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Rotation-powered pulsars represent the main class of identified gamma-ray sources in the Galaxy. The wealth of observational data collected by the AGILE and Fermi gamma-ray space telescopes in the GeV range, and by ground-based Cherenkov telescopes in the TeV band provide invaluable insights into how relativistic plasmas dissipate and accelerate particles. Decoding the information contained in the gamma-ray pulses profile is an important step to understand how pulsars work. In this study, we aim at putting an ab initio plasma model of pulsar magnetospheres to the test, in light of the most recent gamma-ray observations in the GeV and TeV bands. To this end, we present of a new series of global particle-in-cell simulations of an inclined pulsar magnetosphere. High-quality synthetic pulse profiles in the synchrotron and inverse Compton channels are reconstructed to study in greater details their morphology and their energy dependence. We also perform a fit of observed lightcurves with the model, using the third Fermi-LAT gamma-ray pulsar catalog. Reconnection in the wind current sheet powers synchrotron and inverse Compton emission. The modeled pulse profiles reproduce some of the salient features of observed gamma-ray pulsars, including the mysterious Vela-like lightcurves, such as: the generic double-peaked structure, the presence of a bridge or third peak in between the main pulses, the pulse narrowing with increasing energy. The bolometric synchrotron radiative efficiency is strictly limited by the reconnection rate. Our global kinetic simulations are able to match observed pulse profiles. Such direct comparisons will help drive and focus future simulation developments., Comment: 16 pages, 13 figures, 1 appendix, submitted to Astronomy and Astrophysics

Heterostructures made from 2D transition-metal dichalcogenides are known as ideal platforms to explore excitonic phenomena ranging from correlated moir\'e excitons to degenerate interlayer exciton ensembles. So far, it is assumed that the atomic reconstruction appearing in some of the heterostructures gives rise to a dominating localization of the exciton states. We demonstrate that excitonic states in reconstructed MoSe$_2$/WSe$_2$ heterostructures can extend well beyond the moir\'e periodicity of the investigated heterostructures. The results are based on real-space calculations yielding a lateral potential map for interlayer excitons within the strain-relaxed heterostructures and corresponding real-space excitonic wavefunctions. We combine the theoretical results with cryogenic photoluminescence experiments, which support the computed level structure and relaxation characteristics of the interlayer excitons.

Coral reefs are crucial to marine biodiversity and rely on a delicate symbiotic relationship between corals and zooxanthellae algae. Water temperature variations, however, disrupt this association, leading to coral bleaching events that severely affect marine ecosystems. This study presents a mathematical model for the population dynamics of coral and symbiont species considering the coral symbiont network and recurrent warming events. The model incorporates thermal tolerances of species and coupled growth dynamics (between corals and symbionts) to investigate how network structure and thermal tolerance influence the species' growth. Using real data from different ocean regions, results reveal that network connectivity plays a significant role in population growth after successive warming events, with generalist species demonstrating greater growth across all regions analyzed. The comparatively higher correlation between node degree and final population also emphasizes the impact of ecological network structure on species growth, offering valuable insights into coral reef population dynamics under climate change. This research highlights the need to consider network structure beyond species' thermal tolerances when evaluating the ecological responses of corals to environmental changes., Comment: 13 pages, 6 figures

Chernyshev, Rauch, and Rautenbach proved that every connected graph on $n$ vertices with less than $\frac{11}{5}n-\frac{18}{5}$ edges has a vertex cut that induces a forest, and conjectured that the same remains true if the graph has less than $3n-6$ edges. We improve their result by proving that every connected graph on $n$ vertices with less than $\frac{9}{4}n$ edges has a vertex cut that induces a forest. We also study weaker versions of the problem that might lead to an improvement on the bound obtained.

