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The constant increase of devices connected to the Internet, and therefore of cyber-attacks, makes it necessary to analyze network traffic in order to recognize malicious activity. Traditional packet-based analysis methods are insufficient because in large networks the amount of traffic is so high that it is unfeasible to review all communications. For this reason, flows is a suitable approach for this situation, which in future 5G networks will have to be used, as the number of packets will increase dramatically. If this is also combined with unsupervised learning models, it can detect new threats for which it has not been trained. This paper presents a systematic review of the literature on unsupervised learning algorithms for detecting anomalies in network flows, following the PRISMA guideline. A total of 63 scientific articles have been reviewed, analyzing 13 of them in depth. The results obtained show that autoencoder is the most used option, followed by SVM, ALAD, or SOM. On the other hand, all the datasets used for anomaly detection have been collected, including some specialised in IoT or with real data collected from honeypots., Comment: This article has been accepted for publication in Logic Journal of the IGPL Published by Oxford University Press

This article introduces efficient and user-friendly tools for analyzing the intersection curve between a ringed torus and an irreducible quadric surface. Without loose of generality, it is assumed that the torus is centered at the origin, and its axis of revolution coincides with the $z$-axis. The paper primarily focuses on examining the curve's projection onto the plane $z=0$, referred to as the cutcurve, which is essential for ensuring accurate lifting procedures. Additionally, we provide a detailed characterization of the singularities in both the projection and the intersection curve, as well as the existence of double tangents. A key tool for the analysis is the theory of resultant and subresultant polynomials.

Topological semimetals, particularly Weyl semimetals (WSMs), are crucial platforms for exploring emergent quantum phenomena due to their unique electronic structures and potential to transition into various topological phases. In this study, we report the discovery of a ferromagnetic (FM) type-II WSM in Mn(Bi1-xSbx)4Te7, which exhibits a remarkable three-dimensional (3D) quantum Hall effect (QHE). By precisely tuning the chemical potential through Sb doping, we obtained samples with the Fermi level near the charge neutrality point for x = ~ 0.27. This was confirmed by spectroscopy measurements (ARPES and STS), and these samples showed strong quantum oscillations along with a key transport signature of a Weyl state - chiral anomaly, and Fermi surface reconstruction driven by FM ordering. Our theoretical analysis indicates that this Weyl state evolves from a parent nodal ring state, where higher-order k-terms split the nodal line into type-II Weyl nodes. The Weyl state exhibits significant anisotropy, characterized by a pronounced reduction in Fermi velocity along the kz-axis, likely accounting for the observed 3D QHE. These results not only highlight the exceptional tunability of the Mn(Bi1-xSbx)4Te7 system, where precise control of the chemical potential and magnetic properties opens access to novel quantum phases, but also advance the understanding of FM WSMs., Comment: 28 pages, 5 figures

Error mitigation is essential for counteracting noise in quantum computations until fault-tolerant quantum technologies become viable. Here, we introduce Noise-Robust Estimation (NRE), a noise-agnostic framework that systematically reduces estimation bias through a two-step post-processing approach. At its core, NRE exploits a bias-dispersion correlation uncovered in this work, wherein a measurable metric -- normalized dispersion -- quantifies and helps suppress unknown residual bias in expectation value estimations. To reveal this correlation, we leverage bootstrapping on the existing measurement counts. We experimentally validate NRE on an IQM superconducting quantum processor, executing quantum circuits with up to 20 qubits and 240 entangling CZ gates. Our results demonstrate that NRE consistently achieves near bias-free estimations across different implementation settings while maintaining a manageable sampling overhead. These findings establish NRE as a reliable and broadly applicable error mitigation method for quantum computation with noisy hardware., Comment: 15 pages, 7 figures

Robustness in terms of outliers is an important topic and has been formally studied for a variety of problems in machine learning and computer vision. Generalized median computation is a special instance of consensus learning and a common approach to finding prototypes. Related research can be found in numerous problem domains with a broad range of applications. So far, however, robustness of generalized median has only been studied in a few specific spaces. To our knowledge, there is no robustness characterization in a general setting, i.e. for arbitrary spaces. We address this open issue in our work. The breakdown point >=0.5 is proved for generalized median with metric distance functions in general. We also study the detailed behavior in case of outliers from different perspectives. In addition, we present robustness results for weighted generalized median computation and non-metric distance functions. Given the importance of robustness, our work contributes to closing a gap in the literature. The presented results have general impact and applicability, e.g. providing deeper understanding of generalized median computation and practical guidance to avoid non-robust computation.

