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Let $M$ be a closed surface, $q\geq 2$ and $n\geq 2$. In this paper, we analyze the Coxeter-type quotient group $B_n(M)(q)$ of the surface braid group $B_{n}(M)$ by the normal closure of the element $\sigma_1^q$, where $\sigma_1$ is the classic Artin generator of the Artin braid group $B_n$. Also, we study the Coxeter-type quotient groups obtained by taking the quotient of $B_n(M)$ by the commutator subgroup of the respective pure braid group $[P_n(M),P_n(M)]$ and adding the relation $\sigma_1^q=1$, when $M$ is a closed orientable surface or the disk., Comment: 10 pages. All comments are welcome

During the various steps of stellar evolution are formed convectives zones that alter the chemical stratification in stars. Usually, in astrophysics is used the Mixing Length Theory (MLT) for modeling the convective movement and, in general, it is used with the Schwarzschild instability criterion, which neglects the impact of chemical composition gradients in the development of convection. However, towards the end of central helium burning and during the thermal pulses in the Asymptotic Giant Branch (AGB) are produced stratification processes with inversions in the chemical gradient that would produce instabilities beyond the ones predicted by the Schwarzschild criterion. These instabilities would alter the chemical profile in the white dwarfs, with respect to the one predicted by MLT, having observable consequences in the pulsational modes of such objects. In the present work we will explore an extension of MLT in which we will consider the chemical instabilities as generators of convectives and non-convectives instabilities. This theory will be applied in stellar evolution models in comparison with standard MLT and a double diffusive mixing theory, discussing the benefits and shortcomings of each one., Comment: in Spanish language

The iterative bleaching extends multiplexity (IBEX) Knowledge-Base is a central portal for researchers adopting IBEX and related 2D and 3D immunofluorescence imaging methods. The design of the Knowledge-Base is modeled after efforts in the open-source software community and includes three facets: a development platform (GitHub), static website, and service for data archiving. The Knowledge-Base facilitates the practice of open science throughout the research life cycle by providing validation data for recommended and non-recommended reagents, e.g., primary and secondary antibodies. In addition to reporting negative data, the Knowledge-Base empowers method adoption and evolution by providing a venue for sharing protocols, videos, datasets, software, and publications. A dedicated discussion forum fosters a sense of community among researchers while addressing questions not covered in published manuscripts. Together, scientists from around the world are advancing scientific discovery at a faster pace, reducing wasted time and effort, and instilling greater confidence in the resulting data.

Mediation analysis for survival outcomes is challenging. Most existing methods quantify the treatment effect using the hazard ratio (HR) and attempt to decompose the HR into the direct effect of treatment plus an indirect, or mediated, effect. However, the HR is not expressible as an expectation, which complicates this decomposition, both in terms of estimation and interpretation. Here, we present an alternative approach which leverages pseudo-values to simplify estimation and inference. Pseudo-values take censoring into account during their construction, and once derived, can be modeled in the same way as any continuous outcome. Thus, pseudo-values enable mediation analysis for a survival outcome to fit seamlessly into standard mediation software (e.g. CMAverse in R). Pseudo-values are easy to calculate via a leave-one-observation-out procedure (i.e. jackknifing) and the calculation can be accelerated when the influence function of the estimator is known. Mediation analysis for causal effects defined by survival probabilities, restricted mean survival time, and cumulative incidence functions - in the presence of competing risks - can all be performed within this framework. Extensive simulation studies demonstrate that the method is unbiased across 324 scenarios/estimands and controls the type-I error at the nominal level under the null of no mediation. We illustrate the approach using data from the PARADIGMS clinical trial for the treatment of pediatric multiple sclerosis using fingolimod. In particular, we evaluate whether an imaging biomarker lies on the causal path between treatment and time-to-relapse, which aids in justifying this biomarker as a surrogate outcome. Our approach greatly simplifies mediation analysis for survival data and provides a decomposition of the total effect that is both intuitive and interpretable., Comment: Mediation, Pseudo-Values, Time-to-event, Survival Analysis, Restricted Mean Survival Time, Competing Risks

