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The LIGO, Virgo and KAGRA gravitational wave observatories are currently undertaking their O4 observing run offering the opportunity to discover new electromagnetic counterparts to gravitational wave events. We examine the capability of the Neil Gehrels Swift Observatory (Swift) to respond to these triggers, primarily binary neutron star mergers, with both the UV/Optical Telescope (UVOT) and the X-ray Telescope (XRT). We simulate Swift's response to a trigger under different strategies using model skymaps, convolving these with the 2MPZ catalogue to produce an ordered list of observing fields, deriving the time taken for Swift to reach the correct field and simulating the instrumental responses to modelled kilonovae and short gamma-ray burst afterglows. We find that UVOT using the $u$ filter with an exposure time of order 120 s is optimal for most follow-up observations and that we are likely to detect counterparts in $\sim6$% of all binary neutron star triggers. We find that the gravitational wave 90% error area and measured distance to the trigger allow us to select optimal triggers to follow-up. Focussing on sources less than 300 Mpc away or 500 Mpc if the error area is less than a few hundred square degrees, distances greater than previously assumed, offer the best opportunity for discovery by Swift with $\sim5 - 30$% of triggers having detection probabilities $\geq 0.5$. At even greater distances, we can further optimise our follow-up by adopting a longer 250 s or 500 s exposure time., Comment: 16 pages, 13 figures. Submitted to MNRAS

OT 081 is a well-known, luminous blazar that is remarkably variable in many energy bands. We present the first broadband study of the source which includes very-high-energy (VHE, $E>$100\,GeV) $\gamma$-ray data taken by the MAGIC and H.E.S.S. imaging Cherenkov telescopes. The discovery of VHE $\gamma$-ray emission happened during a high state of $\gamma$-ray activity in July 2016, observed by many instruments from radio to VHE $\gamma$-rays. We identify four states of activity of the source, one of which includes VHE $\gamma$-ray emission. Variability in the VHE domain is found on daily timescales. The intrinsic VHE spectrum can be described by a power-law with index $3.27\pm0.44_{\rm stat}\pm0.15_{\rm sys}$ (MAGIC) and $3.39\pm0.58_{\rm stat}\pm0.64_{\rm sys}$ (H.E.S.S.) in the energy range of 55--300\,GeV and 120--500\,GeV, respectively. The broadband emission cannot be sucessfully reproduced by a simple one-zone synchrotron self-Compton model. Instead, an additional external Compton component is required. We test a lepto-hadronic model that reproduces the dataset well and a proton-synchrotron dominated model that requires an extreme proton luminosity. Emission models that are able to successfully represent the data place the emitting region well outside of the Broad Line Region (BLR) to a location at which the radiative environment is dominated by the infrared thermal radiation field of the dusty torus. In the scenario described by this flaring activity, the source appears to be an FSRQ, in contrast with past categorizations. This suggests that the source can be considered to be a transitional blazar, intermediate between BL~Lac and FSRQ objects., Comment: Accepted on MNRAS Corresponding authors: M. Manganaro, J. Becerra Gonz\'alez, M. Seglar-Arroyo, D. A. Sanchez

Given a compact Chern-Ricci flat balanced orbifold, we show that its blow-up at a finite family of smooth points admits constant Chern scalar curvature balanced metrics, extending Arezzo-Pacard's construction to the balanced setting. Moreover, if the orbifold has isolated singularities and admits crepant resolutions, we show that they always carry Chern-Ricci flat balanced metrics, without any further hypothesis. In addition, we discuss the general constant Chern scalar curvature balanced case and discuss another version of the main Theorem assuming the existence of a special (n-2, n-2)-form. We also present several classes of examples in which our results can be applied., Comment: 40 pages, comments are welcome!

Nonlocal, integral operators have become an efficient surrogate for bottom-up homogenization, due to their ability to represent long-range dependence and multiscale effects. However, the nonlocal homogenized model has unavoidable discrepancy from the microscale model. Such errors accumulate and propagate in long-term simulations, making the resultant prediction unreliable. To develop a robust and reliable bottom-up homogenization framework, we propose a new framework, which we coin Embedded Nonlocal Operator Regression (ENOR), to learn a nonlocal homogenized surrogate model and its structural model error. This framework provides discrepancy-adaptive uncertainty quantification for homogenized material response predictions in long-term simulations. The method is built on Nonlocal Operator Regression (NOR), an optimization-based nonlocal kernel learning approach, together with an embedded model error term in the trainable kernel. Then, Bayesian inference is employed to infer the model error term parameters together with the kernel parameters. To make the problem computationally feasible, we use a multilevel delayed acceptance Markov chain Monte Carlo (MLDA-MCMC) method, enabling efficient Bayesian model calibration and model error estimation. We apply this technique to predict long-term wave propagation in a heterogeneous one-dimensional bar, and compare its performance with additive noise models. Owing to its ability to capture model error, the learned ENOR achieves improved estimation of posterior predictive uncertainty.

The high electron temperature in galaxy clusters ($>1\,$keV or $>10^7\,$K) leads to corrections at the level of a few percent in their thermal Sunyaev-Zeldovich effect signatures. Both the size and frequency dependence of these corrections, which are known as relativistic temperature corrections, depend upon the temperature of the objects. In this work we exploit this effect to measure the average temperature of a stack of Compton-$y$ selected clusters. Specifically, we apply the "spectroscopic method" and search for the temperature that best fits the clusters' signal measured at frequencies from 30 to 545 GHz by the Atacama Cosmology Telescope and Planck satellite. We measure the average temperature of clusters detected in the ACT maps to be $8.5\pm 2.4\,$keV, with an additional systematic error of comparable amplitude dominated by passband uncertainty. Upcoming surveys, such as the Simons Observatory and CMB-S4, have the potential to dramatically improve upon these measurements and thereby enable precision studies of cluster temperatures with millimeter observations. The key challenge for future observations will be mitigating instrumental systematic effects, which already limit this analysis., Comment: 21 pages with 17 figures

