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In July 2024, Sunrise completed its third successful science flight. The Sunrise III observatory had been upgraded significantly after the two previous successful flights in 2009 and 2013. Three completely new instruments focus on the small-scale physical processes and their complex interaction from the deepest observable layers in the photosphere up to chromospheric heights. Previously poorly explored spectral regions and lines are exploited to paint a three-dimensional picture of the solar atmosphere with unprecedented completeness and level of detail. The full polarimetric information is captured by all three instruments to reveal the interaction between the magnetic fields and the hydrodynamic processes. Two slit-based spectropolarimeters, the Sunrise UV Spectropolarimeter and Imager (SUSI) and the Sunrise Chromospheric Infrared spectro-Polarimeter (SCIP), focus on the near-ultraviolet and the near-infrared regions respectively, and the imaging spectropolarimeter Tunable Magnetograph (TuMag) simultaneously obtains maps of the full field-of-view of $46 \times 46$ Mm$^2$ in the photosphere and the chromosphere in the visible. The instruments are operated in an orchestrated mode, benefiting from a new Image Stabilization and Light Distribution unit (ISLiD), with the Correlating Wavefront Sensor (CWS) providing the autofocus control and an image stability with a root-mean-square value smaller than 0.005''. A new gondola was constructed to significantly improve the telescope pointing stability, required to achieve uninterrupted observations over many hours. Sunrise III was launched successfully on July 10, 2024, from the Esrange Space Center near Kiruna (Sweden). It reached the landing site between the Mackenzie River and the Great Bear Lake in Canada after a flight duration of 6.5 days. In this paper, we give an overview of the Sunrise III observatory and its instruments., Comment: 67 pages, 25 figures; to be published in Solar Physics Topical Collection "The Sunrise III Solar Observatory" (https://link.springer.com/collections/jegdciedig)

In this paper, we propose a novel censoring policy for energy-efficient transmissions in energy-harvesting sensors. The problem is formulated as an infinite-horizon Markov Decision Process (MDP). The objective to be optimized is the expected sum of the importance (utility) of all transmitted messages. Assuming that such importance can be evaluated at the transmitting node, we show that, under certain conditions on the battery model, the optimal censoring policy is a threshold function on the importance value. Specifically, messages are transmitted only if their importance is above a threshold whose value depends on the battery level. Exploiting this property, we propose a model-based stochastic scheme that approximates the optimal solution, with less computational complexity and faster convergence speed than a conventional Q-learning algorithm. Numerical experiments in single-hop and multi-hop networks confirm the analytical advantages of the proposed scheme.

Over the past decade, specialized computing and storage devices, such as GPUs, TPUs, and high-speed storage, have been increasingly integrated into server nodes within Supercomputers and Data Centers. The advent of high-bandwidth memory (HBM) has facilitated a more compact design for these components, enabling multiple units to be interconnected within a single server node through intra-node networks like PCIe, NVLink, or Ethernet. These networks allow for scaling up the number of dedicated computing and storage devices per node. Additionally, inter-node networks link these devices across thousands of server nodes in large-scale computing systems. However, as communication demands among accelerators grow-especially in workloads like generative AI-both intra- and inter-node networks risk becoming critical bottlenecks. Although modern intra-node network architectures attempt to mitigate this issue by boosting bandwidth, we demonstrate in this paper that such an approach can inadvertently degrade inter-node communication. This occurs when high-bandwidth intra-node traffic interferes with incoming traffic from external nodes, leading to congestion. To evaluate this phenomenon, we analyze the communication behavior of realistic traffic patterns commonly found in generative AI applications. Using OMNeT++, we developed a general simulation model that captures both intra- and inter-node network interactions. Through extensive simulations, our findings reveal that increasing intra-node bandwidth and the number of accelerators per node can actually hinder overall inter-node communication performance rather than improve it., Comment: 15 pages

The geometric distances of active galactic nuclei (AGNs) are challenging to measure because of their exceptionally compact structure yet vast cosmic distances. A combination of spectroastrometry and reverberation mapping (SARM) of broad-line regions (BLRs) constitutes a novel means to probe the geometric distance of AGNs, which has recently become practically feasible owing to successful interferometric observations with VLTI/GRAVITY. Here, we perform SARM analysis of four nearby quasars: Mrk 509, PDS 456, 3C 273, and NGC 3783. Results for the former two are reported for the first time and the latter two are revisited using our improved BLR dynamical modeling that includes the radial-dependent responsivity of BLRs. This allows us to self-consistently account for the emissivity weighting of the BLR in spectroastrometry and responsivity weighting in reverberation mapping. We obtain angular-diameter distances of the four quasars, from which we derive a Hubble constant of $H_0=69_{-10}^{+12}\,\rm km\,s^{-1}\,Mpc^{-1}$. Although this consititutes a large uncertainty for a measurement of $H_0$, it is anticipated that the precision will improve to a competitive level once a greater number of AGNs are accessible following the upgrade of GRAVITY in the near future. From SARM analysis, the black hole masses of the four quasars are also measured with the statistical uncertainty ranging from 0.06 to 0.23 dex, consistent with the correlations between black hole masses and properties of the host bulges., Comment: 21 pages, 14 figures, 4 tables; submitted to ApJ; comments welcome

We investigate the entanglement properties of the Quantum Six-Vertex Model on a cylinder, focusing on the Shannon-Renyi entropy in the limit of Renyi order $n = \infty$. This entropy, calculated from the ground state amplitudes of the equivalent XXZ spin-1/2 chain, allows us to determine the Renyi entanglement entropy of the corresponding Rokhsar-Kivelson wavefunctions, which describe the ground states of certain conformal quantum critical points. Our analysis reveals a novel logarithmic correction to the expected entanglement scaling when the system size is odd. This anomaly arises from the geometric frustration of spin configurations imposed by periodic boundary conditions on odd-sized chains. We demonstrate that the scaling prefactor of this logarithmic term is directly related to the compactification radius of the low-energy bosonic field theory description, or equivalently, the Luttinger parameter. Thus, this correction provides a direct probe of the underlying Conformal Field Theory (CFT) describing the critical point. Our findings highlight the crucial role of system size parity in determining the entanglement properties of this model and offer insights into the interplay between geometry, frustration, and criticality., Comment: 10 pages, 7 figures

In this article, we present a Latent Diffusion Model (LDM) for the generation of brain Magnetic Resonance Imaging (MRI), conditioning its generation based on pathology (Healthy, Glioblastoma, Sclerosis, Dementia) and acquisition modality (T1w, T1ce, T2w, Flair, PD). To evaluate the quality of the generated images, the Fr\'echet Inception Distance (FID) and Multi-Scale Structural Similarity Index (MS-SSIM) metrics were employed. The results indicate that the model generates images with a distribution similar to real ones, maintaining a balance between visual fidelity and diversity. Additionally, the model demonstrates extrapolation capability, enabling the generation of configurations that were not present in the training data. The results validate the potential of the model to increase in the number of samples in clinical datasets, balancing underrepresented classes, and evaluating AI models in medicine, contributing to the development of diagnostic tools in radiology without compromising patient privacy.

