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In the framework of data envelopment analysis (DEA), Tone (2001) introduced the slacks-based measure (SBM) of efficiency, which is a nonradial model that incorporates all the slacks of the evaluated decision-making units (DMUs) into their efficiency scores, unlike classical radial efficiency models. Next, Tone (2002) developed the SBM super-efficiency model in order to differentiate and rank efficient DMUs, whose SBM efficiency scores are always $1$. However, as pointed out by Chen (2013), some interpretation problems arise when the so-called super-efficiency projections are weakly efficient, leading to an overestimation of the SBM super-efficiency score. Moreover, this overestimation is closely related to discontinuity issues when implementing SBM super-efficiency in conjunction with SBM efficiency. Chen (2013) and Chen et al. (2019) treated these problems, but they did not arrive to a fully satisfactory solution. In this paper, we review these papers and propose a new complementary score, called composite SBM, that actually fixes the discontinuity problems by counteracting the overestimation of the SBM super-efficiency score. Moreover, we extend the composite SBM model to different orientations and variable returns to scale, and propose additive versions. Finally, we give examples and state some open problems., Comment: 25 pages, 3 figures

We construct a new family of chance constrained directional models in stochastic data envelopment analysis, generalizing the deterministic directional models and the chance constrained radial models. We prove that chance constrained directional models define the same concept of stochastic efficiency as the one given by chance constrained radial models and, as a particular case, we obtain a stochastic version of the generalized Farrell measure. Finally, we give some examples of application of chance constrained directional models with stochastic and deterministic directions, showing that inefficiency scores obtained with stochastic directions are less or equal than those obtained considering deterministic directions whose values are the means of the stochastic ones., Comment: 15 pages

Quantum Communications is a field that promises advances in cryptography, quantum computing and clock synchronisation, among other potential applications. However, communication based on quantum phenomena requires an extreme level of isolation from external disturbances, making the transmission of quantum signals together with classical ones difficult. A range of techniques has been tested to introduce quantum communications in already deployed optical networks which also carry legacy traffic. This comes with challenges, not only at the physical layer but also at the operations and management layer. To achieve a broad acceptance among network operators, the joint management and operation of quantum and classical resources, compliance with standards, and quality and legal assurance need to be addressed. This article presents a detailed account of solutions to the above issues, deployed and evaluated in the MadQCI (Madrid Quantum Communication Infrastructure) testbed. This network is designed to integrate quantum communications in the telecommunications ecosystem by installing quantum-key-distribution modules from multiple providers in production nodes of two different operators. The modules were connected through an optical-switched network with more than 130 km of deployed optical fibre. The tests were done in compliance with strict service level agreements that protected the legacy traffic of the pre-existing classical network. The goal was to achieve full quantum-classical compatibility at all levels, while limiting the modifications of optical transport and encryption and complying with as many standards as possible. This effort was intended to serve as a blueprint, which can be used as the foundation of large-scale quantum network deployments. To demonstrate the capabilities of MadQCI, end-to-end encryption services were deployed and a variety of use-cases were showcased., Comment: 16 pages

In this work we present the wavScalogram R package, which contains methods based on wavelet scalograms for time series analysis. These methods are related to two main wavelet tools: the windowed scalogram difference and the scale index. The windowed scalogram difference compares two time series, identifying if their scalograms follow similar patterns at different scales and times, and it is thus a useful complement to other comparison tools such as the squared wavelet coherence. On the other hand, the scale index provides a numerical estimation of the degree of non-periodicity of a time series and it is widely used in many scientific areas., Comment: 27 pages, 17 figures

The relation between the Husain-Kucha\v{r} model and some extensions thereof that incorporate a dynamical time variable is explored. To this end, we rely on the geometric approach to the Hamiltonian dynamics of singular (constrained) systems provided by the Gotay-Nester-Hinds method (GNH). We also use recent results regarding the derivation of the Ashtekar formulation for Euclidean general relativity without using the time gauge. The insights provided by the geometric approach followed in the paper shed light on several issues related to the dynamics of general relativity and the role of time., Comment: 27 pages

Artificial Intelligence (AI), particularly large language models (LLMs), holds the potential to bridge language and information gaps, which can benefit the economies of developing nations. However, our analysis of FLORES-200, FLORES+, Ethnologue, and World Development Indicators data reveals that these benefits largely favor English speakers. Speakers of languages in low-income and lower-middle-income countries face higher costs when using OpenAI's GPT models via APIs because of how the system processes the input -- tokenization. Around 1.5 billion people, speaking languages primarily from lower-middle-income countries, could incur costs that are 4 to 6 times higher than those faced by English speakers. Disparities in LLM performance are significant, and tokenization in models priced per token amplifies inequalities in access, cost, and utility. Moreover, using the quality of translation tasks as a proxy measure, we show that LLMs perform poorly in low-resource languages, presenting a ``double jeopardy" of higher costs and poor performance for these users. We also discuss the direct impact of fragmentation in tokenizing low-resource languages on climate. This underscores the need for fairer algorithm development to benefit all linguistic groups., Comment: Project GitHub repository at https://github.com/worldbank/double-jeopardy-in-llms

Context. The gravitational lens system SDSS J1004+4112 was the first known example of a quasar lensed by a galaxy cluster. The interest in this system has been renewed following the publication of r-band light curves spanning 14.5 years and the determination of the time delays between the four brightest quasar images. Aims. We constrained the quasar accretion disk size and the fraction of the lens mass in stars using the signature of microlensing in the quasar image light curves. Methods. We built the six possible histograms of microlensing magnitude differences between the four quasar images and compared them with simulated model histograms, using a $\chi^2$ test to infer the model parameters. Results. We infer a quasar disk half-light radius of $R_{1/2}=(0.70\pm0.04)\, R_E=(6.4\pm0.4) \sqrt{M/0.3M_{\odot}}$ light-days at 2407\r{A} in the rest frame and stellar mass fractions at the quasar image positions of $\alpha_A>0.059$, $\alpha_B=0.056^{+0.021}_{-0.027}$, $\alpha_C=0.030^{+0.031}_{-0.021}$, and $\alpha_D=0.072^{+0.034}_{-0.016}$. Conclusions. The inferred disk size is broadly compatible with most previous estimates, and the stellar mass fractions are within the expected ranges for galaxy clusters. In the region where image C lies, the stellar mass fraction is compatible with a stellar contribution from the brightest cluster galaxy, galaxy cluster members, and intracluster light, but the values at images B, D, and especially A are slightly larger, possibly suggesting the presence of extra stellar components., Comment: 7 pages, 6 figures, 2 tables, A&A in press