Size selection of liquid-dispersed 2D nanomaterials is a prerequisite for size-dependent studies in earlier stage research and for their targeted application in commercial settings. Centrifugation is the most widespread method for reliably sorting suspensions of polydisperse 2D nanosheets according to size. However, whilst centrifugation is effective, no a priori models are available to predict the outcome of centrifugation, making time consuming iterative experiments necessary. Here we present a simple model for the behaviour of 2D nanosheets during centrifugation and benchmark its predictions against experiments. This model uses simple expressions, specific to 2D particles, for the hydrodynamic radius, effective density and viscous resistance to generate the equation of motion of individual nanosheet during centrifugation. Critically, the equation of motion is then used to predict nanosheet size distributions within centrifugation products. This in turn leads to equations for easily measurable properties such as mean and maximum nanosheet sizes obtained during centrifugation-based fractionation. Comparison with experimental data demonstrates the robustness of this model for a range of 2D materials and solvent systems, and its ability to describe quite subtle effects. These results will enable more tailored size selection of nanosheets for specific applications and offer new mechanistic insights to optimise exfoliation conditions., Comment: 23 pages, 3 figures

The Exoplanet Transmission Spectroscopy Imager (ETSI) amalgamates a low resolution slitless prism spectrometer with custom multi-band filters to simultaneously image 15 spectral bandpasses between 430 nm and 975 nm with an average spectral resolution of $R = \lambda/\delta\lambda \sim 20$. ETSI requires only moderate telescope apertures ($\sim2$ m) and is capable of characterizing an exoplanet atmosphere in as little as a single transit, enabling selection of the most interesting targets for further characterization with other ground and space-based observatories and is also well suited to multi-band observations of other variable and transient objects. This enables a new technique, common-path multi-band imaging (CMI), used to observe transmission spectra of exoplanets transiting bright (V$<$14 magnitude) stars. ETSI is capable of near photon-limited observations, with a systematic noise floor on par with the Hubble Space Telescope and below the Earth's atmospheric amplitude scintillation noise limit. We report the as-built instrument optical and optomechanical design, detectors, control system, telescope hardware and software interfaces, and data reduction pipeline. A summary of ETSI's science capabilities and initial results are also included., Comment: 33 pages, 17 figures, published in JATIS

One of the most prolific methods of studying exoplanet atmospheres is transmission spectroscopy, which measures the difference between the depth of an exoplanet's transit signal at various wavelengths and attempts to correlate the depth changes to potential features in the exoplanet's atmosphere. Here we present reconnaissance observations of 21 exoplanet atmospheres measured with the Exoplanet Transmission Spectroscopy Imager (ETSI), a recently deployed spectro-photometer on the McDonald Observatory Otto Struve 2.1 m telescope. ETSI measurements are mostly free of systematics through the use of a novel observing technique called common-path multi-band imaging (CMI), which has been shown to achieve photometric color precision on-par with space-based observations (300ppm or 0.03%). This work also describes the various statistical tests performed on the data to evaluate the efficacy of the CMI method and the ETSI instrument in combination. We find that none of the 8 comparisons of exoplanet atmospheres measured with ETSI and other observatories (including the Hubble Space Telescope) provide evidence that the spectra are statistically dissimilar. These results suggest that ETSI can provide initial transmission spectroscopy observations for a fraction of the observational and monetary overhead previously required to detect an exoplanet's atmosphere. Ultimately these reconnaissance observations increase the number of planets with transmission spectroscopy measurements by ~10% and provide an immediate prioritization of 21 exoplanets for future follow-up with more precious observatories, such as the James Webb Space Telescope. The reconnaissance spectra are available through the Filtergraph visualization portal at the URL: https://filtergraph.com/etsi/., Comment: 28 pages, 8 figures, 11 tables, article published in AJ. ETSI data is available on request and spectra can be found at the URL https://filtergraph.com/etsi/

We investigate the fractional dispersion of solutions to the Helmholtz equation with periodic scattering data. We show that, under appropriate rescaling, the interaction between the different frequencies exhibits the same fluctuating behavior as for the Schr\"{o}dinger equation. To achieve this, we first establish an asymptotic fractional uncertainty principle for solutions to the Helmholtz equation., Comment: 14 pages, 1 figure

