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Massive stars play a crucial role in the cosmic dynamics and chemical evolution of galaxies, however our understanding of their evolution and properties remains limited. An accurate determination of stellar parameters is essential for advancing our knowledge. Detached eclipsing binaries (DEBs) are particularly valuable for these determinations due to the minimal interaction between their stellar components, allowing for precise measurements. This study introduces the Young Massive Detached Binary (YMDB) catalog, designed to address the gap in the high-precision absolute parameter determination for young massive stars. By focusing on DEBs within the spectral range O9-B1, this catalog seeks to provide a reliable database for future studies and improve our understanding of massive star evolution. We conducted a photometric analysis of 87 young massive stars in DEBs using TESS light curves (LCs) that were processed through a custom pipeline. This analysis involved determining the amplitude of magnitude variations, orbital periods, times of minima, eccentricities, and the presence of apsidal motion and heartbeat phenomena. A thorough literature review was performed to obtain MK spectral classifications. We performed our own spectral classification of 19 systems to support the sample where a new classification was lacking or inconclusive. The analysis identified 20 previously unreported binary systems, with 13 newly recognized as variables. Among the 87 stars examined, 30 are confirmed as YMDB members, and 25 are candidates pending spectral classification. The remaining 32 stars belong to unsuitable spectral types or nondetached binary nature. Notable findings include the identification of new LC classifications, eccentricities in 13 systems, and heartbeat phenomena in several targets. The YMDB catalog offers a resource of high-quality LCs and reliable stellar classifications., Comment: 18 pages, 13 figures, 4 tables. Accepted for publication in A&A. Light curves time series and the extended version of Table 2,3,4 will be available on CDS

After shipment to the Daresbury Lab and return to Fermilab, the prototype HB650 cryomodule underwent another phase of 2K RF testing to ascertain any performance issues that may have arisen from the transport of the cryomodule. While measurements taken at room temperature after the conclusion of shipment indicated that there were no negative impacts on cavity alignment, beamline vacuum, or cavity frequency, testing at 2K was required to validate other aspects such as tuner operation, cavity coupling, cryogenic system integrity, and cavity performance. Results of this latest round of limited 2K testing will be presented., Comment: 32nd Linear Accelerator Conference (LINAC 2024)

We theoretically investigate the magnetic response on a three-dimensional superconducting nanobridge system, which is compound of two parallel parallelepiped (samples) connected through a nanobridge of size $\mathbf{L}$ and thickness $\mathbf{x}$, which mediates interactions between them. This study is conducted in the presence of a magnetic field $\mathbf{H}$ and the transport of a direct current $\mathbf{J}$. We use the well-know time dependent Ginzburg-Landau theory ($\mathbf{TDGL}$) for analyzed the possible effects on the density Gibbs free energy $\mathbf{F}$, magnetization $\mathbf{M}$, and superconducting electronic Cooper pair density $|\psi|^{2}$. We are interested in studying two cases: varying the $\mathbf{L}$ and $\mathbf{x}$ of the nanobridge in the absence of induced $\mathbf{J}$, and including the induction of external $\mathbf{J}$ for fixed $\mathbf{L}$ and $\mathbf{x}$. We find that $\mathbf{L}$ and $\mathbf{x}$ play an essential role in stabilizing (controlling) vortex states in the nanobridge, and the presence of induced $\mathbf{J}$ ($\mathbf{J}>0$ and $\mathbf{J}<0$), with a fixed $\mathbf{L}$ and $\mathbf{x}$, causes the movement of vortex states in the nanobridge just when $\mathbf{J}$ is induced at both faces of superconducting nanobridge system., Comment: 11 pages, 10 figures

Annually, at the Conference of Machine Translation (WMT), the Metrics Shared Task organizers conduct the meta-evaluation of Machine Translation (MT) metrics, ranking them according to their correlation with human judgments. Their results guide researchers toward enhancing the next generation of metrics and MT systems. With the recent introduction of neural metrics, the field has witnessed notable advancements. Nevertheless, the inherent opacity of these metrics has posed substantial challenges to the meta-evaluation process. This work highlights two issues with the meta-evaluation framework currently employed in WMT, and assesses their impact on the metrics rankings. To do this, we introduce the concept of sentinel metrics, which are designed explicitly to scrutinize the meta-evaluation process's accuracy, robustness, and fairness. By employing sentinel metrics, we aim to validate our findings, and shed light on and monitor the potential biases or inconsistencies in the rankings. We discover that the present meta-evaluation framework favors two categories of metrics: i) those explicitly trained to mimic human quality assessments, and ii) continuous metrics. Finally, we raise concerns regarding the evaluation capabilities of state-of-the-art metrics, emphasizing that they might be basing their assessments on spurious correlations found in their training data., Comment: Presented at ACL 2024 Main Conference. 29 pages

