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We study the gamma-ray emission from millisecond pulsars within the Milky Way's globular cluster system in order to measure the luminosity function of this source population. We find that these pulsars have a mean luminosity of $\langle L_{\gamma}\rangle \sim (1-8)\times 10^{33}\, {\rm erg/s}$ (integrated between 0.1 and 100 GeV) and a log-normal width of $\sigma_L \sim 1.4-2.8$. If the Galactic Center Gamma-Ray Excess were produced by pulsars with similar characteristics, Fermi would have already detected $N \sim 17-37$ of these sources, whereas only three such pulsar candidates have been identified. We conclude that the excess gamma-ray emission can originate from pulsars only if they are significantly less bright, on average, than those observed within globular clusters or in the Galactic Plane. This poses a serious challenge for pulsar interpretation of the Galactic Center Gamma-Ray Excess., Comment: 23 pages, 5 figures

The integration of artificial intelligence (AI) into the workplace is advancing rapidly, necessitating robust metrics to evaluate its tangible impact on the labour market. Existing measures of AI occupational exposure largely focus on AI's theoretical potential to substitute or complement human labour on the basis of technical feasibility, providing limited insight into actual adoption and offering inadequate guidance for policymakers. To address this gap, we introduce the AI Startup Exposure (AISE) index-a novel metric based on occupational descriptions from O*NET and AI applications developed by startups funded by the Y Combinator accelerator. Our findings indicate that while high-skilled professions are theoretically highly exposed according to conventional metrics, they are heterogeneously targeted by startups. Roles involving routine organizational tasks-such as data analysis and office management-display significant exposure, while occupations involving tasks that are less amenable to AI automation due to ethical or high-stakes, more than feasibility, considerations -- such as judges or surgeons -- present lower AISE scores. By focusing on venture-backed AI applications, our approach offers a nuanced perspective on how AI is reshaping the labour market. It challenges the conventional assumption that high-skilled jobs uniformly face high AI risks, highlighting instead the role of today's AI players' societal desirability-driven and market-oriented choices as critical determinants of AI exposure. Contrary to fears of widespread job displacement, our findings suggest that AI adoption will be gradual and shaped by social factors as much as by the technical feasibility of AI applications. This framework provides a dynamic, forward-looking tool for policymakers and stakeholders to monitor AI's evolving impact and navigate the changing labour landscape., Comment: 24 pages, 6 figures, + Supplementary information

Lumbar spine problems are ubiquitous, motivating research into targeted imaging for treatment planning and guided interventions. While high resolution and high contrast CT has been the modality of choice, MRI can capture both bone and soft tissue without the ionizing radiation of CT albeit longer acquisition time. The critical trade-off between contrast quality and acquisition time has motivated 'thick slice MRI', which prioritises faster imaging with high in-plane resolution but variable contrast and low through-plane resolution. We investigate a recently developed post-acquisition pipeline which segments vertebrae from thick-slice acquisitions and uses a variational autoencoder to enhance quality after an initial 3D reconstruction. We instead propose a latent space diffusion energy-based prior to leverage diffusion models, which exhibit high-quality image generation. Crucially, we mitigate their high computational cost and low sample efficiency by learning an energy-based latent representation to perform the diffusion processes. Our resulting method outperforms existing approaches across metrics including Dice and VS scores, and more faithfully captures 3D features.

This research investigates liquidity dynamics in fractional ownership markets, focusing on illiquid alternative investments traded on a FinTech platform. By leveraging empirical data and employing agent-based modeling (ABM), the study simulates trading behaviors in sell offer-driven systems, providing a foundation for generating insights into how different market structures influence liquidity. The ABM-based simulation model provides a data augmentation environment which allows for the exploration of diverse trading architectures and rules, offering an alternative to direct experimentation. This approach bridges academic theory and practical application, supported by collaboration with industry and Swiss federal funding. The paper lays the foundation for planned extensions, including the identification of a liquidity-maximizing trading environment and the design of a market maker, by simulating the current functioning of the investment platform using an ABM specified with empirical data.