We study classical background electric fields and the Schwinger effect in de Sitter space. We show that having a constant electric field in de Sitter requires the photon to have a tachyonic mass proportional to the Hubble scale. This has physical implications for the induced Schwinger current which affect its IR behaviour. To study this we recompute the Schwinger current in de Sitter space for charged fermions and minimally coupled scalars imposing a physically consistent renormalization condition. We find a finite and positive Schwinger current even in the massless limit. This is in contrast to previous calculations in the literature which found a negative IR divergence. We also obtain the first result of the Schwinger current for a non-minimally coupled scalar, including for a conformally coupled scalar which we find has very similar behaviour to the fermion current. Our results may have physical implications for both magnetogenesis and inflationary dark matter production., Comment: 6 pages, 2 figures

We investigate extensions of the Standard Model (SM) featuring two-component scalar dark matter (DM) stabilized by a $\mathbb{Z}_{2n}$ symmetry. We focus on three specific cases, $\mathbb{Z}_4$, $\mathbb{Z}_6(23)$, and $\mathbb{Z}_6(13)$, each with a complex and a real scalar singlet. Through detailed numerical scans, we explore the viable parameter space, imposing constraints from DM relic abundance (Planck), direct detection (XENON1T, LZ, PandaX-4T), vacuum stability, perturbative unitarity, and coupling perturbativity up to the GUT and Planck scales, using one-loop renormalization group equations (RGEs). Our results demonstrate that these $\mathbb{Z}_{2n}$ models can provide viable two-component DM scenarios, consistent with all imposed constraints, for a range of DM masses and couplings. We identify the key parameters controlling the DM phenomenology and highlight the importance of a combined analysis incorporating both theoretical and experimental bounds., Comment: 39 pages, 14 figures

Yasutaka Nakanishi formulated the following conjecture in 1981: every link is 3-move equivalent to a trivial link. While the conjecture was proved for several specific cases, it remained an open question for over twenty years. In 2002, Mieczys{\l}aw D{\c a}bkowski and the last author showed that it does not hold, in general. In this article, we prove the Montesinos-Nakanishi $3$-move conjecture for links with up to 19 crossings and, with the exception of six pairwise non-isotopic links including the Chen link and its mirror image, for links with 20 crossings. Our work completely classifies links up 20 crossings modulo $3$-moves. This work includes computational methods, including new code in Regina that generalises pre-existing knot functions to work with links., Comment: 18 pages, comments welcomed

The concept of adequate links, introduced by Lickorish and Thistlethwaite as a generalization of alternating links, has recently gained interest among knot theorists in the context of Khovanov homology. Przytycki and Silvero introduced the more general concept of Khovanov adequacy: a diagram is Khovanov-adequate if its associated Khovanov chain complexes at both potential maximal and minimal quantum gradings have non-trivial homology. This article explores Khovanov adequacy within the framework of independence complexes and the calculation of the homotopy type of extreme Khovanov spectra., Comment: 12 pages, 11 figures

Given a swarm of limited-capability robots, we seek to automatically discover the set of possible emergent behaviors. Prior approaches to behavior discovery rely on human feedback or hand-crafted behavior metrics to represent and evolve behaviors and only discover behaviors in simulation, without testing or considering the deployment of these new behaviors on real robot swarms. In this work, we present Real2Sim2Real Behavior Discovery via Self-Supervised Representation Learning, which combines representation learning and novelty search to discover possible emergent behaviors automatically in simulation and enable direct controller transfer to real robots. First, we evaluate our method in simulation and show that our proposed self-supervised representation learning approach outperforms previous hand-crafted metrics by more accurately representing the space of possible emergent behaviors. Then, we address the reality gap by incorporating recent work in sim2real transfer for swarms into our lightweight simulator design, enabling direct robot deployment of all behaviors discovered in simulation on an open-source and low-cost robot platform., Comment: 10 pages, 5 figures. To be included in Proc. of the 24th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2025)

X-ray fluorescence computed tomography (XFCT), a form of X-ray molecular imaging, offers detailed quantitative imaging capabilities for high-Z metal nanoparticles (MNPs), which are widely studied for their applications in multifunctional theranostics. Due to its affordability and accessibility, the benchtop XFCT prototype typically employs a single-pixel detector (SPD) with single-pencil-beam (SPB) X-ray excitation. While this design (resembling the first-generation CT geometry) achieves reliable detection sensitivity, it is hindered by long imaging times. The use of simultaneous multiple-pencil-beam (MPB) excitation presents a promising solution to significantly reduce imaging times. In this study, we developed a repeatable workflow that combines Monte Carlo (MC) simulations and 3D printing to design Nbeam-MPB collimator, where Nbeam is the number of beams generated by the collimator. As an initial test, we fabricated a 2-MPB collimator and evaluated the performance of 2-MPB-based XFCT imaging on a physical phantom and small animals surgically implanted with agarose pellets containing gold chloride (H[AuCl4]). The results demonstrated a 2x acceleration in image acquisition without compromising the contrast-to-noise ratio (CNR). We further investigated the concept of Nbeam-MPB acceleration on the MC computational XFCT system, which confirmed the feasibility of achieving at least 4x acceleration with 4-MPB excitation. Combined with additional system optimization, such as X-ray beam flux optimization, XFCT imaging could be further accelerated, reducing acquisition time from hours to minutes and meeting the requirements for routine MNP imaging.

