Miracle-dz/UniFAR-FTUs huggingface dataset:
The Many-faceted Light Curves of Young Disk-bearing Stars in Upper Sco –– Oph Observed by K2 Campaign 2

The K2 Mission has photometrically monitored thousands of stars at high precision and cadence in a series of ∼80-day campaigns focused on sections of the ecliptic plane. During its second campaign, K2 targeted over 1000 young stellar objects (YSOs) in the ∼1–3 Myr ρ Ophiuchus and 5–10 Myr Upper Scorpius regions. From this set, we have carefully vetted photometry from WISE and Spitzer to identify those YSOs with infrared excess indicative of primordial circumstellar disks. We present here the resulting comprehensive sample of 288 young disk-bearing stars from B through M spectral types and analysis of their associated K2 light curves. Using statistics of periodicity and symmetry, we categorize each light curve into eight different variability classes, notably including “dippers” (fading events), “bursters” (brightening events), stochastic, and quasi-periodic types. Nearly all (96%) of disk-bearing YSOs are identified as variable at 30-minute cadence with the sub-1% precision of K2. Combining our variability classifications with (circum)stellar properties, we find that the bursters, stochastic sources, and the largest amplitude quasi-periodic stars have larger infrared colors, and hence stronger circumstellar disks. They also tend to have larger Hα equivalent widths, indicative of higher accretion rates. The dippers, on the other hand, cluster toward moderate infrared colors and low Hα. Using resolved disk observations, we further find that the latter favor high inclinations, except for a few notable exceptions with close to face-on disks. These observations support the idea that YSO time-domain properties are dependent on several factors, including accretion rate and view angle.
Intense Whistler-mode Waves at Foreshock Transients: Characteristics and Regimes of Wave−Particle Resonant Interaction

Thermalization and heating of plasma flows at shocks result in unstable charged-particle distributions that generate a wide range of electromagnetic waves. These waves, in turn, can further accelerate and scatter energetic particles. Thus, the properties of the waves and their implication for wave−particle interactions are critically important for modeling energetic particle dynamics in shock environments. Whistler-mode waves, excited by the electron heat flux or a temperature anisotropy, arise naturally near shocks and foreshock transients. As a result, they can often interact with suprathermal electrons. The low background magnetic field typical at the core of such transients and the large wave amplitudes may cause such interactions to enter the nonlinear regime. In this study, we present a statistical characterization of whistler-mode waves at foreshock transients around Earth’s bow shock, as they are observed under a wide range of upstream conditions. We find that a significant portion of them are sufficiently intense and coherent (narrowband) to warrant nonlinear treatment. Copious observations of background magnetic field gradients and intense whistler wave amplitudes suggest that phase trapping, a very effective mechanism for electron acceleration in inhomogeneous plasmas, may be the cause. We discuss the implications of our findings for electron acceleration in planetary and astrophysical shock environments.
Distance and reddening of the Local Group dwarf irregular galaxy NGC 6822

On the basis of a new photometric analysis of the Local Group dwarf irregular galaxy NCG 6822 based on observations obtained with the Advanced Camera for Surveys onboard the Hubble Space Telescope, we have obtained a new estimate of the extinction of two fields located in the southeast region of the galaxy. Because of significant differences in the distance estimates to NGC 6822 available in literature, we decided to provide an independent determination of the distance to this galaxy based on an updated and self-consistent theoretical calibration of the tip of the red giant branch brightness. As a result we newly determined the distance to NGC 6822 to be equal to (m − M)0 = 23.54 ± 0.05, and compared our measurement with the most recent determinations of this distance.
On the Privacy Risks of Algorithmic Recourse