Off-road environments pose significant perception challenges for high-speed autonomous navigation due to unstructured terrain, degraded sensing conditions, and domain-shifts among biomes. Learning semantic information across these conditions and biomes can be challenging when a large amount of ground truth data is required. In this work, we propose an approach that leverages a pre-trained Vision Transformer (ViT) with fine-tuning on a small (<500 images), sparse and coarsely labeled (<30% pixels) multi-biome dataset to predict 2D semantic segmentation classes. These classes are fused over time via a novel range-based metric and aggregated into a 3D semantic voxel map. We demonstrate zero-shot out-of-biome 2D semantic segmentation on the Yamaha (52.9 mIoU) and Rellis (55.5 mIoU) datasets along with few-shot coarse sparse labeling with existing data for improved segmentation performance on Yamaha (66.6 mIoU) and Rellis (67.2 mIoU). We further illustrate the feasibility of using a voxel map with a range-based semantic fusion approach to handle common off-road hazards like pop-up hazards, overhangs, and water features., Comment: Accepted to Australasian Conference on Robotics and Automation (ACRA 2024)

We give the first parallel algorithm with optimal $\tilde{O}(m)$ work for the classical problem of computing Single-Source Shortest Paths in general graphs with negative-weight edges. In graphs without negative edges, Dijkstra's algorithm solves the Single-Source Shortest Paths (SSSP) problem with optimal $\tilde O(m)$ work, but is inherently sequential. A recent breakthrough by Bernstein, Nanongkai, Wulff-Nilsen; FOCS '22 achieves the same for general graphs. Parallel shortest path algorithms are more difficult and have been intensely studied for decades. Only very recently, multiple lines of research culminated in parallel algorithms with optimal work $\tilde O(m)$ for various restricted settings, such as approximate or exact algorithms for directed or undirected graphs without negative edges. For general graphs, the best known algorithm by [shvinkumar, Bernstein, Cao, Grunau, Haeupler, Jiang, Nanongkai, Su; ESA '24 still requires $m^{1+o(1)}$ work. This paper presents a randomized parallel algorithm for SSSP in general graphs with near-linear work $\tilde O(m)$ and state-of-the-art span $n^{1/2 + o(1)}$. We follow a novel bottom-up approach leading to a particularly clean and simple algorithm. Our algorithm can be seen as a \emph{near-optimal parallel black-box reduction} from SSSP in general graphs to graphs without negative edges. In contrast to prior works, the reduction in this paper is both parallel and essentially without overhead, only affecting work and span by polylogarithmic factors., Comment: To appear at SOSA 2025

In recent years, diffusion models have emerged as a superior alternative to generative adversarial networks (GANs) for high-fidelity image generation, with wide applications in text-to-image generation, image-to-image translation, and super-resolution. However, their real-time feasibility is hindered by slow training and inference speeds. This study addresses this challenge by proposing a wavelet-based conditional Diffusion GAN scheme for Single-Image Super-Resolution (SISR). Our approach utilizes the diffusion GAN paradigm to reduce the timesteps required by the reverse diffusion process and the Discrete Wavelet Transform (DWT) to achieve dimensionality reduction, decreasing training and inference times significantly. The results of an experimental validation on the CelebA-HQ dataset confirm the effectiveness of our proposed scheme. Our approach outperforms other state-of-the-art methodologies successfully ensuring high-fidelity output while overcoming inherent drawbacks associated with diffusion models in time-sensitive applications., Comment: The paper has been accepted at Italian Workshop on Neural Networks (WIRN) 2024

Fault-tolerant fusion-based photonic quantum computing (FBQC) greatly relies on entangling two-photon measurements, called fusions. These fusions can be realized using linear-optical projective Bell-state measurements (BSMs). These linear-optical BSMs are limited to a success probability of 50%, greatly reducing the performance of FBQC schemes. To improve the performance of FBQC architectures, a boosted BSM scheme taking advantage of ancillary entangled photon pairs and a 4x4 multiport interferometer has been proposed. This scheme allows the success probability to be increased up to 75%. In this work, we experimentally demonstrate this boosted BSM by using two Sagnac photon-pair sources and a fibre-based 4x4 multiport beam splitter. A boosted BSM success probability of $(69.3\pm0.3)\%$ has been achieved, exceeding the 50% limit. Furthermore, based on our BSMs, we calculate photon-loss thresholds for a fusion network using encoded six-ring resource states. We show that with this boosted BSM scheme an individual photon loss probability of 1.4% can be tolerated, while the non-boosted BSM leads to a photon-loss threshold of 0.45%., Comment: 8 pages, 7 figures