Adaptation to climate change requires robust climate projections, yet the uncertainty in these projections performed by ensembles of Earth system models (ESMs) remains large. This is mainly due to uncertainties in the representation of subgrid-scale processes such as turbulence or convection that are partly alleviated at higher resolution. New developments in machine learning-based hybrid ESMs demonstrate great potential for systematically reduced errors compared to traditional ESMs. Building on the work of hybrid (physics + AI) ESMs, we here discuss the additional potential of further improving and accelerating climate models with quantum computing. We discuss how quantum computers could accelerate climate models by solving the underlying differential equations faster, how quantum machine learning could better represent subgrid-scale phenomena in ESMs even with currently available noisy intermediate-scale quantum devices, how quantum algorithms aimed at solving optimisation problems could assist in tuning the many parameters in ESMs, a currently time-consuming and challenging process, and how quantum computers could aid in the analysis of climate models. We also discuss hurdles and obstacles facing current quantum computing paradigms. Strong interdisciplinary collaboration between climate scientists and quantum computing experts could help overcome these hurdles and harness the potential of quantum computing for this urgent topic.

The North Slope of Alaska is dominated by small waterbodies that provide critical ecosystem services for local population and wildlife. Detailed information on the depth of the waterbodies is scarce due to the challenges with collecting such information. In this work we have trained a machine learning (Random Forest Regressor) model to predict depth from multispectral Landsat data in waterbodies across the North Slope of Alaska. The greatest challenge is the scarcity of in situ data, which is expensive and difficult to obtain, to train the model. We overcame this challenge by using modeled depth predictions from a prior study as synthetic training data to provide a more diverse training data pool for the Random Forest. The final Random Forest model was more robust than models trained directly on the in situ data and when applied to 208 Landsat 8 scenes from 2016 to 2018 yielded a map with an overall $r^{2}$ value of 0.76 on validation. The final map has been made available through the Oak Ridge National Laboratory Distribute Active Archive Center (ORNL-DAAC). This map represents a first of its kind regional assessment of waterbody depth with per pixel estimates of depth for the entire North Slope of Alaska., Comment: 24 Pages, 6 Figures, 1 Table. This article is a US Government work. Landsat data from the US Geological Survey Earth Explorer system: https://earthexplorer.usgs.gov. Sonar training measurements: https://doi.org/10.18739/A2JD4PP1H. Output maps from the Oak Ridge National Laboratory Distribute Active Archive Center (ORNL-DAAC): https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=2243

The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20\% increase in the efficiency of sub-1\,cm vertex reconstruction across all neutrino flavours., Comment: 32 pages, 18 figures

Studies of galaxy populations classified according to their kinematic behaviours and dynamical state using the Projected Phase Space Diagram (PPSD) are affected by misclassification and contamination, leading to systematic errors in determining the characteristics of the different galaxy classes. We propose a method to statistically correct the determination of galaxy properties' distributions accounting for the contamination caused by misclassified galaxies from other classes. Using a sample of massive clusters and galaxies in their surroundings taken from the MultiDark Planck 2 simulation combined with the semi-analytic model of galaxy formation SAG, we compute the confusion matrix associated to a classification scheme in the PPSD. Based on positions in the PPSD, galaxies are classified as cluster members, backsplash galaxies, recent infallers, infalling galaxies, and interlopers. This classification is determined using probabilities calculated by the code ROGER, along with a threshold criterion. By inverting the confusion matrix, we are able to get better determinations of distributions of galaxy properties such as colour. Compared to a direct estimation based solely on the predicted galaxy classes, our method provides better estimates of the mass-dependent colour distribution for the galaxy classes most affected by misclassification: cluster members, backsplash galaxies, and recent infallers. We apply the method to a sample of observed X-ray clusters and galaxies. Our method can be applied to any classification of galaxies in the PPSD, and to any other galaxy property besides colour, provided an estimation of the confusion matrix. Blue, low-mass galaxies in clusters are almost exclusively recent infaller galaxies that have not yet been quenched by the environmental action of the cluster. Backsplash galaxies are on average redder than expected., Comment: 8 pages, 4 figures, accepted for publication in A&A

