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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. Future hearing technologies which can identify the focus of a listener's auditory attention, and selectively amplify that sound alone, could improve the experience that this patient group has with their hearing aids. In this work, we present the results of our experiments with an ultra-wearable in-ear electroencephalography (EEG) monitoring device. Participants listened to two competing speakers in an auditory attention experiment whilst their EEG was recorded. We show that typical neural responses to the speech envelope, as well as its onsets, can be recovered from such a device, and that the morphology of the recorded responses is indeed modulated by selective attention to speech. Features of the attended and ignored speech stream can also be reconstructed from the EEG recordings, with the reconstruction quality serving as a marker of selective auditory attention. Using the stimulus-reconstruction method, we show that with this device auditory attention can be decoded from short segments of EEG recordings which are of just a few seconds in duration. The results provide further evidence that ear-EEG systems offer good prospects for wearable auditory monitoring as well as future cognitively-steered hearing aids.

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)

Quality-Diversity (QD) algorithms can discover large and complex behavioural repertoires consisting of both diverse and high-performing skills. However, the generation of behavioural repertoires has mainly been limited to simulation environments instead of real-world learning. This is because existing QD algorithms need large numbers of evaluations as well as episodic resets, which require manual human supervision and interventions. This paper proposes Reset-Free Quality-Diversity optimization (RF-QD) as a step towards autonomous learning for robotics in open-ended environments. We build on Dynamics-Aware Quality-Diversity (DA-QD) and introduce a behaviour selection policy that leverages the diversity of the imagined repertoire and environmental information to intelligently select of behaviours that can act as automatic resets. We demonstrate this through a task of learning to walk within defined training zones with obstacles. Our experiments show that we can learn full repertoires of legged locomotion controllers autonomously without manual resets with high sample efficiency in spite of harsh safety constraints. Finally, using an ablation of different target objectives, we show that it is important for RF-QD to have diverse types solutions available for the behaviour selection policy over solutions optimised with a specific objective. Videos and code available at https://sites.google.com/view/rf-qd.

In recent years, due to a higher demand for portable devices, which provide restricted amounts of processing capacity and battery power, the need for energy and time efficient hard- and software solutions has increased. Preliminary estimations of time and energy consumption can thus be valuable to improve implementations and design decisions. To this end, this paper presents a method to estimate the time and energy consumption of a given software solution, without having to rely on the use of a traditional Cycle Accurate Simulator (CAS). Instead, we propose to utilize a combination of high-level functional simulation with a mechanistic extension to include non-functional properties: Instruction counts from virtual execution are multiplied with corresponding specific energies and times. By evaluating two common image processing algorithms on an FPGA-based CPU, where a mean relative estimation error of 3% is achieved for cacheless systems, we show that this estimation tool can be a valuable aid in the development of embedded processor architectures. The tool allows the developer to reach well-suited design decisions regarding the optimal processor hardware configuration for a given algorithm at an early stage in the design process., Comment: 6 pages, 4 figures, 2015 IEEE International Parallel and Distributed Processing Symposium Workshop (IPDPS)

In this paper, we present a bit stream feature based energy model that accurately estimates the energy required to decode a given HEVC-coded bit stream. Therefore, we take a model from literature and extend it by explicitly modeling the inloop filters, which was not done before. Furthermore, to prove its superior estimation performance, it is compared to seven different energy models from literature. By using a unified evaluation framework we show how accurately the required decoding energy for different decoding systems can be approximated. We give thorough explanations on the model parameters and explain how the model variables are derived. To show the modeling capabilities in general, we test the estimation performance for different decoding software and hardware solutions, where we find that the proposed model outperforms the models from literature by reaching frame-wise mean estimation errors of less than 7% for software and less than 15% for hardware based systems., Comment: 13 pages, 4 figures

The Compact Muon Solenoid Collaboration is designing a new high-granularity endcap calorimeter, HGCAL, to be installed later this decade. As part of this development work, a prototype system was built, with an electromagnetic section consisting of 14 double-sided structures, providing 28 sampling layers. Each sampling layer has an hexagonal module, where a multipad large-area silicon sensor is glued between an electronics circuit board and a metal baseplate. The sensor pads of approximately 1 cm$^2$ are wire-bonded to the circuit board and are readout by custom integrated circuits. The prototype was extensively tested with beams at CERN's Super Proton Synchrotron in 2018. Based on the data collected with beams of positrons, with energies ranging from 20 to 300 GeV, measurements of the energy resolution and linearity, the position and angular resolutions, and the shower shapes are presented and compared to a detailed Geant4 simulation.

