Miracle-dz/UniFAR-FTUs huggingface dataset:
A Stellar Mass Dependence of Structured Disks: A Possible Link with Exoplanet Demographics

Gaps in protoplanetary disks have long been hailed as signposts of planet formation. However, a direct link between exoplanets and disks remains hard to identify. We present a large sample study of ALMA disk surveys of nearby star-forming regions to disentangle this connection. All disks are classified as either structured (transition, ring, extended) or nonstructured (compact) disks. Although low-resolution observations may not identify large-scale substructure, we assume that an extended disk must contain substructure from a dust evolution argument. A comparison across ages reveals that structured disks retain high dust masses up to at least 10 Myr, whereas the dust mass of compact, nonstructured disks decreases over time. This can be understood if the dust mass evolves primarily by radial drift, unless drift is prevented by pressure bumps. We identify a stellar mass dependence of the fraction of structured disks. We propose a scenario linking this dependence with that of giant exoplanet occurrence rates. We show that there are enough exoplanets to account for the observed disk structures if transitional disks are created by exoplanets more massive than Jupiter and ring disks by exoplanets more massive than Neptune, under the assumption that most of those planets eventually migrate inwards. On the other hand, the known anticorrelation between transiting super-Earths and stellar mass implies those planets must form in the disks without observed structure, consistent with formation through pebble accretion in drift-dominated disks. These findings support an evolutionary scenario where the early formation of giant planets determines the disk’s dust evolution and its observational appearance.
The Surprising Performance of Simple Baselines for Misinformation Detection

As social media becomes increasingly prominent in our day to day lives, it is increasingly important to detect informative content and prevent the spread of disinformation and unverified rumours. While many sophisticated and successful models have been proposed in the literature, they are often compared with older NLP baselines such as SVMs, CNNs, and LSTMs. In this paper, we examine the performance of a broad set of modern transformer-based language models and show that with basic fine-tuning, these models are competitive with and can even significantly outperform recently proposed state-of-the-art methods. We present our framework as a baseline for creating and evaluating new methods for misinformation detection. We further study a comprehensive set of benchmark datasets, and discuss potential data leakage and the need for careful design of the experiments and understanding of datasets to account for confounding variables. As an extreme case example, we show that classifying only based on the first three digits of tweet ids, which contain information on the date, gives state-of-the-art performance on a commonly used benchmark dataset for fake news detection –Twitter16. We provide a simple tool to detect this problem and suggest steps to mitigate it in future datasets.
Towards BERT-based Automatic ICD Coding: Limitations and Opportunities

Automatic ICD coding is the task of assigning codes from the International Classification of Diseases (ICD) to medical notes. These codes describe the state of the patient and have multiple applications, e.g., computer-assisted diagnosis or epidemiological studies. ICD coding is a challenging task due to the complexity and length of medical notes. Unlike the general trend in language processing, no transformer model has been reported to reach high performance on this task. Here, we investigate in detail ICD coding using PubMedBERT, a state-of-the-art transformer model for biomedical language understanding. We find that the difficulty of fine-tuning the model on long pieces of text is the main limitation for BERT-based models on ICD coding. We run extensive experiments and show that despite the gap with current state-of-the-art, pretrained transformers can reach competitive performance using relatively small portions of text. We point at better methods to aggregate information from long texts as the main need for improving BERT-based ICD coding.
Weighted holomorphic Dirichlet series and composition operators with polynomial symbols

In this paper, we introduce a general class of weighted spaces of holomorphic Dirichlet series (with real frequencies) analytic in some half-plane and study composition operators on these spaces. In the particular case when the symbol inducing the composition operator is an affine function, we give criteria for boundedness and compactness. We also study the cyclicity property and as a byproduct give a characterization so that the direct sum of the identity plus a weighted forward shift operator on $\ell^2$ is cyclic.
On the implied weights of linear regression for causal inference

