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Spatiotemporal optical vortices (STOV) are space-time structured light pulses with a unique topology that couples spatial and temporal domains and carry transverse orbital angular momentum (OAM). Up to now, their generation has been limited to the visible and infrared regions of the spectrum. During the last decade, it was shown that through the process of high-order harmonic generation (HHG) it is possible to up-convert spatial optical vortices that carry longitudinal OAM from the near-infrared into the extreme-ultraviolet (EUV), thereby producing vortices with distinct femtosecond and attosecond structure. In this work we demonstrate theoretically and experimentally the generation of EUV spatiotemporal and spatiospectral vortices using near infrared STOV driving laser pulses. We use analytical expressions for focused STOVs to perform macroscopic calculations of HHG that are directly compared to the experimental results. As STOV beams are not eigenmodes of propagation, we characterize the highly-charged EUV STOVs both in the near and far fields, to show that they represent conjugated spatiotemporal and spatiospectral vortex pairs. Our work provides high-frequency light beams topologically coupled at the nanometer/attosecond scales domains with transverse OAM, that could be suitable to explore electronic dynamics in magnetic materials, chiral media, and nanostructures., Comment: 17 pages, 7 figures

We report measurements of the absolute branching fractions $\mathcal{B}(B_s^0 \to D_s^{\pm} X)$, $\mathcal{B}(B_s^0 \to D^0/\bar{D}^0 X)$, and $\mathcal{B}(B_s^0 \to D^{\pm} X)$, where the latter is measured for the first time. The results are based on a 121.4\,fb$^{-1}$ data sample collected at the $\Upsilon(10860)$ resonance by the Belle detector at the KEKB asymmetric-energy $e^+ e^-$ collider. We reconstruct one $B_s^0$ meson in $e^+e^- \to \Upsilon(10860) \to B_s^{*} \bar{B}_s^{*}$ events and measure yields of $D_s^+$, $D^0$, and $D^+$ mesons in the rest of the event. We obtain $\mathcal{B}(B_s^0 \to D_s^{\pm} X) = (68.6 \pm 7.2 \pm 4.0)\%$, $\mathcal{B}(B_s^0 \to D^0/\bar{D}^0 X) = (21.5 \pm 6.1 \pm 1.8)\%$, and $\mathcal{B}(B_s^0 \to D^{\pm} X) = (12.6 \pm 4.6 \pm 1.3)\%$, where the first uncertainty is statistical and the second is systematic. Averaging with previous Belle measurements gives $\mathcal{B}(B_s^0 \to D_s^{\pm} X) = (63.4 \pm 4.5 \pm 2.2)\%$ and $\mathcal{B}(B_s^0 \to D^0/\bar{D}^0 X) = (23.9 \pm 4.1 \pm 1.8)\%$. For the $B_s^0$ production fraction at the $\Upsilon(10860)$, we find $f_s = (21.4^{+1.5}_{-1.7})\%$., Comment: 23 pages, 9 figures, submitted to JHEP

We present measurements of $B \to K{}^{*}(892)\gamma$ decays using $365\,{\rm fb}^{-1}$ of data collected from 2019 to 2022 by the Belle~II experiment at the SuperKEKB asymmetric-energy $e^+e^-$ collider. The data sample contains $(387 \pm 6) \times 10^6$ $B\overline{B}$ events. We measure branching fractions ($\mathcal{B}$) and $C\!P$ asymmetries ($\mathcal{A}_{C\!P}$) for both $B^{0}\to K{}^{*0}\gamma$ and $B^{+}\to K{}^{*+}\gamma$ decays. The difference in $C\!P$ asymmetries ($\Delta \mathcal{A}_{C\!P}$) and the isospin asymmetry ($\Delta_{0+}$) between these neutral and charged channels are also measured. We obtain the following branching fractions and $C\!P$ asymmetries: $\mathcal{B} (B^{0} \to K{}^{*0}\gamma) = (4.14 \pm 0.10 \pm 0.11 ) \times 10^{-5}$, $\mathcal{B} (B^{+} \to K{}^{*+}\gamma) = (4.02 \pm 0.13 \pm 0.13 )\times 10^{-5}$, $\mathcal{A}_{C\!P} (B^{0} \to K{}^{*0}\gamma) = (-3.3 \pm 2.3 \pm 0.4 )\%$, and $\mathcal{A}_{C\!P} (B^{+} \to K{}^{*+}\gamma) = (-0.7 \pm 2.9 \pm 0.6 )\%$. The measured difference in $C\!P$ asymmetries is $\Delta \mathcal{A}_{C\!P} = (+2.6 \pm 3.8 \pm 0.7 )\%$, and the measured isospin asymmetry is $\Delta_{0+} = (+5.0 \pm 2.0 \pm 1.5 )\%$. The first uncertainties listed are statistical and the second are systematic. These results are consistent with world-average values and theory predictions.

Very-high-energy (0.1-100 TeV) gamma-ray emission was observed in HAWC data from the lobes of the microquasar SS 433, making them the first set of astrophysical jets that were resolved at TeV energies. In this work, we update the analysis of SS 433 using 2,565 days of data from the High Altitude Water Cherenkov (HAWC) observatory. Our analysis reports the detection of a point-like source in the east lobe at a significance of $6.6\,\sigma$ and in the west lobe at a significance of $8.2\,\sigma$. For each jet lobe, we localize the gamma-ray emission and identify a best-fit position. The locations are close to the X-ray emission sites "e1" and "w1" for the east and west lobes, respectively. We analyze the spectral energy distributions and find that the energy spectra of the lobes are consistent with a simple power-law $\text{d}N/\text{d}E\propto E^{\alpha}$ with $\alpha = -2.44^{+0.13+0.04}_{-0.12-0.04}$ and $\alpha = -2.35^{+0.12+0.03}_{-0.11-0.03}$ for the east and west lobes, respectively. The maximum energy of photons from the east and west lobes reaches 56 TeV and 123 TeV, respectively. We compare our observations to various models and conclude that the very-high-energy gamma-ray emission can be produced by a population of electrons that were efficiently accelerated.

We present a measurement of the branching fraction and time-dependent charge-parity ($CP$) decay-rate asymmetries in $B^0 \to J/\psi \pi^0$ decays. The data sample was collected with the Belle~II detector at the SuperKEKB asymmetric $e^+e^-$ collider in 2019-2022 and contains $(387\pm 6)\times 10^6$ $B\overline{B}$ meson pairs from $\Upsilon(4S)$ decays. We reconstruct $392\pm 24$ signal decays and fit the $CP$ parameters from the distribution of the proper-decay-time difference of the two $B$ mesons. We measure the branching fraction to be $B(B^0 \to J/\psi \pi^0)=(2.02 \pm 0.12 \pm 0.10)\times 10^{-5}$ and the direct and mixing-induced $CP$ asymmetries to be $C_{CP}=0.13 \pm 0.12 \pm 0.03$ and $S_{CP}=-0.88 \pm 0.17 \pm 0.03$, respectively, where the first uncertainties are statistical and the second are systematic. We observe mixing-induced $CP$ violation with a significance of $5.0$ standard deviations for the first time in this mode.

