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Non-destructive X-ray imaging of thruster parts and assemblies down to the scale of several micrometers is a key technology for electric propulsion research and engineering. It allows for thorough product assurance, rapid state acquisition and implementation of more detailed simulation models to understand the physics of device wear and erosion. Being able to inspect parts as 3D density maps allows insight into inner structures hidden from observation. Generating these density maps and also constructing three dimensional mesh objects for further processing depends on the achievable quality of the reconstruction, which is the inverse of Radon's transformation connecting a stack of projections taken from different angles to the original object's structure. Reconstruction is currently flawed by strong mathematical artifacts induced by the many aligned parts and stark density contrasts commonly found in electric propulsion thrusters., Comment: Presented at the 38th International Electric Propulsion Conference, Toulouse, France

The ion-optic grid-system is the essential part of electrostatic ion thrusters governing performance and lifetime. Therefore reliable measurements of the grid and aperture geometry over the lifetime are necessary to understand and predict the behavior of the system. Many different methods of measurement were introduced over the years to tackle the challenges encountered when diagnosing single electrodes or the whole assembly at once. Modern industrial X-ray micro-computer-tomographs (uCT) offer the possibility to obtain a three-dimensional density map of a grid-system or it's components down to microscopic scales of precision. This information allows a spectrum of new diagnostic opportunities, like complete verification of the manufactured parts against CAD models, detecting internal defects or density-changes or the inspection of the assembled ion-optics and its internal alignment, which is normally prohibited by the lack of optical access to all parts at once. Hence uCT imaging is a promising tool to complement established methods and open up new experimental possibilities, however it also has its own weaknesses and pitfalls. The methods developed for grid-erosion and -geometry measurement of a small state-of-the-art radio-frequency-ion-thruster, the obstacles encountered along the route will be discussed and possible solutions demonstrated., Comment: Presented paper at 37th IEPC in Cambridge, MA

Objective. Electroencephalography (EEG) is a widely used neuroimaging technique known for its cost-effectiveness and user-friendliness. However, various artifacts, particularly biological artifacts like Electromyography (EMG) signals, lead to a poor signal-to-noise ratio, limiting the precision of analyses and applications. The currently reported EEG data cleaning performance largely depends on the data used for validation, and in the case of machine learning approaches, also on the data used for training. The data are typically gathered either by recruiting subjects to perform specific artifact tasks or by integrating existing datasets. Prevailing approaches, however, tend to rely on intuitive, concept-oriented data collection with minimal justification for the selection of artifacts and their quantities. Given the substantial costs associated with biological data collection and the pressing need for effective data utilization, we propose an optimization procedure for data-oriented data collection design using deep learning-based artifact detection. Approach. We apply a binary classification between artifact epochs (time intervals containing artifacts) and non-artifact epochs (time intervals containing no artifact) using three different neural architectures. Our aim is to minimize data collection efforts while preserving the cleaning efficiency. Main results. We were able to reduce the number of artifact tasks from twelve to three and decrease repetitions of isometric contraction tasks from ten to three or sometimes even just one. Significance. Our work addresses the need for effective data utilization in biological data collection, offering a systematic and dynamic quantitative approach. By providing clear justifications for the choices of artifacts and their quantity, we aim to guide future studies toward more effective and economical data collection in EEG and EMG research., Comment: Several changes of wording. Caption of figure 10 corrected

Pair-production and single-production of $W$ bosons provide many opportunities to look for new physics via precision measurements, for instance via scrutinising the involved triple-gauge vertices or by measuring CKM matrix elements in an environment very complementary to $B$ hadron decays. This contribution presents the ongoing work based on full simulation of the ILD concept, exploiting the O($10^8$) $W$ bosons produced during the $250$\,GeV stage of the ILC, as well as the CLD concept proposed for the FCC-ee. The projections, which also contribute to two focus topics of the current ECFA study on future Higgs/Top/Electroweak factories, promise improvements of the measurement precision of TGCs and the CKM matrix, respectively, of one to two orders of magnitude with respects to LEP., Comment: Contribution to proceedings of LCWS 2024, 6 pages

