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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)

A $3$-matching of a graph is a partition of its vertices into $3$-element subsets; this generalizes the idea of matchings in graphs. Let $G$ be a complete edge-weighted graph; to each $3$-set of vertices of $G$ assign the sum of its minimum spanning tree as its weight. The $3$-Matching Problem is the combinatorial optimization problem of finding a minimum weight $3$-matching of $G$, where the weight of a $3$-matching is the sum of the weights of its $3$-sets. While the $3$-Matching Problem is known to be NP-hard, the complexity of its Euclidean variant remains open. In the Euclidean $3$-Matching Problem, the vertices of $G$ are a set of points in the plane, and the weight of an edge is given by the distance between its endpoints. In this paper we show that the Euclidean $3$-Matching Problem is NP-hard.

Graph algorithms, such as shortest path finding, play a crucial role in enabling essential applications and services like infrastructure planning and navigation, making their correctness important. However, thoroughly testing graph algorithm implementations poses several challenges, including their vast input space (i.e., arbitrary graphs). Moreover, through our preliminary study, we find that just a few automatically generated graphs (less than 10) could be enough to cover the code of many graph algorithm implementations, rendering the code coverage-guided fuzzing approach -- one of the state-of-the-art search algorithms -- less efficient than expected. To tackle these challenges, we introduce GraphFuzz, the first automated feedback-guided fuzzing framework for graph algorithm implementations. Our key innovation lies in identifying lightweight and algorithm-specific feedback signals to combine with or completely replace the code coverage feedback to enhance the diversity of the test corpus, thereby speeding up the bug-finding process. This novel idea also allows GraphFuzz to effectively work in both black-box (i.e., no code coverage instrumentation/collection is required) and grey-box setups. GraphFuzz applies differential testing to detect both crash-triggering bugs and logic bugs. Our evaluation demonstrates the effectiveness of GraphFuzz. The tool has successfully discovered 12 previously unknown bugs, including 6 logic bugs, in 9 graph algorithm implementations in two popular graph libraries, NetworkX and iGraph. All of them have been confirmed and and 11 bugs have been rectified by the libraries' maintainers.

This work introduces the design, implementation, and validation of a virtual reality (VR) experience aimed at promoting the inclusion of individuals with dyslexia in university settings. Unlike traditional awareness methods, this immersive approach offers a novel way to foster empathy by allowing participants to experience firsthand the challenges faced by students with dyslexia. Specifically, the experience raises awareness by exposing non-dyslexic individuals to the difficulties commonly encountered by dyslexic students. In the virtual environment, participants explore a virtual campus with multiple buildings, navigating between them while completing tasks and simultaneously encountering barriers that simulate some of the challenges faced by individuals with dyslexia. These barriers include reading signs with shifting letters, following directional arrows that may point incorrectly, and dealing with a lack of assistance. The campus is a comprehensive model featuring both indoor and outdoor spaces and supporting various modes of locomotion. To validate the experience, more than 30 non-dyslexic participants from the university environment, mainly professors and students, evaluated it through ad hoc satisfaction surveys. The results indicated heightened awareness of the barriers encountered by students with dyslexia, with participants deeming the experience a valuable tool for increasing visibility and fostering understanding of dyslexic students., Comment: 20 pages, 9 figures

Measuring the accuracy of face recognition (FR) systems is essential for improving performance and ensuring responsible use. Accuracy is typically estimated using large annotated datasets, which are costly and difficult to obtain. We propose a novel method for 1:1 face verification that benchmarks FR systems quickly and without manual annotation, starting from approximate labels (e.g., from web search results). Unlike previous methods for training set label cleaning, ours leverages the embedding representation of the models being evaluated, achieving high accuracy in smaller-sized test datasets. Our approach reliably estimates FR accuracy and ranking, significantly reducing the time and cost of manual labeling. We also introduce the first public benchmark of five FR cloud services, revealing demographic biases, particularly lower accuracy for Asian women. Our rapid test method can democratize FR testing, promoting scrutiny and responsible use of the technology. Our method is provided as a publicly accessible tool at https://github.com/caltechvisionlab/frt-rapid-test, Comment: Accepted as a conference paper for WACV 2025. Manuel Knott, Ignacio Serna, and Ethan Mann contributed equally

Diffusing Wave Spectroscopy (DWS) is an extension of standard dynamic light scattering (DLS), applied to soft materials that are turbid or opaque. The propagation of light is modeled using light diffusion, characterized by a light diffusion coefficient that depends on the transport mean free path l* of the medium. DWS is highly sensitive to small particle displacements or other local fluctuations in the scattering properties and can probe subnanometer displacements. Analyzing the motion of beads in a viscoelastic matrix, known as one-bead microrheology, is one of the most common applications of DWS. Despite significant advancements since its invention in 1987, including two-cell and multi-speckle DWS, challenges such as merging single and multi-speckle data and limited accuracy for short correlation times persist. Here, we address these issues by improving the two-cell Echo DWS scheme. We propose a calibration-free method to blend and merge Echo and two-cell DWS data and demonstrate the use of regularized inversion algorithms to enhance data quality at very short times. Building on this, we introduce stable corrections for bead and fluid inertia, significantly improving the quality of microrheology data at high frequencies., Comment: 14 pages, 5 figures, 4 supplementary figures