Quantum computers with a limited qubit connectivity require inserting SWAP gates for qubit routing, which increases gate execution errors and the impact of environmental noise due to an overhead in circuit depth. In this work, we benchmark various routing techniques considering random quantum circuits on one-dimensional and square lattice connectivities, employing both analytical and numerical methods. We introduce circuit fidelity as a comprehensive metric that captures the effects of SWAP and circuit depth overheads. Leveraging a novel approach based on the SABRE algorithm, we achieve up to $84\%$ higher average circuit fidelity for large devices within the NISQ range, compared to previously existing methods. Additionally, our results highlight that the optimal routing choice critically depends on the qubit count and the hardware characteristics, including gate fidelities and coherence times., Comment: 9+3 pages, 7 figures

Head computed tomography (CT) imaging is a widely-used imaging modality with multitudes of medical indications, particularly in assessing pathology of the brain, skull, and cerebrovascular system. It is commonly the first-line imaging in neurologic emergencies given its rapidity of image acquisition, safety, cost, and ubiquity. Deep learning models may facilitate detection of a wide range of diseases. However, the scarcity of high-quality labels and annotations, particularly among less common conditions, significantly hinders the development of powerful models. To address this challenge, we introduce FM-CT: a Foundation Model for Head CT for generalizable disease detection, trained using self-supervised learning. Our approach pre-trains a deep learning model on a large, diverse dataset of 361,663 non-contrast 3D head CT scans without the need for manual annotations, enabling the model to learn robust, generalizable features. To investigate the potential of self-supervised learning in head CT, we employed both discrimination with self-distillation and masked image modeling, and we construct our model in 3D rather than at the slice level (2D) to exploit the structure of head CT scans more comprehensively and efficiently. The model's downstream classification performance is evaluated using internal and three external datasets, encompassing both in-distribution (ID) and out-of-distribution (OOD) data. Our results demonstrate that the self-supervised foundation model significantly improves performance on downstream diagnostic tasks compared to models trained from scratch and previous 3D CT foundation models on scarce annotated datasets. This work highlights the effectiveness of self-supervised learning in medical imaging and sets a new benchmark for head CT image analysis in 3D, enabling broader use of artificial intelligence for head CT-based diagnosis., Comment: Under Review Preprint

Two novel and direct quantum mechanical representations of the Black-Scholes model are constructed based on the (Wick-rotated) quantization of two specific mechanical systems. The quantum setup is achieved by means of the associated Laplace-Beltrami operator (one for each model), and not by merely applying the usual naive rule. Additionally, the clear identification of the geometric content of the underlying classical framework is exploited in order to arrive at a noncommutative quantum mechanics generalization of the Black-Scholes model. We also consider a system consisting of two degrees of freedom whose (Wick-rotated) quantization leads to a model which can be seen as related to the Merton-Garman family. This model is also generalized via noncommutative quantum mechanics.

Due to the lack of a net magnetic moment, antiferromagnets possess a unique robustness to external magnetic fields and are thus predicted to play an important role in future magnetic technologies. However, this robustness also makes them quite difficult to control, and the development of novel methods to manipulate these systems with external stimuli is a fundamental goal of antiferromagnetic spintronics. In this work, we report evidence for a metastable reorientation of the order parameter in an antiferromagnetic semiconductor triggered by an ultrafast quench of the equilibrium order via photoexcitation above the band gap. The metastable state forms less than 10 ps after the excitation pulse, and persists for longer than 150 ps before decaying to the ground state via thermal fluctuations. Importantly, this transition cannot be induced thermodynamically, and requires the system to be driven out of equilibrium. Broadly speaking, this phenomenology is ultimately the result of large magnetoelastic coupling in combination with a relatively low symmetry of the magnetic ground state. Since neither of these properties are particularly uncommon in magnetic materials, the observations presented here imply a generic path toward novel device technology enabled by ultrafast dynamics in antiferromagnets., Comment: 41 pages, 27 figures