Through automation, deep learning (DL) can enhance the analysis of transesophageal echocardiography (TEE) images. However, DL methods require large amounts of high-quality data to produce accurate results, which is difficult to satisfy. Data augmentation is commonly used to tackle this issue. In this work, we develop a pipeline to generate synthetic TEE images and corresponding semantic labels. The proposed data generation pipeline expands on an existing pipeline that generates synthetic transthoracic echocardiography images by transforming slices from anatomical models into synthetic images. We also demonstrate that such images can improve DL network performance through a left-ventricle semantic segmentation task. For the pipeline's unpaired image-to-image (I2I) translation section, we explore two generative methods: CycleGAN and contrastive unpaired translation. Next, we evaluate the synthetic images quantitatively using the Fr\'echet Inception Distance (FID) Score and qualitatively through a human perception quiz involving expert cardiologists and the average researcher. In this study, we achieve a dice score improvement of up to 10% when we augment datasets with our synthetic images. Furthermore, we compare established methods of assessing unpaired I2I translation and observe a disagreement when evaluating the synthetic images. Finally, we see which metric better predicts the generated data's efficacy when used for data augmentation., Comment: MICCAI2023; DALI Workshop

Ultra-light bosons, naturally appearing in well-motivated extensions to the Standard Model, can constitute all the dark matter. Models with particle mass close to the smallest phenomenologically allowed exhibit coherent field configurations at (sub)galactic scales, oscillating at a frequency corresponding to the fundamental mass of the dark matter particle. The gravitational field of these structures inherits the dark matter field's coherent oscillations, leaving an imprint on gravitational (and electromagnetic) waves sourced close to (or in) such overdensities. This happens via a heterodyning frequency modulation, which can later be decoded in a gravitational-wave detector. An analogous effect occurs in models with universal (conformal) couplings of ultra-light bosons with ordinary matter, generated by the direct interaction with the oscillating field. In this work, we explore this phenomenon in detail and assess the capability of near-future interferometers to probe ultra-light dark matter and its potential conformal couplings to matter. Using astrophysical population models, together with results from cosmological simulations, we find that the observation of gravitational waves from spinning neutron stars at the Galactic Centre with the Einstein Telescope/Cosmic Explorer would be particularly effective in constraining ultra-light dark matter., Comment: 23 pages, 10 figures

Speculative decoding has proven to be an efficient solution to large language model (LLM) inference, where the small drafter predicts future tokens at a low cost, and the target model is leveraged to verify them in parallel. However, most existing works still draft tokens auto-regressively to maintain sequential dependency in language modeling, which we consider a huge computational burden in speculative decoding. We present ParallelSpec, an alternative to auto-regressive drafting strategies in state-of-the-art speculative decoding approaches. In contrast to auto-regressive drafting in the speculative stage, we train a parallel drafter to serve as an efficient speculative model. ParallelSpec learns to efficiently predict multiple future tokens in parallel using a single model, and it can be integrated into any speculative decoding framework that requires aligning the output distributions of the drafter and the target model with minimal training cost. Experimental results show that ParallelSpec accelerates baseline methods in latency up to 62% on text generation benchmarks from different domains, and it achieves 2.84X overall speedup on the Llama-2-13B model using third-party evaluation criteria., Comment: work in progress

We classify all projective surfaces with only T-singularities, $K^2=2p_g-4$, and ample canonical class. In this way, we identify all surfaces, smoothable or not, with only T-singularities in the Koll\'ar--Shepherd-Barron--Alexeev (KSBA) moduli space of Horikawa surfaces. We also prove that they are not smoothable when $p_g \geq 10$, except for the Lee-Park (Fintushel-Stern) examples, which we show to have only one deformation type unless $p_g=6$ (in which case they have two). This demonstrates that the challenging Horikawa problem cannot be addressed through complex T-degenerations, and we propose new questions regarding diffeomorphism types based on our classification. Furthermore, the techniques developed in this paper enable us to classify all KSBA surfaces with only T-singularities and $K^2=2p_g-3$, e.g. quintic surfaces and I-surfaces.

The rapid advancements in autonomous vehicle software present both opportunities and challenges, especially in enhancing road safety. The primary objective of autonomous vehicles is to reduce accident rates through improved safety measures. However, the integration of new algorithms into the autonomous vehicle, such as Artificial Intelligence methods, raises concerns about the compliance with established safety regulations. This paper introduces a novel software architecture based on behavior trees, aligned with established standards and designed to supervise vehicle functional safety in real time. It specifically addresses the integration of algorithms into industrial road vehicles, adhering to the ISO 26262. The proposed supervision methodology involves the detection of hazards and compliance with functional and technical safety requirements when a hazard arises. This methodology, implemented in this study in a Renault M\'egane (currently at SAE level 3 of automation), not only guarantees compliance with safety standards, but also paves the way for safer and more reliable autonomous driving technologies., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Dirac showed that the existence of one magnetic pole in the universe could offer an explanation of the discrete nature of the electric charge. Magnetic poles appear naturally in most grand unified theories. Their discovery would be of greatest importance for particle physics and cosmology. The intense experimental search carried thus far has not met with success. In order to understand this lack of success we studied monopolium a monopole anti-monopole bound state with properties very different from monopoles in which monopoles could be hiding. In here, we propose another mechanism, namely that magnetic poles bind deeply with some neutral states hiding in this way some of the properties of the free monopoles. We study different scenarios of these systems and analyze their detectability. In particular, one scenario is very interesting because it leads to a light state, an analog of an electron with magnetic instead of electric charge, which we call magnetron., Comment: 9 pages, 4 figures

We adopt the two point correlation function (2PCF) as a statistical tool to quantify the spatial clustering of halo stars, for galaxy systems spanning a wide range in host halo virial mass ($11.25<\log_{10}M_{200c}/\mathrm{M}_\odot<15$) and redshifts ($0

Identification of active galactic nuclei (AGNs) is extremely important for understanding galaxy evolution and its connection with the formation and evolution of supermassive black holes (SMBH). With the advent of deep and high angular resolution imaging surveys such as those conducted with the {\it James Webb} Space Telescope (JWST), it is now possible to identify galaxies with a central point source out to the very early Universe. In this study, we develop a fast, accurate and precise method to identify galaxies which host AGNs and recover the intrinsic AGN contribution to the observed total light ($f_{AGN}$). We trained a deep learning (DL) based method Zoobot to estimate the fractional contribution of a central point source to the total light. Our training sample comprises realistic mock JWST images of simulated galaxies from the IllustrisTNG cosmological hydrodynamical simulations. We injected different amounts of the real JWST point spread function (PSF) models to represent galaxies with different levels of $f_{AGN}$. We analyse the performance of our method and compare it with results obtained from the traditional light profile fitting tool GALFIT. We find excellent performance of our DL method in recovering the injected AGN fraction $f_{AGN}$, both in terms of precision and accuracy. The mean difference between the predicted and true injected $f_{AGN}$ is $-0.0006$ and the overall root mean square error (RMSE) is $0.027$. The relative absolute error (RAE) is $0.086$ and the outlier (defined as predictions with RAE $>20\%$) fraction is $7.8\%$. In comparison, using GALFIT on the same dataset, we achieve a mean difference of -$0.024$, RMSE of $0.09$, RAE of $0.19$ and outlier fraction of $19\%$. Our DL-based method to identify AGNs and estimate $f_{AGN}$ is extremely fast and easy to use and has a huge potential in future applications to large galaxy imaging surveys.