T-type brown dwarfs present an opportunity to explore atmospheres teeming with molecules such as H2O, CH4 and NH3, which exhibit a wealth of absorption features in the mid-infrared. With JWST, we can finally explore this chemistry in detail, including for the coldest brown dwarfs that were not yet discovered in the Spitzer era. This allows precise derivations of the molecular abundances, which in turn informs our understanding of vertical transport in these atmospheres and can provide clues about the formation of cold brown dwarfs and exoplanets. This study presents the first JWST/MRS mid-IR spectrum (R ~ 1500-3000) of a T-dwarf: the T8.5+T9 brown dwarf binary WISE J045853.90+643451.9. We fit the spectrum using a parameterized P-T profile and free molecular abundances (i.e., a retrieval analysis), treating the binary as unresolved. We find a good fit with a cloud-free atmosphere and identify H2O, CH4 and NH3 features. Moreover, we make the first detections of HCN and C2H2 (at 13.4$\sigma$ and 9.5$\sigma$ respectively) in any brown dwarf atmosphere. The detection of HCN suggests intense vertical mixing ($K_{zz}\sim10^{11}$cm$^2$s$^{-1}$), challenging previous literature derivations of $K_{zz}$ values for T-type brown dwarfs. Even more surprising is the C2H2 detection, which cannot be explained with existing atmospheric models for isolated objects. This result challenges model assumptions about vertical mixing, and/or our understanding of the C2H2 chemical network, or might hint towards a more complex atmospheric processes such as magnetic fields driving aurorae, or lightning driving ionization. These findings open a new frontier in studying carbon chemistry within brown dwarf atmospheres., Comment: Accepted for publication in The Astrophysical Journal Letters. 13 pages (5 figures) + appendices

Text embeddings are typically evaluated on a limited set of tasks, which are constrained by language, domain, and task diversity. To address these limitations and provide a more comprehensive evaluation, we introduce the Massive Multilingual Text Embedding Benchmark (MMTEB) - a large-scale, community-driven expansion of MTEB, covering over 500 quality-controlled evaluation tasks across 250+ languages. MMTEB includes a diverse set of challenging, novel tasks such as instruction following, long-document retrieval, and code retrieval, representing the largest multilingual collection of evaluation tasks for embedding models to date. Using this collection, we develop several highly multilingual benchmarks, which we use to evaluate a representative set of models. We find that while large language models (LLMs) with billions of parameters can achieve state-of-the-art performance on certain language subsets and task categories, the best-performing publicly available model is multilingual-e5-large-instruct with only 560 million parameters. To facilitate accessibility and reduce computational cost, we introduce a novel downsampling method based on inter-task correlation, ensuring a diverse selection while preserving relative model rankings. Furthermore, we optimize tasks such as retrieval by sampling hard negatives, creating smaller but effective splits. These optimizations allow us to introduce benchmarks that drastically reduce computational demands. For instance, our newly introduced zero-shot English benchmark maintains a ranking order similar to the full-scale version but at a fraction of the computational cost., Comment: Accepted for ICLR: https://openreview.net/forum?id=zl3pfz4VCV

The topological Hall effect (THE) is a hallmark of a non-trivial geometric spin arrangement in a magnetic metal, originating from a finite scalar spin chirality (SSC). The associated Berry phase is often a consequence of non-coplanar magnetic structures identified by multiple k-vectors. For single-k magnetic structures however with zero SSC, the emergence of a finite topological Hall signal presents a conceptual challenge. Here, we report that a fluctuation-driven mechanism involving chiral magnons is responsible for the observed THE in a low-symmetry compound, monoclinic Fe3Ga4. Through neutron scattering experiments, we discovered several nontrivial magnetic phases in this system. In our focus is the helical spiral phase at room temperature, which transforms into a transverse conical state in applied magnetic field, supporting a significant THE signal up to and above room temperature. Our work offers a fresh perspective in the search for novel materials with intertwined topological magnetic and transport properties., Comment: 20 pages, 4 figures

This work explores dyonic black bounce (BB) solutions within the framework of General Relativity (GR), coupled with nonlinear electrodynamics (NLED) and scalar fields (SFs). Previous research has employed NLED and SFs to obtain BB solutions in GR; however, these solutions typically assume the presence of either magnetic monopoles or electric charges exclusively as components of the Maxwell-Faraday tensor. In this study, we examine static and spherically symmetric BB solutions that incorporate both magnetic and electric components, forming what are known as dyon solutions. A dyon is a particle characterized by the coexistence of both magnetic and electric charges. We determine the NLED Lagrangian density and the scalar field potential that produce these solutions and analyze the associated gravitational configurations, focusing on horizons, the behavior of the metric function, and spacetime regularity as described by the Kretschmann scalar. Notably, we present the first BB solution derived from the coupling of a linear electromagnetic Lagrangian and a scalar field with an associated potential as the matter source. This work broadens the class of non-singular geometries in the literature and opens new avenues for investigating dyonic BB solutions within the context of other modified gravity theories., Comment: 24 pages, 26 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 present the discovery of the variable optical counterpart to PSR J2055+1545, a redback millisecond pulsar, and the first radial velocity curve of its companion star. The multi-band optical light curves of this system show a $0.4$$-$$0.6 \ \mathrm{mag}$ amplitude modulation with a single peak per orbit and variable colours, suggesting that the companion is mildly irradiated by the pulsar wind. We find that the flux maximum is asymmetric and occurs at orbital phase $\simeq0.4$, anticipating the superior conjunction of the companion (where the optical emission of irradiated redback companions is typically brightest). We ascribe this asymmetry, well fit with a hot spot in our light curve modelling, to irradiation from the intrabinary shock between pulsar and companion winds. The optical spectra obtained with the \textit{Gran Telescopio Canarias} reveal a G-dwarf companion star with temperatures of $5749 \pm 34 \ \mathrm{K}$ and $6106 \pm 35 \ \mathrm{K}$ at its inferior and superior orbital conjunctions, respectively, and a radial velocity semi-amplitude of $385 \pm 3 \ \mathrm{km}\ \mathrm{s}^{-1}$. Our best-fit model yields a neutron star mass of $1.7^{+0.4}_{-0.1} \ \mathrm{M_{sun}}$ and a companion mass of $0.29^{+0.07}_{-0.01} \ \mathrm{M_{sun}}$. Based on the close similarity between the optical light curve of PSR~J2055$+$1545 and those observed from PSR J1023+0038 and PSR J1227-4853 during their rotation-powered states, we suggest this system may develop an accretion disc in the future and manifest as a transitional millisecond pulsar., Comment: 15 pages, 9 figures, 3 tables. This paper has been accepted for publication in ApJ, and currently in press