Long non-coding RNAs (lncRNAs) serve as crucial regulators in numerous biological processes. Although they share sequence similarities with messenger RNAs (mRNAs), lncRNAs perform entirely different roles, providing new avenues for biological research. The emergence of next-generation sequencing technologies has greatly advanced the detection and identification of lncRNA transcripts and deep learning-based approaches have been introduced to classify long non-coding RNAs (lncRNAs). These advanced methods have significantly enhanced the efficiency of identifying lncRNAs. However, many of these methods are devoid of robustness and accuracy due to the extended length of the sequences involved. To tackle this issue, we have introduced a novel pre-trained bidirectional encoder representation called LoRA-BERT. LoRA-BERT is designed to capture the importance of nucleotide-level information during sequence classification, leading to more robust and satisfactory outcomes. In a comprehensive comparison with commonly used sequence prediction tools, we have demonstrated that LoRA-BERT outperforms them in terms of accuracy and efficiency. Our results indicate that, when utilizing the transformer model, LoRA-BERT achieves state-of-the-art performance in predicting both lncRNAs and mRNAs for human and mouse species. Through the utilization of LoRA-BERT, we acquire valuable insights into the traits of lncRNAs and mRNAs, offering the potential to aid in the comprehension and detection of diseases linked to lncRNAs in humans.

Music digitalization has introduced new forms of composition known as "musical borrowings", where composers use elements of existing songs -- such as melodies, lyrics, or beats -- to create new songs. Using Who Sampled data and Google Trends, we examine how the popularity of a borrowing song affects the original. Employing Regression Discontinuity Design (RDD) for short-term effects and Granger Causality for long-term impacts, we find evidence of causal popularity boosts in some cases. Borrowee songs can revive interest in older tracks, underscoring economic dynamics that may support fairer compensation in the music industry., Comment: To appear in 1st Latin American Music Information Retrieval Workshop

With over a billion active users, TikTok's video-sharing service is currently one of the largest social media websites. This rise in TikTok's popularity has made the website a central platform for music discovery. In this paper, we analyze how TikTok helps to revitalize older songs. To do so, we use both the popularity of songs shared on TikTok and how the platform allows songs to propagate to other places on the Web. We analyze data from TokBoard, a website measuring such popularity over time, and Google Trends, which captures songs' overall Web search interest. Our analysis initially focuses on whether TokBoard can cause (Granger Causality) popularity on Google Trends. Next, we examine whether TikTok and Google Trends share the same virality patterns (via a Bass Model). To our knowledge, we are one of the first works to study song re-popularization via TikTok., Comment: To appear in the 1st Latin American Music Information Retrieval Workshop

Deep learning (DL) models in medical imaging face challenges in generalizability and robustness due to variations in image acquisition parameters (IAP). In this work, we introduce a novel method using conditional denoising diffusion generative models (cDDGMs) to generate counterfactual magnetic resonance (MR) images that simulate different IAP without altering patient anatomy. We demonstrate that using these counterfactual images for data augmentation can improve segmentation accuracy, particularly in out-of-distribution settings, enhancing the overall generalizability and robustness of DL models across diverse imaging conditions. Our approach shows promise in addressing domain and covariate shifts in medical imaging. The code is publicly available at https: //github.com/pedromorao/Counterfactual-MRI-Data-Augmentation

Relativistic magnetic reconnection is one of the most fundamental mechanisms considered responsible for the acceleration of relativistic particles in astrophysical jets and magnetospheres of compact objects. Understanding the properties of the dissipation of magnetic fields and the formation of non-ideal electric fields is of paramount importance to quantify the efficiency of reconnection at energizing charged particles. Recent results from particle-in-cell (PIC) simulations suggest that the fundamental properties of how magnetic fields dissipate in a current sheet might be captured by an "effective resistivity" formulation, which would locally enhance the amount of magnetic energy dissipated and favor the onset of fast reconnection. Our goal is to assess this ansatz quantitatively by comparing fluid models of magnetic reconnection with a non-constant magnetic diffusivity and fully-kinetic models. We perform 2D resistive relativistic magnetohydrodynamic (ResRMHD) simulations of magnetic reconnection combined to PIC simulations using the same initial conditions (namely a Harris current sheet). We explore the impact of crucial parameters such as the plasma magnetization, its mass density, the grid resolution, and the characteristic plasma skin depth. Our ResRMHD models with effective resistivity can quantitatively reproduce the dynamics of fully-kinetic models of relativistic magnetic reconnection. In particular, they lead to reconnection rates consistent with PIC simulations, while for constant-resistivity fluid models the reconnection dynamics is generally 10 times slower. Even at modest resolutions the adoption of an effective resistivity can qualitatively capture the properties of kinetic reconnection models and produce reconnection rates compatible with collisionless models, i.e. of the order of $\sim10^{-1}$., Comment: 17 pages, 17 figures; accepted for publication on A&A