In this work we analyzed the physical origin of the primordial inhomogeneities during the inflation era. The proposed framework is based, on the one hand, on semiclassical gravity, in which only the matter fields are quantized and not the spacetime metric. Secondly, we incorporate an objective collapse mechanism based on the Continuous Spontaneous Localization (CSL) model, and we apply it to the wavefunction associated with the inflaton field. This is introduced due to the close relation between cosmology and the so-called ``measurement problem'' in Quantum Mechanics. In particular, in order to break the homogeneity and isotropy of the initial Bunch-Davies vacuum, and thus obtain the inhomogeneities observed today, the theory requires something akin to a ``measurement'' (in the traditional sense of Quantum Mechanics). This is because the linear evolution driven by Schr\"odinger's equation does not break any initial symmetry. The collapse mechanism given by the CSL model provides a satisfactory mechanism for breaking the initial symmetries of the Bunch-Davies vacuum. The novel aspect in this work is that the constructed CSL model arises from the simplest choices for the collapse parameter and operator. From these considerations, we obtain a primordial spectrum that has the same distinctive features as the standard one, which is consistent with the observations from the Cosmic Microwave Background., Comment: 9 pages. Final version. Published in BAAA 65

We investigate whether neural networks (NNs) can accurately differentiate between growth-rate data of the large-scale structure (LSS) of the Universe simulated via two models: a cosmological constant and $\Lambda$ cold dark matter (CDM) model and a tomographic coupled dark energy (CDE) model. We built an NN classifier and tested its accuracy in distinguishing between cosmological models. For our dataset, we generated $f\sigma_8(z)$ growth-rate observables that simulate a realistic Stage IV galaxy survey-like setup for both $\Lambda$CDM and a tomographic CDE model for various values of the model parameters. We then optimised and trained our NN with \texttt{Optuna}, aiming to avoid overfitting and to maximise the accuracy of the trained model. We conducted our analysis for both a binary classification, comparing between $\Lambda$CDM and a CDE model where only one tomographic coupling bin is activated, and a multi-class classification scenario where all the models are combined. For the case of binary classification, we find that our NN can confidently (with $>86\%$ accuracy) detect non-zero values of the tomographic coupling regardless of the redshift range at which coupling is activated and, at a $100\%$ confidence level, detect the $\Lambda$CDM model. For the multi-class classification task, we find that the NN performs adequately well at distinguishing $\Lambda$CDM, a CDE model with low-redshift coupling, and a model with high-redshift coupling, with 99\%, 79\%, and 84\% accuracy, respectively. By leveraging the power of machine learning, our pipeline can be a useful tool for analysing growth-rate data and maximising the potential of current surveys to probe for deviations from general relativity., Comment: Accepted for publication in A&A

Background: Brain maturation and aging involve significant microstructural changes, resulting in functional and cognitive alterations. Quantitative MRI (qMRI) can measure this evolution, distinguishing the physiological effects of normal aging from pathological deviations. Methods: We conducted a multicentre study using qMRI metrics (R1, R2*, and Quantitative Susceptibility Mapping) to model age trajectories across brain structures, including tractography-based white matter bundles (TWMB), superficial white matter (SWM), and cortical grey matter (CGM). MRI data from 537 healthy subjects, aged 8 to 79 years, were harmonized using two independent methods. We modeled age trajectories and performed regional analyses to capture maturation patterns and aging effects across the lifespan. Findings: Our findings revealed a distinct brain maturation gradient, with early qMRI peak values in TWMB, followed by SWM, and culminating in CGM regions. This gradient was observed as a posterior-to-anterior maturation pattern in the cortex and an inferior-to-superior maturation pattern in white matter tracts. R1 demonstrated the most robust age trajectories, while R2* and susceptibility exhibited greater variability and different patterns. The normative modeling framework confirmed the reliability of our age-modelled trajectories across datasets. Interpretation: Our study highlights the potential of multiparametric qMRI to capture complex, region-specific brain development patterns, addressing the need for comprehensive, age-spanning studies across multiple brain structures. Various harmonization strategies can merge qMRI cohorts, improving the robustness of qMRI-based age models and facilitating the understanding of normal patterns and disease-associated deviations.