This paper presents a method for dynamic adjustment of cable preloads based on the actuation redundancy of \acp{CDPR}, which allows increasing or decreasing the platform stiffness depending on task requirements. This is achieved by computing preload parameters with an extended nullspace formulation of the kinematics. The method facilitates the operator's ability to specify a defined preload within the operation space. The algorithms are implemented in a real-time environment, allowing for the use of optimization in hybrid position-force control. To validate the effectiveness of this approach, a simulation study is performed, and the obtained results are compared to existing methods. Furthermore, the method is investigated experimentally and compared with the conventional position-controlled operation of a cable robot. The results demonstrate the feasibility of adaptively adjusting cable preloads during platform motion and manipulation of additional objects., Comment: Submitted to "Annals of Scientific Society for Assembly, Handling and Industrial Robotics" (MHI2024 conference/colloquium)

The 2024 ICASSP Auditory EEG Signal Processing Grand Challenge concerns the decoding of electroencephalography (EEG) measurements taken from participants who listened to speech material. This work details our solution to the match-mismatch sub-task: given a short temporal segment of EEG recordings and several candidate speech segments, the task is to classify which of the speech segments was time-aligned with the EEG signals. We show that high-frequency gamma-band responses to the speech envelope can be detected with a high accuracy. By jointly assessing gamma-band responses and low-frequency envelope tracking, we develop a match-mismatch decoder which placed first in this task., Comment: Accepted for ICASSP 2024 (challenge track)

Many people with hearing loss struggle to comprehend speech in crowded auditory scenes, even when they are using hearing aids. It has recently been demonstrated that the focus of a listener's selective attention to speech can be decoded from their electroencephalography (EEG) recordings, raising the prospect of smart EEG-steered hearing aids which restore speech comprehension in adverse acoustic environments (such as the cocktail party). To this end, we here assess the feasibility of using a novel, ultra-wearable ear-EEG device to classify the selective attention of normal-hearing listeners who participated in a two-talker competing-speakers experiment. Eighteen participants took part in a diotic listening task, whereby they were asked to attend to one narrator whilst ignoring the other. Encoding models were estimated from the recorded signals, and these confirmed that the device has the ability to capture auditory responses that are consistent with those reported in high-density EEG studies. Several state-of-the-art auditory attention decoding algorithms were next compared, including stimulus-reconstruction algorithms based on linear regression as well as non-linear deep neural networks, and canonical correlation analysis (CCA). Meaningful markers of selective auditory attention could be extracted from the ear-EEG signals of all 18 participants, even when those markers were derived from relatively short EEG segments of just five seconds in duration. Algorithms which related the EEG signals to the rising edges of the speech temporal envelope (onset envelope) were more successful than those which made use of the temporal envelope itself. The CCA algorithm achieved the highest mean attention decoding accuracy, although differences between the performances of the three algorithms were both small and not statistically significant when EEG segments of short durations were employed.

The electroencephalogram (EEG) offers a non-invasive means by which a listener's auditory system may be monitored during continuous speech perception. Reliable auditory-EEG decoders could facilitate the objective diagnosis of hearing disorders, or find applications in cognitively-steered hearing aids. Previously, we developed decoders for the ICASSP Auditory EEG Signal Processing Grand Challenge (SPGC). These decoders aimed to solve the match-mismatch task: given a short temporal segment of EEG recordings, and two candidate speech segments, the task is to identify which of the two speech segments is temporally aligned, or matched, with the EEG segment. The decoders made use of cortical responses to the speech envelope, as well as speech-related frequency-following responses, to relate the EEG recordings to the speech stimuli. Here we comprehensively document the methods by which the decoders were developed. We extend our previous analysis by exploring the association between speaker characteristics (pitch and sex) and classification accuracy, and provide a full statistical analysis of the final performance of the decoders as evaluated on a heldout portion of the dataset. Finally, the generalisation capabilities of the decoders are characterised, by evaluating them using an entirely different dataset which contains EEG recorded under a variety of speech-listening conditions. The results show that the match-mismatch decoders achieve accurate and robust classification accuracies, and they can even serve as auditory attention decoders without additional training.