This thesis presents an innovative approach to automate video thumbnail selection for traditional broadcast content. Our methodology establishes stringent criteria for diverse, representative, and aesthetically pleasing thumbnails, considering factors like logo placement space, incorporation of vertical aspect ratios, and accurate recognition of facial identities and emotions. We introduce a sophisticated multistage pipeline that can select candidate frames or generate novel images by blending video elements or using diffusion models. The pipeline incorporates state-of-the-art models for various tasks, including downsampling, redundancy reduction, automated cropping, face recognition, closed-eye and emotion detection, shot scale and aesthetic prediction, segmentation, matting, and harmonization. It also leverages large language models and visual transformers for semantic consistency. A GUI tool facilitates rapid navigation of the pipeline's output. To evaluate our method, we conducted comprehensive experiments. In a study of 69 videos, 53.6% of our proposed sets included thumbnails chosen by professional designers, with 73.9% containing similar images. A survey of 82 participants showed a 45.77% preference for our method, compared to 37.99% for manually chosen thumbnails and 16.36% for an alternative method. Professional designers reported a 3.57-fold increase in valid candidates compared to the alternative method, confirming that our approach meets established criteria. In conclusion, our findings affirm that the proposed method accelerates thumbnail creation while maintaining high-quality standards and fostering greater user engagement., Comment: 150 pages, 60 figures

We introduce a novel technique to numerically calculate R\'enyi entanglement entropies in lattice quantum field theory using generative models. We describe how flow-based approaches can be combined with the replica trick using a custom neural-network architecture around a lattice defect connecting two replicas. Numerical tests for the $\phi^4$ scalar field theory in two and three dimensions demonstrate that our technique outperforms state-of-the-art Monte Carlo calculations, and exhibit a promising scaling with the defect size., Comment: 10 pages, 9 figures

At frequencies below 1\,Hz, fluctuations in atmospheric emission in the Chajnantor region in northern Chile are the primary source of interference for bolometric millimeter-wave observations. This paper focuses on the statistics of these fluctuations using measurements from the Atacama Cosmology Telescope (ACT) and the Atacama Pathfinder Experiment (APEX) water vapor radiometer. After introducing a method for separating atmospheric effects from other systematic effects, we present a direct measurement of the temporal outer scale of turbulence of $\tau_0\approx50$s corresponding to a spatial scale of $L_0\approx500$m. At smaller scales, the fluctuations are well described by the Kolmogorov 2/3 power law until, at yet smaller scales, the effects of beam smearing become important. As a part of this study, we present measurements of the atmosphere by the APEX radiometer over 20 years, focused on fluctuations in precipitable water vapor (PWV). We find that the 30-minute mean of the total PWV is not in general a robust estimator of the level of fluctuations. We show that the microwave frequency spectrum of these fluctuations is in good agreement with predictions by the \texttt{am} code for bands above 90~GHz. We then show that the variance of fluctuations in ACT's mm-wave bands correlates with the variance of fluctuations in PWV measured by APEX, even though the observatories are 6\,km apart and observe different lines of sight. We find that ACT's atmosphere-determined optical efficiencies are consistent with previous planet-based results., Comment: 13 pages plus appendix, 17 figures. Submitted to PRD

Some WZW models on affine Lie superalgebras at critical level describe string theory on AdS backgrounds at critical values of the string tension. This is the case of $\mathfrak{psu}(1,1|2)_1$ for ${\rm AdS}_3 \times {\rm S}^3$ and potentially of $\mathfrak{u}(2|2)_1$ (or related algebras) for ${\rm AdS}_5 \times {\rm S}^5$. Many interesting features of these superalgebra models are already captured by their affine subalgebra $\mathfrak{su}(2)_{-1}$. In this paper we study the WZW model on $\mathfrak{su}(2)_{-1}$: we classify the representations, introduce a free field realisation, and decompose the free field modules in terms of $\mathfrak{su}(2)_{-1}$. We find continuous and discrete modular invariants and see that the latter naturally leads to considering superalgebra extensions of $\mathfrak{su}(2)_{-1}$. Lastly, we find an invariant for the free field theory of four symplectic bosons.

Time Division Multiplexing (TDM) of cryogenic signal lines is a promising technique that can significantly reduce the required space, minimize the cooldown time, and increase the number of measurable quantum devices per cooldown. Here, we report the TDM of supercurrent with a 1-input-8-outputs voltage-actuated hybrid superconducting demultiplexer for the first time. The device comprises 14 ON/OFF InAsOI-based superconducting Josephson Field Effect Transistors (JoFETs) routed with Al traces. Each JoFET features Al as a superconductor and HfO2 as a gate insulator, and it can entirely suppress the switching current and increase the normal-state resistance by 20 times with a gate voltage of -4.5 V. The superconducting demultiplexer operates up to 100 MHz at 50 mK, features an insertion loss of ~ 0 dB in the superconducting state, and an OFF/ON ratio of ~ 17.5 dB in a 50-Ohm-matched cryogenic measurement setup. The frequency operation range can be extended by designing the demultiplexer with a proper microwave signal transport layout minimizing, at the same time, the impact of the parasitic electrical elements. These achievements open up the practical implementation of superconducting TDM as a key to drastically reducing I/O lines, costs, and space occupation in a cryostat, enabling the scalability of superconducting electronics., Comment: 13 pages, 5 figures, supporting information at the end of the paper. arXiv admin note: text overlap with arXiv:2405.07630

The loop-Amplituhedron $\mathcal{A}^{(L)}_{n}$ is a semialgebraic set in the product of Grassmannians $\mathrm{Gr}_{\mathbb{R}}(2,4)^L$. Recently, many aspects of this geometry for the case of $L=1$ have been elucidated, such as its algebraic and face stratification, the residual arrangement and the existence and uniqueness of the adjoint. This paper extends this analysis to the simplest higher loop case given by the two-loop four-point Amplituhedron $\mathcal{A}^{(2)}_4$., Comment: 22 pages