Analyzing musical influence networks, such as those formed by artist influence or sampling, has provided valuable insights into contemporary Western music. Here, computational methods like centrality rankings help identify influential artists. However, little attention has been given to how influence changes over time. In this paper, we apply Bayesian Surprise to track the evolution of musical influence networks. Using two networks -- one of artist influence and another of covers, remixes, and samples -- our results reveal significant periods of change in network structure. Additionally, we demonstrate that Bayesian Surprise is a flexible framework for testing various hypotheses on network evolution with real-world data., Comment: To appear in the Latin American Musical Information Retrieval Workshop

In recent years, deep learning has achieved formidable results in creative computing. When it comes to music, one viable model for music generation are Transformer based models. However, while transformers models are popular for music generation, they often rely on annotated structural information. In this work, we inquire if the off-the-shelf Music Transformer models perform just as well on structural similarity metrics using only unannotated MIDI information. We show that a slight tweak to the most common representation yields small but significant improvements. We also advocate that searching for better unannotated musical representations is more cost-effective than producing large amounts of curated and annotated data., Comment: Presented at the Music for Machine Learning Workshop with ECML/PKDD. To be published by Springer

A rigorous treatment of light-matter interactions typically requires an interacting quantum field theory. However, many practical results are often derived using classical or semiclassical approximations, which are valid only when quantum-field fluctuations can be neglected. This approximation breaks down in scenarios involving large light intensities or degenerate matter, where additional quantum effects become significant. In this work, we address these limitations by developing a quantum kinetic framework that treats both light and matter fields on equal footing, naturally incorporating both linear and nonlinear interactions. To accurately account for light fluctuations, we introduce a photon distribution function that, together with the classical electromagnetic fields, provides a better description of the photon fluid. From this formalism, we derive kinetic equations from first principles that recover classical electrodynamical results while revealing novel couplings absent in classical theory. Furthermore, by addressing the Coulomb interaction in the Hartree-Fock approximation, we include the role of fermionic exchange exactly in both the kinetic and fluid regimes through a generalized Fock potential. The latter provides corrections not only to the electrostatic forces but also to the plasma velocity field, which are important in degenerate conditions., Comment: Quantum kinetic theory, light matter interaction, quantum degeneracy, warm dense matter, laser-plasma interaction

A vishing attack is a form of social engineering where attackers use phone calls to deceive individuals into disclosing sensitive information, such as personal data, financial information, or security credentials. Attackers exploit the perceived urgency and authenticity of voice communication to manipulate victims, often posing as legitimate entities like banks or tech support. Vishing is a particularly serious threat as it bypasses security controls designed to protect information. In this work, we study the potential for vishing attacks to escalate with the advent of AI. In theory, AI-powered software bots may have the ability to automate these attacks by initiating conversations with potential victims via phone calls and deceiving them into disclosing sensitive information. To validate this thesis, we introduce ViKing, an AI-powered vishing system developed using publicly available AI technology. It relies on a Large Language Model (LLM) as its core cognitive processor to steer conversations with victims, complemented by a pipeline of speech-to-text and text-to-speech modules that facilitate audio-text conversion in phone calls. Through a controlled social experiment involving 240 participants, we discovered that ViKing has successfully persuaded many participants to reveal sensitive information, even those who had been explicitly warned about the risk of vishing campaigns. Interactions with ViKing's bots were generally considered realistic. From these findings, we conclude that tools like ViKing may already be accessible to potential malicious actors, while also serving as an invaluable resource for cyber awareness programs.