We introduce FabricDiffusion, a method for transferring fabric textures from a single clothing image to 3D garments of arbitrary shapes. Existing approaches typically synthesize textures on the garment surface through 2D-to-3D texture mapping or depth-aware inpainting via generative models. Unfortunately, these methods often struggle to capture and preserve texture details, particularly due to challenging occlusions, distortions, or poses in the input image. Inspired by the observation that in the fashion industry, most garments are constructed by stitching sewing patterns with flat, repeatable textures, we cast the task of clothing texture transfer as extracting distortion-free, tileable texture materials that are subsequently mapped onto the UV space of the garment. Building upon this insight, we train a denoising diffusion model with a large-scale synthetic dataset to rectify distortions in the input texture image. This process yields a flat texture map that enables a tight coupling with existing Physically-Based Rendering (PBR) material generation pipelines, allowing for realistic relighting of the garment under various lighting conditions. We show that FabricDiffusion can transfer various features from a single clothing image including texture patterns, material properties, and detailed prints and logos. Extensive experiments demonstrate that our model significantly outperforms state-to-the-art methods on both synthetic data and real-world, in-the-wild clothing images while generalizing to unseen textures and garment shapes., Comment: Accepted to SIGGRAPH Asia 2024. Project page: https://humansensinglab.github.io/fabric-diffusion

The proposed Electron-Ion Collider (EIC) will utilize high-luminosity high-energy electron+proton ($e+p$) and electron+nucleus ($e+A$) collisions to solve several fundamental questions including searching for gluon saturation and studying the proton/nuclear structure. Complementary to the ongoing EIC project detector technical prototype carried out by the ePIC collaboration, a Depleted Monolithic Active Pixel Sensor (i.e., MALTA2) based fast timing silicon tracking detector (FMT) has been proposed to provide additional hits for track reconstruction in the forward and backward region at the EIC to improve the overall track reconstruction quality. The fast timing resolution of the MALTA2 technology will help reject background events at the EIC as well. Progress of latest MALTA2 R$\&$D, the development of a new MALTA2 quad-sensor prototype module and impacts of the proposed FMT in EIC physics studies will be discussed., Comment: 4 pages, 4 figures, conference proceeding submitted for the 21st International Conference on Strangeness in Quark Matter (SQM2024) conference. This work is supported by the EIC generic R&D program

The "IO Wall" problem, in which the gap between computation rate and data access rate grows continuously, poses significant problems to scientific workflows which have traditionally relied upon using the filesystem for intermediate storage between workflow stages. One way to avoid this problem in scientific workflows is to stream data directly from producers to consumers and avoiding storage entirely. However, the manner in which this is accomplished is key to both performance and usability. This paper presents the Sustainable Staging Transport, an approach which allows direct streaming between traditional file writers and readers with few application changes. SST is an ADIOS "engine", accessible via standard ADIOS APIs, and because ADIOS allows engines to be chosen at run-time, many existing file-oriented ADIOS workflows can utilize SST for direct application-to-application communication without any source code changes. This paper describes the design of SST and presents performance results from various applications that use SST, for feeding model training with simulation data with substantially higher bandwidth than the theoretical limits of Frontier's file system, for strong coupling of separately developed applications for multiphysics multiscale simulation, or for in situ analysis and visualization of data to complete all data processing shortly after the simulation finishes.

We report the discovery in TMC-1 of two cyano derivatives of the PAH acenaphthylene (C$_{12}$H$_8$). We have found two series of lines with the QUIJOTE line survey that we assign to 1-C$_{12}$H$_7$CN and 5-C$_{12}$H$_7$CN. For the 1-isomer, we have detected and assigned 173 rotational transitions with $J$ up to 46 and $K_a$ up to 9, corresponding to 107 independent frequencies. For the 5-isomer, the identification is based on 56 individual lines, corresponding to 117 rotational transitions with $J$ up to 40 and $K_a$ up to 8. Identification of the carriers was achieved through a careful analysis of the derived rotational constants, which permit us to focus on molecules larger than naphthalene but smaller than anthracene and phenanthrene. Moreover, the derived rotational constants indicate that the species are planar; this allows us to discard derivatives of fluorene and acenaphthene, which are non-planar species. Quantum chemical calculations and subsequent chemical synthesis of these molecules, as well as the observation of their rotational transitions in the laboratory, unequivocally support our identifications. We also confirm, via a robust line-by-line detection, the previous claimed detection of 1- and 2- cyanonaphthalene, which were obtained through statistical stacking techniques. The column densities of 1- and 5-cyanoacenaphthylene are (9.5\,$\pm$\,0.9)$\,\times$\,10$^{11}$ cm$^{-2}$, while those of 1- and 2-cyanonapthalene are (5.5\,$\pm$\,0.5)$\,\times$\,10$^{11}$ cm$^{-2}$. Hence, it seems that acenaphthylene could be a factor of 1.7 more abundant than naphthalene. These results support a scenario in which PAHs grow in cold dark clouds based on fused five- and six-membered carbon rings., Comment: Accepted as a Letter to the Editor in Astronomy and Astrophysics on 18th September 2024

We investigate the implications of different quantization approaches in Loop Quantum Cosmology for the primordial power spectrum of tensor modes. Specifically, we consider the hybrid and dressed metric approaches to derive the effective mass that governs the evolution of the tensor modes. Our study comprehensively examines the two resulting effective masses and how to estimate them in order to obtain approximated analytic solutions to the tensor perturbation equations. Since Loop Quantum Cosmology incorporates preinflationary effects in the dynamics of the perturbations, we do not have at our disposal a standard choice of privileged vacuum, like the Bunch--Davies state in quasi-de Sitter inflation. We then select the vacuum state by a recently proposed criterion which removes unwanted oscillations in the power spectrum and guarantees an asymptotic diagonalization of the Hamiltonian in the ultraviolet. This vacuum is usually called the NO-AHD (from the initials of Non-Oscillating with Asymptotic Hamiltonian Diagonalization) vacuum. Consequently, we compute the power spectrum by using our analytic approximations and by introducing a suitable numerical procedure, adopting in both cases an NO-AHD vacuum. With this information, we compare the different spectra obtained from the hybrid and the dressed metric approaches, as well as from the analytic and numerical procedures. In particular, this proves the remarkable accuracy of our~approximations., Comment: 22 pages, 10 figures

