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We study the intrinsic and observable shapes of approximately 700 star-forming galaxies with stellar masses $10^8 - 10^{11}$ M$_\odot$ from the FIREbox simulation at $z=0$. We calculate intrinsic axis ratios using inertia tensors weighted by: All Stars, Young Stars, and Luminosity-weighted Stars. Young Stars, in particular, are arranged in systematically different 3D configurations as a function of galaxy stellar mass, with spheroidal, elongated, and disky shapes dominant at stellar masses of $10^{8.5}$ M$_\odot$, $10^{9.5}$ M$_\odot$, and $10^{10.5}$ M$_\odot$, respectively. We construct mock images for each galaxy and show that projected short-to-long axis ratios, $q$, inferred from 2D S\'ersic fits are most closely related to Luminosity-weighted tensor shapes and least resemble the All Stars shapes. This suggests observed 2D shape distributions should not be compared to predictions based on 3D stellar mass shapes. We construct a sample of mock images projected in random orientations and compare them to observed axis ratio distributions from the GAMA survey. For galaxies with stellar masses $10^{10} - 10^{11}$ M$_\odot$, we reproduce axis ratios comparable to the thinnest observed in real galaxies ($q \sim 0.1$), suggesting this model is capable of making thin disk galaxies at Milky Way scale. However, at masses below $10^{10}$ M$_\odot$, we produce an insufficient population of galaxies with observed $q<0.4$ and none with $q<0.2$, suggesting that FIREbox does not produce enough low-mass disk galaxies. Future observational and theoretical programs aimed at understanding low-mass disk fractions will provide crucial tests of galaxy formation models., Comment: 16 pages, 7 figures, 2 tables

During the ESCAPE project, a pilot analysis facility was developed with a bottom-up approach, in collaboration with all the project partners. As a result, the CERN Virtual Research Environment (VRE) initiative proposes a workspace that facilitates data access from the ESCAPE Data Lake, managed by Rucio, a data management framework, and supports interactive analysis via Jupyter notebooks. The facility offers custom software stacks and access to CVMFS, and connects to local data processing resources through REANA, an open-source framework developed by CERN IT for reanalysis and reproducibility. The CERN VRE deploys an instance of REANA, allowing users to utilise its features together with the analysis facility's services. Integrating heterogeneous services with a unified interface significantly eases the user experience. Furthermore, in line with the ESCAPE Open Collaboration, the development of open source tools that can be leveraged by different physics communities with similar analysis strategies, laying the foundation of common lifecycle analysis practices. Therefore, in order to foster accessibility, as well as interactivity, reproducibility and data preservation to more complex infrastructure services, the development of user-friendly middleware should be prioritized. This contribution focuses on the connection of REANA and Zenodo to the CERN Virtual Research Environment's interface through Jupyter extensions. The development of these extensions makes it possible to use the Virtual Research Environment as a single workspace to enhance the lifecycle of research analysis: from data discovery and data access, through interactive analysis and offload to computing resources, to reproducibility and preservation of results., Comment: 8 pages, 4 figures. Proceedings following presentation at the 27th Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)

The Agile Earth Observation Satellite Scheduling Problem (AEOSSP) entails finding the subset of observation targets to be scheduled along the satellite's orbit while meeting operational constraints of time, energy and memory. The problem of deciding what and when to observe is inherently complex, and becomes even more challenging when considering several issues that compromise the quality of the captured images, such as cloud occlusion, atmospheric turbulence, and image resolution. This paper presents a Deep Reinforcement Learning (DRL) approach for addressing the AEOSSP with time-dependent profits, integrating these three factors to optimize the use of energy and memory resources. The proposed method involves a dual decision-making process: selecting the sequence of targets and determining the optimal observation time for each. Our results demonstrate that the proposed algorithm reduces the capture of images that fail to meet quality requirements by > 60% and consequently decreases energy waste from attitude maneuvers by up to 78%, all while maintaining strong observation performance., Comment: This paper has been accepted for presentation at the IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN) Special Sessions 2025