Modern applications are built as large, distributed systems spanning numerous modules, teams, and data centers. Despite robust engineering and recovery strategies, failures and performance issues remain inevitable, risking significant disruptions and affecting end users. Rapid and accurate root cause identification is therefore vital to ensure system reliability and maintain key service metrics. We have developed a novel causality-based Root Cause Identification (RCI) algorithm that emphasizes causation over correlation. This algorithm has been integrated into IBM Instana-bridging research to practice at scale-and is now in production use by enterprise customers. By leveraging "causal AI," Instana stands apart from typical Application Performance Management (APM) tools, pinpointing issues in near real-time. This paper highlights Instana's advanced failure diagnosis capabilities, discussing both the theoretical underpinnings and practical implementations of the RCI algorithm. Real-world examples illustrate how our causality-based approach enhances reliability and performance in today's complex system landscapes.

Inverse kinematics is a fundamental technique for motion and positioning control in robotics, typically applied to end-effectors. In this paper, we extend the concept of inverse kinematics to guiding vector fields for path following in autonomous mobile robots. The desired path is defined by its implicit equation, i.e., by a collection of points belonging to one or more zero-level sets. These level sets serve as a reference to construct an error signal that drives the guiding vector field toward the desired path, enabling the robot to converge and travel along the path by following such a vector field. We start with the formal exposition on how inverse kinematics can be applied to guiding vector fields for single-integrator robots in an m-dimensional Euclidean space. Then, we leverage inverse kinematics to ensure that the level-set error signal behaves as a linear system, facilitating control over the robot's transient motion toward the desired path and allowing for the injection of feed-forward signals to induce precise motion behavior along the path. We then propose solutions to the theoretical and practical challenges of applying this technique to unicycles with constant speeds to follow 2D paths with precise transient control. We finish by validating the predicted theoretical results through real flights with fixed-wing drones., Comment: Yu Zhou and Jes\'us Bautista contributed equally to this work. In the proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2025

In recent neutrino detectors, neutrons produced in neutrino reactions play an important role. Muon capture on oxygen nuclei is one of the processes that produce neutrons in water Cherenkov detectors. We measured neutron multiplicity in the process using cosmic ray muons that stop in the gadolinium-loaded Super-Kamiokande detector. For this measurement, neutron detection efficiency is obtained with the muon capture events followed by gamma rays to be $50.2^{+2.0}_{-2.1}\%$. By fitting the observed multiplicity considering the detection efficiency, we measure neutron multiplicity in muon capture as $P(0)=24\pm3\%$, $P(1)=70^{+3}_{-2}\%$, $P(2)=6.1\pm0.5\%$, $P(3)=0.38\pm0.09\%$. This is the first measurement of the multiplicity of neutrons associated with muon capture without neutron energy threshold.

We obtain a partial answer to the $3$-space problem for isomorphic polyhedrality: every twisted sum of $C(\alpha)$, $\alpha<\omega_1$ with a separable isomorphically polyhedral space with the BAP, is isomorphically polyhedral. We obtain other related results of independent interest; for instance, that every twisted sum of $c_0(\kappa)$ and a Banach space having a boundary with property $(*)$ has a boundary with property $(*)$.

Deep learning training at scale is resource-intensive and time-consuming, often running across hundreds or thousands of GPUs for weeks or months. Efficient checkpointing is crucial for running these workloads, especially in multi-tenant environments where compute resources are shared, and job preemptions or interruptions are common. However, transparent and unified GPU snapshots are particularly challenging because of the hardware architecture differences between CPU and GPU, including memory subsystems, dynamic parallelism, and thread synchronization. State-of-the-art GPU checkpointing techniques typically leverage mechanisms that intercept, log, and replay device API calls. However, this approach adds performance overhead and requires hardware-specific implementation that is difficult to test, maintain, and integrate with existing container platforms. In this paper, we present CRIUgpu - a novel approach for transparent checkpointing of GPU-accelerated workloads that builds on recently introduced driver capabilities, enabling support for CUDA and ROCm applications. Our evaluation results show that CRIUgpu works with a variety of deep learning and high-performance computing workloads running across multiple GPUs, completely eliminating steady-state performance overheads, and significantly reducing recovery times compared to state-of-the-art transparent GPU checkpointing mechanisms.

Full fine-tuning of large language models for alignment and task adaptation has become prohibitively expensive as models have grown in size. Parameter-Efficient Fine-Tuning (PEFT) methods aim at significantly reducing the computational and memory resources needed for fine-tuning these models by only training on a small number of parameters instead of all model parameters. Currently, the most popular PEFT method is the Low-Rank Adaptation (LoRA), which freezes the parameters of the model to be fine-tuned and introduces a small set of trainable parameters in the form of low-rank matrices. We propose simply reducing the number of trainable parameters by randomly selecting a small proportion of the model parameters to train on. In this paper, we compare the efficiency and performance of our proposed approach with PEFT methods, including LoRA, as well as full parameter fine-tuning.

