Hybrid multi-chip assembly of optical communication engines by in situ 3D nano-lithography.

Three-dimensional (3D) nano-printing of freeform optical waveguides, also referred to as photonic wire bonding, allows for efficient coupling between photonic chips and can greatly simplify optical system assembly. As a key advantage, the shape and the trajectory of photonic wire bonds can be adapted to the mode-field profiles and the positions of the chips, thereby offering an attractive alternative to conventional optical assembly techniques that rely on technically complex and costly high-precision alignment. However, while the fundamental advantages of the photonic wire bonding concept have been shown in proof-of-concept experiments, it has so far been unclear whether the technique can also be leveraged for practically relevant use cases with stringent reproducibility and reliability requirements.

Multidimensional Evaluation of Virtual Reality Paradigms in Clinical Neuropsychology: Application of the VR-Check Framework.

Virtual reality (VR) represents a key technology of the 21st century, attracting substantial interest from a wide range of scientific disciplines. With regard to clinical neuropsychology, a multitude of new VR applications are being developed to overcome the limitations of classical paradigms. Consequently, researchers increasingly face the challenge of systematically evaluating the characteristics and quality of VR applications to design the optimal paradigm for their specific research question and study population.

Artificial Intelligence in Dentistry: Chances and Challenges.

The term "artificial intelligence" (AI) refers to the idea of machines being capable of performing human tasks. A subdomain of AI is machine learning (ML), which "learns" intrinsic statistical patterns in data to eventually cast predictions on unseen data. Deep learning is a ML technique using multi-layer mathematical operations for learning and inferring on complex data like imagery.

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods.

Deep learning has recently gained popularity in digital pathology due to its high prediction quality. However, the medical domain requires explanation and insight for a better understanding beyond standard quantitative performance evaluation. Recently, many explanation methods have emerged.

About the selectivity and reactivity of active nickel electrodes in C-C coupling reactions.

Active anodes which are operating in highly stable protic media such as 1,1,1,3,3,3-hexafluoroisopropanol are rare. Nickel forms, within this unique solvent, a non-sacrificial active anode at constant current conditions, which is superior to the reported powerful molybdenum system. The reactivity for dehydrogenative coupling reactions of this novel active anode increases when the electrolyte is not stirred during electrolysis.

Determination of the optical properties of cholesteatoma in the spectral range of 250 to 800 nm.

Cholesteatoma of the ear can lead to life-threatening complications and its only treatment is surgery. The smallest remnants of cholesteatoma can lead to recurrence of this disease. Therefore, the optical properties of this tissue are of high importance to identify and remove all cholesteatoma during therapy.

Asymptotically unbiased estimation of physical observables with neural samplers.

We propose a general framework for the estimation of observables with generative neural samplers focusing on modern deep generative neural networks that provide an exact sampling probability. In this framework, we present asymptotically unbiased estimators for generic observables, including those that explicitly depend on the partition function such as free energy or entropy, and derive corresponding variance estimators. We demonstrate their practical applicability by numerical experiments for the two-dimensional Ising model which highlight the superiority over existing methods.

Analyzing Neuroimaging Data Through Recurrent Deep Learning Models.

The application of deep learning (DL) models to neuroimaging data poses several challenges, due to the high dimensionality, low sample size, and complex temporo-spatial dependency structure of these data. Even further, DL models often act as , impeding insight into the association of cognitive state and brain activity. To approach these challenges, we introduce the DeepLight framework, which utilizes long short-term memory (LSTM) based DL models to analyze functional Magnetic Resonance Imaging (fMRI) data.

UDSMProt: universal deep sequence models for protein classification.

Motivation: Inferring the properties of a protein from its amino acid sequence is one of the key problems in bioinformatics. Most state-of-the-art approaches for protein classification are tailored to single classification tasks and rely on handcrafted features, such as position-specific-scoring matrices from expensive database searches. We argue that this level of performance can be reached or even be surpassed by learning a task-agnostic representation once, using self-supervised language modeling, and transferring it to specific tasks by a simple fine-tuning step.

Manufacturing and Characterization of Femtosecond Laser-Inscribed Bragg Grating in Polymer Waveguide Operation in an IR-A Wavelength Range.

Optical sensors, such as fiber Bragg gratings, offer advantages compared to other sensors in many technological fields due to their outstanding characteristics. This sensor technology is currently transferred to polymer waveguides that provide the potential for cost-effective, easy, and flexible manufacturing of planar structures. While sensor production itself, in the majority of cases, is performed by means of phase mask technique, which is limited in terms of its degrees of freedom, other inscription techniques enable the manufacture of more adaptable sensor elements for a wider range of applications.

Determination of optical properties of human tissues obtained from parotidectomy in the spectral range of 250 to 800 nm.

The optical properties of human tissues are an important parameter in medical diagnostics and therapy. The knowledge of these parameters can encourage the development of automated, computer-driven optical tissue analysis methods. We determine the absorption coefficient μ and scattering coefficient of different tissue types obtained during parotidectomy in the wavelength range of 250 to 800 nm.

Conical microstructuring of titanium by reactive gas assisted laser texturing.

Femtosecond laser micromachining is an important and flexible method to generate precisely targeted surfaces on various materials. On titanium, the laser structuring process strongly depends on the laser parameters. For example, an increasement of the pulse length and repetition rate favors melting processes instead of ablation and microstructuring.