Automating surgical systems enhances precision and safety while reducing human involvement in high-risk environments. A major challenge in automating surgical procedures like suturing is accurately modeling the suture thread, a highly flexible and compliant component. Existing models either lack the accuracy needed for safety critical procedures or are too computationally intensive for real time execution. In this work, we introduce a novel approach for modeling suture thread dynamics using control barrier functions (CBFs), achieving both realism and computational efficiency. Thread like behavior, collision avoidance, stiffness, and damping are all modeled within a unified CBF and control Lyapunov function (CLF) framework. Our approach eliminates the need to calculate complex forces or solve differential equations, significantly reducing computational overhead while maintaining a realistic model suitable for both automation and virtual reality surgical training systems. The framework also allows visual cues to be provided based on the thread's interaction with the environment, enhancing user experience when performing suture or ligation tasks. The proposed model is tested on the MagnetoSuture system, a minimally invasive robotic surgical platform that uses magnetic fields to manipulate suture needles, offering a less invasive solution for surgical procedures., Comment: Accepted for 2025 IEEE International Conference on Robotics and Automation (ICRA). Supplementary video: https://www.youtube.com/watch?v=qO-nroi4Faw

Telegram is a popular platform for public communication, generating large amounts of messages through its channels. pytopicgram is a Python library that helps researchers collect, organize, and analyze these Telegram messages. The library offers key features such as easy message retrieval, detailed channel information, engagement metrics, and topic identification using advanced modeling techniques. By simplifying data extraction and analysis, pytopicgram allows users to understand how content spreads and how audiences interact on Telegram. This paper describes the design, main features, and practical uses of \pytopicgram, showcasing its effectiveness for studying public conversations on Telegram.

Novel materials drive progress across applications from energy storage to electronics. Automated characterization of material structures with machine learning methods offers a promising strategy for accelerating this key step in material design. In this work, we introduce an autoregressive language model that performs crystal structure prediction (CSP) from powder diffraction data. The presented model, deCIFer, generates crystal structures in the widely used Crystallographic Information File (CIF) format and can be conditioned on powder X-ray diffraction (PXRD) data. Unlike earlier works that primarily rely on high-level descriptors like composition, deCIFer performs CSP from diffraction data. We train deCIFer on nearly 2.3M unique crystal structures and validate on diverse sets of PXRD patterns for characterizing challenging inorganic crystal systems. Qualitative and quantitative assessments using the residual weighted profile and Wasserstein distance show that deCIFer produces structures that more accurately match the target diffraction data when conditioned, compared to the unconditioned case. Notably, deCIFer can achieve a 94% match rate on unseen data. deCIFer bridges experimental diffraction data with computational CSP, lending itself as a powerful tool for crystal structure characterization and accelerating materials discovery., Comment: 24 pages, 17 figures, 6 tables. v2: Figure 8 revision

In this article, we consider an inverse problem involving the simultaneous reconstruction of two real valued potentials for a Schr\"odinger equation with mixed boundary conditions: a dynamic boundary condition of Wentzell type and a Dirichler boundary condition. The main result of this paper is a Lipschitz stability estimate for such potentials from a single measurement of the flux. This result is deduced using the Bukhgeim-Klibanov method and a suitable Carleman estimate where the weight function depends on Minkowski's functional.