Global sensing enables parameter estimation across arbitrary parameter ranges with a finite number of shots. While various formulations exist, the Bayesian paradigm offers a flexible approach to optimal protocol design under minimal assumptions. However, there are several sets of assumptions capturing the notions of prior ignorance and uninformed estimation, leading to two main approaches: invariance of the prior distribution under specific parameter transformations, and adherence to the geometry of a state space. While the first approach often leads to simpler priors and estimators and is more broadly applicable in adaptive settings, the second can lead to faster posterior convergence in a well-defined measurement setting. We examine the practical consequences of both approaches and show how to apply them in examples of rate and coherence estimation in noisy scenarios. More importantly, by employing the notion of location-isomorphic parameters, we unify the two approaches into a practical and versatile framework for optimal global quantum sensing, detailing when and how each set of assumptions should be employed - a blueprint for the design of quantum sensors., Comment: 17 pages, 5 figures

In this work we study a generalization of the standard random walk, an homotopic random walk (HRW), using a deformed translation unitary step that arises from a homotopy of the position-dependent masses associated to the Tsallis and Kaniadakis nonexensive statistics. The HRW implies an associated homotopic Fokker-Planck equation (HFPE) provided with a bi-parameterized inhomogeneous diffusion. The trajectories of the HRW exhibit convergence to a position, randomness as well as divergence, according to deformation and homotopic parameters. The HFPE obtained from associated master equation to the HRW presents the features: a) it results an special case of the van Kampen diffusion equation (5) of Ref. [N. G. van Kampen, \emph{Z. Phys. B Condensed Matter} \textbf{68}, 135 (1987)]; b) it exhibits a superdiffusion in function of deformation and homotopic parameters; c) Tsallis and Kaniadakis deformed FPE are recovered as special cases; d) a homotopic mixtured diffusion is observed; and e) it has a stationary entropic density, characterizing a inhomogeneous screening of the medium, obtained from a homotopic version of the H-Theorem.

The standard procedure to explain the accelerated expansion of the Universe is to assume the existence of an exotic component with negative pressure, generically called dark energy. Here, we propose a new accelerating flat cosmology without dark energy, driven by the negative creation pressure of a reduced relativistic gas (RRG). When the hybrid dark matter of the RRG is identified with cold dark matter, it describes the so-called CCDM cosmology whose dynamics is equivalent to the standard $\Lambda$CDM model at both the background and perturbative levels (linear and nonlinear). This effect is quantified by the creation parameter $\alpha$. However, when the pressure from the RRG slightly changes the dynamics of the universe, as measured by a parameter $b$, the model departs slightly from the standard $\Lambda$CDM cosmology. Therefore, this two-parametric model ($\alpha, b$) describes a new scenario whose dynamics is different but close to the late-time scenarios predicted by CCDM and $\Lambda$CDM models. The free parameters of the RRG model with creation are constrained based on SNe Ia data (Pantheon+SH0ES) and also using $H(z)$ from cosmic clocks. In principle, this mild distinction in comparison with both CCDM or $\Lambda$CDM may help alleviate some cosmological problems plaguing the current standard cosmology., Comment: 25 pages, 4 figures, 1 table

The Fourth Industrial Revolution (4IR) technologies, such as cloud computing, machine learning, and AI, have improved productivity but introduced challenges in workforce training and reskilling. This is critical given existing workforce shortages, especially in marginalized communities like Underrepresented Minorities (URM), who often lack access to quality education. Addressing these challenges, this research presents gAI-PT4I4, a Generative AI-based Personalized Tutor for Industrial 4.0, designed to personalize 4IR experiential learning. gAI-PT4I4 employs sentiment analysis to assess student comprehension, leveraging generative AI and finite automaton to tailor learning experiences. The framework integrates low-fidelity Digital Twins for VR-based training, featuring an Interactive Tutor - a generative AI assistant providing real-time guidance via audio and text. It uses zero-shot sentiment analysis with LLMs and prompt engineering, achieving 86\% accuracy in classifying student-teacher interactions as positive or negative. Additionally, retrieval-augmented generation (RAG) enables personalized learning content grounded in domain-specific knowledge. To adapt training dynamically, finite automaton structures exercises into states of increasing difficulty, requiring 80\% task-performance accuracy for progression. Experimental evaluation with 22 volunteers showed improved accuracy exceeding 80\%, reducing training time. Finally, this paper introduces a Multi-Fidelity Digital Twin model, aligning Digital Twin complexity with Bloom's Taxonomy and Kirkpatrick's model, providing a scalable educational framework.

In a recent paper [R. Dale. R. Lapiedra, and J. A. Morales-Lladosa, Phys. Rev. D {\bf 107}, 023506 (2023)] a cosmic-like Clauser-Horne-Shimony-Holt (CHSH) inequality was proved for the temperature fluctuations $\delta T$ of the perturbed Cosmic Microwave Background (CMB), assuming local realism. This inequality can be tested from observational data. In fact, no violation for it has been found from the CMB data provided by the COBE satellite in different sky directions. This is a result which, being negative, is not conclusive in relation to the possible violation of the CHSH inequality in a cosmological context. So, the preceding analysis needs to be extended by using more precise current CMB data, like those provided by WMAP and Planck satellites. This is a task of considerable importance but, technically, much more involved in data treatments. In this work, assuming again local realism behind measurements and observational data, this extension is accomplished. The result is that a sound violation of the CHSH inequality is found, which would mean the failure of the assumed local realism in accordance with the quantum origin of the primordial temperature fluctuations in the framework of standard inflation and the orthodox interpretation of the Quantum Mechanics., Comment: 28 pages, 6 figures

Given a rectangular grid graph with a special vertex at a corner called base station, we study the problem of covering the vertices of the entire graph with tours that start and end at the base station and whose lengths do not exceed a given threshold, while minimizing a quality measure. We consider two objective functions: minimizing the number of tours and minimizing the sum of their lengths. We present an algorithm that computes the optimal solution for both objectives in linear time with respect to the grid size.

We search for parameters defined from photometric images to quantify the ex situ stellar mass fraction of galaxies. We created mock images using galaxies in the cosmological hydrodynamical simulations TNG100, EAGLE, and TNG50 at redshift $z=0$. We define a series of parameters describing their structures. In particular, the inner and outer halo of a galaxy are defined by sectors ranging from $45-135$ degrees from the disk major axis, and with radii ranging from $3.5-10$ kpc and $10-30$ kpc, respectively, to avoid the contamination of disk and bulge. The surface brightness and colour gradients are defined by the same sectors along the minor axis and with similar radii ranges. We used the Random Forest method to create a model that predicts $f_{\rm exsitu}$ from morphological parameters. The model predicts $f_{\rm exsitu}$ well with a scatter smaller than 0.1 compared to the ground truth in all mass ranges. The models trained from TNG100 and EAGLE work similarly well and are cross-validated; they also work well in making predictions for TNG50 galaxies. The analysis using Random Forest reveals that $\nabla \rho_{\rm outer}$, $\nabla (g-r)_{\rm outer}$, $f_{\rm outerhalo}$ and $f_{\rm innerhalo}$ are the most influential parameters in predicting $f_{\rm exsitu}$, underscoring their significance in uncovering the merging history of galaxies. We further analyse how the quality of images will affect the results by using SDSS-like and HSC-like mock images for galaxies at different distances. Our results can be used to infer the ex situ stellar mass fractions for a large sample of galaxies from photometric surveys., Comment: Accepted in A&A