Structural change consists of industrial diversification towards more productive, knowledge intensive activities. However, changes in the productive structure bear inherent links with job creation and income distribution. In this paper, we investigate the consequences of structural change, defined in terms of labour shifts towards more complex industries, on employment growth, wage inequality, and functional distribution of income. The analysis is conducted for European countries using data on disaggregated industrial employment shares over the period 2010-2018. First, we identify patterns of industrial specialisation by validating a country-industry industrial employment matrix using a bipartite weighted configuration model (BiWCM). Secondly, we introduce a country-level measure of labour-weighted Fitness, which can be decomposed in such a way as to isolate a component that identifies the movement of labour towards more complex industries, which we define as structural change. Thirdly, we link structural change to i) employment growth, ii) wage inequality, and iii) labour share of the economy. The results indicate that our structural change measure is associated negatively with employment growth. However, it is also associated with lower income inequality. As countries move to more complex industries, they drop the least complex ones, so the (low-paid) jobs in the least complex sectors disappear. Finally, structural change predicts a higher labour ratio of the economy; however, this is likely to be due to the increase in salaries rather than by job creation., Comment: 34 pages

Major Depressive Disorder and anxiety disorders affect millions globally, contributing significantly to the burden of mental health issues. Early screening is crucial for effective intervention, as timely identification of mental health issues can significantly improve treatment outcomes. Artificial intelligence (AI) can be valuable for improving the screening of mental disorders, enabling early intervention and better treatment outcomes. AI-driven screening can leverage the analysis of multiple data sources, including facial features in digital images. However, existing methods often rely on controlled environments or specialized equipment, limiting their broad applicability. This study explores the potential of AI models for ubiquitous depression-anxiety screening given face-centric selfies. The investigation focuses on high-risk pregnant patients, a population that is particularly vulnerable to mental health issues. To cope with limited training data resulting from our clinical setup, pre-trained models were utilized in two different approaches: fine-tuning convolutional neural networks (CNNs) originally designed for facial expression recognition and employing vision-language models (VLMs) for zero-shot analysis of facial expressions. Experimental results indicate that the proposed VLM-based method significantly outperforms CNNs, achieving an accuracy of 77.6% and an F1-score of 56.0%. Although there is significant room for improvement, the results suggest that VLMs can be a promising approach for mental health screening, especially in scenarios with limited data.

Motivated by the study of the electrodynamics of particles, we propose in this work an arbitrary-order discrete de Rham scheme for the treatment of elliptic problems with potential and flux jumps across a fixed interface. The scheme seamlessly supports general elements resulting from the cutting of a background mesh along the interface. Interface conditions are enforced weakly \`a la Nitsche. We provide a rigorous convergence of analysis of the scheme for a steady model problem and showcase an application to a physical problem inspired by the Leaky Dielectric Model.

Emotion recognition is essential across numerous fields, including medical applications and brain-computer interface (BCI). Emotional responses include behavioral reactions, such as tone of voice and body movement, and changes in physiological signals, such as the electroencephalogram (EEG). The latter are involuntary, thus they provide a reliable input for identifying emotions, in contrast to the former which individuals can consciously control. These signals reveal true emotional states without intentional alteration, thus increasing the accuracy of emotion recognition models. However, multimodal deep learning methods from physiological signals have not been significantly investigated. In this paper, we introduce PHemoNet, a fully hypercomplex network for multimodal emotion recognition from physiological signals. In detail, the architecture comprises modality-specific encoders and a fusion module. Both encoders and fusion modules are defined in the hypercomplex domain through parameterized hypercomplex multiplications (PHMs) that can capture latent relations between the different dimensions of each modality and between the modalities themselves. The proposed method outperforms current state-of-the-art models on the MAHNOB-HCI dataset in classifying valence and arousal using electroencephalograms (EEGs) and peripheral physiological signals. The code for this work is available at https://github.com/ispamm/MHyEEG., Comment: The paper has been accepted at RTSI 2024