Understanding the magnetised Universe is a major challenge in modern astrophysics, and cosmic magnetism has been acknowledged as one of the science key drivers of the most ambitious radio instrument ever planned, the SKA telescope. With this work, we aim to investigate the potential of the SKA telescope and its precursors and pathfinders in the study of magnetic fields in galaxy clusters and filaments through diffuse synchrotron radio emission. Galaxy clusters and filaments of the cosmic web are indeed unique laboratories to investigate turbulent fluid motions and large-scale magnetic fields in action and much of what is known about magnetic fields in galaxy clusters comes from sensitive radio observations. Based on cosmological MHD simulations, we predict radio properties (total intensity and polarisation) of a pair of galaxy clusters connected by a cosmic-web filament. We use our theoretical expectations to explore the potential of polarimetric observations to study large-scale structure magnetic fields in the frequency ranges 50-350MHz and 950-1760MHz. We also present predictions for galaxy cluster polarimetric observations with the SKA precursors and pathfinders (LOFAR2.0 and MeerKAT+). Our findings point out that polarisation observations are particularly powerful for the study of large-scale magnetic fields, since they are not significantly affected by confusion noise. The unprecedented sensitivity and spatial resolution of the intermediate frequency radio telescopes make them the favourite instruments for the study of these sources through polarimetric data, potentially allowing us to understand if the energy density of relativistic electrons is in equipartition with the magnetic field or coupled with the thermal gas density. Our results show that low frequency instruments represent as well a precious tool to study diffuse synchrotron emission in total intensity and polarisation., Comment: Accepted for publication on A&A, 12 pages

We give a method for effectively generating generalised loxodromics in subgroups of graph products, using positive words. This has several consequences for the growth of subsets of these groups. In particular, we show that graph products of groups with strong product set growth properties also share those properties. We additionally show that the set of growth rates of a class of subgroups of any graph product of equationally noetherian groups is well-ordered., Comment: 44 pages, 6 figures

We discuss pioneering heat and fluid flow simulations of fusion and fission energy systems with NekRS on exascale computing facilities, including Frontier and Aurora. The Argonne-based code, NekRS, is a highly-performant open-source code for the simulation of incompressible and low-Mach fluid flow, heat transfer, and combustion with a particular focus on turbulent flows in complex domains. It is based on rapidly convergent high-order spectral element discretizations that feature minimal numerical dissipation and dispersion. State-of-the-art multilevel preconditioners, efficient high-order time-splitting methods, and runtime-adaptive communication strategies are built on a fast OCCA-based kernel library, libParanumal, to provide scalability and portability across the spectrum of current and future high-performance computing platforms. On Frontier, Nek5000/RS has achieved an unprecedented milestone in breaching over 1 trillion degrees of freedom with the spectral element methods for the simulation of the CHIMERA fusion technology testing platform. We also demonstrate for the first time the use of high-order overset grids at scale., Comment: 10 pages, 3 figures, 3 tables

Non-equilibrium Monte Carlo simulations based on Jarzynski's equality are a well-understood method to compute differences in free energy and also to sample from a target probability distribution without the need to thermalize the system under study. In each evolution, the system starts from a given base distribution at equilibrium and it is gradually driven out-of-equilibrium while evolving towards the target parameters. If the target distribution suffers from long autocorrelation times, this approach represents a promising candidate to mitigate critical slowing down. Out-of-equilibrium evolutions are conceptually similar to Normalizing Flows and they can be combined into a recently-developed architecture called Stochastic Normalizing Flows (SNFs). In this contribution we first focus on the promising scaling with the volume guaranteed by the purely stochastic approach in the $\mathrm{SU}(3)$ lattice gauge theory in 4 dimensions; then, we define an SNF by introducing gauge-equivariant layers between the out-of-equilibrium Monte Carlo updates, and we analyse the improvement obtained as well as the inherited scaling with the volume. Finally, we discuss how this approach can be systematically improved and how simulations of lattice gauge theories in four dimensions for large volumes and close to criticality can be realistically achieved., Comment: 1+8 pages, 6 figures, contribution for the 41st International Symposium on Lattice Field Theory (Lattice 2024), 28 July - 3 August 2024, Liverpool, UK; v2: 1+9 pages, 6 figures, reference added

Instruments for gamma-ray astronomy at Very High Energies ($E>100\,{\rm GeV}$) have traditionally derived their scientific results through proprietary data and software. Data standardisation has become a prominent issue in this field both as a requirement for the dissemination of data from the next generation of gamma-ray observatories and as an effective solution to realise public data legacies of current-generation instruments. Specifications for a standardised gamma-ray data format have been proposed as a community effort and have already been successfully adopted by several instruments. We present the first production of standardised data from the Major Atmospheric Gamma-ray Imaging Cherenkov (MAGIC) telescopes. We converted $166\,{\rm h}$ of observations from different sources and validated their analysis with the open-source software Gammapy. We consider six data sets representing different scientific and technical analysis cases and compare the results obtained analysing the standardised data with open-source software against those produced with the MAGIC proprietary data and software. Aiming at a systematic production of MAGIC data in this standardised format, we also present the implementation of a database-driven pipeline automatically performing the MAGIC data reduction from the calibrated down to the standardised data level. In all the cases selected for the validation, we obtain results compatible with the MAGIC proprietary software, both for the manual and for the automatic data productions. Part of the validation data set is also made publicly available, thus representing the first large public release of MAGIC data. This effort and this first data release represent a technical milestone toward the realisation of a public MAGIC data legacy., Comment: Accepted for publication in the Journal of High Energy Astrophysics