One of the primary challenges in live cell culture observation is achieving high-contrast imaging with minimal impact on sample behavior. Quantitative phase imaging (QPI) techniques address this by providing label-free, high-contrast images of transparent samples. Measurement system influence may be further reduced by imaging samples under low illumination intensity (low photon budget - LPB), thereby minimizing photostimulation, phototoxicity, and photodamage, and enabling high-speed imaging. LPB imaging is challenging in QPI due to significant camera shot noise and quantification noise. Digital in-line holographic microscopy (DIHM), working with or without lenses, is a QPI technique known for its robustness to quantification noise. However, reducing simultaneously the camera shot noise and the inherent in-line holographic twin image disturbances remain a critical, yet unaddressed, challenge. This study introduces an innovative iterative Gabor averaging (IGA) algorithm designed specifically for filling this important scientific gap in multi-frame DIHM under LPB conditions. We evaluated the performance of the IGA on simulated data showing that it outperformed traditional algorithms in terms of reconstruction accuracy under high noise conditions. Those results were corroborated by experimental validation involving high-speed imaging of dynamic sperm cells and a phase test target under significantly reduced illumination power. Additionally, the IGA algorithm proved successful in reconstructing optically thin samples, which typically produce low signal-to-noise ratio holograms even under high photon budget conditions. These advancements facilitate photostimulation-free and high-speed imaging of dynamic biological samples and enhance the capability to image samples with extremely low optical thickness, potentially transforming various applications in biomedical and environmental imaging.

We present constraints on the $f(R)$ gravity model using a sample of 1,005 galaxy clusters in the redshift range $0.25 - 1.78$ that have been selected through the thermal Sunyaev-Zel'dovich effect (tSZE) from South Pole Telescope (SPT) data and subjected to optical and near-infrared confirmation with the Multi-component Matched Filter (MCMF) algorithm. We employ weak gravitational lensing mass calibration from the Dark Energy Survey (DES) Year 3 data for 688 clusters at $z < 0.95$ and from the Hubble Space Telescope (HST) for 39 clusters with $0.6 < z < 1.7$. Our cluster sample is a powerful probe of $f(R)$ gravity, because this model predicts a scale-dependent enhancement in the growth of structure, which impacts the halo mass function (HMF) at cluster mass scales. To account for these modified gravity effects on the HMF, our analysis employs a semi-analytical approach calibrated with numerical simulations. Combining calibrated cluster counts with primary cosmic microwave background (CMB) temperature and polarization anisotropy measurements from the Planck2018 release, we derive robust constraints on the $f(R)$ parameter $f_{R0}$. Our results, $\log_{10} |f_{R0}| < -5.32$ at the 95 % credible level, are the tightest current constraints on $f(R)$ gravity from cosmological scales. This upper limit rules out $f(R)$-like deviations from general relativity that result in more than a $\sim$20 % enhancement of the cluster population on mass scales $M_\mathrm{200c}>3\times10^{14}M_\odot$., Comment: 21 pages, 6 figures, submitted to Phys. Rev. D

Resilience refers to the ability of systems to withstand, adapt to, and recover from disruptive events. While studies on resilience have attracted significant attention across various research domains, the precise definition of this concept within the field of cooperative artificial intelligence remains unclear. This paper addresses this gap by proposing a clear definition of `cooperative resilience' and outlining a methodology for its quantitative measurement. The methodology is validated in an environment with RL-based and LLM-augmented autonomous agents, subjected to environmental changes and the introduction of agents with unsustainable behaviors. These events are parameterized to create various scenarios for measuring cooperative resilience. The results highlight the crucial role of resilience metrics in analyzing how the collective system prepares for, resists, recovers from, sustains well-being, and transforms in the face of disruptions. These findings provide foundational insights into the definition, measurement, and preliminary analysis of cooperative resilience, offering significant implications for the broader field of AI. Moreover, the methodology and metrics developed here can be adapted to a wide range of AI applications, enhancing the reliability and effectiveness of AI in dynamic and unpredictable environments., Comment: Supplementary material in https://github.com/mavivi95/resilience/blob/main/Supplementary_File.pdf

Using a combination of HST, JWST, and ALMA data, we perform spatially resolved spectral energy distributions (SED) fitting of fourteen 4

The present work aims to measure speeds of sound $c$ in a biogas mixture of $CH_{4}$ + $N_{2}$ + $CO_{2}$ + $CO$, at $p$ = (1-12) MPa and $T$ = (273, 300 and 325) K, using a spherical acoustical resonator. The results are fitted to the virial acoustic equation of state, and the virial acoustic coefficients are obtained, $\beta_{a}$ and $\gamma_{a}$ and extrapolated to zero pressure, determining the adiabatic coefficient as perfect gas, $\gamma^{pg}$, and the isobaric and isochoric heat capacities as perfect gas, $C_{p}^{pg}$ and $C_{V}^{pg}$, respectively. The speeds of sound are acquired with a mean expanded relative uncertainty of 165 parts in $10^{6}$ ($k$ = 2) and are compared with the results predicted by the reference equation of state for this kind of mixture (natural gas-like mixtures), EoS GERG-2008. Relative deviations between experimental data and values estimated by this model were less than 700 parts in $10^{6}$ at $T$ = 325 K, and below 400 parts in $10^{6}$, and within measurement uncertainty of at $T$ = 300 K, although appreciably higher at isotherm $T$ = 273 K at the highest pressure data for this work, and even reaching values above 3 400 parts in $10^{6}$.

Perpendicular magnetic anisotropy (PMA) and Dzyaloshinskii-Moriya interactions are key interactions in modern spintronics. These interactions are thought to be dominated by the oxidation of the Co|Al interface in the archetypal Platinum-Cobalt-Aluminum oxide system. Here, we observe a double sign change in the anisotropy and about threefold variation in interfacial chiral interaction, influenced not only by the oxidation, but also by the metallic Al thickness. Contrary to previous assumptions about negligible spin-orbit effects at light metal interfaces, we not only observe strong PMA with fully oxidized Al, decreasing and turning negative (in-plane) with less oxygen at the Co|Al interface, we also observe that the magnetic anisotropy reverts to positive (out-of-plane) values at fully metallic Co|Al interface. These findings suggest modification in Co d band via Co|Al orbital hybridization, an effect supported by X-ray absorption spectroscopy and ab initio theory calculations, highlighting the key impact of strain on interfacial mechanisms at fully metallic Co|Al interface., Comment: 6 pages, 5 figures