As predictive models are increasingly being employed to make consequential decisions, there is a growing emphasis on developing techniques that can provide algorithmic recourse to affected individuals. While such recourses can be immensely beneficial to affected individuals, potential adversaries could also exploit these recourses to compromise privacy. In this work, we make the first attempt at investigating if and how an adversary can leverage recourses to infer private information about the underlying model's training data. To this end, we propose a series of novel membership inference attacks which leverage algorithmic recourse. More specifically, we extend the prior literature on membership inference attacks to the recourse setting by leveraging the distances between data instances and their corresponding counterfactuals output by state-of-the-art recourse methods. Extensive experimentation with real world and synthetic datasets demonstrates significant privacy leakage through recourses. Our work establishes unintended privacy leakage as an important risk in the widespread adoption of recourse methods.
Bayesian score calibration for approximate models

Scientists continue to develop increasingly complex mechanistic models to reflect their knowledge more realistically. Statistical inference using these models can be challenging since the corresponding likelihood function is often intractable and model simulation may be computationally burdensome. Fortunately, in many of these situations, it is possible to adopt a surrogate model or approximate likelihood function. It may be convenient to conduct Bayesian inference directly with the surrogate, but this can result in bias and poor uncertainty quantification. In this paper we propose a new method for adjusting approximate posterior samples to reduce bias and produce more accurate uncertainty quantification. We do this by optimizing a transform of the approximate posterior that maximizes a scoring rule. Our approach requires only a (fixed) small number of complex model simulations and is numerically stable. We demonstrate good performance of the new method on several examples of increasing complexity.
Can Transformers Reason in Fragments of Natural Language?

State-of-the-art deep-learning-based approaches to Natural Language Processing (NLP) are credited with various capabilities that involve reasoning with natural language texts. %However, reasoning in this setting is often ill-defined and shallow. In this paper we carry out a large-scale empirical study investigating the detection of formally valid inferences in controlled fragments of natural language for which the satisfiability problem becomes increasingly complex. We find that, while transformer-based language models perform surprisingly well in these scenarios, a deeper analysis reveals that they appear to overfit to superficial patterns in the data rather than acquiring the logical principles governing the reasoning in these fragments.
How Does Sharpness-Aware Minimization Minimize Sharpness?

Sharpness-Aware Minimization (SAM) is a highly effective regularization technique for improving the generalization of deep neural networks for various settings. However, the underlying working of SAM remains elusive because of various intriguing approximations in the theoretical characterizations. SAM intends to penalize a notion of sharpness of the model but implements a computationally efficient variant; moreover, a third notion of sharpness was used for proving generalization guarantees. The subtle differences in these notions of sharpness can indeed lead to significantly different empirical results. This paper rigorously nails down the exact sharpness notion that SAM regularizes and clarifies the underlying mechanism. We also show that the two steps of approximations in the original motivation of SAM individually lead to inaccurate local conclusions, but their combination accidentally reveals the correct effect, when full-batch gradients are applied. Furthermore, we also prove that the stochastic version of SAM in fact regularizes another notion of sharpness, which is most likely to be the preferred notion for practical performance. The key mechanism behind this intriguing phenomenon is the implicit alignment between the gradient and the top eigenvector of Hessian when running SAM.
EARLY EMISSION FROM TYPE Ia SUPERNOVAE

A unique feature of deflagration-to-detonation (DDT) white dwarf explosion models of supernovae of type Ia is the presence of a strong shock wave propagating through the outer envelope. We consider the early emission expected in such models, which is produced by the expanding shock-heated outer part of the ejecta and precedes the emission driven by radioactive decay. We expand on earlier analyses by considering the modification of the pre-detonation density profile by the weak shocks generated during the deflagration phase, the time evolution of the opacity, and the deviation of the post-shock equation of state from that obtained for radiation pressure domination. A simple analytic model is presented and shown to provide an acceptable approximation to the results of one-dimensional numerical DDT simulations. Our analysis predicts a ∼103 s long UV/optical flash with a luminosity of ∼1 to ∼3 × 1039 erg s−1. Lower luminosity corresponds to faster (turbulent) deflagration velocity. The luminosity of the UV flash is predicted to be strongly suppressed at t > tdrop ∼ 1 hr due to the deviation from pure radiation domination.
Unbiased Supervised Contrastive Learning