Buffer gas cooling, one of the most relevant direct cooling techniques for cooling molecules, relies on dissipating the energy of the molecule via collisions with a buffer gas. The cooling efficiency hinges on the molecule-atom scattering properties, concretely, on the transport properties. This work presents a global study on the interactions, collision dynamics, and transport properties of monofluoride molecules (X-F), being X a metal, in the presence of a cold He buffer gas. The interactions are calculated using ab initio quantum chemistry methods, and the dynamics is treated fully quantal, assuming the monofluoride molecule is a rigid rotor. The resulting thermalization and rotational quenching rates are analyzed in light of the Born Distorted Wave Approximation (BDWA), yielding an explanation based on the elemental physical properties of the molecule under consideration. Therefore, the analysis of our results reveals the physics behind the rotational quenching of molecules in the presence of a cold buffer gas.

In recent years, radar-based devices have emerged as an alternative approach for gait monitoring. However, the radar configuration and the algorithms used to extract the gait parameters often differ between contributions, lacking a systematic evaluation of the most appropriate setup. Additionally, radar-based studies often exclude motorically impaired subjects, leaving it unclear whether the existing algorithms are applicable to such populations. In this paper, a radar network is developed and validated by monitoring the gait of five healthy individuals and three patients with Parkinson's disease. Six configurations and four algorithms were compared using Vicon as ground-truth to determine the most appropriate solution for gait monitoring. The best results were obtained using only three nodes: two oriented towards the feet and one towards the torso. The most accurate stride velocity and distance in the state of the art were obtained with this configuration. Moreover, we show that analyzing the feet velocity increases the reliability of the temporal parameters, especially with aged or motorically impaired subjects. The contribution is significant for the implementation of radar networks in clinical and domestic environments, as it addresses critical aspects concerning the radar network configuration and algorithms.

In this work we intend to study homoclinic classes for some classes of flows. To this end we obtain analogous results those obtained by Hertz-Hertz-Tahzibi-Ures in the flow setting. Namely we prove that if the Lesbegue measure gives positive measure to both stable and unstable homoclinic classes of a periodic hyperbolic orbit, then their intersection constitute an ergodic component. Futhermore, with similar techiniques we state several results concerning regular SRB measures., Comment: 15 pages

We describe a concrete device roadmap towards a fault-tolerant quantum computing architecture based on noise-resilient, topologically protected Majorana-based qubits. Our roadmap encompasses four generations of devices: a single-qubit device that enables a measurement-based qubit benchmarking protocol; a two-qubit device that uses measurement-based braiding to perform single-qubit Clifford operations; an eight-qubit device that can be used to show an improvement of a two-qubit operation when performed on logical qubits rather than directly on physical qubits; and a topological qubit array supporting lattice surgery demonstrations on two logical qubits. Devices that enable this path require a superconductor-semiconductor heterostructure that supports a topological phase, quantum dots and coupling between those quantum dots that can create the appropriate loops for interferometric measurements, and a microwave readout system that can perform fast, low-error single-shot measurements. We describe the key design components of these qubit devices, along with the associated protocols for demonstrations of single-qubit benchmarking, Clifford gate execution, quantum error detection, and quantum error correction, which differ greatly from those in more conventional qubits. Finally, we comment on implications and advantages of this architecture for utility-scale quantum computation., Comment: 11+6 pages, 8+5 figures

Topological phases arise as the parameters of a quantum system are varied as a function of time. Under the adiabatic approximation, the time dependency can be removed and the Berry topological phase can be obtained from a trajectory on the parameter space. Although this approach is usually applied in solid-state physics without further reflection, in many cases, say in Dirac or Weyl materials, the adiabatic approximation is never met as many systems are gapless. Here it is shown how to use other time dependent defined topological quantities, in particular the Aharonov-Anandan phase that provides information not only about the topology but also about band transitions. Therefore, we analyze the problem of graphene under electromagnetic radiation from a time-driven perspective, demonstrating how the Aharonov-Anandan and Berry phases provide complementary information about the topology and interband transitions. This is achieved using the Dirac-Bloch formalism and by solving the time-dependent equations within Floquet theory., Comment: 13 pages, 7 figures

Rydberg molecules, Rydberg-atom or Rydberg-molecule, are an essential ingredient of cold molecular sciences. However, due to the richness of Rydberg-neutral interactions, new kinds of Rydberg molecules and binding mechanisms are still to be discovered. In this work, we predict the existence of ultra-long-range van der Waals trimers in dilute atom-gas mixtures. These are bound states of a Rydberg atom and a diatomic polar molecule mediated by the long-range van der Waals interaction. This new binding mechanism gives rise to trimers with sizes between 5-500~nm and binding energies between 2 MHz and 0.2 kHz depending on the atomic principal quantum number $n$ and orbital angular momentum $L$. We show that these molecules can be produced via two-photon photoassociation, with rates on the order of (10$^{-13}$ - 10$^{-11}$) cm$^{3}$s$^{-1}$ for temperatures in the range of (0.5 $\mu K$ - 10$\mu K$), and discuss the feasibility of observing trimer resonances., Comment: arXiv admin note: text overlap with arXiv:2109.06411

Efficient multiple access remains a key challenge for emerging Internet of Things (IoT) networks comprising a large set of devices with sporadic activation, thus motivating significant research in the last few years. In this paper, we consider a network wherein IoT sensors capable of energy harvesting (EH) send updates to a central server to monitor the status of the environment or machinery in which they are located. We develop energy-aware ALOHA-like multiple access schemes for such a scenario using the Age of Information (AoI) metric to quantify the freshness of an information packet. The goal is to minimize the average AoI across the entire system while adhering to energy constraints imposed by the EH process. Simulation results show that applying the designed multiple access scheme improves performance from 24% up to 90% compared to previously proposed age-dependent protocols by ensuring low average AoI and achieving scalability while simultaneously complying with the energy constraints considered., Comment: 6 pages, 7 figures. Paper accepted for presentation at the IEEE International Conference on Communications (ICC), Montreal, Canada, 2025