We derive the equations of motion for relativistic elastic membranes, that is, two-dimensional elastic bodies whose internal energy depends only on their stretching, starting from a variational principle. We show how to obtain conserved quantities for the membrane's motion in the presence of spacetime symmetries, determine the membrane's longitudinal and transverse speeds of sound in isotropic states, and compute the coefficients of linear elasticity with respect to the relaxed configuration. We then use this formalism to discuss two physically interesting systems: a rigidly rotating elastic disk, widely discussed in the context of Ehrenfest's paradox, and a Dyson sphere, that is, a spherical membrane in equilibrium in Schwarzschild's spacetime, with the isotropic tangential pressure balancing the gravitational attraction. Surprisingly, although spherically symmetric perturbations of this system are linearly stable, the axi-symmetric dipolar mode is already unstable. This may be taken as a cautionary tale against misconstruing radial stability as true stability., Comment: 18 pages, 1 figure. arXiv admin note: text overlap with arXiv:1712.05416

We use the Berglund-H\"ubsch transpose rule from classical mirror symmetry in the context of Sasakian geometry and results on relative K-stability in the Sasaki setting developed by Boyer and van Coevering to exhibit examples of Sasaki manifolds of complexity 3 or complexity 4 that do not admit any extremal Sasaki metrics in its whole Sasaki-Reeb cone which is of Gorenstein type. Previously, examples with this feature were produced by Boyer and van Coevering for Brieskorn-Pham polynomials or their deformations. Our examples are based on the more general framework of invertible polynomials. In particular, we construct families of examples of links with the following property: if the link satisfies the Lichnerowicz obstruction of Gauntlett, Martelli, Sparks and Yau, then its Berglund-H\"ubsch dual preserves this obstruction and moreover this dual admits a perturbation in its local moduli which is obstructed to admitting extremal Sasaki metrics in its whole Sasaki-Reeb cone. In particular, we exhibit examples that have the homotopy type of a sphere or are rational homology spheres., Comment: The previous version of this article has been corrected (minor inaccuracies and typos) and improved. We also have added subsection 4.3 with more examples for more general invertible polynomials

The mean square displacement (MSD) of intruders (tracer particles) immersed in a multicomponent granular mixture made up of smooth inelastic hard spheres in a homogeneous cooling state is explicitly computed. The multicomponent granular mixture is constituted by $s$ species with different masses, diameters, and coefficients of restitution. In the hydrodynamic regime, the time decay of the granular temperature of the mixture gives rise to a time decay of the intruder's diffusion coefficient $D_0$. The corresponding MSD of the intruder is determined by integrating the corresponding diffusion equation. As expected from previous works on binary mixtures, we find a logarithmic time dependence of the MSD which involves the coefficient $D_0$. To analyze the dependence of the MSD on the parameter space of the system, the diffusion coefficient is explicitly determined by considering the so-called second Sonine approximation (two terms in the Sonine polynomial expansion of the intruder's distribution function). The theoretical results for $D_0$ are compared with those obtained by numerically solving the Boltzmann equation by means of the direct simulation Monte Carlo method. We show that the second Sonine approximation improves the predictions of the first Sonine approximation, especially when the intruders are much lighter than the particles of the granular mixture. In the long-time limit, our results for the MSD agree with those recently obtained by Bodrova [Phys. Rev. E \textbf{109}, 024903 (2024)] when $D_0$ is determined by considering the first Sonine approximation., Comment: 15 pages, 3 figures

In the burgeoning field of quantum computing, the precise design and optimization of quantum pulses are essential for enhancing qubit operation fidelity. This study focuses on refining the pulse engineering techniques for superconducting qubits, employing a detailed analysis of Square and Gaussian pulse envelopes under various approximation schemes. We evaluated the effects of coherent errors induced by naive pulse designs. We identified the sources of these errors in the Hamiltonian model's approximation level. We mitigated these errors through adjustments to the external driving frequency and pulse durations, thus, implementing a pulse scheme with stroboscopic error reduction. Our results demonstrate that these refined pulse strategies improve performance and reduce coherent errors. Moreover, the techniques developed herein are applicable across different quantum architectures, such as ion-trap, atomic, and photonic systems., Comment: 17 pages, 3 tables, 2 figures

Cardiovascular diseases (CVDs) are the most common health threats worldwide. 2D x-ray invasive coronary angiography (ICA) remains as the most widely adopted imaging modality for CVDs diagnosis. However, in current clinical practice, it is often difficult for the cardiologists to interpret the 3D geometry of coronary vessels based on 2D planes. Moreover, due to the radiation limit, in general only two angiographic projections are acquired, providing limited information of the vessel geometry and necessitating 3D coronary tree reconstruction based only on two ICA projections. In this paper, we propose a self-supervised deep learning method called NeCA, which is based on implicit neural representation using the multiresolution hash encoder and differentiable cone-beam forward projector layer in order to achieve 3D coronary artery tree reconstruction from two projections. We validate our method using six different metrics on coronary computed tomography angiography data in terms of right coronary artery and left anterior descending respectively. The evaluation results demonstrate that our NeCA method, without 3D ground truth for supervision and large datasets for training, achieves promising performance in both vessel topology preservation and branch-connectivity maintaining compared to the supervised deep learning model., Comment: 16 pages, 10 figures, 6 tables

Statistics that capture the directional dependence of the baryon distribution in the cosmic web enable unique tests of cosmology and astrophysical feedback. We use constrained oriented stacking of thermal Sunyaev-Zel'dovich (tSZ) maps to measure the anisotropic distribution of hot gas $2.5-40$ Mpc away from galaxy clusters embedded in massive filaments and superclusters. The cluster selection and orientation (at a scale of $\sim15$ Mpc) use Dark Energy Survey (DES) Year 3 data, while expanded tSZ maps from the Atacama Cosmology Telescope Data Release 6 enable a $\sim3\times$ more significant measurement of the extended gas compared to the technique's proof-of-concept. Decomposing stacks into cosine multipoles of order $m$, we detect a dipole ($m=1$) and quadrupole ($m=2$) at $8-10\sigma$, as well as evidence for $m=4$ signal at up to $6\sigma$, indicating sensitivity to late-time non-Gaussianity. We compare to the Cardinal simulations with spherical gas models pasted onto dark matter halos. The fiducial tSZ data can discriminate between two models that deplete pressure differently in low-mass halos (mimicking astrophysical feedback), preferring higher average pressure in extended structures. However, uncertainty in the amount of cosmic infrared background contamination reduces the constraining power. Additionally, we apply the technique to DES galaxy density and weak lensing to study for the first time their oriented relationships with tSZ. In the tSZ-to-lensing relation, averaged on 7.5 Mpc (transverse) scales, we observe dependence on redshift but not shape or radial distance. Thus, on large scales, the superclustering of gas pressure, galaxies, and total matter is coherent in shape and extent., Comment: 45 pages, 18 figures, submitted to ApJ