The measurements of the temperature and polarisation anisotropies of the Cosmic Microwave Background (CMB) by the ESA Planck mission have strongly supported the current concordance model of cosmology. However, the latest cosmological data release from ESA Planck mission still has a powerful potential to test new data science algorithms and inference techniques. In this paper, we use advanced Machine Learning (ML) algorithms, such as Neural Networks (NNs), to discern among different underlying cosmological models at the angular power spectra level, using both temperature and polarisation Planck 18 data. We test two different models beyond $\Lambda$CDM: a modified gravity model: the Hu-Sawicki model, and an alternative inflationary model: a feature-template in the primordial power spectrum. Furthermore, we also implemented an interpretability method based on SHAP values to evaluate the learning process and identify the most relevant elements that drive our architecture to certain outcomes. We find that our NN is able to distinguish between different angular power spectra successfully for both alternative models and $\Lambda$CDM. We conclude by explaining how archival scientific data has still a strong potential to test novel data science algorithms that are interesting for the next generation of cosmological experiments., Comment: 24 pages, 9 figures, 2 tables, comments welcome

The potential effectiveness of counterspeech as a hate speech mitigation strategy is attracting increasing interest in the NLG research community, particularly towards the task of automatically producing it. However, automatically generated responses often lack the argumentative richness which characterises expert-produced counterspeech. In this work, we focus on two aspects of counterspeech generation to produce more cogent responses. First, by investigating the tension between helpfulness and harmlessness of LLMs, we test whether the presence of safety guardrails hinders the quality of the generations. Secondly, we assess whether attacking a specific component of the hate speech results in a more effective argumentative strategy to fight online hate. By conducting an extensive human and automatic evaluation, we show how the presence of safety guardrails can be detrimental also to a task that inherently aims at fostering positive social interactions. Moreover, our results show that attacking a specific component of the hate speech, and in particular its implicit negative stereotype and its hateful parts, leads to higher-quality generations., Comment: To appear in Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (long paper)

The deposition of a two-dimensional material on the surface of a three-dimensional crystal can generate superlattices with electronic properties modified through the proximity spin-orbit effect. In this study, we found that Kekul\'e graphene superlattices are obtained by placing graphene on Cd-terminated (001)-(1$\times$1) cadmium sulfide (CdS) surface. From an effective model of Kekul\'e superlattices, which is corroborated by Density Functional Theory (DFT) calculations, we identified that the puckered surfqace of CdS modifies the on-site energies (staggered potential) and C-C bonds, giving rise to two possible $\sqrt{3} \times \sqrt{3}$ hexagonal superlattices, which are known as Kekul\'e-O graphene and Kekul\'e-Y with a quadratic band crossing point. Both Kekul\'e superlattices present spin-orbit coupling due to the interaction of graphene with the CdS surface. To guide the experimental realization of Kekul\'e superlattices based on graphene/CdS heterostructures, we simulate electron diffraction patterns, as well as images from High-Resolution Transmission Electron Microscopy (HRTEM), and Scanning Transmission Electron Microscopy in the mode of High Angle Annular Dark Field (STEM-HAADF)., Comment: 9 pages, 4 figures

Este art\'iculo presenta como resultado principal la equivalencia entre, las categor\'ias de espacios topol\'ogicos Hausdorff-Compactos y la categor\'ia de las $C^*-$\'algebras conmutativas con unidad, producto de la ``traducci\'on'' en este lenguaje del teorema de Gelfand--Naimark presentado en 1943. Haremos un recorrido sobre las principales ideas del an\'alisis y el \'algebra, conjugadas con \'exito, en el estudio de la teor\'ia de \'Algebras de Banach. As\'i mismo estableceremos, a forma de conclusi\'on, diversas aplicaciones que resultan naturalmente posibles a la luz de la ``analog\'ia y generalizaci\'on'' que nos permiten la teor\'ia de categor\'ias. Palabras claves: $C^*$-algebras, Categor\'ias, Espacios Topol\'ogicos, Teorema de Gelfand-Naimark, Teor\'ia de Representaciones. The goal of this paper is to prove the categorical equivalence between the category of Hausdorff-Compact topological spaces and the category of Unital Commutative $C^*$-algebras. This equivalence can be interpreted as a way of rewriting the well known Gelfand-Naimark Theorem in a categorical language. We will present the basic concepts in the theory of Banach Algebras as a successful link between Analysis and Algebra. Likewise, we will show some applications due to this new perspective, highlighting the categorical connection through proofs of typical problems that don't have an easy solution in $C^*-$algebra. Keywords: Category Theory, $C^*$-algebras, Gelfand-Naimark Theorem, Topological Spaces, Representation Theory.