Wide band gap oxides are promising host materials for spin defect qubits, offering unique advantages such as a dilute nuclear spin environment. Zinc oxide (ZnO), in particular, can achieve exceptional high purity, which enables long spin coherence time. In this work, we theoretically search for deep-level point defects in ZnO with optimal physical properties for optically-addressable spin qubits. Using first-principles calculations, we predict the Molybdenum-vacancy complex defect $Mo_{Zn}v_O$ in ZnO to own promising spin and optical properties, including spin-triplet ground state, optical transition in the visible to near-infrared range with high quantum yield, allowed intersystem crossings with a sizable optically-detected magnetic resonance contrast, and long spin T$_2$ and T$^*_2$. Notably, we find the Huang-Rhys factor of the defect to be around 5, which is significantly smaller than the typical range of 10-30 for most known defects in ZnO. Furthermore, we compare the spin decoherence driven by the nuclear spin bath and paramagnetic impurity baths. We find that the paramagnetic impurities are very effective in causing spin decoherence even with very low concentrations, implying that they can likely dominate the spin decoherence in ZnO even after isotopic purification. Using the computed excited-state energies and kinetic rates as inputs, we predict the ODMR contrast and propose a new protocol for spin qubit initialization and readout, which could be generalized to other systems with forbidden axial intersystem crossings., Comment: 9 figures

Multivariate Time Series Imputation (MTSI) is crucial for many applications, such as healthcare monitoring and traffic management, where incomplete data can compromise decision-making. Existing state-of-the-art methods, like Denoising Diffusion Probabilistic Models (DDPMs), achieve high imputation accuracy; however, they suffer from significant computational costs and are notably time-consuming due to their iterative nature. In this work, we propose CoSTI, an innovative adaptation of Consistency Models (CMs) for the MTSI domain. CoSTI employs Consistency Training to achieve comparable imputation quality to DDPMs while drastically reducing inference times, making it more suitable for real-time applications. We evaluate CoSTI across multiple datasets and missing data scenarios, demonstrating up to a 98% reduction in imputation time with performance on par with diffusion-based models. This work bridges the gap between efficiency and accuracy in generative imputation tasks, providing a scalable solution for handling missing data in critical spatio-temporal systems., Comment: 20 pages, 5 figures, 13 tables

Experimental Physics and Industrial Control System (EPICS) is a software tool that during last years has become relevant as a main framework to deploy distributed control systems in large scientific environments. At the moment, ESS Bilbao uses this middleware to perform the control of their Ion Source Hydrogen Positive (ISHP) project. The implementation of the control system was based on: PXI Real Time controllers using the LabVIEW-RT and LabVIEW-EPICS tools; and RIO devices based on Field-Programmable Gate Array (FPGA) technology. Intended to provide a full compliant EPICS IOCs for RIO devices and to avoid additional efforts on the system maintainability, a migration of the current system to a derivative Red Hat Linux (CentOS) environment has been conducted. This paper presents a real application case study for using the NIRIO EPICS device support (NIRIO-EDS) to give support to the ISHP. Although RIO FPGA configurations are particular solutions for ISHP performance, the NIRIO-EDS has permitted the control and monitoring of devices by applying a well-defined design methodology into the previous FPGA configuration for RIO/FlexRIO devices. This methodology has permitted a fast and easy deployment for the new robust, scalable and maintainable software to support RIO devices into the ISHP control architecture.

We propose a scale-invariant model with the 3-3-1-1 gauge symmetry that features universal seesaw for all fermion masses. The discrete remnant of the gauge group, the matter parity, stabilizes a fermionic dark matter candidate. The scalar sector contains two triplets, the minimum number to break the 3-3-1 symmetry, and two scalar singlets. With the help of additional vector-like quarks, the universal implementation of the see-saw mechanism across all fermion sectors provides a partial explanation for the observed hierarchy of masses for charged leptons, neutrinos, and quarks. We identify the lightest $ P_M $-odd fermion, $ f_\mathrm{d} $, as a viable dark matter candidate. This fermion satisfies the relic density constraint and the spin-independent constraints within the mass range $ 160 \, \textrm{GeV} \lesssim m_{f_\mathrm{d}} \lesssim 520$ GeV . This range depends on the symmetry-breaking scale $ v_\chi $ with a lower bound $ v_\chi \gtrsim 3.6$ TeV due to LEP bounds on the $ \rho_0 $ parameter. Spin-independent scattering cross-sections for $ f_\mathrm{d} $ align with experimental limits from LZ and PandaX-4T, with some regions of the parameter space nearing the sensitivity of upcoming experiments, such as XLZD and PandaX-xT, which offers promising opportunities for detection., Comment: 34 pages, 5 figures