While elementary particles are the favored candidate for the elusive dark matter, primordial black holes (PBHs) have also been considered to fill that role. Gravitational microlensing is a very well-suited tool to detect and measure the abundance of compact objects in galaxies. Previous studies based on quasar microlensing exclude a significant presence of substellar to intermediate-mass BHs ($\lesssim 100\,\mathrm{M}_\odot$). However, these studies were based on a spatially uniform distribution of BHs while, according to current theories of PBHs formation, they are expected to appear in clusters. We study the impact of clustering in microlensing flux magnification finding that at large scales clusters act like giant pseudo-particles, strongly affecting the emission coming from the Broad Line Region, which can no longer be used to define the zero microlensing baseline. As an alternative, we set this baseline from the intrinsic magnification ratios of quasar images predicted by macro lens models and compare them with the observed flux ratios in emission lines, infrared (IR), and radio. The (magnitude) differences are the flux-ratio anomalies attributable to microlensing, which we estimate for 35 image pairs corresponding to 12 lens systems. A Bayesian analysis indicates that the observed anomalies are incompatible with the existence of a significant population of clustered PBHs. Furthermore, we find that more compact clusters exhibit a stronger microlensing impact. Consequently, we conclude that clustering makes the existence of a significant population of BHs in the substellar to intermediate mass range even more unlikely.

This work presents phase envelope and speed of sound data for the (0.60 $CO_{2}$ + 0.40 $C_{3}H_{8}$) and (0.80 $CO_{2}$ + 0.20 $C_{3}H_{8}$) binary mixtures. Phase equilibria was measured using a cylindrical resonator working in the microwave band whereas an acoustic resonator was used for speed of sound measurements. The experimental results were compared with GERG-2008 equation of state, obtaining average absolute deviations by 0.24% in pressure for phase equilibria data and 0.025% for speed of sound data. Speed of sound values were used to derive perfect-gas heat capacities, acoustic virial coefficients, and second density virial coefficients. In addition, AGA8-DC92 equation of state performance was checked for the results derived from speeds of sound.

The merger timescales of isolated low-mass pairs ($\rm 10^8

Using a sample of 2800 galaxy clusters identified in the Dark Energy Survey across the redshift range $0.20 < z < 0.60$, we characterize the hierarchical assembly of Bright Central Galaxies (BCGs) and the surrounding intracluster light (ICL). To quantify hierarchical formation we use the stellar mass - halo mass (SMHM) relation for the BCG+ICL system and incorporate the magnitude gap (M14), the difference in brightness between the BCG (measured within 30kpc) and 4th brightest cluster member galaxy within 0.5 $R_{200,c}$. The inclusion of M14, which traces BCG hierarchical growth, increases the slope and decreases the intrinsic scatter in the SMHM relation, highlighting that it is a latent variable within the BCG+ICL SMHM relation. Moreover, the correlation with M14 decreases at large radii from the BCG's centre. However, the stellar light within the BCG+ICL transition region (30kpc - 80kpc) most strongly correlates with the dark matter halo mass and has a statistically significant correlation with M14. As the light in the transition region and M14 are independent measurements, the transition region may grow as a result of the BCG's hierarchical two-phase formation. Additionally, as M14 and ICL result from hierarchical growth, we use a stacked sample and find that clusters with large M14 values are characterized by larger ICL and BCG+ICL fractions, which illustrates that the merger processes that build the BCG stellar mass also grow the ICL. Furthermore, this may suggest that M14 combined with the ICL fraction can be used as a method to identify dynamically relaxed clusters., Comment: 16 pages, 5 Figures, submitted to MNRAS on 8/30/2024

We present photometric redshifts for 1 341 559 galaxies from the Physics of the Accelerating Universe Survey (PAUS) over 50.38 ${\rm deg}^{2}$ of sky to $i_{\rm AB}=23$. Redshift estimation is performed using DEEPz, a deep-learning photometric redshift code. We analyse the photometric redshift precision when varying the photometric and spectroscopic samples. Furthermore, we examine observational and instrumental effects on the precision of the photometric redshifts, and we compare photometric redshift measurements with those obtained using a template method-fitting BCNz2. Finally, we examine the use of photometric redshifts in the identification of close galaxy pairs. We find that the combination of samples from W1+W3 in the training of DEEPz significantly enhances the precision of photometric redshifts. This also occurs when we recover narrow band fluxes using broad bands measurements. We show that DEEPz determines the redshifts of galaxies in the prevailing spectroscopic catalogue used in the training of DEEPz with greater precision. For the faintest galaxies ($i_{\rm AB}=21-23$), we find that DEEPz improves over BCNz2 both in terms of the precision (20-50 per cent smaller scatter) and in returning a smaller outlier fraction in two of the wide fields. The catalogues were tested for the identification of close galaxy pairs, showing that DEEPz is effective for the identification of close galaxy pairs for samples with $i_{\rm AB} < 22.5$ and redshift $0.2 < z < 0.6$. In addition, identifying close galaxy pairs common between DEEPz and BCNz2 is a promising approach to improving the purity of the catalogues of these systems., Comment: 21 pages, 13 figures, submitted to A&A, link to the catalogue

Cardiac digital twins (CDTs) offer personalized \textit{in-silico} cardiac representations for the inference of multi-scale properties tied to cardiac mechanisms. The creation of CDTs requires precise information about the electrode position on the torso, especially for the personalized electrocardiogram (ECG) calibration. However, current studies commonly rely on additional acquisition of torso imaging and manual/semi-automatic methods for ECG electrode localization. In this study, we propose a novel and efficient topology-informed model to fully automatically extract personalized ECG electrode locations from 2D clinically standard cardiac MRIs. Specifically, we obtain the sparse torso contours from the cardiac MRIs and then localize the electrodes from the contours. Cardiac MRIs aim at imaging of the heart instead of the torso, leading to incomplete torso geometry within the imaging. To tackle the missing topology, we incorporate the electrodes as a subset of the keypoints, which can be explicitly aligned with the 3D torso topology. The experimental results demonstrate that the proposed model outperforms the time-consuming conventional method in terms of accuracy (Euclidean distance: $1.24 \pm 0.293$ cm vs. $1.48 \pm 0.362$ cm) and efficiency ($2$~s vs. $30$-$35$~min). We further demonstrate the effectiveness of using the detected electrodes for \textit{in-silico} ECG simulation, highlighting their potential for creating accurate and efficient CDT models. The code will be released publicly after the manuscript is accepted for publication., Comment: 12 pages

Quantum simulation with adiabatic annealing can provide insight into difficult problems that are impossible to study with classical computers. However, it deteriorates when the systems scale up due to the shrinkage of the excitation gap and thus places an annealing rate bottleneck for high success probability. Here, we accelerate quantum simulation using digital multi-qubit gates that entangle primary system qubits with the ancillary qubits. The practical benefits originate from tuning the ancillary gauge degrees of freedom to enhance the quantum algorithm's original functionality in the system subspace. For simple but nontrivial short-ranged, infinite long-ranged transverse-field Ising models, and the hydrogen molecule model after qubit encoding, we show improvement in the time to solution by one hundred percent but with higher accuracy through exact state-vector numerical simulation in a digital-analog setting. The findings are further supported by time-averaged Hamiltonian theory.