Emotion recognition is relevant in various domains, ranging from healthcare to human-computer interaction. Physiological signals, being beyond voluntary control, offer reliable information for this purpose, unlike speech and facial expressions which can be controlled at will. They reflect genuine emotional responses, devoid of conscious manipulation, thereby enhancing the credibility of emotion recognition systems. Nonetheless, multimodal emotion recognition with deep learning models remains a relatively unexplored field. In this paper, we introduce a fully hypercomplex network with a hierarchical learning structure to fully capture correlations. Specifically, at the encoder level, the model learns intra-modal relations among the different channels of each input signal. Then, a hypercomplex fusion module learns inter-modal relations among the embeddings of the different modalities. The main novelty is in exploiting intra-modal relations by endowing the encoders with parameterized hypercomplex convolutions (PHCs) that thanks to hypercomplex algebra can capture inter-channel interactions within single modalities. Instead, the fusion module comprises parameterized hypercomplex multiplications (PHMs) that can model inter-modal correlations. The proposed architecture surpasses state-of-the-art models on the MAHNOB-HCI dataset for emotion recognition, specifically in classifying valence and arousal from electroencephalograms (EEGs) and peripheral physiological signals. The code of this study is available at https://github.com/ispamm/MHyEEG., Comment: The paper has been accepted at MLSP 2024

The analysis of the spatial arrangement of colors and roughness/smoothness of figures is relevant due to its wide range of applications. This paper proposes a texture classification method that extracts data from images using the Hilbert curve. Three information theory quantifiers are then computed: permutation entropy, permutation complexity, and Fisher information measure. The proposal exhibits some important properties: (i) it allows to discriminate figures according to varying degrees of correlations (as measured by the Hurst exponent), (ii) it is invariant to rotation and symmetry transformations, (iii) it can be used either in black and white or color images. Validations have been made not only using synthetic images but also using the well-known Brodatz image database.

We study the collection and delivery problem of biomedical specimens (CDSP) with multiple trips, time windows, a homogeneous fleet, and the objective of minimizing total completion time of delivery requests. This is a prominent problem in healthcare logistics, where specimens (blood, plasma, urin etc.) collected from patients in doctor's offices and hospitals are transported to a central laboratory for advanced analysis. To the best of our knowledge, available exact solution approaches for CDSP have been able to solve only small instances with up to 9 delivery requests. In this paper, we propose a two-index mixed-integer programming formulation that, when used with an off-the-shelf solver, results in a fast exact solution approach. Computational experiments on a benchmark data set confirm that the proposed formulation outperforms both the state-of-the-art model and the state-of-the-art metaheuristic from the literature, solving 80 out of 168 benchmark instances to optimality, including a significant number of instances with 100 delivery requests., Comment: This work has been submitted for possible publication

We propose GLAudio: Graph Learning on Audio representation of the node features and the connectivity structure. This novel architecture propagates the node features through the graph network according to the discrete wave equation and then employs a sequence learning architecture to learn the target node function from the audio wave signal. This leads to a new paradigm of learning on graph-structured data, in which information propagation and information processing are separated into two distinct steps. We theoretically characterize the expressivity of our model, introducing the notion of the receptive field of a vertex, and investigate our model's susceptibility to over-smoothing and over-squashing both theoretically as well as experimentally on various graph datasets.

We present a statistical modelling framework for implementing Distributed Lag Models (DLMs), encompassing several extensions of the approach to capture the temporally distributed effect from covariates via regression. We place DLMs in the context of penalised Generalized Additive Models (GAMs) and illustrate that implementation via the R package \texttt{mgcv}, which allows for flexible and interpretable inference in addition to thorough model assessment. We show how the interpretation of penalised splines as random quantities enables approximate Bayesian inference and hierarchical structures in the same practical setting. We focus on epidemiological studies and demonstrate the approach with application to mortality data from Cyprus and Greece. For the Cyprus case study, we investigate for the first time, the joint lagged effects from both temperature and humidity on mortality risk with the unexpected result that humidity severely increases risk during cold rather than hot conditions. Another novel application is the use of the proposed framework for hierarchical pooling, to estimate district-specific covariate-lag risk on morality and the use of posterior simulation to compare risk across districts., Comment: Submitted for review in "Statistics in Medicine" on 4/January/ 2024