Oil and water phases placed on top of a solid surface respond differently to a MHz-level surface acoustic wave (SAW) propagating in the solid due to their different surface tensions and the corresponding capillary stresses. We observe that, under SAW excitation, oil films are extracted continuously from sessile drops of silicone-oil/water/surfactant mixtures. The mechanism responsible for the extraction of oil from the mixtures is the acoustowetting phenomenon: the low surface tension oil phase leaves the mixture in the form of `fingers' that initially spread over the solid in the direction transverse to that of the SAW. Once away from the drop the oil spreads on the solid surface in the direction opposite that of the SAW, as in classic studies on acoustowetting. The high surface tension water phase remains at rest. Increasing the SAW intensity and oil content in the mixtures enhances the rate at which oil leaves the emulsion. We further observe that the oil film develops free surface instabilities in the form of periodic spatial variations, as well as the formation of spatial gradients in the emulsion concentrations in the presence of SAW. Our study suggests the potential for using SAW to remove oil from oil-in-water mixtures to facilitate the small-scale separation of water and oil phases. Furthermore, we describe the peculiar physical effects leading to acoustic-capillary flow instabilities and SAW-induced emulsion dynamics.

We analyze some aspects of the cubic action for gravity recently proposed by Cheung and Remmen, which is a particular instance of a first order (Palatini) action. In this approach both the spacetime metric and the connection are treated as independent fields. We discuss its BRST invariance and compute explicitly the one-loop contribution of quantum fluctuations around flat space, checking that the corresponding Slavnov-Taylor identities are fulfilled. Finally, our results on a first order action are compared with the existing ones corresponding to a second order action., Comment: 25 pages, 4 figures

Gait analysis is critical in the early detection and intervention of motor neurological disorders in infants. Despite its importance, traditional methods often struggle to model the high variability and rapid developmental changes inherent to infant gait. To address these challenges, we propose a probabilistic Gaussian Process (GP)-driven Hidden Markov Model (HMM) to capture the complex temporal dynamics of infant gait cycles and enable automatic recognition of gait anomalies. We use a Multi-Output GP (MoGP) framework to model interdependencies between multiple gait signals, with a composite kernel designed to account for smooth, non-smooth, and periodic behaviors exhibited in gait cycles. The HMM segments gait phases into normal and abnormal states, facilitating the precise identification of pathological movement patterns in stance and swing phases. The proposed model is trained and assessed using a dataset of infants with and without motor neurological disorders via leave-one-subject-out cross-validation. Results demonstrate that the MoGP outperforms Long Short-Term Memory (LSTM) based neural networks in modeling gait dynamics, offering improved accuracy, variance explanation, and temporal alignment. Further, the predictive performance of MoGP provides a principled framework for uncertainty quantification, allowing confidence estimation in gait trajectory predictions. Additionally, the HMM enhances interpretability by explicitly modeling gait phase transitions, improving the detection of subtle anomalies across multiple gait cycles. These findings highlight the MoGP-HMM framework as a robust automatic gait analysis tool, allowing early diagnosis and intervention strategies for infants with neurological motor disorders.

In this study, we explored the use of spectrograms to represent handwriting signals for assessing neurodegenerative diseases, including 42 healthy controls (CTL), 35 subjects with Parkinson's Disease (PD), 21 with Alzheimer's Disease (AD), and 15 with Parkinson's Disease Mimics (PDM). We applied CNN and CNN-BLSTM models for binary classification using both multi-channel fixed-size and frame-based spectrograms. Our results showed that handwriting tasks and spectrogram channel combinations significantly impacted classification performance. The highest F1-score (89.8%) was achieved for AD vs. CTL, while PD vs. CTL reached 74.5%, and PD vs. PDM scored 77.97%. CNN consistently outperformed CNN-BLSTM. Different sliding window lengths were tested for constructing frame-based spectrograms. A 1-second window worked best for AD, longer windows improved PD classification, and window length had little effect on PD vs. PDM.

We review the existing distance estimates to the black hole X-ray binary Swift J1727.8$-$1613 with a discussion of the accuracies and caveats of the associated methodologies. As part of this, we present new line-of-sight HI absorption spectra captured using the MeerKAT radio telescope. We estimate a maximum radial velocity with respect to the local standard of rest of $24.8 \pm 2.8$ km s$^{-1}$, which is significantly lower than that found towards an extragalactic reference source. Given the location of Swift J1727.8$-$1613 at Galactic longitude and latitude $(l, b) \approx (8.6\deg, 10.3\deg)$, we explore the feasibility of the HI absorption method. From this we derive a near kinematic distance of $d_{\rm{near}} = 3.6 \pm 0.3\ (stat) \pm 2.3\ (sys)$ kpc as lower bound for the distance to Swift J1727.8$-$1613. We compare our results with those derived from different distance determination methods including the use of colour excess or reddening along the line of sight, which we constrain to $E(B-V) = 0.37 \pm 0.01\ (stat) \pm 0.025\ (sys)$ using near-UV spectra. By combining this with donor star magnitudes reported by Mata S\'anchez et al. (2024b), we suggest an increased distance of $5.5^{+1.4}_{-1.1}$ kpc, which would imply a natal kick velocity of $190 \pm 30$ km s$^{-1}$., Comment: 12 pages, 4 figures, 2 tables, submitted to ApJL

In the context of the rapid dissemination of multimedia content, identifying disinformation on social media platforms such as TikTok represents a significant challenge. This study introduces a hybrid framework that combines the computational power of deep learning with the interpretability of fuzzy logic to detect suspected disinformation in TikTok videos. The methodology is comprised of two core components: a multimodal feature analyser that extracts and evaluates data from text, audio, and video; and a multimodal disinformation detector based on fuzzy logic. These systems operate in conjunction to evaluate the suspicion of spreading disinformation, drawing on human behavioural cues such as body language, speech patterns, and text coherence. Two experiments were conducted: one focusing on context-specific disinformation and the other on the scalability of the model across broader topics. For each video evaluated, high-quality, comprehensive, well-structured reports are generated, providing a detailed view of the disinformation behaviours.