Fake News are a contemporary phenomenon with potential devastating effects. For inquiry and auditability purposes, it is essential that the news, once classified as false, can be persisted in an immutable means so that interested parties can query it. Although Blockchain clearly satisfies the main requirements for Fake News Management Software Systems, the prescriptive architectural solutions for that domain that cohabit Blockchain with other technologies in a single proposal still need to be made available. This paper's main contribution is presenting a prescriptive architectural solution for blockchain-based fake news management software systems. The Hoffmeister process for software architecture design is systematically followed to culminate in a software solution for that domain. The implementation of two candidate architectures and a brief simulation-based evaluation show the feasibility of the solution to satisfy the functional and quality requirements.

Representation learning is an approach that allows to discover and extract the factors of variation from the data. Intuitively, a representation is said to be disentangled if it separates the different factors of variation in a way that is understandable to humans. Definitions of disentanglement and metrics to measure it usually assume that the factors of variation are independent of each other. However, this is generally false in the real world, which limits the use of these definitions and metrics to very specific and unrealistic scenarios. In this paper we give a definition of disentanglement based on information theory that is also valid when the factors of variation are not independent. Furthermore, we relate this definition to the Information Bottleneck Method. Finally, we propose a method to measure the degree of disentanglement from the given definition that works when the factors of variation are not independent. We show through different experiments that the method proposed in this paper correctly measures disentanglement with non-independent factors of variation, while other methods fail in this scenario.

The gas-phase abundance of carbon, x_C = C/H, and its depletion factors are essential parameters for understanding the gas and solid compositions that are ultimately incorporated into planets. The majority of protoplanetary disks are born in clusters and, as a result, are exposed to external FUV radiation. These FUV photons potentially affect the disk's evolution, chemical composition, and line excitation. We present the first detection of the [CI]609um fine-structure line of neutral carbon (CI), achieved with ALMA, toward one of these disks, d203-506, in the Orion Nebula Cluster. We also report the detection of CI forbidden and permitted lines (from electronically excited states up to 10 eV) observed with JWST in the IR. These lines trace the irradiated outer disk and photo-evaporative wind. Contrary to the common belief that these IR lines are C+ recombination lines, we find that they are dominated by FUV-pumping of CI followed by fluorescence cascades. They trace the transition from atomic to molecular gas, and their intensities scale with G0. The lack of outstanding IR OI fluorescent emission, however, implies a sharper attenuation of external FUV radiation with E > 12 eV (~Lyman-beta). This is related to a lower effective FUV dust absorption cross section compared to that of interstellar grains, implying a more prominent role for FUV shielding by the CI photoionization continuum. The [CI]609um intensity is proportional to N(CI) and can be used to infer x_C. We derive x_C ~ 1.4E-4. This implies that there is no major depletion of volatile carbon compared to x_C measured in the natal cloud, hinting at a young disk. We also show that external FUV radiation impacts the outer disk and wind by vertically shifting the water freeze-out depth, which results in less efficient grain growth and settling. This shift leads to nearly solar gas-phase C/O abundance ratios in these irradiated layers., Comment: Accepted for publication in A&A Letters. 14 pages including Appendices

We introduce a novel graph-based Retrieval-Augmented Generation (RAG) framework specifically designed for the medical domain, called \textbf{MedGraphRAG}, aimed at enhancing Large Language Model (LLM) capabilities for generating evidence-based medical responses, thereby improving safety and reliability when handling private medical data. Graph-based RAG (GraphRAG) leverages LLMs to organize RAG data into graphs, showing strong potential for gaining holistic insights from long-form documents. However, its standard implementation is overly complex for general use and lacks the ability to generate evidence-based responses, limiting its effectiveness in the medical field. To extend the capabilities of GraphRAG to the medical domain, we propose unique Triple Graph Construction and U-Retrieval techniques over it. In our graph construction, we create a triple-linked structure that connects user documents to credible medical sources and controlled vocabularies. In the retrieval process, we propose U-Retrieval which combines Top-down Precise Retrieval with Bottom-up Response Refinement to balance global context awareness with precise indexing. These effort enable both source information retrieval and comprehensive response generation. Our approach is validated on 9 medical Q\&A benchmarks, 2 health fact-checking benchmarks, and one collected dataset testing long-form generation. The results show that MedGraphRAG consistently outperforms state-of-the-art models across all benchmarks, while also ensuring that responses include credible source documentation and definitions. Our code is released at: https://github.com/MedicineToken/Medical-Graph-RAG.

We present a near infrared (NIR) candidate star cluster catalog for the central kiloparsec of M82 based on new JWST NIRCam images. We identify star cluster candidates using the F250M filter, finding 1357 star cluster candidates with stellar masses $>10^4$ M$_\odot$. Compared to previous optical catalogs, nearly all (87%) of the candidates we identify are new. The star cluster candidates have a median intrinsic cluster radius of $\approx$1 pc and have stellar masses up to $10^6$ M$_\odot$. By comparing the color-color diagram to dust-free yggdrasil stellar population models, we estimate that the star cluster candidates have A$_{\rm V}\sim3-24$ mag, corresponding to A$_{\rm 2.5\mu m}\sim0.3-2.1$ mag. There is still appreciable dust extinction towards these clusters into the NIR. We measure the stellar masses of the star cluster candidates, assuming ages of 0 and 8 Myr. The slope of the resulting cluster mass function is $\beta=1.9\pm0.2$, in excellent agreement with studies of star clusters in other galaxies., Comment: Resubmitted to ApJL

Ram pressure stripping is perhaps the most efficient mechanism for removing gas and quenching galaxies in dense environments as they move through the intergalactic medium. Extreme examples of on-going ram pressure stripping are known as jellyfish galaxies, characterized by a tail of stripped material that can be directly observed in multiple wavelengths. Using the largest homogeneous broad-band optical jellyfish candidate sample in local clusters known to date, we measure the angle between the direction of the tails visible in the galaxies, and the direction towards the host cluster center. We find that $33\%$ of the galaxy tails point away from the cluster center, $18\%$ point towards the cluster center, and $49\%$ point elsewhere. Moreover, we find stronger signatures of ram pressure stripping happening on galaxies with a tail pointing away and towards the cluster center, and larger velocity dispersion profiles for galaxies with tails pointing away. These results are consistent with a scenario where ram pressure stripping has a stronger effect for galaxies following radial orbits on first infall. The results also suggest that in many cases, radially infalling galaxies are able to retain their tails after pericenter and continue to experience significant on-going ram pressure stripping. We further constrain the lifespan of the optical tails from the moment they first appear to the moment they disappear, by comparing the observed tail directions with matched N-body simulations through Bayesian parameter estimation. We obtain that galaxy tails appear for the first time at $\sim 1.16$ R$_{200}$ and disappear $\sim660$ Myr after pericenter., Comment: 19 pages, 17 figures