The acorn is the fruit of the oak and is an important crop in the Spanish dehesa extreme\~na, especially for the value it provides in the Iberian pig food to obtain the "acorn" certification. For this reason, we want to maximise the production of Iberian pigs with the appropriate weight. Hence the need to know the area covered by the crowns of the acorn trees, to determine the covered wooded area (CWA, from the Spanish Superficie Arbolada Cubierta SAC) and thereby estimate the number of Iberian pigs that can be released per hectare, as indicated by the royal decree 4/2014. In this work, we propose the automatic estimation of the CWA, through aerial digital images (orthophotos) of the pastureland of Extremadura, and with this, to offer the possibility of determining the number of Iberian pigs to be released in a specific plot of land. Among the main issues for automatic detection are, first, the correct identification of acorn trees, secondly, correctly discriminating the shades of the acorn trees and, finally, detect the arbuscles (young acorn trees not yet productive, or shrubs that are not oaks). These difficulties represent a real challenge, both for the automatic segmentation process and for manual segmentation. In this work, the proposed method for automatic segmentation is based on the clustering algorithm proposed by Gustafson-Kessel (GK) but the modified version of Babuska (GK-B) and on the use of real orthophotos. The obtained results are promising both in their comparison with the real images and when compared with the images segmented by hand. The whole set of orthophotos used in this work correspond to an approximate area of 142 hectares, and the results are of great interest to producers of certified "acorn" pork., Comment: 22 pages, 15 Figures, 2 Tables

Large autoregressive generative models have emerged as the cornerstone for achieving the highest performance across several Natural Language Processing tasks. However, the urge to attain superior results has, at times, led to the premature replacement of carefully designed task-specific approaches without exhaustive experimentation. The Coreference Resolution task is no exception; all recent state-of-the-art solutions adopt large generative autoregressive models that outperform encoder-based discriminative systems. In this work,we challenge this recent trend by introducing Maverick, a carefully designed - yet simple - pipeline, which enables running a state-of-the-art Coreference Resolution system within the constraints of an academic budget, outperforming models with up to 13 billion parameters with as few as 500 million parameters. Maverick achieves state-of-the-art performance on the CoNLL-2012 benchmark, training with up to 0.006x the memory resources and obtaining a 170x faster inference compared to previous state-of-the-art systems. We extensively validate the robustness of the Maverick framework with an array of diverse experiments, reporting improvements over prior systems in data-scarce, long-document, and out-of-domain settings. We release our code and models for research purposes at https://github.com/SapienzaNLP/maverick-coref., Comment: Accepted at main conference of ACL 2024. 15 pages

The Villafranca project is studying Galactic stellar groups with OB stars combining information from $Gaia$ and ground-based surveys. We summarize the status of the project and we present its most important results. The Villafranca project has been used to produce a new astrometric calibration for $Gaia$ (E)DR3, which improves the previous one significantly for bright stars. We have discovered that dynamical interactions among massive stars at a very young age ($\sim$1 Ma or less) can play a significant interaction in the dynamical evolution of clusters. As a consequence, our current view of the massive-star IMF may be distorted and the number of free-floating neutron stars and black holes higher than previously considered., Comment: To appear in Highlights of Spanish Astrophysics XIII, P254

Entity Linking (EL) and Relation Extraction (RE) are fundamental tasks in Natural Language Processing, serving as critical components in a wide range of applications. In this paper, we propose ReLiK, a Retriever-Reader architecture for both EL and RE, where, given an input text, the Retriever module undertakes the identification of candidate entities or relations that could potentially appear within the text. Subsequently, the Reader module is tasked to discern the pertinent retrieved entities or relations and establish their alignment with the corresponding textual spans. Notably, we put forward an innovative input representation that incorporates the candidate entities or relations alongside the text, making it possible to link entities or extract relations in a single forward pass and to fully leverage pre-trained language models contextualization capabilities, in contrast with previous Retriever-Reader-based methods, which require a forward pass for each candidate. Our formulation of EL and RE achieves state-of-the-art performance in both in-domain and out-of-domain benchmarks while using academic budget training and with up to 40x inference speed compared to competitors. Finally, we show how our architecture can be used seamlessly for Information Extraction (cIE), i.e. EL + RE, and setting a new state of the art by employing a shared Reader that simultaneously extracts entities and relations., Comment: To be presented at ACL 2024