We define Gaussian graphical models on directed acyclic graphs with coloured vertices and edges, calling them RDAG (restricted directed acyclic graph) models. If two vertices or edges have the same colour, their parameters in the model must be the same. We present an algorithm to find the maximum likelihood estimate (MLE) in an RDAG model, and characterise when the MLE exists, via linear independence conditions. We relate properties of a graph, and its colouring, to the number of samples needed for the MLE to exist and to be unique. We also characterise when an RDAG model is equal to an associated undirected graphical model and study connections to groups and invariant theory. We provide examples and simulations to study the benefits of RDAGs over uncoloured DAGs., Comment: 27 pages, 3 figures, appendices: 9 pages

Rockfalls pose a substantial threat to ground transportation, due to their rapidity, destructive potential and high probability of occurrence on steep topographies, found along roads and railways. Approaches for assessment of rockfall susceptibility range from purely phenomenological methods and statistical methods, suitable for modeling large areas, to purely deterministic ones, usually easier to use in local analyses. A common requirement is the need to locate potential detachment points, often found uphill on cliffs, and the subsequent assessment of the runout areas of rockfalls stemming from such points. Here, we apply a physically based model to calculate rockfall trajectories along the whole Italian railway network, within a corridor of total length of about 17,000 km and varying width. We propose a data-driven method for the location of rockfall source points based on expert mapping of potential source areas on sample representative locations. Using empirical distributions of gridded slope values in source areas mapped by experts, we derived probabilistic maps of rockfall sources in the proximity of the railway network, regardless of a particular trigger. Source areas act as starting points of simulated trajectories in the three-dimensional model STONE. The program provides a pixel-by-pixel trajectory count, covering 24,500 km2, the largest homogeneous application of the model to date. We classified the map into a vector susceptibility map of the segments of the railway, for which we provide segment-wise rockfall susceptibility. Eventually, we considered a graph representation of the network to classify the segments both on the basis of rockfall susceptibility and the role of each segment in the network, resulting in a network-ranked susceptibility. Both maps are useful for subsequent hazard assessment, and to prioritize safety improvements along the railway, at national scale., Comment: 22 pages, 9 figures, 6 tables. Published version

The development of epitaxy techniques for localized growth of crystalline silicon nanofilms and nanostructures has been crucial to recent advances in electronics and photonics. A precise definition of the crystal growth location, however, requires elaborate pre-epitaxy processes for substrate patterning. Our molecular dynamics simulations reveal that homoepitaxial silicon nanofilms can be directly deposited by a crystalline silicon tip rubbing against the substrate, thus enabling geometrically controlled crystal growth with no need for substrate pre-patterning. We name this solid-phase epitaxial growth triboepitaxy as it solely relies on shear-induced amorphization and recrystallization that occur even at low temperature at the sliding interface between two silicon crystals. The interplay between the two concomitant, shear-induced processes is responsible for the formation of an amorphous sliding interface with constant nanometric thickness. If the two elastically anisotropic crystals slide along different crystallographic orientations, the amorphous layer can move unidirectionally perpendicular to the sliding plane, causing the crystal with lowest elastic energy per atom to grow at the expenses of the other crystal. As triboepitaxial growth is governed by the shear elastic response of the two crystals along the sliding direction, it can be implemented as a mechanical scanning-probe lithography method in which epitaxial growth is controlled by tuning the crystallographic misorientation between tip and substrate, the tip's size or the normal force. These results suggest a radically new way to conceive nanofabrication techniques that are based on tribologically induced materials transformations., Comment: 35 pages, 13 figures, 1 table