We utilize the cosmological volume simulation, FIREbox, to investigate how a galaxy's environment influences its size and dark matter content. Our study focuses on approximately 1,200 galaxies (886 central and 332 satellite halos) in the low-mass regime, with stellar masses between $10^6$ to $10^9$ $M_{\odot}$. We analyze the size-mass relation ($r_{50} - M_{\star}$), inner dark matter mass-stellar mass ($M^{50}_{\rm DM} - M_{\star}$) relation, and the halo mass-stellar mass ($M_{\rm halo} - M_{\star}$) relation. At fixed stellar mass, we find the galaxies experiencing stronger tidal influences, indicated by higher Perturbation Indices (PI $>$ 1) are generally larger and have lower masses relative to their counterparts with lower Perturbation Indices (PI $<$ 1). Applying a Random Forest regression model, we show that both the environment (PI) and halo mass ($M_{rm halo}$) are significant predictors of a galaxy's relative size and dark matter content. Notably, because $M_{\rm halo}$ is also strongly affected by the environment, our findings indicate that environmental conditions not only influence galactic sizes and relative inner dark matter content directly, but also indirectly through their impact on halo mass. Our results highlight a critical interplay between environmental factors and halo mass in shaping galaxy properties, affirming the environment as a fundamental driver in galaxy formation and evolution., Comment: 19 pages, 9 figures; submitted to ApJ

Touch is a crucial sensing modality that provides rich information about object properties and interactions with the physical environment. Humans and robots both benefit from using touch to perceive and interact with the surrounding environment (Johansson and Flanagan, 2009; Li et al., 2020; Calandra et al., 2017). However, no existing systems provide rich, multi-modal digital touch-sensing capabilities through a hemispherical compliant embodiment. Here, we describe several conceptual and technological innovations to improve the digitization of touch. These advances are embodied in an artificial finger-shaped sensor with advanced sensing capabilities. Significantly, this fingertip contains high-resolution sensors (~8.3 million taxels) that respond to omnidirectional touch, capture multi-modal signals, and use on-device artificial intelligence to process the data in real time. Evaluations show that the artificial fingertip can resolve spatial features as small as 7 um, sense normal and shear forces with a resolution of 1.01 mN and 1.27 mN, respectively, perceive vibrations up to 10 kHz, sense heat, and even sense odor. Furthermore, it embeds an on-device AI neural network accelerator that acts as a peripheral nervous system on a robot and mimics the reflex arc found in humans. These results demonstrate the possibility of digitizing touch with superhuman performance. The implications are profound, and we anticipate potential applications in robotics (industrial, medical, agricultural, and consumer-level), virtual reality and telepresence, prosthetics, and e-commerce. Toward digitizing touch at scale, we open-source a modular platform to facilitate future research on the nature of touch., Comment: 28 pages

This paper presents a spatio-temporal inverse optimal control framework for understanding interactions in multi-agent systems (MAS). We employ a graph representation approach and model the dynamics of interactions between agents as state-dependent edge weights in a consensus algorithm, incorporating both spatial and temporal dynamics. Our method learns these edge weights from trajectory observations, such as provided by expert demonstrations, which allows us to capture the complexity of nonlinear, distributed interaction behaviors. We derive necessary and sufficient conditions for the optimality of these interaction weights, explaining how the network topology affects MAS coordination. The proposed method is demonstrated on a multi-agent formation control problem, where we show its effectiveness in recovering the interaction weights and coordination patterns from sample trajectory data., Comment: This work is under review at 2024 American Control Conference

Modeling human trajectories in crowded environments is challenging due to the complex nature of pedestrian behavior and interactions. This paper proposes a geometric graph neural network (GNN) architecture that integrates domain knowledge from psychological studies to model pedestrian interactions and predict future trajectories. Unlike prior studies using complete graphs, we define interaction neighborhoods using pedestrians' field of view, motion direction, and distance-based kernel functions to construct graph representations of crowds. Evaluations across multiple datasets demonstrate improved prediction accuracy through reduced average and final displacement error metrics. Our findings underscore the importance of integrating domain knowledge with data-driven approaches for effective modeling of human interactions in crowds., Comment: \c{opyright} 2024 the authors. This work has been accepted to IFAC for publication under a Creative Commons Licence CC-BY-NC-ND

In this paper, we address the vision-based autonomous landing problem in complex urban environments using deep neural networks for semantic segmentation and risk assessment. We propose employing the SegFormer, a state-of-the-art visual transformer network, for the semantic segmentation of complex, unstructured urban environments. This approach yields valuable information that can be utilized in smart autonomous landing missions, particularly in emergency landing scenarios resulting from system failures or human errors. The assessment is done in real-time flight, when images of an RGB camera at the Unmanned Aerial Vehicle (UAV) are segmented with the SegFormer into the most common classes found in urban environments. These classes are then mapped into a level of risk, considering in general, potential material damage, damaging the drone itself and endanger people. The proposed strategy is validated through several case studies, demonstrating the huge potential of semantic segmentation-based strategies to determining the safest landing areas for autonomous emergency landing, which we believe will help unleash the full potential of UAVs on civil applications within urban areas.