A basic principle in the design of observational studies is to approximate the randomized experiment that would have been conducted under controlled circumstances. Now, linear regression models are commonly used to analyze observational data and estimate causal effects. How do linear regression adjustments in observational studies emulate key features of randomized experiments, such as covariate balance, self-weighted sampling, and study representativeness? In this paper, we provide answers to this and related questions by analyzing the implied (individual-level data) weights of linear regression methods. We derive new closed-form expressions of the weights and examine their properties in both finite and asymptotic regimes. We show that the implied weights of general regression problems can be equivalently obtained by solving a convex optimization problem. Among others, we study doubly and multiply robust properties of regression estimators from the perspective of their implied weights. This equivalence allows us to bridge ideas from the regression modeling and causal inference literatures. As a result, we propose novel regression diagnostics for causal inference that are part of the design stage of an observational study. As special cases, we analyze the implied weights in common settings such as multi-valued treatments and regression adjustment after matching. We implement the weights and diagnostics in the new lmw package for R.
Photometric Redshifts With Machine Learning, Lights and Shadows on a Complex Data Science Use Case

The importance of the current role of data-driven science is constantly increasing within Astrophysics, due to the huge amount of multi-wavelength data collected every day, characterized by complex and high-volume information requiring efficient and, as much as possible, automated exploration tools. Furthermore, to accomplish main and legacy science objectives of future or incoming large and deep survey projects, such as James Webb Space Telescope (JWST), James Webb Space Telescope (LSST), and Euclid, a crucial role is played by an accurate estimation of photometric redshifts, whose knowledge would permit the detection and analysis of extended and peculiar sources by disentangling low-z from high-z sources and would contribute to solve the modern cosmological discrepancies. The recent photometric redshift data challenges, organized within several survey projects, like LSST and Euclid, pushed the exploitation of the observed multi-wavelength and multi-dimensional data or ad hoc simulated data to improve and optimize the photometric redshifts prediction and statistical characterization based on both Spectral Energy Distribution (SED) template fitting and machine learning methodologies. They also provided a new impetus in the investigation of hybrid and deep learning techniques, aimed at conjugating the positive peculiarities of different methodologies, thus optimizing the estimation accuracy and maximizing the photometric range coverage, which are particularly important in the high-z regime, where the spectroscopic ground truth is poorly available. In such a context, we summarize what was learned and proposed in more than a decade of research.
Discrete sticky couplings of functional autoregressive processes

In this paper, we provide bounds in Wasserstein and total variation distances between the distributions of the successive iterates of two functional autoregressive processes with isotropic Gaussian noise of the form $Y_{k+1} = \mathrm{T}_\gamma(Y_k) + \sqrt{\gamma\sigma^2} Z_{k+1}$ and $\tilde{Y}_{k+1} = \tilde{\mathrm{T}}_\gamma(\tilde{Y}_k) + \sqrt{\gamma\sigma^2} \tilde{Z}_{k+1}$. More precisely, we give non-asymptotic bounds on $\rho(\mathcal{L}(Y_{k}),\mathcal{L}(\tilde{Y}_k))$, where $\rho$ is an appropriate weighted Wasserstein distance or a $V$-distance, uniformly in the parameter $\gamma$, and on $\rho(\pi_{\gamma},\tilde{\pi}_{\gamma})$, where $\pi_{\gamma}$ and $\tilde{\pi}_{\gamma}$ are the respective stationary measures of the two processes. The class of considered processes encompasses the Euler-Maruyama discretization of Langevin diffusions and its variants. The bounds we derive are of order $\gamma$ as $\gamma \to 0$. To obtain our results, we rely on the construction of a discrete sticky Markov chain $(W_k^{(\gamma)})_{k \in \mathbb{N}}$ which bounds the distance between an appropriate coupling of the two processes. We then establish stability and quantitative convergence results for this process uniformly on $\gamma$. In addition, we show that it converges in distribution to the continuous sticky process studied in previous work. Finally, we apply our result to Bayesian inference of ODE parameters and numerically illustrate them on two particular problems.
On the distribution of Sudler products and Birkhoff sums for the irrational rotation