Malware attacks pose a significant threat in today's interconnected digital landscape, causing billions of dollars in damages. Detecting and identifying families as early as possible provides an edge in protecting against such malware. We explore a lightweight, order-invariant approach to detecting and mitigating malware threats: analyzing API calls without regard to their sequence. We publish a public dataset of over three hundred thousand samples and their function call parameters for this task, annotated with labels indicating benign or malicious activity. The complete dataset is above 550GB uncompressed in size. We leverage machine learning algorithms, such as random forests, and conduct behavioral analysis by examining patterns and anomalies in API call sequences. By investigating how the function calls occur regardless of their order, we can identify discriminating features that can help us identify malware early on. The models we've developed are not only effective but also efficient. They are lightweight and can run on any machine with minimal performance overhead, while still achieving an impressive F1-Score of over 85\%. We also empirically show that we only need a subset of the function call sequence, specifically calls to the ntdll.dll library, to identify malware. Our research demonstrates the efficacy of this approach through empirical evaluations, underscoring its accuracy and scalability. The code is open source and available at Github along with the dataset on Zenodo.

Understanding how cognitive and social mechanisms shape the evolution of complex artifacts such as songs is central to cultural evolution research. Social network topology (what artifacts are available?), selection (which are chosen?), and reproduction (how are they copied?) have all been proposed as key influencing factors. However, prior research has rarely studied them together due to methodological challenges. We address this gap through a controlled naturalistic paradigm whereby participants (N=2,404) are placed in networks and are asked to iteratively choose and sing back melodies from their neighbors. We show that this setting yields melodies that are more complex and more pleasant than those found in the more-studied linear transmission setting, and exhibits robust differences across topologies. Crucially, these differences are diminished when selection or reproduction bias are eliminated, suggesting an interaction between mechanisms. These findings shed light on the interplay of mechanisms underlying the evolution of cultural artifacts.

We describe a framework and tool specification that represents a step towards cybersecurity testing and monitoring of IoT ecosystems. We begin with challenges from a previous paper and discuss an integrated approach and tools to enable testing and monitoring to address these challenges. We also describe exemplary use cases of IoT ecosystems and propose approaches to address the challenges using the framework and tools. The current status of this work is that the specification and conceptualisation is complete, use cases are understood with clear challenges and implementation / extension of the tools and framework is underway with tools at different stages of development. Several key observations have been made throughout this work, as follows. 1) Tools may be used in multiple different combinations, and ad-hoc use is also encouraged, where one tool may provide clues and other tools executed to undertake further investigations based on initial results. 2) Automated execution of tool chains is supported by workflows. 3) support for immutable storage of audit records of tests and results is an important requirement. 4) Indicators (observations or measurements representing information of relevance for assessment of cyber security) are a key mechanism for intercommunication between one tool and another, or with the operator. 5) Mapping this work to established security development lifecycles is a useful means of determining applicability and utility of the tools and framework. 6) There is a key interplay between devices and systems. 7) Anomaly detection in multiple forms is a key means of runtime monitoring. 8) Considerable investigation is needed related to the specifics of each device / system as an item of further work., Comment: Preprint of Paper submitted to Springer Nature Computer Science

The Worldwide LHC Computing Grid (WLCG) provides the robust computing infrastructure essential for the LHC experiments by integrating global computing resources into a cohesive entity. Simulations of different compute models present a feasible approach for evaluating future adaptations that are able to cope with future increased demands. However, running these simulations incurs a trade-off between accuracy and scalability. For example, while the simulator DCSim can provide accurate results, it falls short on scaling with the size of the simulated platform. Using Generative Machine Learning as a surrogate presents a candidate for overcoming this challenge. In this work, we evaluate the usage of three different Machine Learning models for the simulation of distributed computing systems and assess their ability to generalize to unseen situations. We show that those models can predict central observables derived from execution traces of compute jobs with approximate accuracy but with orders of magnitude faster execution times. Furthermore, we identify potentials for improving the predictions towards better accuracy and generalizability., Comment: Submitted to EPJ Web of Conferences (CHEP 2024)

This work studies Willmore flows of tori and their singularities via a dimension reduction approach. We introduce a Willmore flow that preserves the degenerate constraint of prescribed conformal class and, for rotationally symmetric initial data, we establish a strong relation with the length-preserving elastic flow in the hyperbolic plane. We provide a necessary condition for singularities and a criterion for the initial datum that allows to exclude them. Our results allow for initial data with arbitrarily large energy, in particular exceeding the usual Li-Yau threshold of $8\pi$. As an application, we obtain existence of a new class of conformally constrained Willmore tori. Moreover, we investigate singularities of the classical Willmore flow. For a class of tori, we identify a non-smooth object, the inverted catenoid, as the limit shape and we show that the flow can be restarted at this singular surface and converges to a round sphere., Comment: 44 pages. Comments are welcome!

Recently, domain-uniform stabilizability and detectability has been the central assumption %in order robustness results on the to ensure robustness in the sense of exponential decay of spatially localized perturbations in optimally controlled evolution equations. In the present paper we analyze a chain of transport equations with boundary and point controls with regard to this property. Both for Dirichlet and Neumann boundary and coupling conditions, we show a necessary and sufficient criterion on control domains which allow for the domain-uniform stabilization of this equation. We illustrate the results by means of a numerical example., Comment: 12 pages, 4 figures

We substitute individual Pt atoms into monolayer MoS$_2$ and study the resulting atomic structures with single-sideband (SSB) ptychography supported by ab initio simulations. We demonstrate that while high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) imaging provides excellent Z-contrast, distinguishing some defect types such as single and double sulfur vacancies remains challenging due to their low relative contrast difference. However, SSB with its nearly linear Z-contrast and high phase sensitivity enables reliable identification of these defect configurations as well as various Pt dopant structures at significantly lower electron doses. Our findings uncover the precise atomic placement and highlight the potential of SSB ptychography for detailed structural analysis of dopant-modified 2D materials while minimizing beam-induced damage, offering new pathways for understanding and engineering atomic-scale features in 2D systems.