Many datasets are biased, namely they contain easy-to-learn features that are highly correlated with the target class only in the dataset but not in the true underlying distribution of the data. For this reason, learning unbiased models from biased data has become a very relevant research topic in the last years. In this work, we tackle the problem of learning representations that are robust to biases. We first present a margin-based theoretical framework that allows us to clarify why recent contrastive losses (InfoNCE, SupCon, etc.) can fail when dealing with biased data. Based on that, we derive a novel formulation of the supervised contrastive loss (epsilon-SupInfoNCE), providing more accurate control of the minimal distance between positive and negative samples. Furthermore, thanks to our theoretical framework, we also propose FairKL, a new debiasing regularization loss, that works well even with extremely biased data. We validate the proposed losses on standard vision datasets including CIFAR10, CIFAR100, and ImageNet, and we assess the debiasing capability of FairKL with epsilon-SupInfoNCE, reaching state-of-the-art performance on a number of biased datasets, including real instances of biases in the wild.
Hydrogen 1s-2s transition frequency: Comparison of experiment and theory

Using the Dirac equation, radiative corrections and finite nuclear size and mass corrections, we calculate the $1s$-$2s$ quantum transition frequency $f_{1s,2s}$ of hydrogen and its uncertainty due to the uncertainties $\delta m_e, \delta m_p, \delta \alpha, \delta r_p, \delta R_{\infty}$ of the electron mass $m_e$, proton mass $m_p$, fine structure constant $\alpha$, proton root mean squared charge radius $r_p$, and the Rydberg constant $R_{\infty}$. We use the 2018 CODATA [E. Tiesinga, P. J. Mohr, D. B. Newell, B. N. Taylor, Rev. Mod. Phys. {\bf 93}, 025010 (2021)] procedure for the calculation of $f_{1s,2s}$, and the fundamental constants given therein. We find that the value of the experimental frequency lies outside the theoretical uncertainty (the discrepancy between the theoretical and the experimental frequency is $\Delta f_{1s,2s}^{(2018)} = -23.948$~kHz). But, by fitting $r_p$ we obtain a vanishing discrepancy between the calculated and experimental frequencies and a 6.4 kHz theoretical uncertainty, with $r_p = 0.830734$~fm (and a theoretical uncertainty of $\delta r_p = 0.0022$ fm), consistent with a recent measurement~[W. Xiong, {\it{et al}}. , Nature (London) {\bf 575}, 147 (2019)].
Neutral $B$-anomalies from an $\mathit{on\text{-}shell}$ scalar exchange

The neutral B -anomalies are analysed in terms of the tree-level exchange of a (pseudo)scalar gauge singlet a . Solutions to both R K ( ∗ ) central bin anomalies are found within 1 σ for m 2 a ∈ [1 . 1 , 6] GeV 2 , while the low q 2 -bin anomaly can also be accounted for with masses close to the bin threshold. The impact of these solutions on other b → se + e − observables is discussed in detail. Due to the on-shell enhancement, sizable eﬀects are expected in null tests of the SM, such as the ﬂat term, F H , of the B → Ke + e − angular distribution. At the same time, the observable sensitive to the K ∗ polarisation, F L ( B → K ∗ e + e − ), and the lepton forward-backward asymmetry, A FB ( B → K ∗ e + e − ), can be suppressed with respect to their SM values. Corrections from the new physics to B ( B s → e + e − ) are, on the other hand, negligible. Along with the previous observables, improved measurements of the cross section σ ( e + e − → a ( e + e − ) γ ) could potentially probe the relevant parameter space of the model. A comparison between our results and those stemming from an axion-like particle exchange is also discussed, showing that the exchange of a general scalar singlet oﬀers a noticeably wider parameter space.
CANDELS: THE CORRELATION BETWEEN GALAXY MORPHOLOGY AND STAR FORMATION ACTIVITY AT z ∼ 2