We present the first example where length scale for the growth of ordered regions and the correlation length for the two point correlations of persistent sites scale differently with time. We do so by studying a global spin exchange dynamics in one dimension where a selected spin interacts with its two nearest domains. We found domain growth exponent $z=2.47\pm 0.03$ and the persistence exponent $\theta=0.445\pm0.002$, making $z\theta > d=1$. Unlike any previous study, we found correlation length of two point correlation of persistent sites grows in a power law with exponent $\zeta=1.00\pm 0.03$ by studying the fractal structure created by the persistent sites at the different stages of the dynamics and shown that fractal dimension is not related with any growth exponents., Comment: 6 pages, 8 figures

A myriad of measures to illustrate performance of predictive artificial intelligence (AI) models have been proposed in the literature. Selecting appropriate performance measures is essential for predictive AI models that are developed to be used in medical practice, because poorly performing models may harm patients and lead to increased costs. We aim to assess the merits of classic and contemporary performance measures when validating predictive AI models for use in medical practice. We focus on models with a binary outcome. We discuss 32 performance measures covering five performance domains (discrimination, calibration, overall, classification, and clinical utility) along with accompanying graphical assessments. The first four domains cover statistical performance, the fifth domain covers decision-analytic performance. We explain why two key characteristics are important when selecting which performance measures to assess: (1) whether the measure's expected value is optimized when it is calculated using the correct probabilities (i.e., a "proper" measure), and (2) whether they reflect either purely statistical performance or decision-analytic performance by properly considering misclassification costs. Seventeen measures exhibit both characteristics, fourteen measures exhibited one characteristic, and one measure possessed neither characteristic (the F1 measure). All classification measures (such as classification accuracy and F1) are improper for clinically relevant decision thresholds other than 0.5 or the prevalence. We recommend the following measures and plots as essential to report: AUROC, calibration plot, a clinical utility measure such as net benefit with decision curve analysis, and a plot with probability distributions per outcome category., Comment: 60 pages, 8 tables, 11 figures, two supplementary appendices

In this work we constrain the value of $\sigma_8$ for the normal and self-accelerating branch of a DGP brane embedded in a five-dimensional Minkowski space-time. For that purpose we first constrain the model parameters $H_0$, $\Omega_{m0}$, $\Omega_{r0}$ and $M$ by means of the Pantheon+ catalog and a mock catalog of gravitational waves. Then, we solve numerically the equation for dark matter scalar perturbations using the dynamical scaling solution for the master equation and assuming that $p=4$ for the matter dominated era. Finally, we found that the evolution of matter density perturbations in both branches is different from the $\Lambda$CDM model and that the value of $\sigma_8=0.774\pm0.027$ for the normal branch and $\sigma_8=0.913\pm0.032$ for the self-accelerating branch., Comment: 14 pages, 5 figures

We report on an instance in quantum gravity where a topological expansion resums into an effective description on a single geometry. The original theory whose gravitational path integral we study is JT quantum gravity with one asymptotic boundary at nonperturbatively low temperatures. The effective theory we derive is a deformation of JT gravity by a highly quantum and nonlocal interaction for the dilaton, evaluated only on a disk topology. This emergent description addresses a strongly quantum gravitational regime where all genera contribute at the same order, successfully capturing the doubly nonperturbative physics of the original theory., Comment: 5 pages, 2 figures, 1 geometry

This article deals with the boundary null controllability of some degenerate parabolic equations posed on a square domain, presenting the first study of boundary controllability for such equations in multidimensional settings. The proof combines two classical techniques: the method of moments and the Lebeau-Robbiano strategy. A key novelty of this work lies in the analysis of boundary control localized on a subset of the boundary where the degeneracy occurs. Furthermore, we establish the Kalman rank condition as a full characterization of boundary controllability for coupled degenerate systems. The results are extended to $N$-dimensional domains, and potential extensions and open problems are discussed to motivate further research in this area.

Superclusters are the most massive structures in the universe. To what degree they are actually bound against an accelerating expansion of the background is of significant cosmological and astrophysical interest. In this study, we introduce a cross matched set of superclusters from the SLOW constrained simulations of the local (z<0.05) universe. Identifying the superclusters provides estimates on the efficacy of the constraints in reproducing the local large-scale structure accurately. The simulated counterparts can help identifying possible future observational targets containing interesting features such as bridges between pre-merging and merging galaxy clusters and collapsing filaments and provide comparisons for current observations. By determining the collapse volumes for the superclusters we further elucidate the dynamics of cluster-cluster interactions in those regions. Using catalogs of local superclusters and the most massive simulated clusters, we search for counterparts of supercluster members of six regions. We evaluate the significance of these detections by comparing their geometries to supercluster regions in random simulations. We then run an N-body version of the simulation into the far future and determine which of the member clusters are gravitationally bound to the host superclusters. Furthermore we compute masses and density contrasts for the collapse regions. We demonstrate the SLOW simulation of the local universe to accurately reproduce local supercluster regions in mass of their members and three-dimensional geometrical arrangement. We furthermore find the bound regions of the local superclusters consistent in size and density contrast with previous theoretical studies. This will allow to connect future numerical zoom-in studies of the clusters to the large scale environments and specifically the supercluster environments these local galaxy clusters evolve in., Comment: 18 pages, 20 figures, submitted to A&A

Surface plasmon polaritons have received much attention over the last decades in fields such as photonics or nanotechnology due to their inherent high sensitivity to metal surface variations (e.g., presence of adsorbates or changes in the roughness). Because of this important property, it is expected that they will find promising major applications in widely cross-disciplinary areas ranging from material science to medicine. Motivated by this ever-growing interest, here we introduce a novel theoretical framework suitable to design new types of structured paraxial surface plasmon beams and to control their propagation along the interface between a dielectric and a lossy metal. This is possible by virtue of a convenient Hermite-Gaussian mode expansion, which constitutes a complete basis set upon which new types of structured paraxial plasmon beams can be generated. The beams obtained from this method present a rather peculiar feature, as they exhibit local intensity maxima at different propagation distances. This way of creating tightly confined structured light allows us control where to place the beam energy. This, thus, opens up worth exploring pathways to manipulate light propagation along metal surfaces at the nanoscale. As a proof-of-concept, we provide numerical evidence of the feasibility of the method by considering the analysis of the propagation of Airy-based surface plasmon polaritons along an air-silver interface., Comment: 20 pages, 3 figures (plus 6 pages with supplemental information)