We propose a version of the classical shape lemma for zero-dimensional ideals of a commutative multivariate polynomial ring to the noncommutative setting of zero-dimensional ideals in an algebra of differential operators.

The Athena mission entered a redefinition phase in July 2022, driven by the imperative to reduce the mission cost at completion for the European Space Agency below an acceptable target, while maintaining the flagship nature of its science return. This notably called for a complete redesign of the X-ray Integral Field Unit (X-IFU) cryogenic architecture towards a simpler active cooling chain. Passive cooling via successive radiative panels at spacecraft level is now used to provide a 50 K thermal environment to an X-IFU owned cryostat. 4.5 K cooling is achieved via a single remote active cryocooler unit, while a multi-stage Adiabatic Demagnetization Refrigerator ensures heat lift down to the 50 mK required by the detectors. Amidst these changes, the core concept of the readout chain remains robust, employing Transition Edge Sensor microcalorimeters and a SQUID-based Time-Division Multiplexing scheme. Noteworthy is the introduction of a slower pixel. This enables an increase in the multiplexing factor (from 34 to 48) without compromising the instrument energy resolution, hence keeping significant system margins to the new 4 eV resolution requirement. This allows reducing the number of channels by more than a factor two, and thus the resource demands on the system, while keeping a 4' field of view (compared to 5' before). In this article, we will give an overview of this new architecture, before detailing its anticipated performances. Finally, we will present the new X-IFU schedule, with its short term focus on demonstration activities towards a mission adoption in early 2027., Comment: 44 pages, 14 figures, accepted for publication in Experimental Astronomy

Recently, there has been a significant advancement in designing Self-Supervised Learning (SSL) frameworks for time series data to reduce the dependency on data labels. Among these works, hierarchical contrastive learning-based SSL frameworks, which learn representations by contrasting data embeddings at multiple resolutions, have gained considerable attention. Due to their ability to gather more information, they exhibit better generalization in various downstream tasks. However, when the time series data length is significant long, the computational cost is often significantly higher than that of other SSL frameworks. In this paper, to address this challenge, we propose an efficient way to train hierarchical contrastive learning models. Inspired by the fact that each resolution's data embedding is highly dependent, we introduce importance-aware resolution selection based training framework to reduce the computational cost. In the experiment, we demonstrate that the proposed method significantly improves training time while preserving the original model's integrity in extensive time series classification performance evaluations. Our code could be found here, https://github.com/KEEBVIN/IARS, Comment: Appears in IEEEBigData-2024

Blockchain technology has revolutionized financial markets by enabling decentralized exchanges (DEXs) that operate without intermediaries. Uniswap V2, a leading DEX, facilitates the rapid creation and trading of new tokens, offering high return potential but exposing investors to significant risks. In this work, we analyze the financial impact of newly created tokens, assessing their market dynamics, profitability and liquidity manipulations. Our findings reveal that a significant portion of market liquidity is trapped in honeypots, reducing market efficiency and misleading investors. Applying a simple buy-and-hold strategy, we are able to uncover some major risks associated with investing in newly created tokens, including the widespread presence of rug pulls and sandwich attacks. We extract the optimal sandwich amount, revealing that their proliferation in new tokens stems from higher profitability in low-liquidity pools. Furthermore, we analyze the fundamental differences between token price evolution in swap time and physical time. Using clustering techniques, we highlight these differences and identify typical patterns of honeypot and sellable tokens. Our study provides insights into the risks and financial dynamics of decentralized markets and their challenges for investors.

We present spatially resolved dust-continuum ALMA observations from rest-frame $\sim$60 to $\sim$600 $\mu$m (bands 3-10) of the hyperluminous hot dust-obscured galaxy (hot DOG) WISE J224607.6-052634.9 (W2246-0526), at redshift $z=4.6$. W2246-0526 is interacting with at least three companion galaxies, forming a system connected by tidal streams. We model the multiwavelength ALMA observations of the dust continuum using a modified blackbody, from which we derive the dust properties (mass, emissivity index, area of the emitting region, and temperature) in the hot DOG and resolved structures across a region of nearly $\sim$50 kpc. The peak temperature at the location of the hot DOG, $\sim$110 K, is likely the consequence of heating by the central quasar. The dust temperature drops to $\sim$40 K at a radius of $\sim$8 kpc, suggesting that heating by the quasar beyond that distance is nondominant. The dust in the connecting streams between the host and companion galaxies is at temperatures between 30-40 K, typical of starburst galaxies, suggesting it is most likely heated by recent, in-situ star formation. This is the first time dust properties are spatially resolved over several tens of kpc in a galaxy system beyond Cosmic Noon --this is more than six times the scales previously probed in galaxies at those redshifts., Comment: 10 pages, 4 figures. Accepted for publication in A&A

In this paper, we use a characterization of naturally reductive 2-step nilponent Lie groups via Ambrose-Singer's homogeneous structures to prove that one cannot determine if a closed Riemannian manifold is naturally reductive using the information encoded in the spectrum of the Laplace-Beltrami operator. To do that, we consider a new isospectral pair of 2-step nilmanifolds of dimension 9 such that one of them is naturally reductive and the other is not., Comment: 19 pages