Using an effective Lagrangian model inspired by [1] we describe the early post-merger of a binary Neutron Star (BNS) merger event with a non-vanishing ambient fraction of dark matter. For this we mimic the dynamics of two oscillating Neutron Star (NS) masses in the gravitational potential well as they merge. We parametrize the ejected baryonic matter fraction in the coalescence event forming a surrounding debris disk containing a non-vanishing dark matter fraction. In order to analyze the dark contribution, we start from a dark-matter free modelization using Monte Carlo Markov Chain (MCMC) techniques to replicate the gravitational waveforms obtained from simulations in the CoRe database for three selected equations of state for NS matter (DD2, BHBlp, SFHo). Later, we explore the impact of a dark viscous fluid under a prescribed velocity dependent force and obtain the waveforms and spectral properties originating in the first few ms in the BNS post-merger. Finally we discuss our findings in light of the prospects of detectability of a non-vanishing fraction of dark matter arising in the waveforms and power spectral distribution in present or future experimental settings., Comment: 14 pages, 5 figures

Robustness and generalizability are the key properties of artificial neural network (ANN)-based controllers for maintaining a reliable performance in case of changes. It is demonstrated that exposing the ANNs to variations during training processes can improve their robustness and generalization capabilities. However, the way in which this variation is introduced can have a significant impact. In this paper, we define various training schedules to specify how these variations are introduced during an evolutionary learning process. In particular, we focus on morphological robustness and generalizability concerned with finding an ANN-based controller that can provide sufficient performance on a range of physical variations. Then, we perform an extensive analysis of the effect of these training schedules on morphological generalization. Furthermore, we formalize the process of training sample selection (i.e., morphological variations) to improve generalization as a reinforcement learning problem. Overall, our results provide deeper insights into the role of variability and the ways of enhancing the generalization property of evolved ANN-based controllers.

In the past five years, six quasi-periodic X-ray eruption (QPE) sources have been discovered in the nuclei of nearby galaxies. Their origin remains an open question. We present MUSE integral field spectroscopy of five QPE host galaxies to characterize their properties. We find that 3/5 galaxies host extended emission line regions (EELRs) up to 10 kpc in size. The EELRs are photo-ionized by a non-stellar continuum, but the current nuclear luminosity is insufficient to power the observed emission lines. The EELRs are decoupled from the stars both kinematically and in projected sky position, and the low velocities and velocity dispersions ($<$ 100 km s$^{-1}$ and $\lesssim 75$ km s$^{-1}$ respectively) are inconsistent with being AGN- or shock-driven. The origin of the EELRs is likely a previous phase of nuclear activity. The QPE host galaxy properties are strikingly similar to those of tidal disruption events (Wevers et al. submitted). The preference for a very short-lived (the typical EELR lifetime is $\sim$15000 years), gas-rich phase where the nucleus has recently faded significantly suggests that TDEs and QPEs may share a common formation channel, disfavoring AGN accretion disk instabilities as the origin of QPEs. In the assumption that QPEs are related to extreme mass ratio inspiral systems (EMRIs; stellar-mass objects on bound orbits about massive black holes), the high incidence of EELRs and recently faded nuclear activity can be used to aid in the localization of the host galaxies of EMRIs discovered by low frequency gravitational wave observatories., Comment: Submitted to ApJ Letters. In a companion paper we present an IFU analysis of TDE host galaxies (Wevers & French)

We perform microscopic many-body simulations of the thermal relaxation of a realistic warm Yukawa fluid by solving the dynamics of an ensemble of N particles in a cubic box acting as a heat reservoir kept at fixed temperature. For specific conditions of density, ion size and potential interaction range we find novel anomalous thermal relaxation for these systems, exhibiting faster cooling from increasingly larger temperature values. Our findings show that the interplay among finite particle size, Yukawa interaction range and density are key to understanding the features of the cooling curve $T(t)$. The non-Newtonian thermal behavior is analyzed in terms of a time-dependent relaxation time $\tau(t)$ and a generalized Langevin equation. We compare our results to existing Mpemba-like phenomena and interpret temperature oscillation patterns arising from a non-trivial memory function. Further, we consider the impact of composition admixtures in the simulated system. We discuss these results and the more general implications for non-Markovian systems where Yukawa-like potentials are involved, such as astrophysical plasmas., Comment: 6 pages, 4 figures