Within the AdS/CFT correspondence, computational complexity for reduced density matrices of holographic conformal field theories has been conjectured to be related to certain geometric observables in the dual gravity theory. We study this conjecture from both the gravity and field theory point of view. Specifically, we consider a measure of complexity associated to the Bures metric on the space of density matrices. We compute this complexity measure for mixed states associated to single intervals in descendant states of the vacuum in 2d CFTs. Moreover, we derive from first principles a geometric observable dual to the Bures metric which is localized in the entanglement wedge of the AdS spacetime associated to the quantum circuit on the boundary. Finally, we compare the Bures metric complexity measure with holographic subregion complexity within the ``complexity=volume'' paradigm for perturbatively small transformations of the vacuum. While there is no exact agreement between these two quantities, we find striking similarities as we vary the target state and interval size, suggesting that these quantities are closely related., Comment: 48 pages, 9 figures, 3 appendices

We use effective string theory to study mesons with large spin $J$ in large $N_c$ QCD as rotating open strings. In the first part of this work, we formulate a consistent effective field theory (EFT) for open spinning strings with light quarks. Our EFT provides a consistent treatment of the endpoints' singularities that arise in the massless limit. We obtain results, in a systematic $1/J$ expansion, for the spectrum of the leading and daughter Regge trajectories. Interestingly, we find that the redshift factor associated with the quarks' acceleration implies that the applicability regime of the EFT is narrower compared to that of static flux tubes. In the second part of this work, we discuss several extensions of phenomenological interests, including mesons with heavy quarks, the quarks' spin and the daughter Regge trajectories associated with the worldsheet axion, a massive string mode identified in lattice simulations of $4d$ flux tubes. We compare our predictions with $4d$ QCD spectroscopy data, and suggest potential $stringy$ interpretations of the observed mesons. We finally comment on the relation between the EFT spectrum and the Axionic String Ansatz, a recently proposed characterization of the spectrum of Yang-Mills glueballs., Comment: 66 pages+appendices

We present new orbital solutions for 15 binaries, which were astrometrically measured during 2010-2013. We observe the binary systems using the FastCam ``lucky-imaging'' camera, installed at the 1.5-m Carlos S\'anchez Telescope (CST) at the Observatorio del Teide, Tenerife (Spain). We present first orbital solutions for four binaries and revise orbits for other 11 systems. We apply two orbital calculation techniques, the ``three-dimensional grid search method'' and the Docobo's analytical method. In addition, we use our tool ``Binary Deblending'', which is based on deblending the entire observed multiband photometry into fundamental and photometric parameters for each stellar component based on PARSEC isochrones. This method allows us to obtain the total mass for all systems. Our findings include the identification of a binary system consisting of two M-type dwarfs (WOR 19), a binary of evolved components (twin F6IV-V stars) in BU 1292, accompanied by a newly discovered wide (10.5 arcsec) and faint companion with G=17.05 mag. Additionally, we explore the X-ray emission system STF 147 and a very young quadruple system, WDS 04573+5345. This comprehensive analysis significantly contributes to our understanding on the formation and evolution of these stellar systems., Comment: To be published in PASA journal. 18 pages, 4 figures containing several plots and 6 tables

Low-light image enhancement remains an open problem, and the new wave of artificial intelligence is at the center of this problem. This work describes the use of genetic algorithms for optimizing analytical models that can improve the visualization of images with poor light. Genetic algorithms are part of metaheuristic approaches, which proved helpful in solving challenging optimization tasks. We propose two analytical methods combined with optimization reasoning to approach a solution to the physical and computational aspects of transforming dark images into visible ones. The experiments demonstrate that the proposed approach ranks at the top among 26 state-of-the-art algorithms in the LOL benchmark. The results show evidence that a simple genetic algorithm combined with analytical reasoning can defeat the current mainstream in a challenging computer vision task through controlled experiments and objective comparisons. This work opens interesting new research avenues for the swarm and evolutionary computation community and others interested in analytical and heuristic reasoning., Comment: 26 pages, 6 figures, 6 tables, 34 references

HI-rich absorbers seen within quasar spectra contain the bulk of neutral gas in the Universe. However, the spatial extent of these reservoirs are not extensively studied due to the pencil beam nature of quasar sightlines. Using two giant gravitational arc fields (at redshifts 1.17 and 2.06) as 2D background sources with known strong MgII absorption observed with the MUSE integral field spectrograph (IFS), we investigated whether spatially mapped MgII absorption can predict the presence of strong HI systems, and determine both the physical extent and HI mass of the two absorbing systems. We created a simple model of an ensemble of gas clouds in order to simultaneously predict the HI column density and gas covering fraction of HI-rich absorbers based on observations of the MgII rest-frame equivalent width in IFS spaxels. We first test the model on the field with HI observations already available from the literature, finding that we can recover HI column densities consistent with the previous estimates (although with large uncertainties). We then use our framework to simultaneously predict the gas covering fraction, HI column density and total HI mass ($M_{\rm{HI}}$) for both fields. We find that both of the observed strong systems have a covering fraction of $\approx70$% and are likely damped Lyman $\alpha$ systems (DLAs) with $M_{\rm{HI}}>10^9\ M_{\odot}$. Our model shows that the typical MgII metrics used in the literature to identify the presence of DLAs are sensitive to the gas covering fraction. However, these MgII metrics are still sensitive to strong HI, and can be still applied to absorbers towards gravitational arcs or other spatially extended background sources. Based on our results, we speculate that the two strong absorbers are likely representative of a neutral inner circumgalactic medium and are a significant reservoir of fuel for star formation within the host galaxies., Comment: 15 pages, 6 Figures, 3 appendices. Accepted for publication on Astronomy & Astrophysics

Recent gain in popularity of AI conversational agents has led to their increased use for improving productivity and supporting well-being. While previous research has aimed to understand the risks associated with interactions with AI conversational agents, these studies often fall short in capturing the lived experiences. Additionally, psychological risks have often been presented as a sub-category within broader AI-related risks in past taxonomy works, leading to under-representation of the impact of psychological risks of AI use. To address these challenges, our work presents a novel risk taxonomy focusing on psychological risks of using AI gathered through lived experience of individuals. We employed a mixed-method approach, involving a comprehensive survey with 283 individuals with lived mental health experience and workshops involving lived experience experts to develop a psychological risk taxonomy. Our taxonomy features 19 AI behaviors, 21 negative psychological impacts, and 15 contexts related to individuals. Additionally, we propose a novel multi-path vignette based framework for understanding the complex interplay between AI behaviors, psychological impacts, and individual user contexts. Finally, based on the feedback obtained from the workshop sessions, we present design recommendations for developing safer and more robust AI agents. Our work offers an in-depth understanding of the psychological risks associated with AI conversational agents and provides actionable recommendations for policymakers, researchers, and developers., Comment: 25 pages, 2 figures, 4 tables; Corrected typos