A dedicated search for upward-going air showers at zenith angles exceeding $110^\circ$ and energies $E>0.1$ EeV has been performed using the Fluorescence Detector of the Pierre Auger Observatory. The search is motivated by two "anomalous" radio pulses observed by the ANITA flights I and III which appear inconsistent with the Standard Model of particle physics. Using simulations of both regular cosmic ray showers and upward-going events, a selection procedure has been defined to separate potential upward-going candidate events and the corresponding exposure has been calculated in the energy range [0.1-33] EeV. One event has been found in the search period between 1 Jan 2004 and 31 Dec 2018, consistent with an expected background of $0.27 \pm 0.12$ events from mis-reconstructed cosmic ray showers. This translates to an upper bound on the integral flux of $(7.2 \pm 0.2) \times 10^{-21}$ cm$^{-2}$ sr$^{-1}$ y$^{-1}$ and $(3.6 \pm 0.2) \times 10^{-20}$ cm$^{-2}$ sr$^{-1}$ y$^{-1}$ for an $E^{-1}$ and $E^{-2}$ spectrum, respectively. An upward-going flux of showers normalized to the ANITA observations is shown to predict over 34 events for an $E^{-3}$ spectrum and over 8.1 events for a conservative $E^{-5}$ spectrum, in strong disagreement with the interpretation of the anomalous events as upward-going showers., Comment: 10+5 Pages of Manuscript plus Supplemental Material, 3+8 figures, Accepted for publication in Physical Review Letters

Spin space groups, formed by operations where the rotation of the spins is independent of the accompanying operation acting on the crystal structure, are appropriate groups to describe the symmetry of magnetic structures with null spin-orbit coupling. Their corresponding spin point groups are the symmetry groups to be considered for deriving the symmetry constraints on the form of the crystal tensor properties of such idealized structures. These groups can also be taken as approximate symmetries (with some restrictions) of real magnetic structures, where spin-orbit and magnetic anisotropy are however present. Here we formalize the invariance transformation properties that must satisfy the most important crystal tensors under a spin point group. This is done using modified Jahn symbols, which generalize those applicable to ordinary magnetic point groups [Gallego et al., Acta Cryst. (2019) A{\bf 75}, 438-447]. The analysis includes not only equilibrium tensors, but also transport, optical and non-linear optical susceptibility tensors. The constraints imposed by spin collinearity and coplanarity within the spin group formalism on a series of representative tensors are discussed and compiled. As illustrative examples, the defined tensor invariance equations have been applied to some known magnetic structures, showing the differences of the symmetry-adapted form of some relevant tensors, when considered under the constraints of its spin point group or its magnetic point group. This comparison, with the spin point group implying additional constraints in the tensor form, may allow to distinguish those magnetic-related properties that can be solely attributed to spin-orbit coupling from those that are expected to be present even under negligible spin-orbit effects., Comment: 41 pages, 7 figures

Before a binary system enters into a common envelope (CE) phase, accretion from the primary star onto the companion star through Roche Lobe overflow (RLOF) will lead to the formation of an accretion disk, which may generate jets. Accretion before and during the CE may alter the outcome of the interaction. Previous studies have considered different aspects of this physical mechanism. Here we study the properties of an accretion disk formed via 3D hydrodynamic simulations of the RLOF mass transfer between a 7 M$_\odot$, red supergiant star and a 1.4 M$_\odot$, neutron star companion. We simulate only the volume around the companion for improved resolution. We use a 1D implicit MESA simulation of the evolution of the system during 30,000 years between the on-set of the RLOF and the CE to guide the binary parameters and the mass-transfer rate, while we simulate only 21 years of the last part of the RLOF in 3D using an ideal gas isothermal equation of state. We expect that a pre-CE disk under these parameters will have a mass of $\sim 5\times 10^{-3}$ M$_\odot$ and a radius of $\sim$40 R$_\odot$ with a scale height of $\sim$5 R$_\odot$. The temperature profile of the disk is shallower than that predicted by the formalism of Shakura and Sunyaev, but more reasonable cooling physics would need to be included. We stress test these results with respect to a number of physical and numerical parameters, as well as simulation choices, and we expect them to be reasonable within a factor of a few for the mass and 15% for the radius. We also contextualize our results within those presented in the literature, in particular with respect to the dimensionality of simulations and the adiabatic index. We consider what properties of magnetic fields and jets may be supported by our disk and discuss prospects for future work., Comment: 17 pages, 20 figures

In this paper we proved a pro-$\mathcal{C}$ version of the Rips-Sela Theorems on splittings as an amalgamated free product or HNN-extension over a cyclic subgroup, where $\mathcal{C}$ is a class of finite groups closed for subgroups, quotients, finite direct products and extensions with abelian kernel.

Diffuse photons of energy above 0.1 PeV, produced through the interactions between cosmic rays and either interstellar matter or background radiation fields, are powerful tracers of the distribution of cosmic rays in the Galaxy. Furthermore, the measurement of a diffuse photon flux would be an important probe to test models of super-heavy dark matter decaying into gamma-rays. In this work, we search for a diffuse photon flux in the energy range between 50 PeV and 200 PeV using data from the Pierre Auger Observatory. For the first time, we combine the air-shower measurements from a 2 km$^2$ surface array consisting of 19 water-Cherenkov surface detectors, spaced at 433 m, with the muon measurements from an array of buried scintillators placed in the same area. Using 15 months of data, collected while the array was still under construction, we derive upper limits to the integral photon flux ranging from 13.3 to 13.8 km$^{-2}$ sr$^{-1}$ yr$^{-1}$ above tens of PeV. We extend the Pierre Auger Observatory photon search program towards lower energies, covering more than three decades of cosmic-ray energy. This work lays the foundation for future diffuse photon searches: with the data from the next 10 years of operation of the Observatory, this limit is expected to improve by a factor of $\sim$20., Comment: Submitted to JCAP