In the last decades, various events have shown that electromechanical oscillations are a major concern for large interconnected Alternating Current (AC) power systems. Segmentation of AC power systems with High Voltage Direct Current (HVDC) systems (DC segmentation, for short) is a method that consists in turning large AC grids into a set of asynchronous AC clusters linked by HVDC links. It is a promising solution to mitigate electromechanical oscillations and other issues. In particular, an appropriately placed DC segmentation can stop a selected inter-area electromechanical oscillation mode. However, without supplementary controllers, DC segmentation will not contribute to the damping of the intra-area oscillation modes in the remaining AC clusters and will deteriorate the frequency stability of the power system. This paper aims at filling this gap and proposes the use of DC segmentation with HVDC systems based on Voltage Source Converters (VSC-HVDC) with supplementary controllers in the converter stations: (a) active-power supplementary controllers for frequency support among the asynchronous AC clusters and (b) a reactive-power supplementary controllers for Power Oscillation Damping (POD-Q), in order to damp the intra-area oscillation modes. The proposed supplementary controllers and their design will be presented, and their efficiency will be demonstrated on the Nordic 44 test system with DC segmentation by means of non-linear time-domain simulation and small-signal stability analysis., Comment: arXiv admin note: text overlap with arXiv:2302.09931

Gottlob Frege ingeniously presented a purely logical definition of the concept of number. However, one can claim that his definition is, in some way, circular, as it relies on the concept of one-to-one relation. The concept of number only makes sense when it presents the property of projection/reflection or binding. When we consider a number as an abstraction of objects, whatever they may be, saying that a number that belongs to the concept F is the same as that which belongs to the concept G means there is a projection/reflection, or binding, between the objects in F and the objects in G. We present a definition based on both equivalent approaches. First, we introduce the definition based on the relations of projection and reflection; then, we present the definition based on the relation of binding.

Quantum teleportation has proven to be fundamental for many quantum information and communication processes. The core concept can be exploited in many tasks, from the transmission of quantum states, quantum repeaters, to quantum computing. However, for linear-optical systems, the efficiency of teleportation is directly linked to the success probability of the involved Bell-state measurement. In most implementations, this is realized by linear optics with an intrinsically limited success probability of 50%. Here, we demonstrate quantum teleportation surpassing this limit. We achieve an average fidelity of the teleported states of $0.8677\pm0.0024$, leading to an overall acceptance rate of the teleportation of $69.71\pm0.75\%$. We obtain this boosted success probability by generating ancillary photonic states that are interfered with the Bell states. Thus, our work demonstrates the boosting Bell-state measurements in quantum-technology applications and our scheme could directly be applied to e.g. quantum repeaters.

The AEFishBIT tri-axial accelerometer was externally attached to the operculum to assess the divergent activity and respiratory patterns of two marine farmed fish, the gilthead sea bream (Sparus aurata) and European sea bass (Dicentrarchus labrax). Analysis of raw data from exercised fish highlighted the large amplitude of operculum aperture and body tail movements in European sea bass, which were overall more stable at low-medium exercise intensity levels. Cosinor analysis in free-swimming fish (on-board data processing) highlighted a pronounced daily rhythmicity of locomotor activity and respiratory frequency in both gilthead sea bream and European sea bass. Acrophases of activity and respiration were coupled in gilthead sea bream, acting feeding time (once daily at 11:00 h) as a main synchronizing factor. By contrast, locomotor activity and respiratory frequency were out of phase in European sea bass with activity acrophase on early morning and respiration acrophase on the afternoon. The daily range of activity and respiration variation was also higher in European sea bass, probably as part of the adaptation of this fish species to act as a fast swimming predator. In any case, lower locomotor activity and enhanced respiration were associated with larger body weight in both fish species. This agrees with the notion that selection for fast growth in farming conditions is accompanied by a lower activity profile, which may favor an efficient feed conversion for growth purposes. Therefore, the use of behavioral monitoring is becoming a reliable and large-scale promising tool for selecting more efficient farmed fish, allowing researchers and farmers to establish stricter criteria of welfare for more sustainable and ethical fish production.