We construct and count the microstates of out-of-equilibrium eternal AdS black holes in which a shell carrying an order one fraction of the black hole mass is emitted from the past horizon and re-absorbed at the future horizon. Our microstates have semiclassical interpretations in terms of matter propagating behind the horizon. We show that they span a Hilbert space with a dimension equal to the exponential of the horizon area of the intermediate black hole. This is consistent with the idea that, in a non-equilibrium setting, entropy is the logarithm of the number of microscopic configurations consistent with the dynamical macroscopic state. In our case, therefore, the entropy should measure the number of microstates consistent with a large and atypical macroscopic black hole fluctuation due to which part of the early time state becomes fully known to an external observer., Comment: 49 pages, 12 figures, 2 appendices

Isolated attosecond pulses (IAPs) generated by few-cycle femtosecond lasers are essential for capturing ultrafast dynamics in atoms, molecules, and solids. Nonetheless, the advancement of attosecond science critically depends on achieving stable, high-temporal-contrast IAPs. Our study reveals a universal scenario in which self-compression of the infrared driver in high harmonic generation in extended gas media leads to high-contrast high-frequency IAP generation. Our experimental and theoretical results reveal that filamentation in a semi-infinite gas cell not only shapes the infrared driving pulse spatially and temporally, but also creates a stable propagation region where high harmonic generation is phase-matched, leading to the production of bright IAPs. In an argon-filled gas cell, filamentation notably reduces the pulse duration of Yb-based 1030 nm pulses from 4.7 fs to 3.5 fs, while simultaneously generating high-contrast 200-attosecond IAPs at 70 eV. We demonstrate the universality of filamentation-assisted IAP generation, showing that post-compressed Yb-based laser filaments in neon and helium yield even shorter IAPs: 69-attoseconds at 100 eV, and 65-attoseconds IAPs at 135 eV, respectively. This spatiotemporal reshaping of few-cycle pulses through filamentation possesses immediate impacts on both post-compression techniques and attosecond-based technologies.

The mass accretion rate of galaxy clusters is a key factor in determining their structure, but a reliable observational tracer has yet to be established. We present a state-of-the-art machine learning model for constraining the mass accretion rate of galaxy clusters from only X-ray and thermal Sunyaev-Zeldovich observations. Using idealized mock observations of galaxy clusters from the MillenniumTNG simulation, we train a machine learning model to estimate the mass accretion rate. The model constrains 68% of the mass accretion rates of the clusters in our dataset to within 33% of the true value without significant bias, a ~58% reduction in the scatter over existing constraints. We demonstrate that the model uses information from both radial surface brightness density profiles and asymmetries., Comment: 13 pages, 9 figures, 1 table

We report on a search for millicharged particles (mCPs) produced in cosmic ray proton atmospheric interactions using data collected during the first science run of the LUX-ZEPLIN experiment. The mCPs produced by two processes -- meson decay and proton bremsstrahlung -- are considered in this study. This search utilized a novel signature unique to liquid xenon (LXe) time projection chambers (TPCs), allowing sensitivity to mCPs with masses ranging from 10 to 1000 MeV/c$^2$ and fractional charges between 0.001 and 0.02 of the electron charge e. With an exposure of 60 live days and a 5.5 tonne fiducial mass, we observed no significant excess over background. This represents the first experimental search for atmospheric mCPs and the first search for mCPs using an underground LXe experiment.

This work explores the topological properties of altermagnets, a novel class of collinear magnetic materials. We employ equivariant K-theory of magnetic groups and Hamiltonian models to formulate a robust $C^z_4 \mathbb{T}$ topological invariant to classify 2D and 3D altermagnetic systems. Our findings demonstrate that the spin Chern number serves as a robust topological index, corresponding to the half-quantized Chern number of the divided Brillouin zone. This indicator enables the prediction of a topologically protected 2D altermagnetic insulators and 3D Weyl altermagnetic semimetals, highlighting the relationship between altermagnetism and topological phases. Furthermore, our results provide a pathway to the exploration of topological applications in $d$-wave altermagnetic materials., Comment: 12 pages, 5 figures

In the era of large-scale astronomical surveys, fast modeling of strong lens systems has become increasingly vital. While significant progress has been made for galaxy-scale lenses, the development of automated methods for modeling larger systems, such as groups and clusters, is not as extensive. Our study aims to extend the capabilities of the GIGA-Lens code, enhancing its efficiency in modeling multi-galaxy strong lens systems. We focus on demonstrating the potential of GPU-accelerated Bayesian inference in handling complex lensing scenarios with a high number of free parameters. We employ an improved inference approach that combines image position and pixelated data with an annealing sampling technique to obtain the posterior distribution of complex models. This method allows us to overcome the challenge of limited prior information, a high number of parameters, and memory usage. Our process is exemplified through the analysis of the compact group lens system DES J0248-3955, for which we present VLT/X-shooter spectra. We measure a redshift of $z = 0.69 \pm 0.04$ for the group, and $z = 1.2722 \pm 0.0005$ for one of the extended arcs. Our enhanced method successfully constrained a lens model with 29 free parameters and lax priors in a remarkably short time. The mass of the lens is well described by a single dark-matter halo with a velocity dispersion of $\sigma_v = (690 \pm 30) \, km \, s^{-1}$. The model predicts the presence of a second source at the same redshift and a third source at approximately $z \sim 2.7$. Our study demonstrates the effectiveness of our lens modeling technique for dealing with a complex system in a short time using ground-based data. This presents considerable potential within the context of large surveys such as LSST., Comment: 10 pages, 6 figures. Submitted to A&A. The final version may differ slightly after production edits

Recently, there has been an increasing interest in performing post-hoc uncertainty estimation about the predictions of pre-trained deep neural networks (DNNs). Given a pre-trained DNN via back-propagation, these methods enhance the original network by adding output confidence measures, such as error bars, without compromising its initial accuracy. In this context, we introduce a novel family of sparse variational Gaussian processes (GPs), where the posterior mean is fixed to any continuous function when using a universal kernel. Specifically, we fix the mean of this GP to the output of the pre-trained DNN, allowing our approach to effectively fit the GP's predictive variances to estimate the DNN prediction uncertainty. Our approach leverages variational inference (VI) for efficient stochastic optimization, with training costs that remain independent of the number of training points, scaling efficiently to large datasets such as ImageNet. The proposed method, called fixed mean GP (FMGP), is architecture-agnostic, relying solely on the pre-trained model's outputs to adjust the predictive variances. Experimental results demonstrate that FMGP improves both uncertainty estimation and computational efficiency when compared to state-of-the-art methods., Comment: 12 pages, 6 figures and 2 tables. Submitted to IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