We investigate the spatial extent of the [CII] line emission in a sample of 34 galaxies at $z=4-6$ from the ALMA-CRISTAL Survey. By modeling the [CII] line emission in the visibility data directly, we derive the effective radius of [CII] line emission assuming exponential distribution. These measurements comprise not only isolated galaxies but also interacting systems, identified thanks to the high spatial resolution of the data. The [CII] line radius ranges from 0.5 to 3.5 kpc with an average value of 1.9 kpc. We compare the [CII] sizes with the sizes of UV and FIR continua, which were measured from the HST F160W and ALMA Band-7 continuum images, respectively. We confirm that the [CII] line emission is more spatially extended than the continuum emission, with average size ratios of $R_{e,[CII]}/R_{e,UV}=2.90$ and $R_{e,[CII]}/R_{e,FIR}=1.54$, although about half of the FIR-detected sample show comparable spatial extent between [CII] line and FIR continuum emission ($R_{e,[CII]}\approx R_{e, FIR}$). The residual visibility data of the best-fit model do not show evidence of flux excesses either individually or in stacking analysis. This indicates that the [CII] line emission in star-forming galaxies can be characterized by an extended exponential disk profile. Overall, our results suggest that the spatial extent of [CII] line emission can primarily be explained by photodissociation regions associated with star formation activity, while the contribution from diffuse neutral medium (atomic gas) and the effects of mergers may further expand the [CII] line distributions, causing their variations among our sample. We report the correlations between the [CII] line, dust, and Lya line properties, which may be in line with our scenario. Future 3D-analysis of Lya and Ha lines will shed light on the association of the extended [CII] line emission with atomic gas and outflows., Comment: Submitted to A&A, 21 pages, 14 figures, 4 tables

Solving very large linear systems of equations is a key computational task in science and technology. In many cases, the coefficient matrix of the linear system is rank-deficient, leading to systems that may be underdetermined, inconsistent, or both. In such cases, one generally seeks to compute the least squares solution that minimizes the residual of the problem, which can be further defined as the solution with smallest norm in cases where the coefficient matrix has a nontrivial nullspace. This work presents several new techniques for solving least squares problems involving coefficient matrices that are so large that they do not fit in main memory. The implementations include both CPU and GPU variants. All techniques rely on complete orthogonal decompositions that guarantee that both conditions of a least squares solution are met, regardless of the rank properties of the matrix. Specifically, they rely on the recently proposed "randUTV" algorithm that is particularly effective in strongly communication-constrained environments. A detailed precision and performance study reveals that the new methods, that operate on data stored on disk, are competitive with state-of-the-art methods that store all data in main memory., Comment: 26 pages, 12 figures

While the nitrile group is by far the most prevalent one among interstellar molecules, the existence of interstellar dinitriles (molecules containing two -CN groups) has recently been proven. Here we report the discovery of two new dinitriles in the cold dense cloud TMC-1. These newly identified species are malononitrile, CH2(CN)2, and maleonitrile, the Z isomer of NC-CH=CH-CN, which can be seen as the result of substituting two H atoms with two -CN groups in methane and ethylene, respectively. These two molecules were detected using data from the ongoing QUIJOTE line survey of TMC-1 that is being carried out with the Yebes 40m telescope. We derive column densities of 1.8e11 cm-2 and 5.1e10 cm-2 for malononitrile and maleonitrile, respectively. This means that they are eight and three times less abundant than HCC-CH2-CN and (E)-HCC-CH=CH-CN, respectively, which are analog molecules detected in TMC-1 in which one -CN group is converted into a -CCH group. This is in line with previous findings in which -CCH derivatives are more abundant than the -CN counterparts in TMC-1. We examined the potential chemical pathways to these two dinitriles, and we find that while maleonitrile can be efficiently formed through the reaction of CN with CH2CHCN, the formation of malononitrile is not clear because the neutral-neutral reactions that could potentially form it are not feasible under the physical conditions of TMC-1., Comment: Accepted for publication in A&A Letters

Computer vision systems have witnessed rapid progress over the past two decades due to multiple advances in the field. As these systems are increasingly being deployed in high-stakes real-world applications, there is a dire need to ensure that they do not propagate or amplify any discriminatory tendencies in historical or human-curated data or inadvertently learn biases from spurious correlations. This paper presents a comprehensive survey on fairness that summarizes and sheds light on ongoing trends and successes in the context of computer vision. The topics we discuss include 1) The origin and technical definitions of fairness drawn from the wider fair machine learning literature and adjacent disciplines. 2) Work that sought to discover and analyze biases in computer vision systems. 3) A summary of methods proposed to mitigate bias in computer vision systems in recent years. 4) A comprehensive summary of resources and datasets produced by researchers to measure, analyze, and mitigate bias and enhance fairness. 5) Discussion of the field's success, continuing trends in the context of multimodal foundation and generative models, and gaps that still need to be addressed. The presented characterization should help researchers understand the importance of identifying and mitigating bias in computer vision and the state of the field and identify potential directions for future research., Comment: 20 pages, 4 figures

Characterization of the redshift distribution of ensembles of galaxies is pivotal for large scale structure cosmological studies. In this work, we focus on improving the Self-Organizing Map (SOM) methodology for photometric redshift estimation (SOMPZ), specifically in anticipation of the Dark Energy Survey Year 6 (DES Y6) data. This data set, featuring deeper and fainter galaxies than DES Year 3 (DES Y3), demands adapted techniques to ensure accurate recovery of the underlying redshift distribution. We investigate three strategies for enhancing the existing SOM-based approach used in DES Y3: 1) Replacing the Y3 SOM algorithm with one tailored for redshift estimation challenges; 2) Incorporating $\textit{g}$-band flux information to refine redshift estimates (i.e. using $\textit{griz}$ fluxes as opposed to only $\textit{riz}$); 3) Augmenting redshift data for galaxies where available. These methods are applied to DES Y3 data, and results are compared to the Y3 fiducial ones. Our analysis indicates significant improvements with the first two strategies, notably reducing the overlap between redshift bins. By combining strategies 1 and 2, we have successfully managed to reduce redshift bin overlap in DES Y3 by up to 66$\%$. Conversely, the third strategy, involving the addition of redshift data for selected galaxies as an additional feature in the method, yields inferior results and is abandoned. Our findings contribute to the advancement of weak lensing redshift characterization and lay the groundwork for better redshift characterization in DES Year 6 and future stage IV surveys, like the Rubin Observatory.