We study the value distribution of the Sudler product $\prod_{n=1}^N |2 \sin (\pi n \alpha )|$ and the Diophantine product $\prod_{n=1}^N (2e\| n \alpha \|)$ for various irrational $\alpha$, as $N$ ranges in a long interval of integers. At badly approximable irrationals these products exhibit strong concentration around $N^{1/2}$, and at certain quadratic irrationals they even satisfy a central limit theorem. In contrast, at almost every $\alpha$ we observe an interesting anticoncentration phenomenon when the typical and the extreme values are of the same order of magnitude. Our methods are equally suited for the value distribution of Birkhoff sums $\sum_{n=1}^N f(n \alpha )$ for circle rotations. Using Diophantine approximation and Fourier analysis, we find the first and second moment for an arbitrary periodic $f$ of bounded variation, and (almost) prove a conjecture of Bromberg and Ulcigrai on the appropriate scaling factor in a so-called temporal limit theorem. Birkhoff sums also satisfy a central limit theorem at certain quadratic irrationals.
Significant interstellar object production by close stellar flybys

Context. Within just two years, two interstellar objects (ISOs) – 1I/‘Oumuamua and 2I/Borisov – have been discovered, the first of their kind. Large quantities of planetesimals form as a by-product of planet formation. Therefore, it seems likely that ISOs are former planetesimals that became somehow unbound from their parent star. The discoveries raise the question of the dominant ISO formation process. Aims. Here, we concentrate on planetesimals released during another star’s close flybys. Such close flybys happen most frequently during the first 10 Myr of a star’s life. Here, we quantify the amount of planetesimals released during close stellar flybys, their ejection velocity and likely composition. Methods. We numerically study the dependence of the effect of parabolic flybys on the mass ratio between the perturber and parent star, the periastron distance, inclination, and angle of periastron. Results. As expected, close prograde flybys of high-mass stars produce the most considerable amount of ISOs. Especially flybys of stars with M > 5 M⊙ on trajectories closer than 250 AU can lead to more planetesimals turning into ISOs than remaining bound to the parent star. Even strongly inclined orbits do not significantly reduce the ISO production; only retrograde flybys lead to a significantly lower ISO production. For perturbers slightly more massive than the parent star, there is a competition between ISO production and planetesimals being captured by the perturber. Whenever ISOs are produced, they leave their parent system typically with velocities in the range of 0.5–2 km s−1. This ejection velocity is distinctly different to that of ISOs produced by planet scattering (~4–8 km s−1) and those shed during the stellar post-main-sequence phase (~0.1–0.2 km s−1). Using the typical disc truncation radius in various cluster environments, we find that clusters like the Orion nebula cluster are likely to produce the equivalent of 0.85 Earth-masses of ISOs per star. In contrast, compact clusters like NGC 3603 could produce up to 50 Earth-masses of ISOs per star. Our solar-system probably produced the equivalent of 2–3 Earth masses of ISOs, which left our solar system at a mean ejection velocity of 0.7 km s−1. Conclusions. Most ISOs produced by flybys should be comet-like, similar to Borisov and unlike ‘Oumuamua. ISOs originating from compact long-lived clusters would often show a deficiency in CO. As soon as a statistically significant sample of ISOs is discovered, the combined information of their observed velocities and composition might help in constraining the dominant production process.
Reward Shaping with Subgoals for Social Navigation

Social navigation has been gaining attentions with the growth in machine intelligence. Since reinforcement learning can select an action in the prediction phase at a low computational cost, it has been formulated in a social navigation tasks. However, reinforcement learning takes an enormous number of iterations until acquiring a behavior policy in the learning phase. This negatively affects the learning of robot behaviors in the real world. In particular, social navigation includes humans who are unpredictable moving obstacles in an environment. We proposed a reward shaping method with subgoals to accelerate learning. The main part is an aggregation method that use subgoals to shape a reinforcement learning algorithm. We performed a learning experiment with a social navigation task in which a robot avoided collisions and then reached its goal. The experimental results show that our method improved the learning efficiency from a base algorithm in the task.
Cutting cosmological correlators