The integration of computing with memory is essential for distributed, massively parallel, and adaptive architectures such as neural networks in artificial intelligence (AI). Accelerating AI can be achieved through photonic computing, but it requires nonvolatile photonic memory capable of rapid updates during on-chip training sessions or when new information becomes available during deployment. Phase-change materials (PCMs) are promising for providing compact, nonvolatile optical weighting; however, they face limitations in terms of bit precision, programming speed, and cycling endurance. Here, we propose a novel photonic memory cell that merges nonvolatile photonic weighting using PCMs with high-speed, volatile tuning enabled by an integrated PN junction. Our experiments demonstrate that the same PN modulator, fabricated via a foundry compatible process, can achieve dual functionality. It supports coarse programmability for setting initial optical weights and facilitates high-speed fine-tuning to adjust these weights dynamically. The result showcases a 400-fold increase in volatile tuning speed and a 10,000-fold enhancement in efficiency. This multifunctional photonic memory with volatile and nonvolatile capabilities could significantly advance the performance and versatility of photonic memory cells, providing robust solutions for dynamic computing environments.

Chalcogenide optical phase change materials (PCMs) have garnered significant interest for their growing applications in programmable photonics, optical analog computing, active metasurfaces, and beyond. Limited endurance or cycling lifetime is however increasingly becoming a bottleneck toward their practical deployment for these applications. To address this issue, we performed a systematic study elucidating the cycling failure mechanisms of Ge$_2$Sb$_2$Se$_4$Te (GSST), a common optical PCM tailored for infrared photonic applications, in an electrothermal switching configuration commensurate with their applications in on-chip photonic devices. We further propose a set of design rules building on insights into the failure mechanisms, and successfully implemented them to boost the endurance of the GSST device to over 67,000 cycles.

To date, several methods have been developed to explain deep learning algorithms for classification tasks. Recently, an adaptation of two of such methods has been proposed to generate instance-level explainable maps in a semantic segmentation scenario, such as multiple sclerosis (MS) lesion segmentation. In the mentioned work, a 3D U-Net was trained and tested for MS lesion segmentation, yielding an F1 score of 0.7006, and a positive predictive value (PPV) of 0.6265. The distribution of values in explainable maps exposed some differences between maps of true and false positive (TP/FP) examples. Inspired by those results, we explore in this paper the use of characteristics of lesion-specific saliency maps to refine segmentation and detection scores. We generate around 21000 maps from as many TP/FP lesions in a batch of 72 patients (training set) and 4868 from the 37 patients in the test set. 93 radiomic features extracted from the first set of maps were used to train a logistic regression model and classify TP versus FP. On the test set, F1 score and PPV were improved by a large margin when compared to the initial model, reaching 0.7450 and 0.7817, with 95% confidence intervals of [0.7358, 0.7547] and [0.7679, 0.7962], respectively. These results suggest that saliency maps can be used to refine prediction scores, boosting a model's performances.

Iterative Bleaching Extends multipleXity (IBEX) is a versatile method for highly multiplexed imaging of diverse tissues. Based on open science principles, we created the IBEX Knowledge-Base, a resource for reagents, protocols and more, to empower innovation., Comment: 8 pages, 1 figure, 9 references

Uncertainty quantification (UQ) has become critical for evaluating the reliability of artificial intelligence systems, especially in medical image segmentation. This study addresses the interpretability of instance-wise uncertainty values in deep learning models for focal lesion segmentation in magnetic resonance imaging, specifically cortical lesion (CL) segmentation in multiple sclerosis. CL segmentation presents several challenges, including the complexity of manual segmentation, high variability in annotation, data scarcity, and class imbalance, all of which contribute to aleatoric and epistemic uncertainty. We explore how UQ can be used not only to assess prediction reliability but also to provide insights into model behavior, detect biases, and verify the accuracy of UQ methods. Our research demonstrates the potential of instance-wise uncertainty values to offer post hoc global model explanations, serving as a sanity check for the model. The implementation is available at https://github.com/NataliiaMolch/interpret-lesion-unc.

Sample Return Capsules (SRCs) entering Earth's atmosphere at hypervelocity from interplanetary space are a valuable resource for studying meteor phenomena. The 24 September 2023 arrival of the OSIRIS-REx (Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer) SRC provided an unprecedented chance for geophysical observations of a well-characterized source with known parameters, including timing and trajectory. A collaborative effort involving researchers from 16 institutions executed a carefully planned geophysical observational campaign at strategically chosen locations, deploying over 400 ground-based sensors encompassing infrasound, seismic, distributed acoustic sensing (DAS), and GPS technologies. Additionally, balloons equipped with infrasound sensors were launched to capture signals at higher altitudes. This campaign (the largest of its kind so far) yielded a wealth of invaluable data anticipated to fuel scientific inquiry for years to come. The success of the observational campaign is evidenced by the near-universal detection of signals across instruments, both proximal and distal. This paper presents a comprehensive overview of the collective scientific effort, field deployment, and preliminary findings. The early findings have the potential to inform future space missions and terrestrial campaigns, contributing to our understanding of meteoroid interactions with planetary atmospheres. Furthermore, the dataset collected during this campaign will improve entry and propagation models as well as augment the study of atmospheric dynamics and shock phenomena generated by meteoroids and similar sources., Comment: 87 pages, 14 figures