In this work, we study the relaxation of a degenerate functional with linear growth, depending on a weight $w$ that does not exhibit doubling or Muckenhoupt-type conditions. In order to obtain an explicit representation of the relaxed functional and its domain, our main tools for are Sobolev inequalities with double weight., Comment: arXiv admin note: substantial text overlap with arXiv:2404.02919

Photons and optical circuits are among the most promising platforms for implementation of quantum technologies, because of its potential use in quantum computing, quantum cryptography and long-distance quantum communication. One of the main requirements to achieve reliable quantum communications are on-demand sources of highly entangled photon pairs, and semiconductor quantum dots have emerged as prominent candidates to satisfy the necessary conditions of brightness and entanglement fidelity. However, in most cases the biexciton-exciton-vacuum cascade produces a pair of maximally polarization-entangled photons with a dephasing, due to the exciton fine structure splitting. This work focuses on the performance of the E91 quantum key distribution protocol under the variation of two elements: first, the phase in the input state when the protocol is implemented using entangled photons generated via the radiative cascade, and second, the relative directions of the polarization analyzers. We obtain analytical expressions for the protocol's secret key rate and Bell's parameter as functions of the studied phase and angles. Then, we validate those expressions by means of a quantum computational implementation with the IBM's API Qiskit. Our results show that the performance of the quantum transmission is highly impacted by the product between the exciton lifetime and the quantum dot's fine structure splitting and that such an impact may be modulated through the orientation of the polarizers. These findings provide important insight for the scalable implementation of quantum key distribution protocols with quantum dots as entanglement sources., Comment: 5 figures and 10 pages long

We demonstrate that the temporal reflection of a weak dispersive pulse on a soliton in media with a frequency-dependent nonlinearity leads to the generation of new solitons, whose number can be selected by tuning parameters of the dispersive pulse. By carefully analyzing the different processes involved, we show that a virtuous interplay between Raman scattering and a zero-nonlinearity wavelength is a key enabler for soliton generation to occur, limiting the initial soliton redshift and allowing for an efficient energy transfer between the dispersive pulse and its reflection. Finally, we believe results presented in this work may contribute guidelines for the generation of short and intense pulses for various photonic applications., Comment: 4 pages (+ 1 full references page), letter

While deep learning models can learn human-like features at earlier levels, which suggests their utility in modeling human vision, few attempts exist to incorporate these features by design. Current approaches mostly optimize all parameters blindly, only constraining minor architectural aspects. This paper demonstrates how parametrizing neural network layers enables more biologically-plausible operations while reducing trainable parameters and improving interpretability. We constrain operations to functional forms present in human vision, optimizing only these functions' parameters rather than all convolutional tensor elements independently. We present two parametric model versions: one with hand-chosen biologically plausible parameters, and another fitted to human perception experimental data. We compare these with a non-parametric version. All models achieve comparable state-of-the-art results, with parametric versions showing orders of magnitude parameter reduction for minimal performance loss. The parametric models demonstrate improved interpretability and training behavior. Notably, the model fitted to human perception, despite biological initialization, converges to biologically incorrect results. This raises scientific questions and highlights the need for diverse evaluation methods to measure models' humanness, rather than assuming task performance correlates with human-like behavior.

We study quantum neural networks where the generated function is the expectation value of the sum of single-qubit observables across all qubits. In [Girardi \emph{et al.}, arXiv:2402.08726], it is proven that the probability distributions of such generated functions converge in distribution to a Gaussian process in the limit of infinite width for both untrained networks with randomly initialized parameters and trained networks. In this paper, we provide a quantitative proof of this convergence in terms of the Wasserstein distance of order $1$. First, we establish an upper bound on the distance between the probability distribution of the function generated by any untrained network with finite width and the Gaussian process with the same covariance. This proof utilizes Stein's method to estimate the Wasserstein distance of order $1$. Next, we analyze the training dynamics of the network via gradient flow, proving an upper bound on the distance between the probability distribution of the function generated by the trained network and the corresponding Gaussian process. This proof is based on a quantitative upper bound on the maximum variation of a parameter during training. This bound implies that for sufficiently large widths, training occurs in the lazy regime, \emph{i.e.}, each parameter changes only by a small amount. While the convergence result of [Girardi \emph{et al.}, arXiv:2402.08726] holds at a fixed training time, our upper bounds are uniform in time and hold even as $t \to \infty$.

Knowledge about how well a robot can perform a specific task is currently present only in engineering reports which are inaccessible to the robot. Artificial Intelligence techniques, such as hypergraphs and automated reasoning, can provide such engineering knowledge online while enabling updates in the knowledge with new experiences. This requires a sound knowledge structure and maintenance routines for keeping this knowledge-base about the robot's capabilities truthful. A robot with such up-to-date information can reason about if and how well it can accomplish a task. This article introduces a knowledge representation that combines an ontology on system engineering, a deductive reasoning on the connections between system components, and an inductive reasoning on the performance of these components in the current system configuration. This representation is further used to derive the expected performance for the overall system based on a continuous evaluation of the actual performance per component. Our real-life implementation shows a robot that can answer questions on whether it can do a specific task with the desired performance., Comment: 18 pages

Recent observations reveal a rapid dust build-up in high-redshift galaxies (z > 4), challenging current models of galaxy formation. While our understanding of dust production and destruction in the interstellar medium (ISM) is advancing, probing baryonic processes in the early Universe remains a complex task. We characterize the evolution of 98 z~5 star-forming galaxies observed as part of the ALPINE survey by constraining the physical processes underpinning the gas and dust production, consumption, and destruction in their ISM. We make use of chemical evolution models to simultaneously reproduce the observed dust and gas content. For each galaxy, we estimate initial gas mass, inflows and outflows, and efficiencies of dust growth and destruction. We test the models with the canonical Chabrier and top-heavy initial mass functions (IMFs), with the latter enabling rapid dust production on shorter timescales. Our models successfully reproduce gas and dust content in older galaxies (> 600 Myr) regardless of the IMF, with Type II SNe as the primary dust source and no dust growth in ISM with moderate inflow of primordial gas. In case of intermediate-age galaxies (300 - 600 Myr), we reproduce the gas and dust content through Type II SNe and dust growth in ISM, though we observe an over-prediction of dust mass in older galaxies, potentially indicating an unaccounted dust destruction mechanism and/or an overestimation of the observed dust masses. The number of young galaxies (< 300 Myr) reproduced, increases for models assuming top-heavy IMF but with maximal prescriptions of dust production. Galactic outflows are necessary to reproduce observed gas and dust masses. The Chabrier IMF models reproduce 65% of galaxies, while top-heavy IMF models improve this to 93%, easing tensions with observations. Upcoming JWST data will refine these models by resolving degeneracies in intrinsic galaxy properties., Comment: Accepted for publication in A&A