Detector studies for future experiments rely on advanced software tools to estimate performance and optimize their design and technology choices. The Key4hep project provides a flexible turnkey solution for the full experiment life-cycle based on established community tools such as ROOT, Geant4, DD4hep, Gaudi, podio and spack. Members of the CEPC, CLIC, EIC, FCC, and ILC communities have joined to develop this framework and have merged, or are in the progress of merging, their respective software environments into the Key4hep stack. These proceedings will give an overview over the recent progress in the Key4hep project: covering the developments towards adaptation of state-of-the-art tools for simulation (DD4hep, Gaussino), track and calorimeter reconstruction (ACTS, CLUE), particle flow (PandoraPFA), analysis via RDataFrame, and visualization with Phoenix, as well as tools for testing and validation., Comment: Proceedings of CHEP 2023

We import the algebro-geometric notion of a complete collineation into the study of maximum likelihood estimation in directed Gaussian graphical models. A complete collineation produces a perturbation of sample data, which we call a stabilisation of the sample. While a maximum likelihood estimate (MLE) may not exist or be unique given sample data, it is always unique given a stabilisation. We relate the MLE given a stabilisation to the MLE given original sample data, when one exists, providing necessary and sufficient conditions for the MLE given a stabilisation to be one given the original sample. For linear regression models, we show that the MLE given any stabilisation is the minimal norm choice among the MLEs given an original sample. We show that the MLE has a well-defined limit as the stabilisation of a sample tends to the original sample, and that the limit is an MLE given the original sample, when one exists. Finally, we study which MLEs given a sample can arise as such limits. We reduce this to a question regarding the non-emptiness of certain algebraic varieties., Comment: 35 pages

We present a non-reactive force field for molecular dynamics simulations of interfaces between passivated amorphous surfaces and their interaction with water. The force field enables large-scale dynamic simulations of dry and lubricated tribological contacts and is tailored to surfaces with hydrogen, hydroxyl and aromatic surface passivation. To favour its compatibility with existing force-field parameterizations for liquids and allow a straightforward extension to other types of surface passivation species, we adopt the commonly used OPLS functional form. The optimisation of the force-field parameters is systematic and follows a protocol that can be reused for other surface-molecule combinations. Reference data are calculated with gradient- and dispersion-corrected density functional theory and include the bonding structure and elastic deformation of bulk and surface structures as well as surface adhesion and water adsorption energy landscapes. The conventions adopted to define the different force-field atom types are based on the hybridisation of carbon orbitals and enable a simple and efficient parameter optimisation strategy based on quantum-mechanical calculations performed only on crystalline reference structures. Transferability tests on amorphous interfaces demonstrate the effectiveness of this approach. After testing the force field, we present two examples of application to tribological problems. Namely, we investigate relationships between dry friction and the corrugation of the contact potential energy surface and the dependency of friction on the thickness of interface water films. We finally discuss the limitations of the force field and propose strategies for its improvement and extension.

We present a new event generator for the simulation of both neutral- and charged-current deep inelastic scattering (DIS) at next-to-leading order in QCD matched to parton showers using the POWHEG method. Our implementation builds on the existing POWHEG BOX framework originally designed for hadron-hadron collisions, supplemented by considerable extensions to account for the genuinely different kinematics inherent to lepton-hadron collisions. In particular, we present new momentum mappings that conserve the special kinematics found in DIS, which we use to modify the POWHEG BOX implementation of the Frixione-Kunszt-Signer subtraction mechanism. We compare our predictions to fixed-order and resummed predictions, as well as to data from the HERA ep collider. Finally we study a few representative distributions for the upcoming Electron Ion Collider., Comment: 54 pages, 25 figures, code obtainable from svn://powhegbox.mib.infn.it/trunk/User-Processes-RES/DIS