Bilayers of NdCo$_5$/Ni$_8$Fe$_2$ can act as reconfigurable racetracks thanks to the parallel stripe domain configuration present in the hard magnetic material with weak perpendicular anisotropy (NdCo$_5$), and its imprint on the soft magnetic layer (Ni$_8$Fe$_2$). This pattern hosts spin textures with well defined topological charges and establishes paths for their deterministic propagation under the effect of pulsed currents, which has been studied as a function of externally applied fields by using Magnetic Transmission X-ray Microscopy. The experiments show guided vortex/antivortex propagation events within the Ni$_8$Fe$_2$ above a threshold current of $3\cdot10^{11}$ A/m$^2$. Opposite propagation senses have been observed depending on the topological charge of the spin texture, both in the remnant state and under an applied external field. Micromagnetic simulations of our multilayer reveal that the guiding effect and asymmetric propagation sense are due to the magnetic history of the hard magnetic layer. An exchange-bias-based memory effect acts as a magnetic spring and controls the propagation sense by favoring a specific orientation of the in plane magnetization, leading to a system which behaves as a hard-soft magnetic composite with reconfigurable capabilities for a controlled propagation of magnetic topological textures.

Despite their successes, deep learning models struggle with tasks requiring complex reasoning and function composition. We present a theoretical and empirical investigation into the limitations of Structured State Space Models (SSMs) and Transformers in such tasks. We prove that one-layer SSMs cannot efficiently perform function composition over large domains without impractically large state sizes, and even with Chain-of-Thought prompting, they require a number of steps that scale unfavorably with the complexity of the function composition. Multi-layer SSMs are constrained by log-space computational capacity, limiting their reasoning abilities. Our experiments corroborate these theoretical findings. Evaluating models on tasks including various function composition settings, multi-digit multiplication, dynamic programming, and Einstein's puzzle, we find significant performance degradation even with advanced prompting techniques. Models often resort to shortcuts, leading to compounding errors. These findings highlight fundamental barriers within current deep learning architectures rooted in their computational capacities. We underscore the need for innovative solutions to transcend these constraints and achieve reliable multi-step reasoning and compositional task-solving, which is critical for advancing toward general artificial intelligence., Comment: 29 pages, 4 theorems, 17 figures, 4 tables

On a locally finite, infinite tree $T$, let $p_c(T)$ denote the critical probability for Bernoulli percolation. We prove that every positively associated, finite-range dependent percolation model on $T$ with marginals $p > p_c(T)$ must percolate. Among finite-range dependent models on trees, positive association is thus a favourable property for percolation to occur. On general graphs of bounded degree, Liggett, Schonmann and Stacey (1997) proved that finite-range dependent percolation models with sufficiently large marginals stochastically dominate product measures. Under the additional assumption of positive association, we prove that stochastic domination actually holds for arbitrary marginals. Our result thereby generalises Proposition 3.4 in Liggett, Schonmann and Stacey (1997) which was restricted to the special case $G = \mathbb{Z}$. Studying the class of 1-independent percolation models has proven useful in bounding critical probabilities of various percolation models via renormalization. In many cases, the renormalized model is not only 1-independent but also positively associated. This motivates us to introduce the smallest parameter $p_a^+(G)$ such that every positively associated, 1-independent bond percolation model on a graph $G$ with marginals $p > p_a^+(G)$ percolates. We obtain quantitative upper and lower bounds on $p_a^+(\mathbb{Z}^2)$ and on $p_a^+(\mathbb{Z}^n)$ as $n\to \infty$, and also study the case of oriented bond percolation. In proving these results, we revisit several techniques originally developed for Bernoulli percolation, which become applicable thanks to a simple but seemingly new way of combining positive association with finite-range dependence., Comment: 41 pages, 5 figures