Nonvolatile photonic integrated circuits employing phase change materials have relied either on optical switching mechanisms with precise multi-level control but poor scalability or electrical switching with seamless integration and scalability but mostly limited to a binary response. Recent works have demonstrated electrical multi-level switching; however, they relied on the stochastic nucleation process to achieve partial crystallization with low demonstrated repeatability and cyclability. Here, we re-engineer waveguide-integrated microheaters to achieve precise spatial control of the temperature profile (i.e., hotspot) and, thus, switch deterministic areas of an embedded phase change material cell. We experimentally demonstrate this concept using a variety of foundry-processed doped-silicon microheaters on a silicon-on-insulator platform to trigger multi-step amorphization and reversible switching of Sb$_{2}$Se$_{3}$ and Ge$_{2}$Sb$_{2}$Se$_{4}$Te alloys. We further characterize the response of our microheaters using Transient Thermoreflectance Imaging. Our approach combines the deterministic control resulting from a spatially resolved glassy-crystalline distribution with the scalability of electro-thermal switching devices, thus paving the way to reliable multi-level switching towards robust reprogrammable phase-change photonic devices for analog processing and computing., Comment: 20 pages, 7 figures, 1 table

In recent years, explainable methods for artificial intelligence (XAI) have tried to reveal and describe models' decision mechanisms in the case of classification tasks. However, XAI for semantic segmentation and in particular for single instances has been little studied to date. Understanding the process underlying automatic segmentation of single instances is crucial to reveal what information was used to detect and segment a given object of interest. In this study, we proposed two instance-level explanation maps for semantic segmentation based on SmoothGrad and Grad-CAM++ methods. Then, we investigated their relevance for the detection and segmentation of white matter lesions (WML), a magnetic resonance imaging (MRI) biomarker in multiple sclerosis (MS). 687 patients diagnosed with MS for a total of 4043 FLAIR and MPRAGE MRI scans were collected at the University Hospital of Basel, Switzerland. Data were randomly split into training, validation and test sets to train a 3D U-Net for MS lesion segmentation. We observed 3050 true positive (TP), 1818 false positive (FP), and 789 false negative (FN) cases. We generated instance-level explanation maps for semantic segmentation, by developing two XAI methods based on SmoothGrad and Grad-CAM++. We investigated: 1) the distribution of gradients in saliency maps with respect to both input MRI sequences; 2) the model's response in the case of synthetic lesions; 3) the amount of perilesional tissue needed by the model to segment a lesion. Saliency maps (based on SmoothGrad) in FLAIR showed positive values inside a lesion and negative in its neighborhood. Peak values of saliency maps generated for these four groups of volumes presented distributions that differ significantly from one another, suggesting a quantitative nature of the proposed saliency. Contextual information of 7mm around the lesion border was required for their segmentation.

We propose a novel approach using neural networks (NNs) to differentiate between cosmological models, especially in the case where they are nested and the additional model parameters are close to zero, making it difficult to discriminate them with traditional approaches. Our method complements Bayesian analyses for cosmological model selection, which heavily depend on the chosen priors and average the unnormalized posterior over potentially large prior volumes. By analyzing simulated realistic data sets of the growth rate of the large scale structure (LSS) of the Universe, based on current galaxy-clustering survey specifications, for the cosmological constant and cold dark matter ($\Lambda$CDM) model and the Hu-Sawicki $f(R)$ model, we demonstrate the potential of NNs to enhance the extraction of meaningful information from cosmological LSS data. We find that the NN can successfully distinguish between $\Lambda$CDM and the $f(R)$ models, by predicting the correct model with approximately $97\%$ overall accuracy, thus demonstrating that NNs can maximise the potential of current and next generation surveys to probe for deviations from general relativity., Comment: 8 pages, 7 figures, comments are welcome

Among the open problems in fundamental physics, few are as conceptually significant as the measurement problem in Quantum Mechanics. One of the proposed solutions to this problem is the Continuous Spontaneous Localization (CSL) model, which introduces a non-linear and stochastic modification of the Schr\"odinger equation. This model incorporates two parameters that can be subjected to experimental constraints. One of the most notable consequences of this theory is the spontaneous heating of massive objects; this anomalous heating is dependent on the CSL parameters. In this work, we will revisit some astrophysical bounds previously found, and introduce new methods for testing the spontaneous heating in a variety of compact objects. Finally, we will compare our different bounds and discuss the benefits and shortcomings of each one.