The James Webb Space Telescope (JWST) has uncovered a new population of candidate broad-line AGN emerging in the early Universe, named ''little red dots'' (LRDs) because of their compactness and red colors at optical wavelengths. LRDs appear to be surprisingly abundant ($\approx 10^{-5}\,\mathrm{cMpc}^{-3}$) given that their inferred bolometric luminosities largely overlap with the ones of the UV-luminous quasars identified at high $z$ in wide-field spectroscopic surveys. In this work, we investigate how the population of LRDs and/or other UV-obscured AGN relates to the one of unobscured, UV-selected quasars. By comparing their number densities, we infer an extremely large and rapidly evolving obscured:unobscured ratio, ranging from $\approx20:1$ at $z\approx4$ to $\approx2300:1$ at $z\approx7$, and possibly extending out to very high ($\approx10^{47}\,\mathrm{erg}\,\mathrm{s}^{-1}$) bolometric luminosities. This large obscured:unobscured ratio is incompatible with the UV-luminous duty cycle measured for unobscured quasars at $z\approx4-6$, suggesting that LRDs are too abundant to be hosted by the same halos as unobscured quasars. This implies that either (a) the bolometric luminosities of LRDs are strongly overestimated (possibly because LRDs are dominated by stellar galaxy light) or (b) LRDs follow different scaling relations than the ones of UV-selected quasars, representing a new population of accreting SMBHs emerging in the early Universe. A direct comparison between the clustering of LRDs and the one of faint UV-selected quasars will ultimately confirm these findings, and shed light on key properties of LRDs such as their host mass distribution and duty cycle. We provide a mock analysis for the clustering of LRDs and show that it is feasible with current and upcoming JWST surveys., Comment: 15 pages. Submitted to MNRAS

Flow-based architectures have recently proved to be an efficient tool for numerical simulations of Effective String Theories regularized on the lattice that otherwise cannot be efficiently sampled by standard Monte Carlo methods. In this work we use Stochastic Normalizing Flows, a state-of-the-art deep-learning architecture based on non-equilibrium Monte Carlo simulations, to study different effective string models. After testing the reliability of this approach through a comparison with exact results for the Nambu-Got\={o} model, we discuss results on observables that are challenging to study analytically, such as the width of the string and the shape of the flux density. Furthermore, we perform a novel numerical study of Effective String Theories with terms beyond the Nambu-Got\={o} action, including a broader discussion on their significance for lattice gauge theories. These results establish the reliability and feasibility of flow-based samplers for Effective String Theories and pave the way for future applications on more complex models., Comment: 1+28 pages, 11 figures, 8 tables

Quantum Machine Learning (QML) is considered one of the most promising applications of Quantum Computing in the Noisy Intermediate Scale Quantum (NISQ) era for the impact it is thought to have in the near future. Although promising theoretical assumptions, the exploration of how QML could foster new discoveries in Medicine and Biology fields is still in its infancy with few examples. In this study, we aimed to assess whether Quantum Kernels (QK) could effectively classify subtypes of Breast Cancer (BC) patients on the basis of molecular characteristics. We performed an heuristic exploration of encoding configurations with different entanglement levels to determine a trade-off between kernel expressivity and performances. Our results show that QKs yield comparable clustering results with classical methods while using fewer data points, and are able to fit the data with a higher number of clusters. Additionally, we conducted the experiments on the Quantum Processing Unit (QPU) to evaluate the effect of noise on the outcome. We found that less expressive encodings showed a higher resilience to noise, indicating that the computational pipeline can be reliably implemented on the NISQ devices. Our findings suggest that QK methods show promises for application in Precision Oncology, especially in scenarios where the dataset is limited in size and a granular non-trivial stratification of complex molecular data cannot be achieved classically., Comment: 10 pages, 6 figures

Inter-area oscillations have been extensively studied in conventional power systems dominated by synchronous machines, as well as methods to mitigate them. Several publications have addressed Power Oscillation Damping (POD) controllers in grid-following voltage source converters (GFOL). However, the performance of POD controllers for Grid-Forming voltage source converters (GFOR) in modern power systems with increased penetration of power electronics requires further investigation. This paper investigates the performance of GFORs and supplementary POD controllers in the damping of electromechanical oscillations in modern power systems. This paper proposes POD controllers in GFORs by supplementary modulation of active- and reactive-power injections of the converter and both simultaneously (POD- P, POD-Q and POD-PQ, respectively). The proposed POD controllers use the frequency imposed by the GFOR as the input signal, which has a simple implementation and it eliminates the need for additional measurements. Eigenvalue-sensitivity methods using a synthetic test system are applied to the design of POD controllers in GFORs, which is useful when limited information of the power system is available. This paper demonstrates the effectiveness of POD controllers in GFOR converters to damp electromechanical oscillations, by small-signal stability analysis and non-linear time-domain simulations in a small test system and in a large-scale power system., Comment: 11 pages, 15 figures

We do experiments on physical agents with dynamic binary ideologies, deep memories of previous probes of neigboring agents, but fixed personalities that interpret the memory content to make ideological decisions. We find experimentally a critical memory depth below which complete ideological polarization of the collective cannot occur, and above which it is inevitable, an emergent symmetry breaking that is memory depth dependent. Depending on the details of the personalities, the polarization can be static or dynamic in time, even in certain cases chaotic due to nonreciprocity in how the agents respond to other agents. Thus, agents with different personalities and depths of memory serve as a physics analog of the ideology dynamics among biased individuals, illuminating how decisions influenced by individual memories of past interactions can shape and influence subsequent polarization. Perhaps such applications of physics-based systems to political systems will help us to understand the ideological instabilities observed today.

This study presents the design, simulation, and experimental characterization of a superconducting transmon qubit circuit prototype for potential applications in dark matter detection experiments. We describe a planar circuit design featuring two non-interacting transmon qubits, one with fixed frequency and the other flux tunable. Finite-element simulations were employed to extract key Hamiltonian parameters and optimize component geometries. The qubit was fabricated and then characterized at $20$ mK, allowing for a comparison between simulated and measured qubit parameters. Good agreement was found for transition frequencies and anharmonicities (within 1\% and 10\% respectively) while coupling strengths exhibited larger discrepancies (30\%). We discuss potential causes for measured coherence times falling below expectations ($T_1\sim\,$1-2 \textmu s) and propose strategies for future design improvements. Notably, we demonstrate the application of a hybrid 3D-2D simulation approach for energy participation ratio evaluation, yielding a more accurate estimation of dielectric losses. This work represents an important first step in developing planar Quantum Non-Demolition (QND) single-photon counters for dark matter searches, particularly for axion and dark photon detection schemes.