Relativistic magnetic reconnection studies have focused on symmetric configurations so far, where the upstream plasma has identical properties on each side of the layer. The boundary layer between a relativistic jet and an accretion flow forming around a supermassive black hole may present an asymmetric configuration in terms of plasma composition, bulk velocity, temperature and magnetization. In this work, we aim to conduct the first study of relativistic magnetic reconnection where the upstream plasma is composed of electron-positron pairs on one side, and electrons and ions on the other. We also investigate the role of a relativistic symmetric shear flow applied along the reconnecting field lines. We simulate magnetic reconnection using two-dimensional particle-in-cell simulations. The initial setup is adapted from a classic Harris layer without guide field, modified to accommodate plasma-composition and shear asymmetries in the upstream medium. For a composition-asymmetric setup, we find that the reconnection dynamics is driven by the electron-ion side, which is the plasma with the lowest magnetization. The energy partition favors accelerating ions at the expense of electrons even more than in a corresponding symmetric setup. With respect to shear, a super-Alfv\'enic upstream decreases the laboratory-frame reconnection rate, but, unlike in non-relativistic studies, does not shut off reconnection completely. The asymmetries examined in this work diminish the overall efficiency of electron acceleration relative to corresponding symmetric configurations. In the context of a black hole jet-disk boundary, asymmetric reconnection alone is probably not efficient at accelerating electrons to very high energies, but it might facilitate plasma mixing and particle injection for other acceleration channels at the interface., Comment: 10 pages, 9 figures, accepted for publication in Astronomy & Astrophysics

Many natural phenomena are quantified by counts of observable events, from the annihilation of quasiparticles in a lattice to predator-prey encounters on a landscape to spikes in a neural network. These events are triggered at random intervals when an underlying dynamical system occupies a set of reactive states in its phase space. We derive a general expression for the distribution of times between events in such counting processes assuming the underlying triggering dynamics is a stochastic process that converges to a stationary distribution. Our results contribute to resolving a long-standing dichotomy in the study of reaction-diffusion processes, showing the inter-reaction point process interpolates between a reaction- and a diffusion-limited regime. At low reaction rates, the inter-reaction process is Poisson with a rate depending on stationary properties of the event-triggering stochastic process. At high reaction rates, inter-reaction times are dominated by the hitting times to the reactive states. To further illustrate the power of this approach we apply our framework to obtain the counting statistics of two counting processes appearing in several biophysical scenarios. First, we study the common situation of estimating an animal's activity level by how often it crosses a detector, showing that the mean number of crossing events can decrease monotonically with the hitting rate, a seemingly 'paradoxical' result that could possibly lead to misinterpretation of experimental count data. Second, we derive the ensemble statistics for the detection of many particles, recovering and generalizing known results in the biophysics of chemosensation. Overall, we develop a unifying theoretical framework to quantify inter-event time distributions in reaction-diffusion systems that clarifies existing debates in the literature and provide examples of application to real-world scenarios., Comment: 14 pages, 3 figures

It has been a long-standing challenge to define a canonical loop integrand for non-supersymmetric gluon scattering amplitudes in the planar limit. Naive integrands are inflicted with $1/0$ ambiguities associated with tadpoles and massless external bubbles, which destroy integrand-level gauge invariance as well as consistent on-shell factorization on single loop-cuts. In this letter, we show that this essentially kinematical obstruction to defining "the" integrand for Yang-Mills theory has a structural solution, handed to us by the formulation of gluon amplitudes in terms of curves on surfaces. This defines "surface kinematics" generalizing momenta, making it possible to define "the" integrand satisfying both a (surface generalized) notion of gauge-invariance and consistent loop-cuts. The integrand also vanishes at infinity in appropriate directions, allowing it to be recursively computed for non-supersymmetric Yang-Mills theory in any number of dimensions. We illustrate these ideas through one loop for all multiplicity, and for the simplest two-loop integrand., Comment: 13 pages, 4 figures, explicit expressions at tree-level and one-loop given in supplementary materials

The article deals with finite groups in which commutators have prime power order (CPPO-groups). We show that if G is a soluble CPPO-group, then the order of the commutator subgroup G' is divisible by at most two primes.