In one-dimensional quantum materials, conducting electrons and the underlying lattices can undergo a spontaneous translational symmetry breaking, known as Peierls transition. For nearly a century, the Peierls transition has been understood within the paradigm of electron-electron interactions mediated by longitudinal acoustic phonons. This classical picture has recently been revised in topological semimetals, where transverse acoustic phonons can couple with conducting p-orbital electrons and give rise to an unconventional Fermi surface instability, dubbed the transverse Peierls transition (TPT). Most interestingly, the TPT induced lattice distortions can further break rotation or mirror/inversion symmetries, leading to nematic or chiral charge density waves (CDWs). Quantum materials that host the TPT, however, have not been experimentally established. Here, we report the experimental discovery of an incommensurate TPT in the tetragonal Dirac semimetal EuAl$_4$. Using inelastic x-ray scattering with meV resolution, we observe the complete softening of a transverse acoustic phonon at the CDW wavevector upon cooling, whereas the longitudinal acoustic phonon is nearly unchanged. Combining with first principles calculations, we show that the incommensurate CDW wavevector matches the calculated charge susceptibility peak and connects the nested Dirac bands with Al 3$p_{x}$ and 3$p_{y}$ orbitals. Supplemented by second harmonic generation measurements, we show that the CDW induced lattice distortions break all vertical and diagonal mirrors whereas the four-fold rotational symmetry is retained below the CDW transition. Our observations strongly suggest a chiral CDW in EuAl$_4$ and highlight the TPT as a new avenue for chiral quantum states., Comment: Supplementary materials are available upon request

Earth Observation (EO) has traditionally involved the transmission of a large volume of raw data to map the Earth surface. This results in congestion to the satellite network and delays in the availability of the results, invalidating the approach for timing-sensitive applications. Instead, the computation resources at the satellites can be used as an edge layer for compressing the data and/or doing inferences. In this paper, we investigate satellite edge computing for vessel detection with a LEO satellite constellation. First, we distribute the computation and inference load among the neighbouring satellites of the one taking the images, based on the VHRShips data set and YOLOv8. This semantic and fragmented information is then routed to a remote ground monitor through the whole constellation. The average and peak Age of Information (AoI) are reformulated to measure the freshness of the aggregated information at the receiver in this image-capture scenario. We then dimension the network (number of orbital planes and satellites per orbital plane) for a given target age and covered area that quantify the level of achievement of the task. The results show that 20 orbital planes with 20 satellites are necessary to keep the peak AoI below 60 seconds with a compression ratio > 23000, i.e., a size reduction of 99.996%, and for a approximately 100% probability of coverage., Comment: This paper was presented at the ComSoc Challenge on 'Future 6G NTN Systems' held at ICCSPA'24, where it was awarded second place

We present Syndeo: a software framework for container orchestration of Ray on Slurm. In general the idea behind Syndeo is to write code once and deploy anywhere. Specifically, Syndeo is designed to addresses the issues of portability, scalability, and security for parallel computing. The design is portable because the containerized Ray code can be re-deployed on Amazon Web Services, Microsoft Azure, Google Cloud, or Alibaba Cloud. The process is scalable because we optimize for multi-node, high-throughput computing. The process is secure because users are forced to operate with unprivileged profiles meaning administrators control the access permissions. We demonstrate Syndeo's portable, scalable, and secure design by deploying containerized parallel workflows on Slurm for which Ray does not officially support., Comment: IEEE HPEC 2024. 6 Pages