The Euler characteristic of a very affine variety encodes the algebraic complexity of solving likelihood (or scattering) equations on this variety. We study this quantity for the Grassmannian with $d$ hyperplane sections removed. We provide a combinatorial formula, and explain how to compute this Euler characteristic in practice, both symbolically and numerically. Our particular focus is on generic hyperplane sections and on Schubert divisors. We also consider special Schubert arrangements relevant for physics. We study both the complex and the real case., Comment: 20 pages

We study upward pointset embeddings (UPSEs) of planar $st$-graphs. Let $G$ be a planar $st$-graph and let $S \subset \mathbb{R}^2$ be a pointset with $|S|= |V(G)|$. An UPSE of $G$ on $S$ is an upward planar straight-line drawing of $G$ that maps the vertices of $G$ to the points of $S$. We consider both the problem of testing the existence of an UPSE of $G$ on $S$ (UPSE Testing) and the problem of enumerating all UPSEs of $G$ on $S$. We prove that UPSE Testing is NP-complete even for $st$-graphs that consist of a set of directed $st$-paths sharing only $s$ and $t$. On the other hand, for $n$-vertex planar $st$-graphs whose maximum $st$-cutset has size $k$, we prove that it is possible to solve UPSE Testing in $O(n^{4k})$ time with $O(n^{3k})$ space, and to enumerate all UPSEs of $G$ on $S$ with $O(n)$ worst-case delay, using $O(k n^{4k} \log n)$ space, after $O(k n^{4k} \log n)$ set-up time. Moreover, for an $n$-vertex $st$-graph whose underlying graph is a cycle, we provide a necessary and sufficient condition for the existence of an UPSE on a given poinset, which can be tested in $O(n \log n)$ time. Related to this result, we give an algorithm that, for a set $S$ of $n$ points, enumerates all the non-crossing monotone Hamiltonian cycles on $S$ with $O(n)$ worst-case delay, using $O(n^2)$ space, after $O(n^2)$ set-up time., Comment: This is the long version of a paper to appear at the 32nd International Symposium on Graph Drawing and Network Visualization (GD '24)

Recent progress in Text-to-Image (T2I) generative models has enabled high-quality image generation. As performance and accessibility increase, these models are gaining significant attraction and popularity: ensuring their fairness and safety is a priority to prevent the dissemination and perpetuation of biases. However, existing studies in bias detection focus on closed sets of predefined biases (e.g., gender, ethnicity). In this paper, we propose a general framework to identify, quantify, and explain biases in an open set setting, i.e. without requiring a predefined set. This pipeline leverages a Large Language Model (LLM) to propose biases starting from a set of captions. Next, these captions are used by the target generative model for generating a set of images. Finally, Vision Question Answering (VQA) is leveraged for bias evaluation. We show two variations of this framework: OpenBias and GradBias. OpenBias detects and quantifies biases, while GradBias determines the contribution of individual prompt words on biases. OpenBias effectively detects both well-known and novel biases related to people, objects, and animals and highly aligns with existing closed-set bias detection methods and human judgment. GradBias shows that neutral words can significantly influence biases and it outperforms several baselines, including state-of-the-art foundation models. Code available here: https://github.com/Moreno98/GradBias., Comment: Under review. Code: https://github.com/Moreno98/GradBias

Emergent language research has made significant progress in recent years, but still largely fails to explore how communication emerges in more complex and situated multi-agent systems. Existing setups often employ a reference game, which limits the range of language emergence phenomena that can be studied, as the game consists of a single, purely language-based interaction between the agents. In this paper, we address these limitations and explore the emergence and utility of token-based communication in open-ended multi-agent environments, where situated agents interact with the environment through movement and communication over multiple time-steps. Specifically, we introduce two novel cooperative environments: Multi-Agent Pong and Collectors. These environments are interesting because optimal performance requires the emergence of a communication protocol, but moderate success can be achieved without one. By employing various methods from explainable AI research, such as saliency maps, perturbation, and diagnostic classifiers, we are able to track and interpret the agents' language channel use over time. We find that the emerging communication is sparse, with the agents only generating meaningful messages and acting upon incoming messages in states where they cannot succeed without coordination., Comment: 10 pages, 4 figures, 4 tables, preprint

One of the main statistical features of near-neutral atmospheric boundary layer (ABL) turbulence is the positive vertical velocity skewness $Sk_w$ above the roughness sublayer or the buffer region in smooth-walls. The $Sk_w$ variations are receiving renewed interest in many climate-related parameterizations of the ABL given their significance to cloud formation and to testing sub-grid schemes for Large Eddy Simulations (LES). The vertical variations of $Sk_w$ are explored here using high Reynolds number wind tunnel and flume experiments collected above smooth, rough, and permeable-walls in the absence of buoyancy and Coriolis effects. These laboratory experiments form a necessary starting point to probe the canonical structure of $Sk_w$ as they deal with a key limiting case (i.e. near-neutral conditions) that has received much less attention compared to its convective counterpart in atmospheric turbulence studies. Diagnostic models based on cumulant expansions, realizability constraints, and the now-popular constant mass flux approach routinely employed in the convective boundary layer as well as prognostic models based on third-order budgets are used to explain variations in $Sk_w$ for the idealized laboratory conditions. The failure of flux-gradient relations to model $Sk_w$ from the gradients of the vertical velocity variance $\sigma_w^2$ are explained and corrections based on models of energy transport offered. Novel links between the diagnostic and prognostic models are also featured, especially for the inertial term in the third order budget of the vertical velocity fluctuation. The co-spectral properties of $w'/\sigma_w$ versus $w'^2/\sigma_w^2$ are also presented for the first time to assess the dominant scales governing $Sk_w$ in the inner and outer layers, where $w'$ is the fluctuating vertical velocity and $\sigma_w$ is the vertical velocity standard deviation.