In this work, we explore the implications of the Cohen and Glashow Very Special Relativity (VSR) theory, a framework that introduces Lorentz invariance violation through the presence of a preferred direction. Our analysis focuses on the impact of VSR on the Cherenkov angle, revealing modifications to the dispersion relation of particles, particularly the photon and the electron, which acquire an effective inertial mass. This modification also implies a deviation in the speed of light, which can be constrained through precise experimental measurements. Using data from the RICH system of the LHCb experiment, we take advantage of its capability to reconstruct Cherenkov angles within the momentum range of the particles of 2.6-100 GeV/c. These measurements, combined with the most stringent laboratory tests of the isotropy of the speed of light ($\Delta c / c \sim 10^{-17}$), allow us to impose new upper bounds on the parameter $\Omega$, which quantifies a deviation from the standard Special Relativity. Furthermore, we establish an analogy between VSR and Minkowski's electrodynamics in a dielectric medium for particles with very high velocity, offering a physically intuitive interpretation of the parameter $\Omega$., Comment: 23 pages, 9 pages

The softmax function is used as an activation function placed in the output layer of a neural network. It allows extracting the probabilities of the output classes, while introduces a non-linearity to the model. In the field of low-end FPGAs, implementations of Deep Neural Networks (DNNs) require the exploration of optimisation techniques to improve computational efficiency and hardware resource consumption. This work explores approximate computing techniques to implement the softmax function, using Taylor and Pad\'e approximations, and interpolation methods with Look-Up Tables (LUTs). The introduction of approximations aims to reduce the required execution time while reducing the precision of results produced by the softmax function. Each implementation is evaluated using Root Mean Square Error (RMSE) for accuracy assessment, and individual performance is verified by taking measurements of execution times. From our evaluation, quadratic interpolation with LUTs achieves the lowest error, but in terms of performance, Taylor and Pad\'e approximations show better execution times, which highlights the existing design trade-off between numerical accuracy and power consumption.

Quiescent galaxies over $34$, although our measurements are consistent within errors. This technique may be applied to other JWST surveys., Comment: Submitted to The Astrophysical Journal. 19 pages, 7 figures. Code to reproduce Figs. 2 - 4 can be found at https://github.com/alex-delavega/umap_jades_2025. Comments are welcome!

Achieving extremely high-quality and truly immersive interactive Virtual Reality (VR) is expected to require a wireless link to the cloud, providing multi-gigabit throughput and extremely low latency. A prime candidate for fulfilling these requirements is millimeter-wave (mmWave) communications, operating in the 30 to 300 GHz bands, rather than the traditional sub-6 GHz. Evaluations with first-generation mmWave Wi-Fi hardware, based on the IEEE 802.11ad standard, have so far largely remained limited to lower-layer metrics. In this work, we present the first experimental analysis of the capabilities of mmWave for streaming VR content, using a novel testbed capable of repeatably creating blockage through mobility. Using this testbed, we show that (a) motion may briefly interrupt transmission, (b) a broken line of sight may degrade throughput unpredictably, and (c) TCP-based streaming frameworks need careful tuning to behave well over mmWave., Comment: Published and presented at International Conference on Network of the Future 2022

Extended Reality (XR) enables a plethora of novel interactive shared experiences. Ideally, users are allowed to roam around freely, while audiovisual content is delivered wirelessly to their Head-Mounted Displays (HMDs). Therefore, truly immersive experiences will require massive amounts of data, in the range of tens of gigabits per second, to be delivered reliably at extremely low latencies. We identify Millimeter-Wave (mmWave) communications, at frequencies between 24 and 300 GHz, as a key enabler for such experiences. In this article, we show how the mmWave state of the art does not yet achieve sufficient performance, and identify several key active research directions expected to eventually pave the way for extremely-high-quality mmWave-enabled interactive multi-user XR., Comment: Published in IEEE Communications Magazine

The avoidance of water freezing is the holy grail in the cryopreservation of biological samples, food, and organs. Fast cooling rates are used to beat ice nucleation and avoid cell damage. This strategy can be enhanced by applying high pressures to decrease the nucleation rate, but the physics behind this procedure has not been fully understood yet. We perform computer experiments to investigate ice nucleation at high pressures consisting in embedding ice seeds in supercooled water. We find that the slowing down of the nucleation rate is mainly due to an increase of the ice I-water interfacial free energy with pressure. Our work also clarifies the molecular mechanism of ice nucleation for a wide pressure range. This study is not only relevant to cryopreservation, but also to water amorphization and climate change modeling., Comment: 6 pages and 3 figures