The Southern Photometric Local Universe Survey (S-PLUS) is a project to map $\sim9300$ sq deg of the sky using twelve bands (seven narrow and five broadbands). Observations are performed with the T80-South telescope, a robotic telescope located at the Cerro Tololo Observatory in Chile. The survey footprint consists of several large contiguous areas, including fields at high and low galactic latitudes, and towards the Magellanic Clouds. S-PLUS uses fixed exposure times to reach point source depths of about $21$ mag in the $griz$ and $20$ mag in the $u$ and the narrow filters. This paper describes the S-PLUS Data Release 4 (DR4), which includes calibrated images and derived catalogues for over 3000 sq deg, covering the aforementioned area. The catalogues provide multi-band photometry performed with the tools \texttt{DoPHOT} and \texttt{SExtractor} -- point spread function (\PSF) and aperture photometry, respectively. In addition to the characterization, we also present the scientific potential of the data. We use statistical tools to present and compare the photometry obtained through different methods. Overall we find good agreement between the different methods, with a slight systematic offset of 0.05\,mag between our \PSF and aperture photometry. We show that the astrometry accuracy is equivalent to that obtained in previous S-PLUS data releases, even in very crowded fields where photometric extraction is challenging. The depths of main survey (MS) photometry for a minimum signal-to-noise ratio $S/N = 3$ reach from $\sim19.5$ for the bluer bands to $\sim21.5$ mag on the red. The range of magnitudes over which accurate \PSF photometry is obtained is shallower, reaching $\sim19$ to $\sim20.5$ mag depending on the filter. Based on these photometric data, we provide star-galaxy-quasar classification and photometric redshift for millions of objects., Comment: 26 pages, 17 figures, 14 tables, accepted for A&A

Software architecture often consists of interconnected components dispersed across source code and other development artifacts, making visualization difficult without costly additional documentation. Although some tools can automatically generate architectural diagrams, these hardly fully reflect the architecture of the system. We propose the value of automatic architecture recovery from multiple software artifacts, combined with the ability to manually adjust recovered models and automate the recovery process. We present a general approach to achieve this and describe a tool that generates architectural diagrams for a software system by analyzing its software artifacts and unifying them into a comprehensive system representation. This representation can be manually modified while ensuring that changes are reintegrated into the diagram when it is regenerated. We argue that adopting a similar approach in other types of documentation tools is possible and can render similar benefits.

We discuss the constraints and phenomenology of the $Z_2 \times Z_2$ three Higgs doublet model (3HDM) with two inert scalars, originating two dark matter (DM) particles. We elucidate the competing vacua and we submit the model to all theoretical and current experimental constraints. We find unexplored regions of parameter space and investigate their experimental signatures. In particular, we find regions where the two DM particles contribute equally to the relic DM density., Comment: 24 pages, 17 figures, revtex

Point cloud models are a common shape representation for several reasons. Three-dimensional scanning devices are widely used nowadays and points are an attractive primitive for rendering complex geometry. Nevertheless, there is not much literature on collision detection for point cloud models. This paper presents a novel collision detection algorithm for large point cloud models using voxels, octrees and bounding spheres hierarchies (BSH). The scene graph is divided in voxels. The objects of each voxel are organized into an octree. Due to the high number of points in the scene, each non-empty cell of the octree is organized in a bounding sphere hierarchy, based on an R-tree hierarchy like structure. The BSH hierarchies are used to group neighboring points and filter out very quickly parts of objects that do not interact with other models. Points derived from laser scanned data typically are not segmented and can have arbitrary spatial resolution thus introducing computational and modeling issues. We address these issues and our results show that the proposed collision detection algorithm effectively finds intersections between point cloud models since it is able to reduce the number of bounding volume checks and updates.

