind uncertainties in civil engineering structural design. Optimising the design of a structure for safety or operability without factoring in these uncertainties can result in a design that is not robust to these per ...

The performance of SwissSPAD2 (SS2), a large scale, widefield time-gated CMOS SPAD imager developed for fluorescence lifetime imaging, has recently been described in the context of visible range and fluorescence lifetime imaging microscopy (FLIM) of dyes w ...

Hexagonal boron nitride (hBN) has emerged as a promising material platform for nanophotonics and quantum sensing, hosting optically active defects with exceptional properties such as high brightness and large spectral tuning. However, precise control over ...

The variety of bioimage data and their quality have dramatically increased over the last decade. In parallel, the number of proposed deep learning (DL) models for their analysis grows by the day. Yet, the adequate reuse of published tools by practitioners ...

In real-time optimization, the solution quality depends on the model ability to predict the plant KarushKuhn-Tucker (KKT) conditions. In the case of non-parametric plant-model mismatch, one can add input-affine modifiers to the model cost and constraints a ...

The construction of dry-joint brick masonry that is subject to seismic loading can be modelled as a strategy problem wherein bricks must be laid to form a load resisting structure. To learn how to mount bricks while considering relevant engineering criteri ...

Data-driven control in atomic force microscopy using a genetic algorithm

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Improving the accuracy and interpretability of neural networks for wind power forecasting

2D Metalorganic Ferromagnets

Engineering Spin Interaction Channels of FePc on Au(111)

Learning Barrier-Certified Polynomial Dynamical Systems for Obstacle Avoidance with Robots

Learning to Optimize with Convergence Guarantees Using Nonlinear System Theory

Diffusion Moving-Average Adaptation Over Networks

Selectively Exciting and Probing Radiative Plasmon Modes on Short Gold Nanorods by Scanning Tunneling Microscope-Induced Light Emission

CONFIG: Constrained Efficient Global Optimization for Closed-Loop Control System Optimization with Unmodeled Constraints

Multi-Level Monte Carlo Methods for Uncertainty Quantification and Risk-Averse Optimisation

NIR Fluorescence lifetime macroscopic imaging with a time-gated SPAD camera

Engineering Optically Active Defects in Hexagonal Boron Nitride Using Focused Ion Beam and Water

A Practical Guide to Supervised Deep Learning for Bioimage Analysis: Challenges and good practices

On improving the efficiency of modifier adaptation via directional information

Deep Q-learning agent for building earthquake resisting wall