We present full 3-28 micron JWST MIRI/MRS and NIRSpec/IFU spectra of the western nucleus of Arp 220, the nearest ultraluminous infrared galaxy. This nucleus has long been suggested to possibly host an embedded Compton-thick AGN. Millimeter observations of the dust continuum suggest the presence of a distinct 20 pc core with a dust temperature of $T_\mathrm{d} \gtrsim 500~\mathrm{K}$, in addition to a 100 pc circumnuclear starburst disk. However, unambiguously identifying the nature of this core is challenging, due to the immense obscuration, the nuclear starburst activity, and the nearby eastern nucleus. With the JWST integral field spectrographs, we can, for the first time, separate the two nuclei across this full wavelength range, revealing a wealth of molecular absorption features towards the western nucleus. We analyse the rovibrational bands detected at 4-22 micron, deriving column densities and rotational temperatures for 10 distinct species. Optically thick features of C$_2$H$_2$, HCN and HNC suggest that this molecular gas is hidden behind a curtain of cooler dust, and indicate that the column densities of C$_2$H$_2$ and HCN are an order of magnitude higher than previously derived from Spitzer observations. We identify a warm HCN component with rotational temperature $T_\mathrm{rot} = 330~\mathrm{K}$, which we associate with radiative excitation by the hot inner nucleus. We propose a geometry where the detected molecular gas is located in the inner regions of the starburst disk, directly surrounding the hot 20 parsec core. The chemical footprint of the western nucleus is reminiscent of that of hot cores, with additional evidence for shocks. No evidence for the presence of an AGN in the form of X-ray-driven chemistry or extreme excitation is found., Comment: Submitted to A&A

We consider one-dimensional, interacting spinless bosons on a tight-binding lattice described by the Bose-Hubbard model. Besides attractive on-site two-body interactions, we include a three-body repulsive term such that the competition between these two forces allows the formation of self-bound liquid states. We investigate the properties of this system using the Gutzwiller approximation, showing that, indeed, this mean-field approach also supports liquid states. We find that for densities lower than the equilibrium density, the Gutzwiller method, and other mean-field approaches -- such as the Gross-Pitaevskii theory -- feature a sharp transition to the vacuum state. This, however, is avoided by considering local minima of the functional in the standard manner. We also study the excitation spectrum, and calculate the speed of sound, in full agreement with the usual expression obtained from the thermodynamic equation of state. We study their corresponding quantum droplets variationally and find that the results behave in accordance with the one-dimensional liquid drop model., Comment: 8 pages, 7 figures

The increasing use and implementation of Autonomous Surface Vessels (ASVs) for various activities in maritime environments is expected to drive a rise in developments and research on their control. Particularly, the coordination of multiple ASVs presents novel challenges and opportunities, requiring interdisciplinary research efforts at the intersection of robotics, control theory, communication systems, and marine sciences. The wide variety of missions or objectives for which these vessels can be collectively used allows for the application and combination of different control techniques. This includes the exploration of machine learning to consider aspects previously deemed infeasible. This review provides a comprehensive exploration of coordinated ASV control while addressing critical gaps left by previous reviews. Unlike previous works, we adopt a systematic approach to ensure integrity and minimize bias in article selection. We delve into the complex world of sub-actuated ASVs with a focus on customized control strategies and the integration of machine learning techniques for increased autonomy. By synthesizing recent advances and identifying emerging trends, we offer insights that drive this field forward, providing both a comprehensive overview of state-of-the-art techniques and guidance for future research efforts., Comment: 29 pages

Large Language Models (LLMs) have revolutionized natural language processing, setting new standards across a wide range of applications. However, their relevant memory and computational demands make them impractical for deployment on technologically-constrained tiny devices such as wearable devices and Internet-of-Things units. To address this limitation, we introduce EmbBERT-Q, a novel tiny language model specifically designed for tiny devices with stringent memory constraints. EmbBERT-Q achieves state-of-the-art (SotA) accuracy in Natural Language Processing tasks in this scenario, with a total memory footprint (weights and activations) of just 781 kB, representing a 25x reduction in size with respect to SotA models. By combining architectural innovations with hardware-compatible 8-bit quantization, EmbBERT-Q consistently outperforms several baseline models scaled down to a 2 MB memory budget (i.e., the maximum memory typically available in tiny devices), including heavily compressed versions of BERT and MAMBA. Extensive experimental evaluations on both a selected benchmark dataset, TinyNLP, specifically curated to evaluate Tiny Language Models in NLP tasks and real-world scenarios, and the GLUE benchmark, demonstrate EmbBERT-Q ability to deliver competitive accuracy with respect to existing approaches, achieving an unmatched balance between memory and performance. To ensure the complete and immediate reproducibility of all our results, we release all code, scripts, and model checkpoints at https://github.com/RiccardoBravin/tiny-LLM., Comment: 24 pages, 4 figures, 14 tables

A major open question in exoplanet research is whether secondary atmospheres are rare around Earth-sized rocky exoplanets. In this work we determine the distance at which an Earth-sized planet orbiting a variety of stellar hosts could retain a CO2- or N2-dominated atmosphere and compare this atmospheric retention distance (ARD) with that of the liquid-water HZ. We combined planetary atmosphere models with stellar evolution models. The atmospheric models produced by the thermochemical Kompot code allowed us to calculate the Jeans escape rates for different stellar masses, rotation rates, and ages. These loss rates allowed us to determine the closest distance a planet is likely to retain a CO2- or N2-dominated atmosphere. Using stellar rotation evolution models, we modelled how these retention distances evolve as the X-ray and ultraviolet activity of the star evolves. We find that the overlap of the HZ and the ARD occurs earlier around slowly rotating stars. Additionally, we find that HZ planets orbiting stars with masses under 0.4 M_\odot are unlikely to retain any atmosphere, due to the lower spin-down rate of these fully convective stars. We also show that the initial rotation rate of the star can impact the likelihood of a planet retaining an atmosphere, as an initially fast-rotating star maintains high levels of short-wavelength irradiance for much longer. The orbits of all Earth-like rocky exoplanets observed by JWST in cycles 1 and 2, including HZ planets, fall outside the ARD. Our results will have implications for future target selections of small exoplanet observing programmes with JWST or future instruments such as the Ariel space mission., Comment: 10 pages, 5 figures, accepted for publication in A&A