Molecularly Imprinted Sol-Gel for TNT Detection with Optical Micro-Ring Resonator Sensor Chips.

A sensor for trinitrotoluene (TNT) detection was developed by using a combination of optical micro-ring technology and a receptor coating based on molecularly imprinted sol-gel layers. Two techniques for deposition of receptor layers were compared: Airbrush technology and electrospray ionization. A concentration of less than 5 ppb for TNT in the gas-phase, using electrospray deposition of the receptor layer, was detected.

Blueprint and Implementation of Rural Stand-Alone Power Grids with Second-Life Lithium Ion Vehicle Traction Battery Systems for Resilient Energy Supply of Tropical or Remote Regions.

Developed societies with advanced economic performance are undoubtedly coupled with the availability of electrical energy. Whilst industrialized nations already started to decrease associated carbon emissions in many business sectors, e.g.

EEGSourceSim: A framework for realistic simulation of EEG scalp data using MRI-based forward models and biologically plausible signals and noise.

Background: Electroencephalography (EEG) is widely used to investigate human brain function. Simulation studies are essential for assessing the validity of EEG analysis methods and the interpretability of results.

New Method: Here we present a simulation environment for generating EEG data by embedding biologically plausible signal and noise into MRI-based forward models that incorporate individual-subject variability in structure and function.

Cooperation dynamics in networked geometric Brownian motion.

Recent works suggest that pooling and sharing may constitute a fundamental mechanism for the evolution of cooperation in well-mixed fluctuating environments. The rationale is that, by reducing the amplitude of fluctuations, pooling and sharing increases the steady-state growth rate at which individuals self-reproduce. However, in reality interactions are seldom realized in a well-mixed structure, and the underlying topology is in general described by a complex network.

Information Theory Applications in Signal Processing.

The birth of Information Theory, right after the pioneering work of Claude Shannon and his celebrated publication of the paper "A mathematical theory of Communication" [...

Unmasking Clever Hans predictors and assessing what machines really learn.

Current learning machines have successfully solved hard application problems, reaching high accuracy and displaying seemingly intelligent behavior. Here we apply recent techniques for explaining decisions of state-of-the-art learning machines and analyze various tasks from computer vision and arcade games. This showcases a spectrum of problem-solving behaviors ranging from naive and short-sighted, to well-informed and strategic.

Explaining the unique nature of individual gait patterns with deep learning.

Machine learning (ML) techniques such as (deep) artificial neural networks (DNN) are solving very successfully a plethora of tasks and provide new predictive models for complex physical, chemical, biological and social systems. However, in most cases this comes with the disadvantage of acting as a black box, rarely providing information about what made them arrive at a particular prediction. This black box aspect of ML techniques can be problematic especially in medical diagnoses, so far hampering a clinical acceptance.

Rapid Drop-Test for Lectin Binding with Glycopolymer-Coated Optical Ring Resonators.

We fabricated a simple sensor system for qualitative analysis of glycan-mediated interactions. Our main aim was to establish a ronbbust system that allowes drop-tests without complex fluidics. The test system should be usable in routine analytics in the future and bear sufficient sensitivity to detect binding events in the nanomolar range.

Detecting and interpreting myocardial infarction using fully convolutional neural networks.

Objective: We aim to provide an algorithm for the detection of myocardial infarction that operates directly on ECG data without any preprocessing and to investigate its decision criteria.

Approach: We train an ensemble of fully convolutional neural networks on the PTB ECG dataset and apply state-of-the-art attribution methods.

Main Results: Our classifier reaches 93.

Shearlet-based compressed sensing for fast 3D cardiac MR imaging using iterative reweighting.

High-resolution three-dimensional (3D) cardiovascular magnetic resonance (CMR) is a valuable medical imaging technique, but its widespread application in clinical practice is hampered by long acquisition times. Here we present a novel compressed sensing (CS) reconstruction approach using shearlets as a sparsifying transform allowing for fast 3D CMR (3DShearCS) using 3D radial phase encoding (RPE). An iterative reweighting scheme was applied during image reconstruction to ensure fast convergence and high image quality.

Economic complexity unfolded: Interpretable model for the productive structure of economies.

Economic complexity reflects the amount of knowledge that is embedded in the productive structure of an economy. It resides on the premise of hidden capabilities-fundamental endowments underlying the productive structure. In general, measuring the capabilities behind economic complexity directly is difficult, and indirect measures have been suggested which exploit the fact that the presence of the capabilities is expressed in a country's mix of products.

Cooperation dynamics of generalized reciprocity in state-based social dilemmas.

We introduce a framework for studying social dilemmas in networked societies where individuals follow a simple state-based behavioral mechanism based on generalized reciprocity, which is rooted in the principle "help anyone if helped by someone." Within this general framework, which applies to a wide range of social dilemmas including, among others, public goods, donation, and snowdrift games, we study the cooperation dynamics on a variety of complex network examples. By interpreting the studied model through the lenses of nonlinear dynamical systems, we show that cooperation through generalized reciprocity always emerges as the unique attractor in which the overall level of cooperation is maximized, while simultaneously exploitation of the participating individuals is prevented.