The initial conditions of our universe appear to us in the form of a classical probability distribution that we probe with cosmological observations. In the current leading paradigm, this probability distribution arises from a quantum mechanical wavefunction of the universe. Here we ask what the imprint of quantum mechanics is on the late time observables. We show that the requirement of unitary time evolution, colloquially the conservation of probabilities, fixes the analytic structure of the wavefunction and of all the cosmological correlators it encodes. In particular, we derive in perturbation theory an infinite set of single-cut rules that generalize the Cosmological Optical Theorem and relate a certain discontinuity of any tree-level n-point function to that of lower-point functions. Our rules are closely related to, but distinct from the recently derived Cosmological Cutting Rules. They follow from the choice of the Bunch-Davies vacuum and a simple property of the (bulk-to-bulk) propagator and are astoundingly general: we prove that they are valid for fields with a linear dispersion relation and any mass, any integer spin and arbitrary local interactions with any number of derivatives. They also apply to general FLRW spacetimes admitting a Bunch-Davies vacuum, including de Sitter, slow-roll inflation, power-law cosmologies and even resonant oscillations in axion monodromy. We verify the single-cut rules in a number of non-trivial examples, including four massless scalars exchanging a massive scalar, as relevant for cosmological collider physics, four gravitons exchanging a graviton, and a scalar five-point function.
Dynamical masses and mass-to-light ratios of resolved massive star clusters – II. Results for 26 star clusters in the Magellanic Clouds

We present spectroscopy of individual stars in 26 Magellanic Cloud (MC) star clusters with the aim of estimating dynamical masses and V-band mass-to-light (M/LV ) ratios over a wide range in age and metallicity. We obtained 3137 high-resolution stellar spectra with M2FS on the Magellan/Clay Telescope. Combined with 239 published spectroscopic results of comparable quality, we produced a final sample of 2787 stars with good quality spectra for kinematic analysis in the target clusters. Line-of-sight velocities measured from these spectra and stellar positions within each cluster were used in a customized expectation-maximization (EM) technique to estimate cluster membership probabilities. Using appropriate cluster structural parameters and corresponding single-mass dynamical models, this technique ultimately provides self-consistent total mass and M/LV estimates for each cluster. Mean metallicities for the clusters were also obtained and tied to a scale based on calcium IR triplet metallicites. We present trends of the cluster M/LV values with cluster age, mass and metallicity, and find that our results run about 40 per cent on average lower than the predictions of a set of simple stellar population (SSP) models. Modified SSP models that account for internal and external dynamical effects greatly improve agreement with our results, as can models that adopt a strongly bottom-light IMF. To the extent that dynamical evolution must occur, a modified IMF is not required to match data and models. In contrast, a bottom-heavy IMF is ruled out for our cluster sample as this would lead to higher predicted M/LV values, significantly increasing the discrepancy with our observations.
Volatile compounds as potential bio-fumigants against plant-parasitic nematodes – a mini review

Abstract Soil fumigation remains the standard practice to manage soilborne pathogens such as plant-parasitic nematodes, bacteria, and fungi, especially in high-value crops. However, increasing regulatory pressure due to the inherent and broad-spectrum toxicity and negative environmental impact of chemical soil fumigants, its negative effect on overall soil health, and increasing demand for organic produce, has created a growing interest in biological fumigants. Many plants and microorganisms emit volatile compounds, which can potentially be used as bio-fumigants. In this mini-review, we summarize the current status of nematology studies focused on the development of volatile compounds emitted from plants and microorganisms as fumigants to control plant-parasitic nematodes. The gap of knowledge and challenges of studying volatile compounds are also addressed.
Patterns of Abnormal Glucose Metabolism in Acromegaly and Impact of Treatment Modalities on Glucose Metabolism