A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated., Comment: Prepared for submission to JINST

PLATO (PLAnetary Transits and Oscillations of stars) is ESA's M3 mission designed to detect and characterise extrasolar planets and perform asteroseismic monitoring of a large number of stars. PLATO will detect small planets (down to <2 R_(Earth)) around bright stars (<11 mag), including terrestrial planets in the habitable zone of solar-like stars. With the complement of radial velocity observations from the ground, planets will be characterised for their radius, mass, and age with high accuracy (5 %, 10 %, 10 % for an Earth-Sun combination respectively). PLATO will provide us with a large-scale catalogue of well-characterised small planets up to intermediate orbital periods, relevant for a meaningful comparison to planet formation theories and to better understand planet evolution. It will make possible comparative exoplanetology to place our Solar System planets in a broader context. In parallel, PLATO will study (host) stars using asteroseismology, allowing us to determine the stellar properties with high accuracy, substantially enhancing our knowledge of stellar structure and evolution. The payload instrument consists of 26 cameras with 12cm aperture each. For at least four years, the mission will perform high-precision photometric measurements. Here we review the science objectives, present PLATO's target samples and fields, provide an overview of expected core science performance as well as a description of the instrument and the mission profile at the beginning of the serial production of the flight cameras. PLATO is scheduled for a launch date end 2026. This overview therefore provides a summary of the mission to the community in preparation of the upcoming operational phases.

The differential cross section for $\gamma p\rightarrow K^+\Lambda(1520)$ was measured from threshold to a centre-of-mass energy of 2090\,MeV at forward angles at the BGOOD experiment. The high statistical precision and resolution in centre-of-mass energy and angle allows a detailed characterisation of this low-momentum transfer kinematic region. The data agree with a previous LEPS measurement and support effective Lagrangian models that indicate that the contact term dominates the cross section near threshold., Comment: 6 pages, 7 figures

H$\alpha$ emission is a clear indicator of circumstellar activity in Be stars, historically employed to assess the classical Be star (CBe) population in young open clusters (YOCs). The YOC NGC330 in the Small Magellanic Cloud exhibits a large known fraction of {CBe} stars and was selected for a pilot study to establish a comprehensive methodology for identifying H$\alpha$ emitters in the Magellanic Clouds, encompassing the entire B-type spectral range. Using the SOAR Adaptative Module Imager (SAMI), we investigated the stellar population of NGC330 using multi-band BVRI+H$\alpha$ imaging. We identified H$\alpha$ emitters within the entire V-band range covered by SAMI/SOAR observations ($V\lesssim22$), comprising the complete B-type stellar population and offering a unique opportunity to explore the Be phenomenon across all spectral sub-classes. The stellar radial distribution shows a clear bimodal pattern between the most massive (B5 or earlier) and the lower-mass main-sequence objects (later than B6) within the cluster. The former is concentrated towards the cluster center (showing a dispersion of $\sigma=4.26\pm0.20$ pc), whereas the latter extends across larger radii ($\sigma=10.83\pm0.65$ pc), indicating mass stratification within NGC330. The total fraction of emitters is $4.4\pm0.5\%$, notably smaller than previous estimates from flux- or seeing-limited observations. However, a higher fraction of H$\alpha$ emitters is observed among higher-mass stars ($32.8\pm3.4\%$) than within lower-mass ($4.4\pm0.9\%$). Consequently, the putative CBe population exhibits distinct dynamical characteristics compared to the bulk of the stellar population in NGC330. These findings highlight the significance of the current observations in providing a complete picture of the CBe population in NGC330., Comment: Accepted for publication in the Astrophysical Journal. 24 pages, 17 figures, 3 tables