Synthetic data is increasingly being used to address the lack of labeled images in uncommon domains for deep learning tasks. A prominent example is 2D pose estimation of animals, particularly wild species like zebras, for which collecting real-world data is complex and impractical. However, many approaches still require real images, consistency and style constraints, sophisticated animal models, and/or powerful pre-trained networks to bridge the syn-to-real gap. Moreover, they often assume that the animal can be reliably detected in images or videos, a hypothesis that often does not hold, e.g. in wildlife scenarios or aerial images. To solve this, we use synthetic data generated with a 3D photorealistic simulator to obtain the first synthetic dataset that can be used for both detection and 2D pose estimation of zebras without applying any of the aforementioned bridging strategies. Unlike previous works, we extensively train and benchmark our detection and 2D pose estimation models on multiple real-world and synthetic datasets using both pre-trained and non-pre-trained backbones. These experiments show how the models trained from scratch and only with synthetic data can consistently generalize to real-world images of zebras in both tasks. Moreover, we show it is possible to easily generalize those same models to 2D pose estimation of horses with a minimal amount of real-world images to account for the domain transfer. Code, results, trained models; and the synthetic, training, and validation data, including 104K manually labeled frames, are provided as open-source at https://zebrapose.is.tue.mpg.de/, Comment: 8 pages, 5 tables, 7 figures

We propose a new convex integration scheme in fluid mechanics, and we provide an application to the two-dimensional Euler equations. We prove the flexibility and nonuniqueness of $L^\infty L^2$ weak solutions with vorticity in $L^\infty L^p$ for some $p>1$, surpassing for the first time the critical scaling of the standard convex integration technique. To achieve this, we introduce several new ideas, including: (i) A new family of building blocks built from the Lamb-Chaplygin dipole. (ii) A new method to cancel the error based on time averages and non-periodic, spatially-anisotropic perturbations., Comment: 46 pages, 3 figures

We use data from the ALMA Evolutionary Study of High Mass Protocluster Formation in the Galaxy (ALMAGAL) survey to study 100 ALMAGAL regions at $\sim$ 1 arsecond resolution located between $\sim$ 2 and 6 kpc distance. Using ALMAGAL $\sim$ 1.3mm line and continuum data we estimate flow rates onto individual cores. We focus specifically on flow rates along filamentary structures associated with these cores. Our primary analysis is centered around position velocity cuts in H$_2$CO (3$_{0,3}$ - 2$_{0,2}$) which allow us to measure the velocity fields, surrounding these cores. Combining this work with column density estimates we derive the flow rates along the extended filamentary structures associated with cores in these regions. We select a sample of 100 ALMAGAL regions covering four evolutionary stages from quiescent to protostellar, Young Stellar Objects (YSOs), and HII regions (25 each). Using dendrogram and line analysis, we identify a final sample of 182 cores in 87 regions. In this paper, we present 728 flow rates for our sample (4 per core), analysed in the context of evolutionary stage, distance from the core, and core mass. On average, for the whole sample, we derive flow rates on the order of $\sim$10$^{-4}$ M$_{sun}$yr$^{-1}$ with estimated uncertainties of $\pm$50%. We see increasing differences in the values among evolutionary stages, most notably between the less evolved (quiescent/protostellar) and more evolved (YSO/HII region) sources. We also see an increasing trend as we move further away from the centre of these cores. We also find a clear relationship between the flow rates and core masses $\sim$M$^{2/3}$ which is in line with the result expected from the tidal-lobe accretion mechanism. Overall, we see increasing trends in the relationships between the flow rate and the three investigated parameters; evolutionary stage, distance from the core, and core mass., Comment: 11 pages, 11 figures, accepted for publication in A&A

Miniaturized cyber-physical systems (CPSes) powered by tiny machine learning (TinyML), such as nano-drones, are becoming an increasingly attractive technology. Their small form factor (i.e., ~10cm diameter) ensures vast applicability, ranging from the exploration of narrow disaster scenarios to safe human-robot interaction. Simple electronics make these CPSes inexpensive, but strongly limit the computational, memory, and sensing resources available on board. In real-world applications, these limitations are further exacerbated by domain shift. This fundamental machine learning problem implies that model perception performance drops when moving from the training domain to a different deployment one. To cope with and mitigate this general problem, we present a novel on-device fine-tuning approach that relies only on the limited ultra-low power resources available aboard nano-drones. Then, to overcome the lack of ground-truth training labels aboard our CPS, we also employ a self-supervised method based on ego-motion consistency. Albeit our work builds on top of a specific real-world vision-based human pose estimation task, it is widely applicable for many embedded TinyML use cases. Our 512-image on-device training procedure is fully deployed aboard an ultra-low power GWT GAP9 System-on-Chip and requires only 1MB of memory while consuming as low as 19mW or running in just 510ms (at 38mW). Finally, we demonstrate the benefits of our on-device learning approach by field-testing our closed-loop CPS, showing a reduction in horizontal position error of up to 26% vs. a non-fine-tuned state-of-the-art baseline. In the most challenging never-seen-before environment, our on-device learning procedure makes the difference between succeeding or failing the mission., Comment: This paper has been accepted for publication in the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. Copyright 2024 IEEE

Heat pipes can efficiently and passively remove heat in nuclear microreactors. Nevertheless, the flow dynamics within heat pipes present a significant challenge in designing and optimizing them for nuclear energy applications. This work aims to explore the vapor core of heat pipes through comprehensive two- and three-dimensional simulations, with a primary focus on modeling the pressure recovery observed in the condenser section. The goal is to establish improved correlations for one-dimensional heat pipe codes. The simulations are validated against experimental data from a vapor pipe documented in the literature. The turbulence model is employed in the two-dimensional simulations through the open-source spectral-element code Nek5000. This model provides insights into pressure recovery within heat pipes with low computational cost. In addition, Large Eddy Simulations (LES) are used to capture turbulent flow features in a three-dimensional vapor pipe model, utilizing the code NekRS. Using LES is crucial for comprehending the impact of laminar-to-turbulent transition on pressure recovery. A simulation framework is created to model the heat pipe's vapor core, laying the foundation for an enhanced understanding of heat pipe behavior. The ultimate goal is to improve and optimize heat pipe designs, provide data to validate lower-fidelity approaches and enhance their performance in nuclear reactors., Comment: 14th International Topical Meeting on Nuclear Reactor Thermal-Hydraulics, Operation and Safety (NUTHOS-14)