The RPCs employed at the LHC experiments are currently operated in avalanche mode, with a mixture containing a large fraction of C$_{2}$H$_{2}$F$_{4}$ ($\approx$90\% or more) with the addition of i-C$_{4}$H$_{10}$ and SF$_{6}$ in different concentrations. However, C$_{2}$H$_{2}$F$_{4}$ and SF$_{6}$ are fluorinated greenhouse gases (F-gases) with Global Warming Potential (GWP) of $\approx$1400 and $\approx$22800, respectively. EU regulations imposed a progressive phase-down of C$_{2}$H$_{2}$F$_{4}$ production and consumption, aiming at strongly reducing its emission. This is already resulting in an increase of its price and reduction in availability. The most desirable long-term solution to this problem is to find an alternative, F-gases-free gas mixture, able to maintain similar detector performance. To address this challenge, the RPC ECOGasas@GIF++ collaboration (including RPC experts of ALICE, ATLAS, CMS, SHiP/LHCb, and the CERN EP-DT group) was created in 2019. The collaboration is currently studying a gas from the olefine family, the C$_{3}$H$_{2}$F$_{4}$ (or simply HFO, with GWP $\approx$6), to be used, in combination with CO$_{2}$, as a substitute for C$_{2}$H$_{2}$F$_{4}$. This contribution will focus on the signal shape studies that have been carried out by the collaboration during dedicated beam test periods. The methodology used in the data analysis will be presented, together with the results obtained with several HFO-based gas mixtures, and with the currently employed one. Furthermore, results on the counting-rate dependence of the RPC performance, obtained by combining the muon beam with the GIF++ $^{137}$Cs source with different attenuation factors, will also be presented.

The Dragonfly topology is currently one of the most popular network topologies in high-performance parallel systems. The interconnection networks of many of these systems are built from components based on the InfiniBand specification. However, due to some constraints in this specification, the available versions of the InfiniBand network controller (OpenSM) do not include routing engines based on some popular deadlock-free routing algorithms proposed theoretically for Dragonflies, such as the one proposed by Kim and Dally based on Virtual-Channel shifting. In this paper we propose a straightforward method to integrate this routing algorithm in OpenSM as a routing engine, explaining in detail the configuration required to support it. We also provide experiment results, obtained both from a real InfiniBand-based cluster and from simulation, to validate the new routing engine and to compare its performance and requirements against other routing engines currently available in OpenSM.

During brain function, groups of neurons fire synchronously. When these groups are large enough, the resulting electrical signals can be measured on the scalp using Electroencephalography (EEG). The amplitude of these signals can be significant depending on the size and synchronization of the neural activity. EEG waves exhibit distinct patterns based on the brain's state, such as whether it is asleep, awake, engaged in mental calculations, or performing other cognitive functions. Additionally, these patterns can be modified by external factors, such as transcranial magnetic stimulation (TMS). TMS involves bringing an antenna that generates variable electromagnetic fields close to specific areas of the skull to treat certain pathologies. Given that the human body naturally generates magnetic fields, a question arises: Can these fields influence the EEG by modulating neuronal function, causing a resonance effect, or through some unknown interaction? This study investigated whether approaching the palm of the hand to the top of the head (Intervention) could induce effects in the EEG. Power Spectral Density (PSD) was obtained for the 30 seconds preceding the intervention (PSD_pre) and the final 30 seconds of the intervention (PSD_last). The exact Wilcoxon signed-rank test suggests that the median of PSD_pre is greater than the median of PSD_last at the 95% confidence level (p-value = 0.004353). In contrast, in the control group, the test indicates that at the 95% confidence level (p-value = 0.7667), the median of PSD_pre is not greater than the median of PSD_last., Comment: 7 pages, 2 figures

Quantum computation with electron spin qubits requires coherent and efficient manipulation of these spins, typically accomplished through the application of alternating magnetic or electric fields for electron spin resonance (ESR). In particular, electrical driving allows us to apply localized fields on the electrons, which benefits scale-up architectures. However, we have found that Electric Dipole Spin Resonance (EDSR) is insufficient for modeling the Rabi behavior in recent experimental studies. Therefore, we propose that the electron spin is being driven by a new method of electric spin qubit control which generalizes the spin dynamics by taking into account a quadrupolar contribution of the quantum dot: electric quadrupole spin resonance (EQSR). In this work, we explore the electric quadrupole driving of a quantum dot in silicon, specifically examining the cases of 5 and 13 electron occupancies., Comment: Main: 5 pages, 4 figures Supp: 4 pages

In this 2022 work we argued that, despite claims about successful modeling of the visual brain using artificial nets, the problem is far from being solved (even for low-level vision). Examples of open issues include: where should we read from ANNs in order to reproduce human behavior?, this ad-hoc read-out is considered part of the brain model or not?, should we use artificial psychophysics or artificial physiology?, in the case of ANNs, artificial experiments should literally match the experiments done with humans?. There is a clear need of rigorous procedures for experimental tests for ANNs devoted to model the visual brain, and more generally, to understand ANNs devoted to generic vision tasks. Following our experience in using low-level facts from Quantitative Visual Neuroscience in computer vision, in this work we presented the idea of developing a low-level dataset compiling the basic spatio-temporal and chromatic facts that are known to happen in the retina-V1 pathway, and they are not currently available in existing databases such as BrainScore. In our results we checked the behavior of three recently proposed models with similar architecture: (1) A parametric model tuned via Maximum Differentiation [Malo & Simoncelli SPIE 15, Martinez et al. PLOS 18, Martinez et al. Front. Neurosci. 19], (2) A non-parametric model called PerceptNet tuned to maximize the correlation with human opinion on subjective distortions [Hepburn et al. IEEE ICIP 19], and (3) A model with the same encoder as PerceptNet, but tuned for image segmentation (published as Hernandez-Camara et al. Patt.Recogn.Lett. 23). Results on 10 compelling psycho/physio visual facts show that the first model is the one with closer behavior to the humans in terms of receptive fields, but more interestingly, on the nonlinear behavior when facing complex spatio-chromatic patterns of a range of luminances and contrasts.