We discuss the state of the assembly of the Hubble sequence in the mix of bright galaxies at redshift 1.4 < z ⩽ 2.5 with a large sample of 1671 galaxies down to HAB ∼ 26, selected from the HST/ACS and WFC3 images of the GOODS-South field obtained as part of the GOODS and CANDELS observations. We investigate the relationship between the star formation properties and morphology using various parametric diagnostics, such as the Sérsic light profile, Gini (G), M20, concentration (C), asymmetry (A), and multiplicity (Ψ) parameters. Our sample clearly separates into massive, red, and passive galaxies versus less massive, blue, and star-forming ones, and this dichotomy correlates very well with the galaxies' morphological properties. Star-forming galaxies show a broad variety of morphological features, including clumpy structures and bulges mixed with faint low surface brightness features, generally characterized by disky-type light profiles. Passively evolving galaxies, on the other hand, very often have compact light distribution and morphology typical of today's spheroidal systems. We also find that artificially redshifted local galaxies have a similar distribution with z ∼ 2 galaxies in a G–M20 plane. Visual inspection between the rest-frame optical and UV images show that there is a generally weak morphological k-correction for galaxies at z ∼ 2, but the comparison with non-parametric measures show that galaxies in the rest-frame UV are somewhat clumpier than rest-frame optical. Similar general trends are observed in the local universe among massive galaxies, suggesting that the backbone of the Hubble sequence was already in place at z ∼ 2.
Measuring expansion velocities in Type II-P supernovae

We estimate photospheric velocities of Type II-P supernovae using model spectra created with SYNOW, and compare the results with those obtained by more conventional techniques, such as cross-correlation, or measuring the absorption minimum of P Cygni features. Based on a sample of 81 observed spectra of 5 SNe, we show that SYNOW provides velocities that are similar to ones obtained by more sophisticated NLTE modeling codes, but they can be derived in a less computation-intensive way. The estimated photopheric velocities (vmodel) are compared to ones measured from Doppler-shifts of the absorption minima of the Hβ and the Feii λ5169 features. Our results confirm that the Feii velocities (vFe) have tighter and more homogeneous correlation with the estimated photospheric velocities than the ones measured from Hβ, but both suffer from phase-dependent systematic deviations from those. The same is true for comparison with the cross-correlation velocities. We verify and im
The Sample Complexity of Online Contract Design

Contract theory studies the interactions between a principal and an agent when the two parties transact in the presence of private information [Bolton and Dewatripont, 2004, Faure-Grimaud et al., 2001, Salanié, 2005]. The principal would like to achieve her desired outcomes by hiring agents to work for her. The agent wishes to make money by working for the principal. They develop agreements in the form of a contract, which specifies how much the principal would pay under the different possible outcomes of the agent's work.
Mapping the Milky Way Disk with Gaia DR3: 3D Extended Kinematic Maps and Rotation Curve to ≈30 kpc

We apply a statistical deconvolution of the parallax errors based on Lucy’s inversion method (LIM) to the Gaia DR3 sources to measure their 3D velocity components in the range of Galactocentric distances R between 8 and 30 kpc with their corresponding errors and rms values. We find results that are consistent with those obtained by applying LIM to the Gaia DR2 sources, and we conclude that the method gives convergent and more accurate results by improving the statistics of the data set and lowering observational errors. The kinematic maps reconstructed with LIM up to R ≈ 30 kpc show that the Milky Way is characterized by asymmetrical motions with significant gradients in all velocity components. Furthermore, we determine the Galaxy rotation curve V C (R) up to ≈27.5 kpc with the cylindrical Jeans equation assuming an axisymmetric gravitational potential. We find that V C (R) is significantly declining up to the largest radius investigated. Finally, we also measure V C (R) at different vertical heights, showing that, for R < 15 kpc, there is a marked dependence on Z, whereas at larger R the dependence on Z is negligible.
MuMIC - Multimodal Embedding for Multi-label Image Classification with Tempered Sigmoid