This study explores the design and preliminary evaluation of the "Well-being Journey" (WB Journey), a digital tool aimed at enhancing student well-being within educational environments through tailored recommendations for students. The study examines the WB Journey prototype's user experience and its effectiveness in meeting learning analytics goals related to student preferences. To achieve both goals, we employ a mixed-methods approach, combining quantitative data from the User Experience Questionnaire (UEQ) and the Student Expectations of Learning Analytics Questionnaire (SELAQ) with qualitative feedback from a student discussion. Conducted among 25 students from an engineering school in a Spanish University, the study's data collection involved a 120-minute workshop. The findings suggest opportunities for enhancing the prototype, highlighting the importance of aligning similar digital tools with student needs and preferences for a supportive learning environment, which can be achieved by leveraging tools such as UEQ and SELAQ., Comment: 15 pages, 4 figures, 2 tables

Metric depth estimation from visual sensors is crucial for robots to perceive, navigate, and interact with their environment. Traditional range imaging setups, such as stereo or structured light cameras, face hassles including calibration, occlusions, and hardware demands, with accuracy limited by the baseline between cameras. Single- and multi-view monocular depth offers a more compact alternative, but is constrained by the unobservability of the metric scale. Light field imaging provides a promising solution for estimating metric depth by using a unique lens configuration through a single device. However, its application to single-view dense metric depth is under-addressed mainly due to the technology's high cost, the lack of public benchmarks, and proprietary geometrical models and software. Our work explores the potential of focused plenoptic cameras for dense metric depth. We propose a novel pipeline that predicts metric depth from a single plenoptic camera shot by first generating a sparse metric point cloud using machine learning, which is then used to scale and align a dense relative depth map regressed by a foundation depth model, resulting in dense metric depth. To validate it, we curated the Light Field & Stereo Image Dataset (LFS) of real-world light field images with stereo depth labels, filling a current gap in existing resources. Experimental results show that our pipeline produces accurate metric depth predictions, laying a solid groundwork for future research in this field., Comment: 8 pages (6 for text + 2 for references), 6 figures, 2 tables. Submitted to IEEE ICRA 2025

Cosmological analyses of redshift space clustering data are primarily based on using luminous ``red'' galaxies (LRGs) and ``blue'' emission line galaxies (ELGs) to trace underlying dark matter. Using the large high-fidelity high-resolution MillenniumTNG (MTNG) and Astrid simulations, we study these galaxies with the effective field theory (EFT)-based field level forward model. We confirm that both red and blue galaxies can be accurately modeled with EFT at the field level and their parameters match those of the phenomenological halo-based models. Specifically, we consider the state of the art Halo Occupation Distribution (HOD) and High Mass Quenched (HMQ) models for the red and blue galaxies, respectively. Our results explicitly confirm the validity of the halo-based models on large scales beyond the two-point statistics. In addition, we validate the field-level HOD/HMQ-based priors for EFT full-shape analysis. We find that the local bias parameters of the ELGs are in tension with the predictions of the LRG-like HOD models and present a simple analytic argument explaining this phenomenology. We also confirm that ELGs exhibit weaker non-linear redshift-space distortions (``fingers-of-God''), suggesting that a significant fraction of their data should be perturbative. We find that the response of EFT parameters to galaxy selection is sensitive to assumptions about baryonic feedback, suggesting that a detailed understanding of feedback processes is necessary for robust predictions of EFT parameters. Finally, using neural density estimation based on paired HOD-EFT parameter samples, we obtain optimal HOD models that reproduce the clustering of Astrid and MTNG galaxies., Comment: 27 pages, 16 figures

Ionizable lipids are essential in developing lipid nanoparticles (LNPs) for effective messenger RNA (mRNA) delivery. While traditional methods for designing new ionizable lipids are typically time-consuming, deep generative models have emerged as a powerful solution, significantly accelerating the molecular discovery process. However, a practical challenge arises as the molecular structures generated can often be difficult or infeasible to synthesize. This project explores Monte Carlo tree search (MCTS)-based generative models for synthesizable ionizable lipids. Leveraging a synthetically accessible lipid building block dataset and two specialized predictors to guide the search through chemical space, we introduce a policy network guided MCTS generative model capable of producing new ionizable lipids with available synthesis pathways.

Lipid nanoparticles (LNPs) are vital in modern biomedicine, enabling the effective delivery of mRNA for vaccines and therapies by protecting it from rapid degradation. Among the components of LNPs, ionizable lipids play a key role in RNA protection and facilitate its delivery into the cytoplasm. However, designing ionizable lipids is complex. Deep generative models can accelerate this process and explore a larger candidate space compared to traditional methods. Due to the structural differences between lipids and small molecules, existing generative models used for small molecule generation are unsuitable for lipid generation. To address this, we developed a deep generative model specifically tailored for the discovery of ionizable lipids. Our model generates novel ionizable lipid structures and provides synthesis paths using synthetically accessible building blocks, addressing synthesizability. This advancement holds promise for streamlining the development of lipid-based delivery systems, potentially accelerating the deployment of new therapeutic agents, including mRNA vaccines and gene therapies., Comment: NeurIPS 2024 Workshop on AI for New Drug Modalities

Disk winds play a crucial role in the evolution of protoplanetary disks. Typical conditions for star and planet formation are in regions with intermediate or strong UV radiation fields produced by massive stars. The $\sigma$-Orionis cluster is the ideal site to study disk winds under these conditions; its outer parts can be used to study disk evolution, while its innermost regions to study the effect of external irradiation. For this, we analyze the $\rm [OI]\,\lambda$6300, $\rm [NII]\,\lambda$6583, and $\rm [SII]\,\lambda$6731,$\lambda$6716 lines using high-resolution MIKE spectra of 27 classical T Tauri stars and complemented by intermediate-resolution X-shooter data. We decompose the line profiles into multiple Gaussian components. We calculated luminosities, line ratios, and kinematic properties of these components. We found that the $\rm [OI]\,\lambda$6300 line luminosity and kinematic properties are similar to those found in low-mass star-forming regions (SFRs). The frequency of single-component $\rm [OI]\,\lambda$6300 line profiles reflects the expected evolutionary stage given the intermediate age of $\sigma$-Orionis. This points to internal processes contributing to the line emission. However, the highly irradiated disks do not follow the accretion - [OI] luminosity relation found in low-mass SFRs, and all exhibit single-component line profiles. Line ratios of highly ionized species of [NII] and [SII] show higher ratios than typical values found in low-mass SFRs. The innermost regions of $\sigma$-Orionis are clearly affected by external irradiation, evidenced by the lack of correlation in the accretion - [OI] luminosity relation. The broad line widths of close-in sources, however, indicate a contribution from internal processes, such as magnetohydrodynamical winds and/or internal photoevaporation. This suggests a coevolution of internal and external winds in $\sigma$-Orionis., Comment: Accepted for publication on Astronomy & Astrophysics. 14 pages, 11 figures + appendix. Abstract abridged to meet arXiv requirements