We consider a network of identical agents, coupled through linear asymmetric coupling. An important case is when each agent has an asymptotically stable periodic orbit, so that the full network inherits a synchronous periodic orbit, but also chaotic trajectories are of interest. In this work, we will restrict to "nearest-neighbor" type of couplings. The Master Stability Function (MSF) is a powerful tool to establish local stability of the synchronous orbit, in particular a negative MSF implies asymptotic stability. But not every network structure gives a negative MSF. Moreover, there are many situations where in order to obtain a negative MSF, symmetric networks need a coupling strength so large, that the model bears little physical interest. We make two main contributions: (i) Given a tridiagonal nearest neighbor topology, we show how it is possible to choose appropriate coupling so that the synchronous orbit is stable, and (ii) we show that this stability comes without the need of a large coupling strength if the structure is not symmetric. Our construction is based on solving inverse eigenvalue problems. We will see that the coupling of the agents cannot always be chosen to be symmetric so that the underlying graph structure is that of a directed graph with edges having different weights. We provide numerical implementation of our technique on networks of van der Pol and of chaotic R\"ossler oscillators, where the standard symmetric nearest neighbor coupling fails to give stability of the synchronous orbit., Comment: 14 pages, 4 figures

Biodiversity loss driven by agricultural intensification is a pressing global issue, with significant implications for ecosystem stability and human well-being. We design an integrated bio-economic agent-based model, informed by historical data from the French agricultural sector, to project future biodiversity trends and evaluate policy interventions. Our model predicts further biodiversity decline under a business-as-usual scenario, primarily due to intensified land consolidation. We evaluate two policy options: reducing pesticide use and subsidizing small farmers. While pesticide reduction rapidly benefits biodiversity in the beginning, it eventually leads to increased land consolidation and further biodiversity loss. In contrast, subsidizing small farmers by reallocating a small fraction of existing subsidies, stabilizes farm sizes and enhances biodiversity in the long run. The most effective strategy results from combining both policies, leveraging pesticide reduction alongside targeted subsidies to balance economic pressures and consistently improve biodiversity., Comment: 17 pages, 5 figures, 1 table

In the context of the CERN EP R&D on monolithic sensors and the ALICE ITS3 upgrade, the Tower Partners Semiconductor Co (TPSCo) 65 nm process has been qualified for use in high energy physics, and adopted for the ALICE ITS3 upgrade. An Analog Pixel Test Structure (APTS) featuring fast per pixel operational-amplifier-based buffering for a small matrix of four by four pixels, with a sensor with a small collection electrode and a very non-uniform electric field, was designed to allow detailed characterization of the pixel performance in this technology. Several variants of this chip with different pixel designs have been characterized with a (120 GeV/$c$) positive hadron beam. This result indicates that the APTS-OA prototype variants with the best performance achieve a time resolution of 63 ps with a detection efficiency exceeding 99% and a spatial resolution of 2 $\mu$m, highlighting the potential of TPSCo 65nm CMOS imaging technology for high-energy physics and other fields requiring precise time measurement, high detection efficiency, and excellent spatial resolution.

When objects are packed in a cluster, physical interactions are unavoidable. Such interactions emerge because of the objects geometric features; some of these features promote entanglement, while others create repulsion. When entanglement occurs, the cluster exhibits a global, complex behaviour, which arises from the stochastic interactions between objects. We hereby refer to such a cluster as an entangled granular metamaterial. We investigate the geometrical features of the objects which make up the cluster, henceforth referred to as grains, that maximise entanglement. We hypothesise that a cluster composed from grains with high propensity to tangle, will also show propensity to interact with a second cluster of tangled objects. To demonstrate this, we use the entangled granular metamaterials to perform complex robotic picking tasks, where conventional grippers struggle. We employ an electromagnet to attract the metamaterial (ferromagnetic) and drop it onto a second cluster of objects (targets, non-ferromagnetic). When the electromagnet is re-activated, the entanglement ensures that both the metamaterial and the targets are picked, with varying degrees of physical engagement that strongly depend on geometric features. Interestingly, although the metamaterials structural arrangement is random, it creates repeatable and consistent interactions with a second tangled media, enabling robust picking of the latter.

Low-luminosity AGNs with low-mass black holes (BHs) in the early universe are fundamental to understanding the BH growth and their co-evolution with the host galaxies. Utilizing JWST NIRCam Wide Field Slitless Spectroscopy (WFSS), we perform a systematic search for broad-line ${\rm H\alpha}$ emitters (BHAEs) at $z\approx 4-5$ in 25 fields of the ASPIRE (A SPectroscopic survey of biased halos In the Reionization Era) project, covering a total area of 275 arcmin$^2$. We identify 16 BHAEs with FWHM of the broad components spanning from $\sim$ 1000 km s$^{-1}$ to 3000 km s$^{-1}$. Assuming the broad linewidths arise due to Doppler broadening around BHs, the implied BH masses range from $10^7$ to $10^{8}~M_\odot$, with broad ${\rm H\alpha}$-converted bolometric luminosity of $10^{44.5}-10^{45.5}$ erg s$^{-1}$ and Eddington ratios of $0.07-0.47$. The spatially extended structure of the F200W stacked image may trace the stellar light from the host galaxies. The ${\rm H\alpha}$ luminosity function indicates an increasing AGN fraction towards the higher ${\rm H\alpha}$ luminosities. We find possible evidence for clustering of BHAEs: two sources are at the same redshift with a projected separation of 519 kpc; one BHAE appears as a composite system residing in an overdense region with three close companion ${\rm H\alpha}$ emitters. Three BHAEs exhibit blueshifted absorption troughs indicative of the presence of high-column-density gas. We find the broad-line and photometrically selected BHAE samples exhibit different distributions in the optical continuum slopes, which can be attributed to their different selection methods. The ASPIRE broad-line ${\rm H\alpha}$ sample provides a good database for future studies of faint AGN populations at high redshift., Comment: 19 pages, 13 figures, 4 tables. Accepted by the ApJ