In this paper, it is shown that $N$naturalness scenarios have an intrinsic mechanism to suppress neutrino masses. As in other infrared neutrino mass models like extra-dimensional theories or many species theories, the large number of mixing partners is responsible for the neutrino mass suppression. It is shown how neutrino mass matrices arise in $N$naturalness models and the resulting neutrino mixing is analyzed. The first result is that a totally democratic coupling among the different sectors like in the original models is already ruled out by the fact that neutrinos are not massless. In the case where the sector couplings deviate from the intersector ones a tower of additional neutrino mass eigenstates appears whose difference between the squared masses, $\Delta m_{ij}^2$, is determined fully by the theory. The resulting phenomenology of such a tower is investigated and the unique signals in neutrino oscillation experiments, neutrino mass measurements, and neutrinoless double beta decay experiments are discussed. This opens the door for terrestrial tests of $N$naturalness whose phenomenology was so far focused on Cosmology.

This paper offers a thorough examination of the univariate predictability in cryptocurrency time-series. By exploiting a combination of complexity measure and model predictions we explore the cryptocurrencies time-series forecasting task focusing on the exchange rate in USD of Litecoin, Binance Coin, Bitcoin, Ethereum, and XRP. On one hand, to assess the complexity and the randomness of these time-series, a comparative analysis has been performed using Brownian and colored noises as a benchmark. The results obtained from the Complexity-Entropy causality plane and power density spectrum analysis reveal that cryptocurrency time-series exhibit characteristics closely resembling those of Brownian noise when analyzed in a univariate context. On the other hand, the application of a wide range of statistical, machine and deep learning models for time-series forecasting demonstrates the low predictability of cryptocurrencies. Notably, our analysis reveals that simpler models such as Naive models consistently outperform the more complex machine and deep learning ones in terms of forecasting accuracy across different forecast horizons and time windows. The combined study of complexity and forecasting accuracies highlights the difficulty of predicting the cryptocurrency market. These findings provide valuable insights into the inherent characteristics of the cryptocurrency data and highlight the need to reassess the challenges associated with predicting cryptocurrency's price movements., Comment: This is the author's accepted manuscript, modified per ACM self-archiving policy. The definitive Version of Record is available at https://doi.org/10.1145/3703412.3703420

We consider the problem of approaching real numbers with rational numbers with prime denominator and with a single numerator allowed for each denominator. We obtain basic results, both probabilistic and deterministic, draw connections to twisted diophantine approximation, and present a simple application, related to possible correlations between trace functions and dynamical sequences., Comment: 26 pages

We report the results of deep H-alpha and [O III] images of the bright WN7/WC4 Wolf-Rayet star WR 8 (HD 62910). These data show considerably more surrounding nebulosity than seen in prior imaging. The brighter portions of the nebula span 6' in diameter and exhibit considerable fine-scale structure including numerous emission clumps and bright head-tail like features presumably due to the effects of the WR star's stellar winds. Due to the overlap of a relatively bright band of unrelated foreground diffuse interstellar H-alpha emission, WR 8's nebula is best viewed via its [O III] emission. A faint 9' x 13' diffuse outer nebulosity is detected surrounding the nebula's main ring of emission. The nebula's optical structure is substantially different from that of its thermal continuum dust emission seen in WISE 22 micron infrared images which show a smaller and sharply defined emission shell., Comment: 8 pages, 6 figures, 1 table

Unmanned Aerial Vehicles (UAVs) are increasingly being utilized to enhance the Quality of Service (QoS) in wireless networks due to their flexibility and cost-effectiveness. However, optimizing UAV placement in dynamic and obstacle-prone environments remains a research challenge. Reinforcement Learning (RL) has proven to be an effective approach that offers adaptability and robustness in such environments. This paper introduces RLpos-3, a novel framework that integrates standard RL techniques and existing libraries with Network Simulator 3 (ns-3) to facilitate the development and evaluation of UAV positioning algorithms. RLpos-3 serves as a complementary tool for researchers, enabling the implementation, analysis, and benchmarking of UAV positioning strategies across different environmental settings while ensuring user traffic demands are met. To validate its effectiveness, we present a use case demonstrating the performance of RLpos-3 in optimizing UAV placement under realistic conditions.

Barren plateaus are fundamentally a statement about quantum loss landscapes on average but there can, and generally will, exist patches of barren plateau landscapes with substantial gradients. Previous work has studied certain classes of parameterized quantum circuits and found example regions where gradients vanish at worst polynomially in system size. Here we present a general bound that unifies all these previous cases and that can tackle physically-motivated ans\"atze that could not be analyzed previously. Concretely, we analytically prove a lower-bound on the variance of the loss that can be used to show that in a non-exponentially narrow region around a point with curvature the loss variance cannot decay exponentially fast. This result is complemented by numerics and an upper-bound that suggest that any loss function with a barren plateau will have exponentially vanishing gradients in any constant radius subregion. Our work thus suggests that while there are hopes to be able to warm-start variational quantum algorithms, any initialization strategy that cannot get increasingly close to the region of attraction with increasing problem size is likely inadequate.

Estimating the expectation of a Bernoulli random variable based on N independent trials is a classical problem in statistics, typically addressed using Binomial Proportion Confidence Intervals (BPCI). In the control systems community, many critical tasks-such as certifying the statistical safety of dynamical systems-can be formulated as BPCI problems. Conformal Prediction (CP), a distribution-free technique for uncertainty quantification, has gained significant attention in recent years and has been applied to various control systems problems, particularly to address uncertainties in learned dynamics or controllers. A variant known as training-conditional CP was recently employed to tackle the problem of safety certification. In this note, we highlight that the use of training-conditional CP in this context does not provide valid safety guarantees. We demonstrate why CP is unsuitable for BPCI problems and argue that traditional BPCI methods are better suited for statistical safety certification.