Understanding how interacting particles approach thermal equilibrium is a major challenge of quantum simulators. Unlocking the full potential of such systems toward this goal requires flexible initial state preparation, precise time evolution, and extensive probes for final state characterization. We present a quantum simulator comprising 69 superconducting qubits which supports both universal quantum gates and high-fidelity analog evolution, with performance beyond the reach of classical simulation in cross-entropy benchmarking experiments. Emulating a two-dimensional (2D) XY quantum magnet, we leverage a wide range of measurement techniques to study quantum states after ramps from an antiferromagnetic initial state. We observe signatures of the classical Kosterlitz-Thouless phase transition, as well as strong deviations from Kibble-Zurek scaling predictions attributed to the interplay between quantum and classical coarsening of the correlated domains. This interpretation is corroborated by injecting variable energy density into the initial state, which enables studying the effects of the eigenstate thermalization hypothesis (ETH) in targeted parts of the eigenspectrum. Finally, we digitally prepare the system in pairwise-entangled dimer states and image the transport of energy and vorticity during thermalization. These results establish the efficacy of superconducting analog-digital quantum processors for preparing states across many-body spectra and unveiling their thermalization dynamics.

The term 'active galactic nuclei' (AGN) subtends a huge variety of objects, classified on their properties at different wavelengths. Peaked sources (PS) represent a class of AGN at the first stage of evolution, characterised by a peaked radio spectrum. Among these radio sources, low-luminosity compact (LLC) sources can be identified as PS accreting with a high Eddington rate, harbouring low-power jets and hosting low-mass black holes. These properties are also shared by narrow-line Seyfert 1 galaxies (NLS1s). In 2016, LLCs were hypothesised to be the parent population of NLS1s with a flat radio spectrum (F-NLS1s), suggesting the former to be the same objects as the latter, seen at higher inclination. Based on radio luminosity functions and optical spectra analysis, 10 LLCs were identified as valid candidates for F-NLS1s. To account for the missing puzzle piece, verifying if these LLCs could be hosted in late-type galaxies as NLS1s, we performed the photometric decomposition of their Pan-STARRS1 images in all five filters. We used the 2D fitting algorithm GALFIT for the single-band analysis, and its extension GALFITM for the multi-band analysis. Considering that the morphological type and the structural parameters of the host can be dependent on the wavelength, we found six out of ten LLCs hosted in late-type galaxies, probably with pseudo-bulges, three point-like sources and one object of uncertain classification. Although this study is based on a small sample, it represents the first morphological analysis of LLC host galaxies. The results confirm the trend observed in NLS1s, indicating late-type/disc-like host galaxies for LLCs and supporting the validity of the parent population scenario., Comment: 15 pages, 21 figures, accepted for publication in A&A

Concerns about the effect of greenhouse gases have motivated the development of certification protocols to quantify the industrial carbon footprint (CF). These protocols are manual, work-intensive, and expensive. All of the above have led to a shift towards automatic data-driven approaches to estimate the CF, including Machine Learning (ML) solutions. Unfortunately, the decision-making processes involved in these solutions lack transparency from the end user's point of view, who must blindly trust their outcomes compared to intelligible traditional manual approaches. In this research, manual and automatic methodologies for CF estimation were reviewed, taking into account their transparency limitations. This analysis led to the proposal of a new explainable ML solution for automatic CF calculations through bank transaction classification. Consideration should be given to the fact that no previous research has considered the explainability of bank transaction classification for this purpose. For classification, different ML models have been employed based on their promising performance in the literature, such as Support Vector Machine, Random Forest, and Recursive Neural Networks. The results obtained were in the 90 % range for accuracy, precision, and recall evaluation metrics. From their decision paths, the proposed solution estimates the CO2 emissions associated with bank transactions. The explainability methodology is based on an agnostic evaluation of the influence of the input terms extracted from the descriptions of transactions using locally interpretable models. The explainability terms were automatically validated using a similarity metric over the descriptions of the target categories. Conclusively, the explanation performance is satisfactory in terms of the proximity of the explanations to the associated activity sector descriptions.