Many studies conclude that galaxies quench from the inside-out by examining profiles of specific star-formation rate (sSFR). These are usually measured by fitting spectral energy distributions (SEDs) assuming a fixed dust law and uniform priors on all parameters. Here, we examine the effects of more physically motivated priors: a flexible dust law, an exponential prior on the dust attenuation $A_V$, and Gaussian priors that favor extended star-formation histories. This results in model colors that better trace observations. We then perform radial SED fits to multiband flux profiles measured from Hubble Space Telescope images for 1,440 galaxies at $0.4

We present the Cosmic Evolution Early Release Science (CEERS) Survey, a 77.2 hour Director's Discretionary Early Release Science Program. CEERS demonstrates, tests, and validates efficient extragalactic surveys using coordinated, overlapping parallel observations with the JWST instrument suite, including NIRCam and MIRI imaging, NIRSpec low (R~100) and medium (R~1000) resolution spectroscopy, and NIRCam slitless grism (R~1500) spectroscopy. CEERS targets the Hubble Space Telescope-observed region of the Extended Groth Strip (EGS) field, supported by a rich set of multiwavelength data. CEERS facilitated immediate community science in both of the extragalactic core JWST science drivers ``First Light" and ``Galaxy Assembly," including: 1) The discovery and characterization of large samples of galaxies at z >~ 10 from ~90 arcmin^2 of NIRCam imaging, constraining their abundance and physical nature; 2) Deep spectra of >1000 galaxies, including dozens of galaxies at 63; and 4) Characterizing galaxy mid-IR emission with MIRI to study dust-obscured star-formation and supermassive black hole growth at z~1-3. As a legacy product for the community, the CEERS team has provided several data releases, accompanied by detailed notes on the data reduction procedures and notebooks to aid in reproducibility. In addition to an overview of the survey and quality of the data, we provide science highlights from the first two years with CEERS data., Comment: 38 pages, 13 figures, 6 tables

Near Earth Asteroids are of great interest to the scientific community due to their proximity to Earth, making them both potential hazards and possible targets for future missions, as they are relatively easy to reach by spacecraft. A number of techniques and models can be used to constrain their physical parameters and build a comprehensive assessment of these objects. In this work, we compare physical property results obtained from improved $H_V$ absolute magnitude values, thermophysical modeling, and polarimetry data for the well-known Amor-class NEO 1627 Ivar. We show that our fits for albedo are consistent with each other, thus demonstrating the validity of this cross-referencing approach, and propose a value for Ivar's albedo of $0.24^{+0.04}_{-0.02}$ . Future observations will extend this work to a larger sample size, increasing the reliability of polarimetry for rapid asteroid property characterization, as a technique independent of previously established methods and requiring significantly fewer observations., Comment: 11 pages, 5 figures (13 sub-figures), 4 tables. Accepted by the Planetary Science Journal

This work raises the question of whether finding an equivalent bottom-up description to a given top-down one is possible. We consider the vector meson spectrum derived in the D3/D7 system to answer this question. Using WKB analysis, we reconstruct a bottom-up confining potential that resembles the geometric structure of the so-called hardwall model. We compute some properties for this bottom-up model, including the thermal deconfinement phase transition, the $\rho$ radial Regge trajectory, and the configurational entropy., Comment: 10 pages, 4 figures

Lattice QCD is notorious for its computational expense. Modern lattice simulations require large-scale computational resources to handle the large number of Dirac operator inversions used to construct correlation functions. Machine learning (ML) techniques that can increase, at the analysis level, the information inferred from the correlation functions would therefore be beneficial. We apply supervised learning to infer two-point lattice correlation functions at different target masses. Our work proposes a new method for separating data into training and bias correction subsets for efficient uncertainty estimation. We also benchmark our ML models against a simple ratio method., Comment: 7 pages, 3 figures. Contribution to the 41st International Symposium on Lattice Field Theory (LATTICE2024), 28 July - 3 August 2024, Liverpool, UK. Removed section numbers for acknowledgments and disclaimer, fixed bibliography formatting

Polynomial invariants constitute a dynamic and essential area of study in the mathematical theory of knots. From the pioneer Alexander polynomial, the revolutionary Jones polynomial, to the collectively discovered HOMFLYPT polynomial, just to mention a few, these algebraic expressions have been central to the understanding of knots and links. The introduction of Khovanov homology has sparked significant interest in the categorification of these polynomials, offering deeper insights into their topological and algebraic properties. In this work, we revisit two prominent polynomial invariants, the Alexander-Conway and the Kauffman bracket polynomials, and focus specifically on the polynomials associated with the family of three strand pretzel links $P(1,1,n)$., Comment: 10 pages, 18 figures