Gaia XP spectra for over two hundred million stars have great potential for mapping metallicity across the Milky Way. Several recent studies have analyzed this data set to derive parameters and characterize systematics in the fluxes. We aim to construct an alternative catalog of atmospheric parameters from Gaia XP spectra by fitting them with synthetic spectra based on model atmospheres, and provide corrections to the XP fluxes according to stellar colors, magnitudes, and extinction. We use GaiaXPy to obtain calibrated spectra and apply FERRE to match the corrected XP spectra with models and infer atmospheric parameters. We train a neural network using stars in APOGEE to predict flux corrections as a function of wavelength for each target. Based on the comparison with APOGEE parameters, we conclude that our estimated parameters have systematic errors and uncertainties in $T_{\mathrm{eff}}$, $\log g$, and [M/H] about $-38 \pm 167$ K, $0.05 \pm 0.40$ dex, and $-0.12 \pm 0.19$ dex, respectively, for stars in the range $4000 \le T_{\mathrm{eff}} \le 7000$ K. The corrected XP spectra show better agreement with both models and Hubble Space Telescope CALSPEC data. Our correction increases the precision of the relative spectrophotometry of the XP data from $3.2\% - 3.7\%$ to $1.2\% - 2.4\%$. Finally, we have built a catalog of atmospheric parameters for stars within $4000 \le T_{\mathrm{eff}} \le 7000$ K, comprising $68,394,431$ sources, along with a subset of $124,188$ stars with $\mathrm{[M/H]} \le -2.5$. Our results confirm that the Gaia XP flux calibrated spectra show systematic patterns as a function of wavelength that are tightly related to colors, magnitudes, and extinction. Our optimization algorithm can give us accurate atmospheric parameters of stars with a clear and direct link to models of stellar atmospheres, and can be used to efficiently search for extremely metal-poor stars., Comment: 16 pages, 11 figures, submitted to A&A, catalogs and code in https://doi.org/10.5281/zenodo.14028589

KM3NeT/ORCA is an underwater neutrino telescope under construction in the Mediterranean Sea. Its primary scientific goal is to measure the atmospheric neutrino oscillation parameters and to determine the neutrino mass ordering. ORCA can constrain the oscillation parameters $\Delta m^{2}_{31}$ and $\theta_{23}$ by reconstructing the arrival direction and energy of multi-GeV neutrinos crossing the Earth. Searches for deviations from the Standard Model of particle physics in the forward scattering of neutrinos inside Earth matter, produced by Non-Standard Interactions, can be conducted by investigating distortions of the standard oscillation pattern of neutrinos of all flavours. This work reports on the results of the search for non-standard neutrino interactions using the first six detection units of ORCA and 433 kton-years of exposure. No significant deviation from standard interactions was found in a sample of 5828 events reconstructed in the 1 GeV$-$1 TeV energy range. The flavour structure of the non-standard coupling was constrained at 90\% confidence level to be $|\varepsilon_{\mu\tau} | \leq 5.4 \times 10^{-3}$, $|\varepsilon_{e\tau} | \leq 7.4 \times 10^{-2}$, $|\varepsilon_{e\mu} | \leq 5.6 \times 10^{-2}$ and $-0.015 \leq \varepsilon_{\tau\tau} - \varepsilon_{\mu\mu} \leq 0.017$. The results are comparable to the current most stringent limits placed on the parameters by other experiments.

The relativistic Vlasov-Maxwell system is a kinetic model for collisionless plasmas. For the two-dimensional model, global well-posedness of this model is known and was proven by deriving global bounds on the momentum support of the particle density function. In this paper, we prove bounds on the magnitude of the momentum support in one direction depending on the magnitude of the support in the corresponding orthogonal direction.

White Matter Hyperintensities (WMH) are key neuroradiological markers of small vessel disease present in brain MRI. Assessment of WMH is important in research and clinics. However, WMH are challenging to segment due to their high variability in shape, location, size, poorly defined borders, and similar intensity profile to other pathologies (e.g stroke lesions) and artefacts (e.g head motion). In this work, we apply the most effective techniques for uncertainty quantification (UQ) in segmentation to the WMH segmentation task across multiple test-time data distributions. We find a combination of Stochastic Segmentation Networks with Deep Ensembles yields the highest Dice and lowest Absolute Volume Difference % (AVD) score on in-domain and out-of-distribution data. We demonstrate the downstream utility of UQ, proposing a novel method for classification of the clinical Fazekas score using spatial features extracted for WMH segmentation and UQ maps. We show that incorporating WMH uncertainty information improves Fazekas classification performance and calibration, with median class balanced accuracy for classification models with (UQ and spatial WMH features)/(spatial WMH features)/(WMH volume only) of 0.71/0.66/0.60 in the Deep WMH and 0.82/0.77/0.73 in the Periventricular WMH regions respectively. We demonstrate that stochastic UQ techniques with high sample diversity can improve the detection of poor quality segmentations. Finally, we qualitatively analyse the semantic information captured by UQ techniques and demonstrate that uncertainty can highlight areas where there is ambiguity between WMH and stroke lesions, while identifying clusters of small WMH in deep white matter unsegmented by the model., Comment: 34 pages (or 22 not including appendix) 26 figures (or 11 not including appendix)

Detecting user frustration in modern-day task-oriented dialog (TOD) systems is imperative for maintaining overall user satisfaction, engagement, and retention. However, most recent research is focused on sentiment and emotion detection in academic settings, thus failing to fully encapsulate implications of real-world user data. To mitigate this gap, in this work, we focus on user frustration in a deployed TOD system, assessing the feasibility of out-of-the-box solutions for user frustration detection. Specifically, we compare the performance of our deployed keyword-based approach, open-source approaches to sentiment analysis, dialog breakdown detection methods, and emerging in-context learning LLM-based detection. Our analysis highlights the limitations of open-source methods for real-world frustration detection, while demonstrating the superior performance of the LLM-based approach, achieving a 16\% relative improvement in F1 score on an internal benchmark. Finally, we analyze advantages and limitations of our methods and provide an insight into user frustration detection task for industry practitioners.

In this work, we present MOLPIPx, a versatile library designed to seamlessly integrate Permutationally Invariant Polynomials (PIPs) with modern machine learning frameworks, enabling the efficient development of linear models, neural networks, and Gaussian process models. These methodologies are widely employed for parameterizing potential energy surfaces across diverse molecular systems. MOLPIPx leverages two powerful automatic differentiation engines -JAX and EnzymeAD-Rust- to facilitate the efficient computation of energy gradients and higher-order derivatives, which are essential for tasks such as force field development and dynamic simulations. MOLPIPx is available at https://github.com/ChemAI-Lab/molpipx.