Massive stars (those larger than 8 solar masses at formation) have radiative envelopes that cannot sustain a dynamo, the mechanism that produces magnetic fields in lower-mass stars. Despite this, approximately 7\% of massive stars have observed magnetic fields, the origin of which is debated. We used multi-epoch interferometric and spectroscopic observations to characterize HD 148937, a binary system of two massive stars. We found that only one star is magnetic and that it appears younger than its companion. The system properties and a surrounding bipolar nebula can be reproduced with a model in which two stars merged (in a previous triple system) to produce the magnetic massive star. Our results provide observational evidence that magnetic fields form in at least some massive stars through stellar mergers., Comment: Full paper with supplementary materials. 59 pages, 18 figures

Short texts are omnipresent in real-time news, social network commentaries, etc. Traditional text representation methods have been successfully applied to self-contained documents of medium size. However, information in short texts is often insufficient, due, for example, to the use of mnemonics, which makes them hard to classify. Therefore, the particularities of specific domains must be exploited. In this article we describe a novel system that combines Natural Language Processing techniques with Machine Learning algorithms to classify banking transaction descriptions for personal finance management, a problem that was not previously considered in the literature. We trained and tested that system on a labelled dataset with real customer transactions that will be available to other researchers on request. Motivated by existing solutions in spam detection, we also propose a short text similarity detector to reduce training set size based on the Jaccard distance. Experimental results with a two-stage classifier combining this detector with a SVM indicate a high accuracy in comparison with alternative approaches, taking into account complexity and computing time. Finally, we present a use case with a personal finance application, CoinScrap, which is available at Google Play and App Store.

We present the results of a comparison between different methods to estimate the power of relativistic jets from active galactic nuclei (AGN). We selected a sample of 32 objects (21 flat-spectrum radio quasars, 7 BL Lacertae Objects, 2 misaligned AGN, and 2 changing-look AGN) from the Very Large Baseline Array (VLBA) observations at 43 GHz of the Boston University blazar program. We then calculated the total, radiative, and kinetic jet power from both radio and high-energy gamma-ray observations, and compare the values. We found an excellent agreement between the radiative power calculated by using the Blandford and K\"onigl model with 37 or 43 GHz data, and the values derived from the high-energy $\gamma-$ray luminosity. The agreement is still acceptable if 15 GHz data are used, although with a larger dispersion, but it improves if we use a constant fraction of the $\gamma-$ray luminosity. We found a good agreement also for the kinetic power calculated with Blandford and K\"onigl model with 15 GHz data, and the value from the extended radio emission. We also propose some easy-to-use equations to estimate the jet power., Comment: 23 pages, 12 figures, 6 tables. Accepted for publication on Universe, Special Issue: Recent Advances in Gamma Ray Astrophysics and Future Perspectives

This document describes the development of a video game prototype designed to encourage physical activity among children and older adults. The prototype consists of a laptop, a camera with 3D sensors, and optionally requires an LCD screen or a projector. The programming component of this prototype was developed in Scratch, a programming language geared towards children, which greatly facilitates the creation of a game tailored to the users' preferences. The idea to create such a prototype originated from the desire to offer an option that promotes physical activity among children and adults, given that a lack of physical exercise is a predominant factor in the development of chronic degenerative diseases such as diabetes and hypertension, to name the most common. As a result of this initiative, an active video game prototype was successfully developed, based on a ping-pong game, which allows both children and adults to interact in a fun way while encouraging the performance of physical activities that can positively impact the users' health., Comment: 13 pages, in Spanish language, 11 figures

Physics-informed neural networks (PINNs) are neural networks (NNs) that directly encode model equations, like Partial Differential Equations (PDEs), in the network itself. While most of the PINN algorithms in the literature minimize the local residual of the governing equations, there are energy-based approaches that take a different path by minimizing the variational energy of the model. We show that in the case of the steady thermal equation weakly coupled to magnetic equation, the energy-based approach displays multiple advantages compared to the standard residual-based PINN: it is more computationally efficient, it requires a lower order of derivatives to compute, and it involves less hyperparameters. The analyzed benchmark problems are the single- and multi-objective optimal design of an inductor for the controlled heating of a graphite plate. The optimized device is designed involving a multi-physics problem: a time-harmonic magnetic problem and a steady thermal problem. For the former, a deep neural network solving the direct problem is supervisedly trained on Finite Element Analysis (FEA) data. In turn, the solution of the latter relies on a hypernetwork that takes as input the inductor geometry parameters and outputs the model weights of an energy-based PINN (or ePINN). Eventually, the ePINN predicts the temperature field within the graphite plate.