Phenalenyl (C$_{13}$H$_9$) is an open-shell spin-$1/2$ nanographene. Using scanning tunneling microscopy (STM) inelastic electron tunneling spectroscopy (IETS), covalently-bonded phenalenyl dimers have been shown to feature conductance steps associated with singlet-triplet excitations of a spin-$1/2$ dimer with antiferromagnetic exchange. Here, we address the possibility of tuning the magnitude of the exchange interactions by varying the dihedral angle between the two molecules within a dimer. Theoretical methods, ranging from density functional theory calculations to many-body model Hamiltonians solved within different levels of approximation, are used to explain STM-IETS measurements of twisted phenalenyl dimers on a h-BN/Rh(111) surface. By means of first-principles calculations, we also propose strategies to induce sizable twist angles in surface-adsorbed phenalenyl dimers via functional groups, including a photoswitchable scheme. This work paves the way toward tuning magnetic couplings in carbon-based spin chains and two-dimensional lattices.

We present results from a search for X-ray/gamma-ray counterparts of gravitational-wave (GW) candidates from the third observing run (O3) of the LIGO-Virgo-KAGRA (LVK) network using the Swift Burst Alert Telescope (Swift-BAT). The search includes 636 GW candidates received in low latency, 86 of which have been confirmed by the offline analysis and included in the third cumulative Gravitational-Wave Transient Catalogs (GWTC-3). Targeted searches were carried out on the entire GW sample using the maximum--likelihood NITRATES pipeline on the BAT data made available via the GUANO infrastructure. We do not detect any significant electromagnetic emission that is temporally and spatially coincident with any of the GW candidates. We report flux upper limits in the 15-350 keV band as a function of sky position for all the catalog candidates. For GW candidates where the Swift-BAT false alarm rate is less than 10$^{-3}$ Hz, we compute the GW--BAT joint false alarm rate. Finally, the derived Swift-BAT upper limits are used to infer constraints on the putative electromagnetic emission associated with binary black hole mergers., Comment: 50 pages, 10 figures, 4 tables

We present a review of popular simulation engines and frameworks used in reinforcement learning (RL) research, aiming to guide researchers in selecting tools for creating simulated physical environments for RL and training setups. It evaluates nine frameworks (Brax, Chrono, Gazebo, MuJoCo, ODE, PhysX, PyBullet, Webots, and Unity) based on their popularity, feature range, quality, usability, and RL capabilities. We highlight the challenges in selecting and utilizing physics engines for RL research, including the need for detailed comparisons and an understanding of each framework's capabilities. Key findings indicate MuJoCo as the leading framework due to its performance and flexibility, despite usability challenges. Unity is noted for its ease of use but lacks scalability and simulation fidelity. The study calls for further development to improve simulation engines' usability and performance and stresses the importance of transparency and reproducibility in RL research. This review contributes to the RL community by offering insights into the selection process for simulation engines, facilitating informed decision-making., Comment: 11 pages, 3 figures

Audio-language models (ALMs) process sounds to provide a linguistic description of sound-producing events and scenes. Recent advances in computing power and dataset creation have led to significant progress in this domain. This paper surveys existing datasets used for training audio-language models, emphasizing the recent trend towards using large, diverse datasets to enhance model performance. Key sources of these datasets include the Freesound platform and AudioSet that have contributed to the field's rapid growth. Although prior surveys primarily address techniques and training details, this survey categorizes and evaluates a wide array of datasets, addressing their origins, characteristics, and use cases. It also performs a data leak analysis to ensure dataset integrity and mitigate bias between datasets. This survey was conducted by analyzing research papers up to and including December 2023, and does not contain any papers after that period.

We provide empirical evidence that within the inertial sub layer of adiabatic turbulent flows over smooth walls, the skewness of the vertical velocity component $Sk_w$ displays universal behaviour, being constant and constrained within the range $Sk_w \approx 0.1-0.16$, regardless of flow configuration and Reynolds number. A theoretical model is proposed to explain the observed behaviour, including the observed range of variations of $Sk_w$. The model clarifies why $Sk_w$ cannot be predicted from down-gradient closure approximations routinely employed in meteorological and climate models whereby $Sk_w$ impacts cloud formation and dispersion processes. The model also offers an alternative and implementable approach.

We prove a generalization of the Kolmogorov-Barzdin theorem for maps from simplicial complexes into Euclidean space. Along the way we introduce the notion of sparse maps and discuss maps from simplicial complexes with controlled 1-waist.

We consider the affine-additive group as a metric measure space with a canonical left-invariant measure and a left-invariant sub-Riemannian metric. We prove that this metric measure space is locally 4-Ahlfors regular and it is hyperbolic, meaning that it has a non-vanishing 4-capacity at infinity. This implies that the affine-additive group is not quasiconformally equivalent to the Heisenberg group or to the roto-translation group in contrast to the fact that both of these groups are globally contactomorphic to the affine-additive group. Moreover, each quasiregular map, from the Heisenberg group to the affine-additive group must be constant., Comment: 17 pages

Decentralized learning (DL) offers a powerful framework where nodes collaboratively train models without sharing raw data and without the coordination of a central server. In the iterative rounds of DL, models are trained locally, shared with neighbors in the topology, and aggregated with other models received from neighbors. Sharing and merging models contribute to convergence towards a consensus model that generalizes better across the collective data captured at training time. In addition, the energy consumption while sharing and merging model parameters is negligible compared to the energy spent during the training phase. Leveraging this fact, we present SkipTrain, a novel DL algorithm, which minimizes energy consumption in decentralized learning by strategically skipping some training rounds and substituting them with synchronization rounds. These training-silent periods, besides saving energy, also allow models to better mix and finally produce models with superior accuracy than typical DL algorithms that train at every round. Our empirical evaluations with 256 nodes demonstrate that SkipTrain reduces energy consumption by 50% and increases model accuracy by up to 12% compared to D-PSGD, the conventional DL algorithm.