Careful determination of the heating performance of magnetic nanoparticles under AC fields is critical for magnetic hyperthermia applications. However, most interpretations of experimental data are based on the uniaxial anisotropy approximation, which in first instance can be correlated with particle aspect ratio. This is to say, the intrinsic magnetocrystalline anisotropy is discarded, under the assumption that the shape contribution dominates. We show in this work that such premise, generally valid for large field amplitudes, does not hold for describing hyperthermia experiments carried out under small field values. Specifically, given its relevance for \textit{in vivo} applications, we focus our analysis on the so-called "Brezovich criterion", $H\cdot{f}=4.85\times{10^8}A/m\cdot{s}$. By means of a computational model, we show that the intrinsic magnetocrystalline anisotropy plays a critical role in defining the heat output, determining also the role of shape and aspect ratio of the particles on the SLP. Our results indicate that even small deviations from spherical shape have an important impact in optimizing the heating performance. The influence of interparticle interactions on the dissipated heat is also evaluated. Our results call therefore for an improvement in the theoretical models used to interpret magnetic hyperthermia performance., Comment: 10 pages, 9 figures, accepted for publication in Nanoscale journal

Deep learning is computationally intensive, with significant efforts focused on reducing arithmetic complexity, particularly regarding energy consumption dominated by data movement. While existing literature emphasizes inference, training is considerably more resource-intensive. This paper proposes a novel mathematical principle by introducing the notion of Boolean variation such that neurons made of Boolean weights and inputs can be trained -- for the first time -- efficiently in Boolean domain using Boolean logic instead of gradient descent and real arithmetic. We explore its convergence, conduct extensively experimental benchmarking, and provide consistent complexity evaluation by considering chip architecture, memory hierarchy, dataflow, and arithmetic precision. Our approach achieves baseline full-precision accuracy in ImageNet classification and surpasses state-of-the-art results in semantic segmentation, with notable performance in image super-resolution, and natural language understanding with transformer-based models. Moreover, it significantly reduces energy consumption during both training and inference., Comment: Under review

The current standard model of cosmology successfully describes a variety of measurements, but the nature of its main ingredients, dark matter and dark energy, remains unknown. Euclid is a medium-class mission in the Cosmic Vision 2015-2025 programme of the European Space Agency (ESA) that will provide high-resolution optical imaging, as well as near-infrared imaging and spectroscopy, over about 14,000 deg^2 of extragalactic sky. In addition to accurate weak lensing and clustering measurements that probe structure formation over half of the age of the Universe, its primary probes for cosmology, these exquisite data will enable a wide range of science. This paper provides a high-level overview of the mission, summarising the survey characteristics, the various data-processing steps, and data products. We also highlight the main science objectives and expected performance., Comment: Accepted for publication in the A&A special issue`Euclid on Sky'

We present the design and user study of an installation for office workers, enabling moments of temporal diversity and connection to nature. The installation is a form of creative computing experience that departs from the traditional focus on office technologies for productivity. Drawing on neuroscience insights and the slowing effect of nature sounds on time perception, we created an immersive, slow interaction, generative AI installation that composes an audiovisual space - serving as a perceptual portal into temporal realms beyond the linear rhythm of the office. Our study investigates the lived experiences of 18 office workers, gathered via explicitation interviews, observational notes, and video recordings, analysed through an inductive thematic analysis. Key findings highlight the ephemeral qualities in creative computing experiences using generative AI, its potential to foster contemplative practices, amplify ecological temporalities, and reshape office workers' engagement with their environment. Our design and user study offer research and practical implications for utilising creative computing to enrich office experiences., Comment: Paper to be published at ICCC'24

Following previous work on congruence subgroups and crystallographic braid groups, we study the lower central series of congruence braid groups related to the braid group $B_3$, showing in particular that corresponding quotients are almost crystallographic., Comment: All comments are welcome