The interconnection network is a crucial subsystem in High-Performance Computing clusters and Data-centers, guaranteeing high bandwidth and low latency to the applications' communication operations. Unfortunately, congestion situations may spoil network performance unless the network design applies specific countermeasures. Adaptive routing algorithms are a traditional approach to dealing with congestion since they provide traffic flows with alternative routes that bypass congested areas. However, adaptive routing decisions at switches are typically based on local information without a global network traffic perspective, leading to congestion spreading throughout the network beyond the original congested areas. In this paper, we propose a new efficient congestion management strategy that leverages adaptive routing notifications currently available in some interconnect technologies and efficiently isolates the congesting flows in reserved spaces at switch buffers. The experiment results based on simulations of realistic traffic scenarios show that our proposal removes the congestion impact., Comment: 34 pages

The interconnection network is a key element in High-Performance Computing (HPC) and Datacenter (DC) systems whose performance depends on several design parameters, such as the topology, the switch architecture, and the routing algorithm. Among the most common topologies in HPC systems, the Fat-Tree offers several shortest-path routes between any pair of end-nodes, which allows multi-path routing schemes to balance traffic flows among the available links, thus reducing congestion probability. However, traffic balance cannot solve by itself some congestion situations that may still degrade network performance. Another approach to reduce congestion is queue-based flow separation, but our previous work shows that multi-path routing may spread congested flows across several queues, thus being counterproductive. In this paper, we propose a set of restrictions to improve alternative routes selection for multi-path routing algorithms in Fat-Tree networks, so that they can be positively combined with queuing schemes., Comment: 53 pages

Parkinson's disease (PD) is a neurological disorder requiring early and accurate diagnosis for effective management. Machine learning (ML) has emerged as a powerful tool to enhance PD classification and diagnostic accuracy, particularly by leveraging wearable sensor data. This survey comprehensively reviews current ML methodologies used in classifying Parkinsonian tremors, evaluating various tremor data acquisition methodologies, signal preprocessing techniques, and feature selection methods across time and frequency domains, highlighting practical approaches for tremor classification. The survey explores ML models utilized in existing studies, ranging from traditional methods such as Support Vector Machines (SVM) and Random Forests to advanced deep learning architectures like Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM). We assess the efficacy of these models in classifying tremor patterns associated with PD, considering their strengths and limitations. Furthermore, we discuss challenges and discrepancies in current research and broader challenges in applying ML to PD diagnosis using wearable sensor data. We also outline future research directions to advance ML applications in PD diagnostics, providing insights for researchers and practitioners., Comment: 28 pages, 9 figures, 3 tables, Journal Artificial Intelligence In Medicine

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.

Visual prostheses are designed to restore partial functional vision in patients with total vision loss. Retinal visual prostheses provide limited capabilities as a result of low resolution, limited field of view and poor dynamic range. Understanding the influence of these parameters in the perception results can guide prostheses research and design. In this work, we evaluate the influence of field of view with respect to spatial resolution in visual prostheses, measuring the accuracy and response time in a search and recognition task. Twenty-four normally sighted participants were asked to find and recognize usual objects, such as furniture and home appliance in indoor room scenes. For the experiment, we use a new simulated prosthetic vision system that allows simple and effective experimentation. Our system uses a virtual-reality environment based on panoramic scenes. The simulator employs a head-mounted display which allows users to feel immersed in the scene by perceiving the entire scene all around. Our experiments use public image datasets and a commercial head-mounted display. We have also released the virtual-reality software for replicating and extending the experimentation. Results show that the accuracy and response time decrease when the field of view is increased. Furthermore, performance appears to be correlated with the angular resolution, but showing a diminishing return even with a resolution of less than 2.3 phosphenes per degree. Our results seem to indicate that, for the design of retinal prostheses, it is better to concentrate the phosphenes in a small area, to maximize the angular resolution, even if that implies sacrificing field of view.

In this work we prove that quantum error correcting codes do not fix isotropic errors, even assuming that their correction circuits do not introduce new errors. We say that a quantum code does not fix a quantum computing error if its application does not reduce the variance of the error. We also prove for isotropic errors that, if the correction circuit of a quantum code detects an error, the corrected logical $m-$qubit has uniform distribution and as a result, it already loses all the computing information., Comment: arXiv admin note: substantial text overlap with arXiv:2101.03971

Context: This paper focuses on a class of galaxies characterised by an extremely low surface brightness: the ultra-diffuse galaxies (UDGs). We used new integral-field spectroscopic data from the ESO Large Programme Looking into the faintEst WIth MUSE (LEWIS) project. Aims: Our main goals are addressing the formation channels and investigating possible correlations of their observational properties. In particular, we derive their stellar kinematics and dynamical properties. Methods: We extract the 1D stacked spectrum inside the effective radius to obtain an unbiased measure of $\sigma_{\rm eff}$. To derive the spatially-resolved stellar kinematics, we first apply the Voronoi tessellation algorithm to bin the spaxels in the datacube and then follow the same prescription adopted for the 1D case. In addition, we extract the velocity profiles along the galaxy's major and minor axes. Results: We find that 7 out of 18 UDGs in LEWIS show a mild rotation, 5 do not have evidence of any rotation, and the remaining 6 UDGs are unconstrained cases. This is the first large census of velocity profiles for UDGs. On average, UDGs in LEWIS are characterised by low values of $\sigma_{\rm eff}$, comparable with available values from the literature. In the Faber-Jackson relation plane, we found a group of UDGs consistent with the relation within the errorbars, whereas outliers are objects with non-negligible rotation components. UDGs and LSBs in LEWIS have larger dark matter content than dwarf galaxies with similar total luminosity. We do not find clear correlations between the derived properties and the local environment. Conclusions: Based on the stellar kinematics, two classes of UDGs are found in the Hydra I cluster: the rotating and non-rotating systems. This result, combined with other structural properties, can help to discriminate between the several formation scenarios proposed for UDGs., Comment: Accepted for publication in Astronomy and Astrophysics

This study explores a data-driven approach to discovering novel clinical and genetic markers in ovarian cancer (OC). Two main analyses were performed: (1) a nonlinear examination of an OC dataset using autoencoders, which compress data into a 3-dimensional latent space to detect potential intrinsic separability between platinum-sensitive and platinum-resistant groups; and (2) an adaptation of the informative variable identifier (IVI) to determine which features (clinical or genetic) are most relevant to disease progression. In the autoencoder analysis, a clearer pattern emerged when using clinical features and the combination of clinical and genetic data, indicating that disease progression groups can be distinguished more effectively after supervised fine tuning. For genetic data alone, this separability was less apparent but became more pronounced with a supervised approach. Using the IVI-based feature selection, key clinical variables (such as type of surgery and neoadjuvant chemotherapy) and certain gene mutations showed strong relevance, along with low-risk genetic factors. These findings highlight the strength of combining machine learning tools (autoencoders) with feature selection methods (IVI) to gain insights into ovarian cancer progression. They also underscore the potential for identifying new biomarkers that integrate clinical and genomic indicators, ultimately contributing to improved patient stratification and personalized treatment strategies.