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Spiking neural networks mimic the way the human brain processes data and excel in efficiency. With the advent of the memristor, foundations are laid for scalable and low-power integrated electronic implementations. [...]

For self-triggered readout of SiPM sum signals, a waveform classification can aid a simple threshold trigger to reliably extract calorimetric particle hit information online at an early stage and thus reduce the volume of transmitted data. Typically, the ADC data acquisition is based on FPGAs for edge data processing. [...]

Memristors are promising devices for scalable and low power, in-memory computing to improve the energy efficiency of a rising computational demand. The crossbar array architecture with memristors is used for vector matrix multiplication (VMM) and acts as kernels in neuromorphic computing. [...]

Chiplet-based processor design, which combines small dies called chiplets to form a larger chip, enables scalable designs at economical costs. This trend has received high attention such that standards for chiplet design have rapidly established, including packaging, protocols, and Chiplet-to-Chiplet (C2C) interfaces. [...]

Integrated circuit implementations of coupled oscillator networks have recently gained increased attention. The focus is usually on using these networks for analogue computing, for example for solving computational optimization tasks. [...]

BackgroundApplications of artificial intelligence (AI) are now widely used in medicine and are becoming increasingly more important.ObjectiveSome of the ethical challenges associated with the use of AI applications in medicine are highlighted.Material and methodsAgainst the backdrop of established ethical principles, several recent studies are cited and discussed from an ethical perspective.ConclusionThe AI is a disruptive technology that offers great benefits but also entails certain risks. Medical ethics provide principles that can be used to identify risks and develop strategies for the responsible implementation of AI applications..

Predictive analytics based on machine learning (ML) and artificial intelligence is a powerful tool enabling precision psychiatry and providing insights into brain-behavior relationships. However, given the mixed results observed in the field so far, making meaningful progress requires careful consideration of several key challenges to ensure the validity of models and findings, including overfitting, confounding biases, site effect harmonization, and interpretability, among others [...]

The continued generation of $N_f=2+1$ quark flavor gauge configurations using stabilized Wilson fermions by the open lattice initiative (OpenLat) is reported. We present the status of our ongoing production and show updates on increasing statistics at the four lattice spacings $a=0.12,0.094,0.077$ and $0.064$ fm. [...]

Using forward matrix elements of local leading twist operators, we present a determination of the third Mellin moments $\left< x^2\right>$ of nucleon's unpolarized, polarized and transversity parton distribution functions. Two lattice QCD ensembles at the physical pion mass are used: these were generated using a tree-level Symanzik-improved gauge action and $2+1$ flavor tree-level improved Wilson Clover fermions coupling via 2-level HEX-smearing. [...]