Hypercomplex algebras have recently been gaining prominence in the field of deep learning owing to the advantages of their division algebras over real vector spaces and their superior results when dealing with multidimensional signals in real-world 3D and 4D paradigms. This paper provides a foundational framework that serves as a roadmap for understanding why hypercomplex deep learning methods are so successful and how their potential can be exploited. Such a theoretical framework is described in terms of inductive bias, i.e., a collection of assumptions, properties, and constraints that are built into training algorithms to guide their learning process toward more efficient and accurate solutions. We show that it is possible to derive specific inductive biases in the hypercomplex domains, which extend complex numbers to encompass diverse numbers and data structures. These biases prove effective in managing the distinctive properties of these domains, as well as the complex structures of multidimensional and multimodal signals. This novel perspective for hypercomplex deep learning promises to both demystify this class of methods and clarify their potential, under a unifying framework, and in this way promotes hypercomplex models as viable alternatives to traditional real-valued deep learning for multidimensional signal processing., Comment: Accepted for Publication in IEEE Signal Processing Magazine

Transient stability is a complex phenomenon presented in multi-machine and multi-converter systems, and it is still considered a key limiting factor for stressed power systems. The increasing integration of non-synchronous generation further emphasises the need to address the challenges of improving the transient stability faced by these power systems. Several studies have focused on developing control strategies for GFM-VSCs to improve transient stability. These strategies include the use of current limiting algorithms and/or control of active/reactive power injections. This paper investigates the impact of fast voltage boosters (FVBs) and hybrid current limiters (HCLs) on transient stability of power systems with 100% grid-forming VSC-based generators. Short-circuit simulations and critical clearing time analysis are performed to evaluate the effectiveness of HCLs and FVBs in improving transient stability. The simulation results demonstrate the effectiveness of these approaches in avoiding the loss of synchronism. This research contributes to the current studies on transient stability in power systems and provides valuable insights into the potential of HCLs and FVBs as effective approaches to improve system stability., Comment: 23 pages, 20 figures

$ $Low density cosmic voids gravitationally lens the cosmic microwave background (CMB), leaving a negative imprint on the CMB convergence $\kappa$. This effect provides insight into the distribution of matter within voids, and can also be used to study the growth of structure. We measure this lensing imprint by cross-correlating the Planck CMB lensing convergence map with voids identified in the Dark Energy Survey Year 3 data set, covering approximately 4,200 deg$^2$ of the sky. We use two distinct void-finding algorithms: a 2D void-finder which operates on the projected galaxy density field in thin redshift shells, and a new code, Voxel, which operates on the full 3D map of galaxy positions. We employ an optimal matched filtering method for cross-correlation, using the MICE N-body simulation both to establish the template for the matched filter and to calibrate detection significances. Using the DES Y3 photometric luminous red galaxy sample, we measure $A_\kappa$, the amplitude of the observed lensing signal relative to the simulation template, obtaining $A_\kappa = 1.03 \pm 0.22$ ($4.6\sigma$ significance) for Voxel and $A_\kappa = 1.02 \pm 0.17$ ($5.9\sigma$ significance) for 2D voids, both consistent with $\Lambda$CDM expectations. We additionally invert the 2D void-finding process to identify superclusters in the projected density field, for which we measure $A_\kappa = 0.87 \pm 0.15$ ($5.9\sigma$ significance). The leading source of noise in our measurements is Planck noise, implying that future data from the Atacama Cosmology Telescope (ACT), South Pole Telescope (SPT) and CMB-S4 will increase sensitivity and allow for more precise measurements., Comment: 16 pages, 9 figures. Accepted for publication in MNRAS

Clustering algorithms or methods for GPS trajectories are in constant evolution due to the interest aroused in part of the scientific community. With the development of clustering algorithms considered traditional, improvements to these algorithms and even unique methods considered as "novelty" for science have emerged. This work aims to analyze the scientific production that exists around the topic "GPS trajectory clustering" by means of bibliometrics. Therefore, a total of 559 articles from the main collection of Scopus were analyzed, previously filtering the generated sample to discard any article that does not have a direct relationship with the topic to be analyzed. This analysis establishes an ideal environment for other disciplines and researchers, since it provides a current state of the trend of the subject of study in their field of research. -- Los algoritmos o m\'etodos de agrupamiento para trayectorias GPS se encuentran en constante evoluci\'on debido al inter\'es que despierta en parte de la comunidad cient\'ifica. Con el desarrollo de los algoritmos de agrupamiento considerados tradicionales han surgido mejoras a estos algoritmos e incluso m\'etodos \'unicos considerados como "novedad" para la ciencia. Este trabajo tiene como objetivo analizar la producci\'on cient\'ifica que existe alrededor del tema "agrupamiento de trayectorias GPS" mediante la bibliometr\'ia. Por lo tanto, fueron analizados un total de 559 art\'iculos de la colecci\'on principal de Scopus, realizando previamente un filtrado de la muestra generada para descartar todo aquel art\'iculo que no tenga una relaci\'on directa con el tema a analizar. Este an\'alisis establece un ambiente ideal para otras disciplinas e investigadores, ya que entrega un estado actual de la tendencia que lleva la tem\'atica de estudio en su campo de investigaci\'on., Comment: 16 pages, in Spanish, 8 figures