We report on time-resolved coherent diffraction imaging of gas-phase silver nanoparticles, strongly heated via their plasmon resonance. The x-ray diffraction images reveal a broad range of phenomena for different excitation strengths, from simple melting over strong cavitation to explosive disintegration. Molecular dynamics simulations fully reproduce this behavior and show that the heating induces rather similar trajectories through the phase diagram in all cases, with the very different outcomes being due only to whether and where the stability limit of the metastable superheated liquid is crossed., Comment: 17 pages, 8 figures (including supplemental material)

Automatic code generation has recently attracted large attention and is becoming more significant to the software development process. Solutions based on Machine Learning and Artificial Intelligence are being used to increase human and software efficiency in potent and innovative ways. In this paper, we aim to leverage these developments and introduce a novel approach to generating frontend component code for the popular Angular framework. We propose to do this using behavior-driven development test specifications as input to a transformer-based machine learning model. Our approach aims to drastically reduce the development time needed for web applications while potentially increasing software quality and introducing new research ideas toward automatic code generation., Comment: Accepted for publication at the International Conference on AI Engineering (CAIN) 2023

In this paper we consider the fundamental operations dilation and erosion of mathematical morphology. Many powerful image filtering operations are based on their combinations. We establish homomorphism between max-plus semi-ring of integers and subset of polynomials over the field of real numbers. This enables to reformulate the task of computing morphological dilation to that of computing sums and products of polynomials. Therefore, dilation and its dual operation erosion can be computed by convolution of discrete linear signals, which is efficiently accomplished using a Fast Fourier Transform technique. The novel method may deal with non-flat filters and incorporates no restrictions on shape or size of the structuring element, unlike many other fast methods in the field. In contrast to previous fast Fourier techniques it gives exact results and is not an approximation. The new method is in practice particularly suitable for filtering images with small tonal range or when employing large filter sizes. We explore the benefits by investigating an implementation on FPGA hardware. Several experiments demonstrate the exactness and efficiency of the proposed method.

Angle-Resolved Photoemission Spectroscopy (ARPES) is a premier technique for understanding the electronic excitations in conductive, crystalline matter, in which the induced photocurrent is collected and dispersed in energy and angle of emission to reveal the energy- and momentum-dependent single particle spectral function $A(\mathbf{k},\omega)$. So far, ARPES in a magnetic field has been precluded due to the need to preserve the electron paths between the sample and detector. In this paper we report progress towards "magnetoARPES", a variant of ARPES that can be conducted in a magnetic field. It is achieved by applying a microscopic probe beam ($\lesssim$ 10 $\mu$m ) to a thinned sample mounted upon a special sample holder that generates magnetic field confined to a thin layer near the sample surface. In this geometry we could produce ARPES in magnetic fields up to around $\pm$ 100 mT. The magnetic fields can be varied from purely in-plane to nearly purely out-of-plane, by scanning the probe beam across different parts of the device. We present experimental and simulated data for graphene to explore the aberrations induced by the magnetic field. These results demonstrate the viability of the magnetoARPES technique for exploring symmetry breaking effects in weak magnetic fields., Comment: 21 pages, 6 figures

During speech perception, a listener's electroencephalogram (EEG) reflects acoustic-level processing as well as higher-level cognitive factors such as speech comprehension and attention. However, decoding speech from EEG recordings is challenging due to the low signal-to-noise ratios of EEG signals. We report on an approach developed for the ICASSP 2023 'Auditory EEG Decoding' Signal Processing Grand Challenge. A simple ensembling method is shown to considerably improve upon the baseline decoder performance. Even higher classification rates are achieved by jointly decoding the speech-evoked frequency-following response and responses to the temporal envelope of speech, as well as by fine-tuning the decoders to individual subjects. Our results could have applications in the diagnosis of hearing disorders or in cognitively steered hearing aids., Comment: 2 pages, 3 figures. Accepted for ICASSP 2023 (challenge track)