Multi-label image classification is a foundational topic in various domains. Multimodal learning approaches have recently achieved outstanding results in image representation and single-label image classification. For instance, Contrastive Language-Image Pretraining (CLIP) demonstrates impressive image-text representation learning abilities and is robust to natural distribution shifts. This success inspires us to leverage multimodal learning for multi-label classification tasks, and benefit from contrastively learnt pretrained models. We propose the Multimodal Multi-label Image Classification (MuMIC) framework, which utilizes a hardness-aware tempered sigmoid based Binary Cross Entropy loss function, thus enables the optimization on multi-label objectives and transfer learning on CLIP. MuMIC is capable of providing high classification performance, handling real-world noisy data, supporting zero-shot predictions, and producing domain-specific image embeddings. In this study, a total of 120 image classes are defined, and more than 140K positive annotations are collected on approximately 60K Booking.com images. The final MuMIC model is deployed on Booking.com Content Intelligence Platform, and it outperforms other state-of-the-art models with 85.6% GAP@10 and 83.8% GAP on all 120 classes, as well as a 90.1% macro mAP score across 32 majority classes. We summarize the modelling choices which are extensively tested through ablation studies. To the best of our knowledge, we are the first to adapt contrastively learnt multimodal pretraining for real-world multi-label image classification problems, and the innovation can be transferred to other domains.
NGC 6240: merger-induced star formation and gas dynamics

We present spatially resolved integral field spectroscopic K-band data at a resolution of 0.13 �� (60 pc) and interferometric CO(2–1) line observations of the prototypical merging system NGC 6240. Despite the clear rotational signature, the stellar kinematics in the two nuclei are dominated by dispersion. We use Jeans modelling to derive the masses and the mass-to-light ratios of the nuclei. Combining the luminosities with the spatially resolved Brγ equivalent width shows that only 1/ 3o f theK-band continuum from the nuclei is associated with the most recent star forming episode; and that less than 30% of the system’s bolometric luminosity and only 9% of its stellar mass is due to this starburst. The star formation properties, calculated from typical merger star formation histories, demonstrate the impact of different assumptions about the star formation history. The properties of the nuclei, and the existence of a prominent old stellar population, indicate that the nuclei are remnants of the progenitor galaxies’ bulges.
A kinetic approach to consensus-based segmentation of biomedical images

In this work, we apply a kinetic version of a bounded confidence consensus model to biomedical segmentation problems. In the presented approach, time-dependent information on the microscopic state of each particle/pixel includes its space position and a feature representing a static characteristic of the system, i.e. the gray level of each pixel. From the introduced microscopic model we derive a kinetic formulation of the model. The large time behavior of the system is then computed with the aid of a surrogate Fokker-Planck approach that can be obtained in the quasi-invariant scaling. We exploit the computational efficiency of direct simulation Monte Carlo methods for the obtained Boltzmann-type description of the problem for parameter identification tasks. Based on a suitable loss function measuring the distance between the ground truth segmentation mask and the evaluated mask, we minimize the introduced segmentation metric for a relevant set of 2D gray-scale images. Applications to biomedical segmentation concentrate on different imaging research contexts.
Nano: Nested Human-in-the-Loop Reward Learning for Few-shot Language Model Control

Pretrained language models have demonstrated extraordinary capabilities in language generation. However, real-world tasks often require controlling the distribution of generated text in order to mitigate bias, promote fairness, and achieve personalization. Existing techniques for controlling the distribution of generated text only work with quantified distributions, which require pre-defined categories, proportions of the distribution, or an existing corpus following the desired distributions. However, many important distributions, such as personal preferences, are unquantified. In this work, we tackle the problem of generating text following arbitrary distributions (quantified and unquantified) by proposing Nano, a few-shot human-in-the-loop training algorithm that continuously learns from human feedback. Nano achieves state-of-the-art results on single topic/attribute as well as quantified distribution control compared to previous works. We also show that Nano is able to learn unquantified distributions, achieves personalization, and captures differences between different individuals' personal preferences with high sample efficiency.