This article is devoted to studying gonosomal algebras and operators with a single male genotype. We compute the limit points of the trajectories of the corresponding normalised gonosomal operators, describing the development of specific populations and providing the corresponding biological interpretations., Comment: 22 pages

Flip graphs were recently introduced in order to discover new matrix multiplication methods for matrix sizes. The technique applies to other tensors as well. In this paper, we explore how it performs for polynomial multiplication.

In statistics and machine learning, when we train a fitted model on available data, we typically want to ensure that we are searching within a model class that contains at least one accurate model -- that is, we would like to ensure an upper bound on the model class risk (the lowest possible risk that can be attained by any model in the class). However, it is also of interest to establish lower bounds on the model class risk, for instance so that we can determine whether our fitted model is at least approximately optimal within the class, or, so that we can decide whether the model class is unsuitable for the particular task at hand. Particularly in the setting of interpolation learning where machine learning models are trained to reach zero error on the training data, we might ask if, at the very least, a positive lower bound on the model class risk is possible -- or are we unable to detect that "all models are wrong"? In this work, we answer these questions in a distribution-free setting by establishing a model-agnostic, fundamental hardness result for the problem of constructing a lower bound on the best test error achievable over a model class, and examine its implications on specific model classes such as tree-based methods and linear regression.

The goal of the TANGERINE project is to develop the next generation of monolithic silicon pixel detectors using a 65 nm CMOS imaging process, which offers a higher logic density and overall lower power consumption compared to previously used processes. A combination of Technology Computer-Aided Design (TCAD) and Monte Carlo (MC) simulations is used to understand the physical processes within the sensing element and thus the overall performance of the pixel detector. The response of the sensors can then be tested in laboratory and test beam facilities and compared to simulation results. Transient simulations allow for studying the response of the sensor as a function of time, such as the signal produced after a charged particle passes through the sensor. The study of these signals is important to understand the magnitude and timing of the response from the sensors and improve upon them. While TCAD simulations are accurate, the time required to produce a single pulse is large compared to a combination of MC and TCAD simulations. In this work, a validation of the transient simulation approach and studies on charge collection are presented., Comment: TIPP 2023 Proceedings

The advance of quantum computing poses a significant threat to classical cryptography, compromising the security of current encryption schemes such as RSA and ECC. In response to this challenge, two main approaches have emerged: quantum cryptography and post-quantum cryptography (PQC). However, both have implementation and security limitations. In this paper, we propose a secure key distribution protocol for Quantum Key Distribution Networks (QKDN), which incorporates encapsulation techniques in the key-relay model for QKDN inspired by onion routing and combined with PQC to guarantee confidentiality, integrity, authenticity and anonymity in communication. The proposed protocol optimizes security by using post-quantum public key encryption to protect the shared secrets from intermediate nodes in the QKDN, thereby reducing the risk of attacks by malicious intermediaries. Finally, relevant use cases are presented, such as critical infrastructure networks, interconnection of data centers and digital money, demonstrating the applicability of the proposal in critical high-security environments., Comment: 6 pages, 4 figures

The high energy physics community recently gained access to the TPSCo 65 nm ISC (Image Sensor CMOS), which enables a higher in-pixel logic density in monolithic active pixel sensors (MAPS) compared to processes with larger feature sizes. To explore this novel technology, the Hybrid-to-Monolithic (H2M) test chip has been designed and manufactured. The design followed a digital-on-top design workflow and ports a hybrid pixel-detector architecture, with digital pulse processing in each pixel, into a monolithic chip. The chip matrix consists of 64$\times$16 square pixels with a size of 35$\times$35 um2, and a total active area of approximately 1.25 um2. The chip has been successfully integrated into the Caribou DAQ system. It is fully functional, and the measured threshold dispersion and noise agree with the expectation from front-end simulations. However, a non-uniform in-pixel response related to the size and location of the n-wells in the analog circuitry has been observed in test beam measurements and will be discussed in this contribution. This asymmetry in the pixel response, enhanced by the 35 um pixel pitch - larger than in other prototypes - and certain features of the readout circuit, has not been observed in prototypes with smaller pixel pitches in this technology.

Given an input acyclic digraph $G = (V,E)$ and a positive integer $k$, the problem of Maximum Coverage $k$-Antichains (resp., Chains) denoted as MA-$k$ (resp., MC-$k$) asks to find $k$ sets of pairwise unreachable vertices, known as antichains (resp., $k$ subsequences of paths, known as chains), maximizing the number of vertices covered by these antichains (resp. chains). While MC-$k$ has been recently solved in (almost) optimal $O(|E|^{1+o(1)})$ time [Kogan and Parter, ICALP 2022], the fastest known algorithm for MA-$k$ is a recent $(k|E|)^{1+o(1)}$-time solution [Kogan and Parter, ESA 2024] as well as a $1/2$ approximation running in $|E|^{1+o(1)}$ time in the same paper. In this paper, we leverage a paths-based proof of the Greene-Kleitmann (GK) theorem with the help of the greedy algorithm for set cover and recent advances on fast algorithms for flows and shortest paths to obtain the following results for MA-$k$: - The first (exact) algorithm running in $|E|^{1+o(1)}$ time, hence independent in $k$. - A randomized algorithm running in $\tilde{O}(\alpha_k|E|)$ time, where $\alpha_k$ is the size of the optimal solution. That is, a near-linear parameterized running time, generalizing the result of [M\"akinen et al., ACM TALG] obtained for $k=1$. - An approximation algorithm running in time $O(\alpha_1^2|V| + (\alpha_1+k)|E|)$ with approximation ratio of $(1-1/e) > 0.63 > 1/2$. Our last two solutions rely on the use of greedy set cover, first exploited in [Felsner et al., Order 2003] for chains, which we now apply to antichains. We complement these results with two examples (one for chains and one for antichains) showing that, for every $k \ge 2$, greedy misses a $1/4$ portion of the optimal coverage. We also show that greedy is a $\Omega(\log{|V|})$ factor away from minimality when required to cover all vertices: previously unknown for sets of chains or antichains.