Future neuromorphic architectures will require millions of artificial synapses, making understanding the physical mechanisms behind their plasticity functionalities mandatory. In this work, we propose a simplified spin memristor, where the resistance can be controlled by magnetic field pulses, based on a Co/Pt multilayer with perpendicular magnetic anisotropy as a synapsis emulator. We demonstrate plasticity and spike time dependence plasticity (STDP) in this device and explored the underlying magnetic mechanisms using Kerr microscopy imaging and Hall magneto-transport measurements. A well-defined threshold for magnetization reversal and the continuous resistance states associated with the micromagnetic configuration are the basic properties allowing plasticity and STDP learning mechanisms in this device., Comment: 7 pages, 5 figures research paper

This article studies an inverse problem for a transmission wave equation, a system where the main coefficient has a variable jump across an internal interface given by the boundary between two subdomains. The main result obtains Lipschitz stability in recovering a zeroth-order coefficient in the equation. The proof is based on the Bukhgeim-Klibanov method and uses a new one-parameter global Carleman inequality, specifically constructed for the case of a variable main coefficient which is discontinuous across a strictly convex interface. In particular, our hypothesis allows the main coefficient to vary smoothly within each subdomain up to the interface, thereby extending the preceding literature on the subject.

Most deep learning models of multiclass arrhythmia classification are tested on fingertip photoplethysmographic (PPG) data, which has higher signal-to-noise ratios compared to smartwatch-derived PPG, and the best reported sensitivity value for premature atrial/ventricular contraction (PAC/PVC) detection is only 75%. To improve upon PAC/PVC detection sensitivity while maintaining high AF detection, we use multi-modal data which incorporates 1D PPG, accelerometers, and heart rate data as the inputs to a computationally efficient 1D bi-directional Gated Recurrent Unit (1D-Bi-GRU) model to detect three arrhythmia classes. We used motion-artifact prone smartwatch PPG data from the NIH-funded Pulsewatch clinical trial. Our multimodal model tested on 72 subjects achieved an unprecedented 83% sensitivity for PAC/PVC detection while maintaining a high accuracy of 97.31% for AF detection. These results outperformed the best state-of-the-art model by 20.81% for PAC/PVC and 2.55% for AF detection even while our model was computationally more efficient (14 times lighter and 2.7 faster).

The integration of Semantic Communications (SemCom) and edge computing in space networks enables the optimal allocation of the scarce energy, computing, and communication resources for data-intensive applications. We use Earth Observation (EO) as a canonical functionality of satellites and review its main characteristics and challenges. We identify the potential of the space segment, represented by a low Earth orbit (LEO) satellite constellation, to serve as an edge layer for distributed intelligence. Based on that, propose a system architecture that supports semantic and goal-oriented applications for image reconstruction and object detection and localization. The simulation results show the intricate trade-offs among energy, time, and task-performance using a real dataset and State-of-the-Art (SoA) processing and communication parameters., Comment: Submitted for publication to IEEE Communications Magazine

Charge density wave (CDW) and their interplay with correlated and topological quantum states are forefront of condensed matter research. The 4$H_{b}$-TaS$_2$ is a CDW ordered quantum heterostructure that is formed by alternative stacking of Mott insulating 1T-TaS$_2$ and Ising superconducting 1H-TaS$_2$. While the $\sqrt{13}\times\sqrt{13}$ and 3$\times$3 CDWs have been respectively observed in the bulk 1T-TaS$_2$ and 2H-TaS$_2$, the CDWs and their pivotal role for unconventional superconductivity in the 4$H_{b}$-TaS$_2$ remain unsolved. In this letter, we reveal the 2-dimensional (2D) $\sqrt{13}\times\sqrt{13}$ chiral CDW in the 1T-layers and intra-unit cell coupled 2D 2$\times$2 CDW in the 1H and 1H' layers of 4$H_{b}$-TaS$_2$. Our results establish 4$H_{b}$-TaS$_2$ a novel 2.5D quantum heterostructure, where 2D quantum states emerge from 3D crystalline structure.