Understanding the structural and magnetic properties in layered hybrid organic-inorganic metal halide perovskites (HOIPs) is key for their design and integration in spin-electronic devices. Here, we have conducted a systematic study on ten compounds to understand the effect of the transition metal (Cu$^{2+}$, Mn$^{2+}$, Co$^{2+}$), organic spacer (alkyl- and aryl-ammonium) and perovskite phase (Ruddlesden-Popper and Dion-Jacobson) on the properties of these materials. Temperature-dependent Raman measurements show that the crystals' structural phase transitions are triggered by the motional freedom of the organic cations as well as by the flexibility of the inorganic metal-halide lattice. In the case of Cu$^{2+}$ HOIPs, an increase of the in-plane anisotropy and a reduction of the octahedra interlayer distance is found to change the behavior of the HOIP from that of a 2D ferromagnet to that of a quasi-3D antiferromagnet. Mn$^{2+}$ HOIPs show inherent antiferromagnetic octahedra intralayer interactions and a phenomenologically rich magnetism, presenting spin-canting, spin-flop transitions and metamagnetism controlled by the crystal anisotropy. Co$^{2+}$ crystals with non-linked tetrahedra show a dominant paramagnetic behavior irrespective of the organic spacer and the perovskite phase. This work demonstrates that the chemical flexibility of HOIPs can be exploited to develop novel layered magnetic materials with tailored magnetic properties., Comment: 5 figures

The Interior-Point Methods are a class for solving linear programming problems that rely upon the solution of linear systems. At each iteration, it becomes important to determine how to solve these linear systems when the constraint matrix of the linear programming problem includes dense columns. In this paper, we propose a preconditioner to handle linear programming problems with dense columns, and we prove theoretically that the final linear system to solve is uniformly bounded when the Interior-Point Method is converging to an optimal solution. This result is illustrated through computational experiments, which show that our proposed method is robust and competitive in terms of running time and/or number of iterations compared with existing methods.

Topological defects, or singularities, play a key role in the statics and dynamics of complex systems. In magnetism, Bloch point singularities represent point defects that mediate the nucleation of textures such as skyrmions and hopfions. However, while the textures are typically stabilised in chiral magnets, the influence of chirality on the Bloch point singularities remains relatively unexplored. Here we harness advanced three-dimensional nanofabrication to explore the influence of chirality on Bloch point singularities by introducing curvature-induced symmetry breaking in a ferromagnetic nanowire. Combining X-ray magnetic microscopy with the application of in situ magnetic fields, we demonstrate that Bloch point singularity-containing domain walls are stabilised in straight regions of the sample, and determine that curvature can be used to tune the energy landscape of the Bloch points. Not only are we able to pattern pinning points but, by controlling the gradient of curvature, we define asymmetric potential wells to realise a robust Bloch point shift-register with non-reciprocal behaviour. These insights into the influence of symmetry and chirality on singularities offers a route to the controlled nucleation and propagation of topological textures, providing opportunities for logic and computing devices.

This paper addresses the task of tracking Lissajous trajectories in the presence of quantized positioning sensors. To do so, theoretical results on tracking of continuous time periodic signals in the presence of output quantization are provided. With these results in hand, the application to Lissajous tracking is explored. The method proposed relies on the internal model principle and dispenses perfect knowledge of the system equations. Numerical results show that an arbitrary small scanning resolution is achievable despite large sensor quantization intervals.