Navigation precision, speed and stability are crucial for safe Unmanned Aerial Vehicle (UAV) flight maneuvers and effective flight mission executions in dynamic environments. Different flight missions may have varying objectives, such as minimizing energy consumption, achieving precise positioning, or maximizing speed. A controller that can adapt to different objectives on the fly is highly valuable. Proportional Integral Derivative (PID) controllers are one of the most popular and widely used control algorithms for drones and other control systems, but their linear control algorithm fails to capture the nonlinear nature of the dynamic wind conditions and complex drone system. Manually tuning the PID gains for various missions can be time-consuming and requires significant expertise. This paper aims to revolutionize drone flight control by presenting the AirPilot, a nonlinear Deep Reinforcement Learning (DRL) - enhanced Proportional Integral Derivative (PID) drone controller using Proximal Policy Optimization (PPO). AirPilot controller combines the simplicity and effectiveness of traditional PID control with the adaptability, learning capability, and optimization potential of DRL. This makes it better suited for modern drone applications where the environment is dynamic, and mission-specific performance demands are high. We employed a COEX Clover autonomous drone for training the DRL agent within the simulator and implemented it in a real-world lab setting, which marks a significant milestone as one of the first attempts to apply a DRL-based flight controller on an actual drone. Airpilot is capable of reducing the navigation error of the default PX4 PID position controller by 90%, improving effective navigation speed of a fine-tuned PID controller by 21%, reducing settling time and overshoot by 17% and 16% respectively., Comment: 9 pages, 20 figures

Training machine learning interatomic potentials often requires optimizing a loss function composed of three variables: potential energies, forces, and stress. The contribution of each variable to the total loss is typically weighted using fixed coefficients. Identifying these coefficients usually relies on iterative or heuristic methods, which may yield sub-optimal results. To address this issue, we propose an adaptive loss weighting algorithm that automatically adjusts the loss weights of these variables during the training of potentials, dynamically adapting to the characteristics of the training dataset. The comparative analysis of models trained with fixed and adaptive loss weights demonstrates that the adaptive method not only achieves a more balanced predictions across the three variables but also improves overall prediction accuracy.

Monitoring diseases that affect the brain's structural integrity requires automated analysis of magnetic resonance (MR) images, e.g., for the evaluation of volumetric changes. However, many of the evaluation tools are optimized for analyzing healthy tissue. To enable the evaluation of scans containing pathological tissue, it is therefore required to restore healthy tissue in the pathological areas. In this work, we explore and extend denoising diffusion models for consistent inpainting of healthy 3D brain tissue. We modify state-of-the-art 2D, pseudo-3D, and 3D methods working in the image space, as well as 3D latent and 3D wavelet diffusion models, and train them to synthesize healthy brain tissue. Our evaluation shows that the pseudo-3D model performs best regarding the structural-similarity index, peak signal-to-noise ratio, and mean squared error. To emphasize the clinical relevance, we fine-tune this model on data containing synthetic MS lesions and evaluate it on a downstream brain tissue segmentation task, whereby it outperforms the established FMRIB Software Library (FSL) lesion-filling method.

Let $n\geq 2$. Let $VB_n$ (resp. $VP_n$) denote the virtual braid group (resp. virtual pure braid group), let $WB_n$ (resp. $WP_n$) denote the welded braid group (resp. welded pure braid group) and let $UVB_n$ (resp. $UVP_n$) denote the unrestricted virtual braid group (resp. unrestricted virtual pure braid group). In the first part of this paper we prove that, for $n\geq 4$, the group $VP_n$ and for $n\geq 3$ the groups $WP_n$ and $UVP_n$ are characteristic subgroups of $VB_n$, $WB_n$ and $UVB_n$, respectively. In the second part of the paper we show that, for $n\geq 2$, the virtual braid group $VB_n$, the unrestricted virtual pure braid group $UVP_n$, and the unrestricted virtual braid group $UVB_n$ have the R$_\infty$-property. As a consequence of the technique used for few strings we also prove that, for $n=2,3,4$, the welded braid group $WB_n$ has the R$_\infty$-property and that for $n=2$ the corresponding pure braid groups have the R$_\infty$-property. On the other hand for $n\geq 3$ it is unknown if the R$_\infty$-property holds or not for the virtual pure braid group $VP_n$ and the welded pure braid group $WP_n$., Comment: 26 pages