The most important aspects of scattering amplitudes have long been thought to be associated with their poles. But recently a very different sort of "split" factorizations for a wide range of particle and string tree amplitudes have been discovered away from poles. In this paper, we give a simple, conceptual origin for these splits arising from natural properties of the binary geometry of the curve integral formulation for scattering amplitudes for Tr$(\Phi^3)$ theory. The most natural way of "joining" smaller surfaces to build larger ones directly produces a choice of kinematics for which higher amplitudes factor into lower ones. This gives a generalization of splits to all orders in the topological expansion. These splits allow us to access and compute loop-integrated multi-soft limits for particle and string amplitudes, at all loop orders. This includes split factorizations and multi-soft limits for pion and gluon amplitudes, that are related to Tr$(\Phi^3)$ theory by a simple kinematical shift., Comment: 59 pages, 17 figures

The coherent reaction, $\gamma d \rightarrow \pi^0\eta d$ was studied with the BGOOD experiment at ELSA from threshold to a centre-of-mass energy of 3200\,MeV. A full kinematic reconstruction was made, with final state deuterons identified in the forward spectrometer and $\pi^0$ and $\eta$ decays in the central BGO Rugby Ball. The strength of the differential cross section exceeds what can be described by models of coherent photoproduction at forward angles by orders of magnitude. The distribution of the differential cross section has an excellent agreement with a model including quasi-free $\Delta \pi$ photoproduction, pion re-scattering and $N(1535)$ formation and subsequent nucleon coalescence to the deuteron. This also gives a reasonable description of the two-body invariant mass distributions and naturally explains the similar magnitudes of this channel and $\pi^0\pi^0 d$ coherent photoproduction., Comment: 7 pages, 5 figures

Adapting the user interface (UI) of software systems to meet the needs and preferences of users is a complex task. The main challenge is to provide the appropriate adaptations at the appropriate time to offer value to end-users. Recent advances in Machine Learning (ML) techniques may provide effective means to support the adaptation process. In this paper, we instantiate a reference framework for Intelligent User Interface Adaptation by using Reinforcement Learning (RL) as the ML component to adapt user interfaces and ultimately improving the overall User Experience (UX). By using RL, the system is able to learn from past adaptations to improve the decision-making capabilities. Moreover, assessing the success of such adaptations remains a challenge. To overcome this issue, we propose to use predictive Human-Computer Interaction (HCI) models to evaluate the outcome of each action (ie adaptations) performed by the RL agent. In addition, we present an implementation of the instantiated framework, which is an extension of OpenAI Gym, that serves as a toolkit for developing and comparing RL algorithms. This Gym environment is highly configurable and extensible to other UI adaptation contexts. The evaluation results show that our RL-based framework can successfully train RL agents able to learn how to adapt UIs in a specific context to maximize the user engagement by using an HCI model as rewards predictor., Comment: To be published in Companion of the16th ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS Companion '24). 9 pages, 2 figures, 28 references

We present a detailed chemical abundance and kinematic analysis of six extremely metal-poor ($-4.2 \leq$ [Fe/H] $\leq-$2.9) halo stars with very low neutron-capture abundances ([Sr/H] and [Ba/H]) based on high-resolution Magellan/MIKE spectra. Three of our stars have [Sr/Ba] and [Sr/H] ratios that resemble those of metal-poor stars in ultra-faint dwarf galaxies (UFDs). Since early UFDs may be the building blocks of the Milky Way, extremely metal-poor halo stars with low, UFD-like Sr and Ba abundances may thus be ancient stars from the earliest small galactic systems that were accreted by the proto-Milky Way. We label these objects as Small Accreted Stellar System (SASS) stars, and we find an additional 61 similar ones in the literature. A kinematic analysis of our sample and literature stars reveals them to be fast-moving halo objects, all with retrograde motion, indicating an accretion origin. Because SASS stars are much brighter than typical UFD stars, identifying them offers promising ways towards detailed studies of early star formation environments. From the chemical abundances of SASS stars, it appears that the earliest accreted systems were likely enriched by a few supernovae whose light element yields varied from system to system. Neutron-capture elements were sparsely produced and/or diluted, with $r$-process nucleosynthesis playing a role. These insights offer a glimpse into the early formation of the Galaxy. Using neutron-capture elements as a distinguishing criterion for early formation, we have access to a unique metal-poor population that consists of the oldest stars in the universe., Comment: 18 pages, 7 figures, accepted to MNRAS