Jupyter notebooks have become central in data science, integrating code, text and output in a flexible environment. With the rise of machine learning (ML), notebooks are increasingly used for ML prototyping and data analysis. However, due to their dependence on complex ML libraries and the flexible notebook semantics that allow cells to be run in any order, notebooks are susceptible to software bugs that may lead to program crashes. This paper presents a comprehensive empirical study focused on crashes in ML notebooks. We collect 64,031 ML notebooks containing 92,542 crashes from GitHub and Kaggle, and manually analyze a sample of 746 crashes across various aspects, including exception types and root causes. Our analysis highlights unique root causes related to notebook semantics, including out-of-order execution and previous cell error, that have not been thoroughly covered in earlier research. Furthermore, we categorize crashes as ML bugs or general Python bugs and examine how the crashes are distributed across different stages of the ML pipeline. Our analysis reveals that 87% of crashes are caused by API misuse, data confusion, notebook-specific issues, environment problems, and implementation errors. Crashes are more commonly related to ML bugs than general Python bugs, particularly in Kaggle notebooks, where over 70% of crashes are ML-related. The distribution of crashes across ML pipeline stages differs between the two platforms. Additionally, most crashes (58%) occur during data preparation, model training, and evaluation/prediction stages of the ML pipeline. GitHub and Kaggle exhibit different crash distributions across these stages., Comment: submitted to a journal

Bayesian optimization (BO) methods based on information theory have obtained state-of-the-art results in several tasks. These techniques heavily rely on the Kullback-Leibler (KL) divergence to compute the acquisition function. In this work, we introduce a novel information-based class of acquisition functions for BO called Alpha Entropy Search (AES). AES is based on the {\alpha}-divergence, that generalizes the KL divergence. Iteratively, AES selects the next evaluation point as the one whose associated target value has the highest level of the dependency with respect to the location and associated value of the global maximum of the optimization problem. Dependency is measured in terms of the {\alpha}-divergence, as an alternative to the KL divergence. Intuitively, this favors the evaluation of the objective function at the most informative points about the global maximum. The {\alpha}-divergence has a free parameter {\alpha}, which determines the behavior of the divergence, trading-off evaluating differences between distributions at a single mode, and evaluating differences globally. Therefore, different values of {\alpha} result in different acquisition functions. AES acquisition lacks a closed-form expression. However, we propose an efficient and accurate approximation using a truncated Gaussian distribution. In practice, the value of {\alpha} can be chosen by the practitioner, but here we suggest to use a combination of acquisition functions obtained by simultaneously considering a range of values of {\alpha}. We provide an implementation of AES in BOTorch and we evaluate its performance in both synthetic, benchmark and real-world experiments involving the tuning of the hyper-parameters of a deep neural network. These experiments show that the performance of AES is competitive with respect to other information-based acquisition functions such as JES, MES or PES., Comment: 31 pages, 12 figures, 3 tables, Journal KBS

Motivation: Automated bug detection in dynamically typed languages such as Python is essential for maintaining code quality. The lack of mandatory type annotations in such languages can lead to errors that are challenging to identify early with traditional static analysis tools. Recent progress in deep neural networks has led to increased use of neural bug detectors. In statically typed languages, a type checker is integrated into the compiler and thus taken into consideration when the neural bug detector is designed for these languages. Problem: However, prior studies overlook this aspect during the training and testing of neural bug detectors for dynamically typed languages. When an optional type checker is used, assessing existing neural bug detectors on bugs easily detectable by type checkers may impact their performance estimation. Moreover, including these bugs in the training set of neural bug detectors can shift their detection focus toward the wrong type of bugs. Contribution: We explore the impact of type checking on various neural bug detectors for variable misuse bugs, a common type targeted by neural bug detectors. Existing synthetic and real-world datasets are type-checked to evaluate the prevalence of type-related bugs. Then, we investigate how type-related bugs influence the training and testing of the neural bug detectors. Findings: Our findings indicate that existing bug detection datasets contain a significant proportion of type-related bugs. Building on this insight, we discover integrating the neural bug detector with a type checker can be beneficial, especially when the code is annotated with types. Further investigation reveals neural bug detectors perform better on type-related bugs than other bugs. Moreover, removing type-related bugs from the training data helps improve neural bug detectors' ability to identify bugs beyond the scope of type checkers., Comment: Accepted by ICSE'25 Research Track

We present tonalli, a spectroscopic analysis python code that efficiently predicts effective temperature, stellar surface gravity, metallicity, $\alpha$-element abundance, and rotational and radial velocities for stars with effective temperatures between 3200 and 6250 K, observed with the Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2). tonalli implements an asexual genetic algorithm to optimise the finding of the best comparison between a target spectrum and the continuum-normalised synthetic spectra library from the Model Atmospheres with a Radiative and Convective Scheme (MARCS), which is interpolated in each generation. Using simulated observed spectra and the APOGEE-2 solar spectrum of Vesta, we study the performance, limitations, accuracy and precision of our tool. Finally, a Monte Carlo realisation was implemented to estimate the uncertainties of each derived stellar parameter. The ad hoc continuum-normalised library is publicly available on Zenodo (DOI 10.5281/zenodo.12736546)., Comment: 35 pages, 22 figures. Accepted to RASTI. Supplemental continuum-normalised library available at: 10.5281/zenodo.12736546

CHANCES, the CHileAN Cluster galaxy Evolution Survey, will study the evolution of galaxies in and around ${\sim}$150 massive galaxy clusters, from the local universe out to z=0.45. CHANCES will use the new 4MOST Spectroscopic Survey Facility on the VISTA 4m telescope to obtain spectra for ${\sim}$500,000 galaxies with magnitudes $r_\mathrm{AB} < 20.5$, providing comprehensive spectroscopic coverage of each cluster out to $5r_{200}$. Its wide and deep scope will trace massive and dwarf galaxies from the surrounding filaments and groups to the cores of galaxy clusters, enabling the study of galaxy pre-processing and the role of the evolving environment on galaxy evolution. In this paper we present and characterize the sample of clusters and superclusters to be targeted by CHANCES. We used literature catalogues based on X-ray emission and Sunyaev-Zel'dovich effect to define the cluster sample in a homogeneous way, with attention to cluster mass and redshift, as well as the availability of ancillary data. We calibrated literature mass estimates from various surveys against each other and provide an initial mass estimate for each cluster, which we used to define the radial extent of the 4MOST coverage. We also present an initial assessment of the structure surrounding these clusters based on the redMaPPer red-sequence algorithm as a preview of some of the science CHANCES will enable., Comment: 11 pages, 9 figures, plus references and appendix containing catalog tables, submitted to A&A