The finite momentum superconducting pairing states (FMPs), where Cooper pairs carry non-zero momentum, are believed to give rise to exotic physical phenomena including the pseudogap phase of cuprate high-Tc superconductors and Majorana fermions in topological superconductivity. FMPs can emerge in intertwined electronic liquids with strong spin-spin interactions or be induced by lifting the spin degeneracy under magnetic field as originally proposed by Fulde-Ferrell and Larkin-Ovchinnikov. In quantum materials with strong Ising-type spin-orbit coupling, such as the 2D transition metal dichalcogenides (TMDs), the spin degree of freedom is frozen enabling novel orbital driven FMPs via magnetoelectric effect. While evidence of orbital driven FMPs has been revealed in bilayer TMDs, its realization in 3D bulk materials remains an unresolved challenge. Here we report experimental signatures of FMP in a locally noncentrosymmetric bulk superconductor 4Hb-TaS2. Using hard X-ray diffraction and angle-resolved photoemission spectroscopy, we reveal unusual 2D chiral charge density wave (CDW) and weak interlayer hopping in 4Hb-TaS2. Below the superconducting transition temperature, the upper critical field, Hc2, linearly increases via decreasing temperature, and well exceeds the Pauli limit, thus establishing the dominant orbital pair-breaking mechanism. Remarkably, we discover a field-induced superconductivity-to-superconductivity transition that breaks continuous rotational symmetry of the s-wave uniform pairing in the Bardeen-Cooper-Schrieffer theory down to the six-fold rotation symmetry. Combining with a Ginzburg-Landau free energy analysis that incorporates magnetoelectric effect, our observations provide strong evidence of orbital driven FMP in the 3D quantum heterostructure 4Hb-TaS2.

We present $\sim300$ stellar metallicity measurements in two faint M31 dwarf galaxies, Andromeda XVI ($M_V = -7.5$) and Andromeda XXVIII ($M_V = -8.8$) derived using metallicity-sensitive Calcium H & K narrow-band Hubble Space Telescope imaging. These are the first individual stellar metallicities in And~XVI (95 stars). Our And~XXVIII sample (191 stars) is a factor of $\sim15$ increase over literature metallicities. For And~XVI, we measure $\langle \mbox{[Fe/H]}\rangle = -2.17^{+0.05}_{-0.05}$, $\sigma_{\mbox{[Fe/H]}}=0.33^{+0.07}_{-0.07}$, and $\nabla_{\mbox{[Fe/H]}} = -0.23\pm0.15$ dex $R_e^{-1}$. We find that And XVI is more metal-rich than MW UFDs of similar luminosity, which may be a result of its unusually extended star formation history. For And XXVIII, we measure $\langle \mbox{[Fe/H]}\rangle = -1.95^{+0.04}_{-0.04}$, $\sigma_{\mbox{[Fe/H]}}=0.34^{+0.07}_{-0.07}$, and $\nabla_{\mbox{[Fe/H]}} = -0.46 \pm 0.10$~dex~$R_e^{-1}$, placing it on the dwarf galaxy mass-metallicity relation. Neither galaxy has a metallicity distribution function with an abrupt metal-rich truncation, suggesting that star formation fell off gradually. The stellar metallicity gradient measurements are among the first for faint ($L \lesssim 10^6~L_{\odot}$) galaxies outside the Milky Way halo. Both galaxies' gradients are consistent with predictions from the FIRE simulations, where an age-gradient strength relationship is the observational consequence of stellar feedback that produces dark matter cores. We include a catalog for community spectroscopic follow-up, including 19 extremely metal poor ($\mbox{[Fe/H]} < -3.0$) star candidates, which make up 7% of And~XVI's MDF and 6% of And~XXVIII's., Comment: 17 pages, 5 figures, 4 tables, ApJ submitted; comments welcome!