The rapid proliferation of large language models and natural language processing (NLP) applications creates a crucial need for uncertainty quantification to mitigate risks such as hallucinations and to enhance decision-making reliability in critical applications. Conformal prediction is emerging as a theoretically sound and practically useful framework, combining flexibility with strong statistical guarantees. Its model-agnostic and distribution-free nature makes it particularly promising to address the current shortcomings of NLP systems that stem from the absence of uncertainty quantification. This paper provides a comprehensive survey of conformal prediction techniques, their guarantees, and existing applications in NLP, pointing to directions for future research and open challenges.

Corrosion, a naturally occurring process leading to the deterioration of metallic materials, demands diligent detection for quality control and the preservation of metal-based objects, especially within industrial contexts. Traditional techniques for corrosion identification, including ultrasonic testing, radio-graphic testing, and magnetic flux leakage, necessitate the deployment of expensive and bulky equipment on-site for effective data acquisition. An unexplored alternative involves employing lightweight, conventional camera systems, and state-of-the-art computer vision methods for its identification. In this work, we propose a complete system for semi-automated corrosion identification and mapping in industrial environments. We leverage recent advances in LiDAR-based methods for localization and mapping, with vision-based semantic segmentation deep learning techniques, in order to build semantic-geometric maps of industrial environments. Unlike previous corrosion identification systems available in the literature, our designed multi-modal system is low-cost, portable, semi-autonomous and allows collecting large datasets by untrained personnel. A set of experiments in an indoor laboratory environment, demonstrate quantitatively the high accuracy of the employed LiDAR based 3D mapping and localization system, with less then $0.05m$ and 0.02m average absolute and relative pose errors. Also, our data-driven semantic segmentation model, achieves around 70\% precision when trained with our pixel-wise manually annotated dataset.

For a property $\mathcal{P}$ of graphs, the $\mathcal{P}$-\textsc{Sandwich-Problem}, introduced by Golumbic and Shamir (1993), is the following: Given a pair of graphs $(G_1, G_2)$ on the same vertex set $V$, does there exist a graph $G$ such that $V(G)=V$, $E(G_{1})\subseteq E(G) \subseteq E(G_{2})$, and $G$ satisfies $\mathcal{P}$? A {\em hole} in a graph is an induced subgraph which is a cycle of length at least four. An odd (respectively even) hole is a hole of odd (respectively even) length. Given a class of graphs $\mathcal{C}$ and a graph $G$ we say that $G$ is {\em $\mathcal{C}$-free} if it contains no induced subgraph isomorphic to a member of $\mathcal{C}$. In this paper we prove that if $\mathcal{P}$ is the property of being odd-hole-free or the property of being even-hole-free, then the $\mathcal{P}$-\textsc{Sandwich-Problem} is NP-hard.

This paper describes the technical and conceptual development of the LuminLab platform, an online tool that integrates a purpose-fit human-centric AI chatbot and predictive energy model into a streamlined front-end that can rapidly produce and discuss building retrofit plans in natural language. The platform provides users with the ability to engage with a range of possible retrofit pathways tailored to their individual budget and building needs on-demand. Given the complicated and costly nature of building retrofit projects, which rely on a variety of stakeholder groups with differing goals and incentives, we feel that AI-powered tools such as this have the potential to pragmatically de-silo knowledge, improve communication, and empower individual homeowners to undertake incremental retrofit projects that might not happen otherwise.