We present the X-ray spectral analysis of two complementary sets of intermediate Seyfert galaxies (ISs). Analyzing X-ray data, we estimate the hydrogen abundance $N_H$ and test its connection with the [O III] luminosity acquired from optical observations. The results confirm the conclusions drawn in a previous study concerning the lack of a direct correlation between the obscuration measure ($N_H$) and the intrinsic characteristics of the active nuclei ([O III] luminosity). Instead, we validate the existence of a correlation between the Seyfert type and the $N_H$ parameter, employing a separation threshold of approximately 10$^{22}$ atoms cm$^{-2}$. Simultaneously, our findings align with prior research, corroborating the relationship between X-ray luminosity and the [O III] luminosity.

We present microscopic Molecular Dynamics simulations including the efficient Ewald sum procedure to study warm and dense stellar plasmas consisting of finite-size ion charges immerse in a relativistic neutralizing electron gas. For densities typical of Supernova matter and crust in a proto-neutron star, we select a representative single ion composition and obtain the virialized equation of state (vEoS). We scrutinize the finite-size and screening corrections to the Coulomb potential appearing in the virial coefficients $B_2, B_3$ and $B_4$ as a function of temperature. In addition, we study the thermal heat capacity at constant volume, $C_V$, and the generalized Mayer's relation i.e. the difference $C_P-C_V$ with $C_P$ being the heat capacity at constant pressure, obtaining clear features signaling the onset of the liquid-gas phase transition. Our findings show that microscopic simulations reproduce the discontinuity in $C_V$, whose value lies between that of idealized gas and crystallized configurations. We study the pressure isotherms marking the boundary of the metastable region before the gaseous transition takes place. The resulting vEoS displays a behaviour where effective virial coefficients include extra density dependence showing a generalized density-temperature form. As an application we parametrize pressure as a function of density and temperature under the form of an artificial neural network showing the potential of machine learning for future regression analysis in more refined multicomponent approaches. This is of interest to size the importance of these corrections in the liquid-gas phase transition in warm and dense plasma phases contributing to the cooling behaviour of early Supernova phases and proto-neutron stars., Comment: 11 pages, 8 figures, published in MNRAS

We present preliminary results of the analysis of optical spectra of two complementary samples of Seyfert galaxies. The first sample was extracted from a selection of the 4th Fermi Gamma-ray Large Area Telescope (4FGL) catalog, and consists of 9 $\gamma$-ray emitting jetted Seyfert galaxies. The second one was extracted from the Swift-BAT AGN Spectroscopic Survey (BASS), and is composed of 38 hard-X ray selected Active Galactic Nuclei (AGN). These two samples are complementary, with the former expected to have smaller viewing angles, while the latter may include objects with larger viewing angles. We measured emission line ratios to investigate whether the behavior of these Seyferts can be explained in terms of obscuration, as suggested by the well-known Unified Model (UM) of AGN, or if there are intrinsic differences due to the presence of jets, outflows, or the evolution. We found no indications of intrinsic differences. The UM remains the most plausible interpretation for these classes of objects even if some results can be challenging for this model., Comment: 24 pages, 10 figures, conference proceeding "14th Serbian Conference on Spectral Line Shapes in Astrophysics"

Diffusion models have emerged as a pivotal advancement in generative models, setting new standards to the quality of the generated instances. In the current paper we aim to underscore a discrepancy between conventional training methods and the desired conditional sampling behavior of these models. While the prevalent classifier-free guidance technique works well, it's not without flaws. At higher values for the guidance scale parameter $w$, we often get out of distribution samples and mode collapse, whereas at lower values for $w$ we may not get the desired specificity. To address these challenges, we introduce an updated loss function that better aligns training objectives with sampling behaviors. Experimental validation with FID scores on CIFAR-10 elucidates our method's ability to produce higher quality samples with fewer sampling timesteps, and be more robust to the choice of guidance scale $w$. We also experiment with fine-tuning Stable Diffusion on the proposed loss, to provide early evidence that large diffusion models may also benefit from this refined loss function., Comment: Accepted at NeurIPS Diffusion Workshop 2023