Hyperspectral image (HSI) classification plays a pivotal role in domains such as environmental monitoring, agriculture, and urban planning. However, it faces significant challenges due to the high-dimensional nature of the data and the complex spectral-spatial relationships inherent in HSI. Traditional methods, including conventional machine learning and convolutional neural networks (CNNs), often struggle to effectively capture these intricate spectral-spatial features and global contextual information. Transformer-based models, while powerful in capturing long-range dependencies, often demand substantial computational resources, posing challenges in scenarios where labeled datasets are limited, as is commonly seen in HSI applications. To overcome these challenges, this work proposes GraphMamba, a hybrid model that combines spectral-spatial token generation, graph-based token prioritization, and cross-attention mechanisms. The model introduces a novel hybridization of state-space modeling and Gated Recurrent Units (GRU), capturing both linear and nonlinear spatial-spectral dynamics. GraphMamba enhances the ability to model complex spatial-spectral relationships while maintaining scalability and computational efficiency across diverse HSI datasets. Through comprehensive experiments, we demonstrate that GraphMamba outperforms existing state-of-the-art models, offering a scalable and robust solution for complex HSI classification tasks.

Topological superconductors are essential elements of the periodic table of topological quantum matter. However, the relevant odd-parity spin-triplet superconductors are rare. We report high-resolution measurements of the complex electrical impedance of YbRh$_2$Si$_2$ down to 0.4 mK, that reveal the presence of several superconducting states, suppressed differently by magnetic field, both Pauli-limited and beyond the Pauli limit. Superconductivity is abruptly switched off at the critical field of the primary antiferromagnetic order. The onset of electro-nuclear spin density wave order enhances the superconductivity, which we account for by the simultaneous formation of a spin-triplet pair density wave. Together these observations provide compelling evidence for odd-parity superconductivity, and its underpinning by antiferromagnetism, and allow us to identify the topological helical state., Comment: 24 pages, 4 main and 15 supplementary figures, 1 supplementary table

The main objective of this paper is to study the relationship between a solvable evolution algebra and its subalgebra lattice, emphasizing two of its main properties: distributivity and modularity. First, we will focus on the nilpotent case, where distributivity is characterised, and a necessary condition for modularity is deduced. Subsequently, we comment on some results for solvable non-nilpotent evolution algebras, finding that the ones with maximum index of solvability have the best properties. Finally, we characterise modularity in this particular case by introducing supersolvable evolution algebras and computing the terms of the derived series., Comment: 20 pages

Bent monolithic active pixel sensors are the basis for the planned fully cylindrical ultra low material budget tracking detector ITS3 of the ALICE experiment. This paper presents results from testbeam campaigns using high-energy particles to verify the performance of 50 um thick bent ALPIDE chips in terms of efficiency and spatial resolution. The sensors were bent to radii of 18, 24 and 30 mm, slightly smaller than the foreseen bending radii of the future ALICE ITS3 layers. An efficiency larger than $99.9\%$ and a spatial resolution of approximately 5 um, in line with the nominal operation of flat ALPIDE sensors, is obtained at nominal operating conditions. These values are found to be independent of the bending radius and thus constitute an additional milestone in the demonstration of the feasibility of the planned ITS3 detector. In addition, a special geometry in which the beam particles graze the chip and traverse it laterally over distances of up to 3 mm is investigated.

Statistical tools are crucial for studying and modeling turbulent flows, where chaotic velocity fluctuations span a wide range of spatial and temporal scales. Advances in image velocimetry, especially in tracking-based methods, now allow for high-speed, high-density particle image processing, enabling the collection of detailed 3D flow fields. This lecture provides a set of tutorials on processing such datasets to extract essential quantities like averages, second-order moments (turbulent stresses) and coherent patterns using modal decompositions such as the Proper Orthogonal Decomposition (POD). After a brief review of the fundamentals of statistical and modal analysis, the lecture delves into the challenges of processing scattered data from tracking velocimetry, comparing it to traditional gridded-data approaches. It also covers research topics, including physics-based Radial Basis Function (RBF) regression for meshless computation of turbulent statistics and the definition of an RBF inner product, which enables meshless computation of data-driven decompositions. These include traditional methods like Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD), as well as advanced variants such as Spectral POD (SPOD) and Multiscale POD (mPOD). We refer to this approach as the "Meshless Data-Driven Decomposition" framework. All codes are available at https://github.com/mendezVKI/PIV_LS_2024_Signal, Comment: Book in preparation! Chapter from VKI Lecture Series "Particle Image Velocimetry 2024", held at the von Karman Institute, 2-5 December 2024

With the increasing ubiquity of safety-critical autonomous systems operating in uncertain environments, there is a need for mathematical methods for formal verification of stochastic models. Towards formally verifying properties of stochastic systems, methods based on discrete, finite Markov approximations -- abstractions -- thereof have surged in recent years. These are found in contexts where: either a) one only has partial, discrete observations of the underlying continuous stochastic process, or b) the original system is too complex to analyze, so one partitions the continuous state-space of the original system to construct a handleable, finite-state model thereof. In both cases, the abstraction is an approximation of the discrete stochastic process that arises precisely from the discretization of the underlying continuous process. The fact that the abstraction is Markov and the discrete process is not (even though the original one is) leads to approximation errors. Towards accounting for non-Markovianity, we introduce memory-dependent abstractions for stochastic systems, capturing dynamics with memory effects. Our contribution is twofold. First, we provide a formalism for memory-dependent abstractions based on transfer operators. Second, we quantify the approximation error by upper bounding the total variation distance between the true continuous state distribution and its discrete approximation., Comment: This pre-print was accepted for publication and presentation at the 2025 Hybrid Systems: Computation and Control conference (HSCC 2025). This version is not the camera-ready version of the paper