Background: Chronic exposure to high levels of growth hormone (GH) and insulin-like growth factor 1 (IGF-1) leads to metabolic complications, most importantly dysglycemia in the form of diabetes mellitus and pre-diabetes. Dysglycemia if diagnosed early in the course of the disease can decrease complications. Treatment modalities in the form of surgery and medical therapy have varied impacts on glucose metabolism. Objective: To determine the frequency of diabetes mellitus, impaired glucose tolerance, and impaired fasting glucose in Pakistani patients with acromegaly and to establish the impact of the intervention (surgery/medical) on glucose metabolism. Methods: This study was a retrospective review of patient records. Eighty-nine patients fulfilling the Endocrine Society criteria for acromegaly diagnosis were included. A data of baseline, GH, IGF-1 level, hemoglobin A1C (HbA1C), fasting blood glucose (FBG), and random blood glucose (RBS) levels were reviewed before and after the intervention (surgery/medical therapy). Normal glucose tolerance (NGT), impaired fasting glucose (IFG), impaired glucose tolerance (IGT), and diabetes mellitus (DM) were defined based on the American Diabetes Association (ADA) criteria. Patients were grouped into normoglycemic (NGT) and dysglycemic (IFG, IGT, and DM) based on FBG, RBS, and HbA1C. Results: Major risk factors for dysglycemia included age (15-45 years), male sex (33.70%), obesity (45.7%), and macroadenoma (77.52%). Both mean GH levels (58.29 vs. 54.36 ng/dl) and IGF-1 levels (862.98 vs. 824.32 ng/dl) were higher among the normoglycemic than dysglycemia. Pre-surgery NGT, IFG, IGT, IFG, and IGT/DM combined were found in 48.31%, 5.61%, 1.1%, 5.61%, and 39.32% of the subjects respectively. Post-surgery, HbA1C improved in 79.5%, deteriorated in 6.8%, and remained the same in 13.6%. Similarly, it improved in 67% post-medical therapy. Both FBG and RBS improved post-surgery and medical therapy. Further, the number of anti-diabetic drugs used also decreased post-surgery. Conclusion: Dysglycemia is more common among patients with acromegaly as compared to the general population and tends to be poorly controlled in untreated acromegaly. Glycemic control improves significantly after surgery and medical therapy.
Long noncoding RNAs may impact podocytes and proximal tubule function through modulating miRNAs expression in Early Diabetic Kidney Disease of Type 2 Diabetes Mellitus patients

Aims: Long noncoding RNAs (lncRNAs) play key roles in the pathophysiology of DKD involving actions of microRNAs (miRNAs). The aims of the study were to establish the involvement of selected lncRNAs in the epigenetic mechanisms of podocyte damage and tubular injury in DKD of type 2 diabetes mellitus (DM) patients in relation to a particular miRNAs profile. Methods: A total of 136 patients with type 2 DM and 25 healthy subjects were assessed in a cross-sectional study concerning urinary albumin: creatinine ratio (UACR), eGFR, biomarkers of podocyte damage (synaptopodin, podocalyxin) and of proximal tubule (PT) dysfunction (Kidney injury molecule-1-KIM-1, N-acetyl-D-glucosaminidase-NAG), urinary lncRNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), nuclear-enriched abundant transcript 1 (NEAT1), myocardial infarction-associated transcript (MIAT), taurine-upregulated gene 1 (TUG1), urinary miRNA21, 124, 93, 29a. Results: Multivariable regression analysis showed that urinary lncMALAT1 correlated directly with urinary synaptopodin, podocalyxin, KIM-1, NAG, miRNA21, 124, UACR, and negatively with eGFR, miRNA93, 29a (p<0.0001; R2=0.727); urinary lncNEAT1 correlated directly with synaptopodin, KIM-1, NAG, miRNA21, 124, and negatively with eGFR, miRNA93, 29a (p<0.0001; R2=0.702); urinary lncMIAT correlated directly with miRNA93 and 29a, eGFR (p<0.0001; R2=0.671) and negatively with synaptopodin, KIM-1, NAG, UACR, miRNA21, 124 (p<0.0001; R2=0.654); urinary lncTUG1 correlated directly with eGFR, miRNA93, 29a, and negatively with synaptopodin, podocalyxin, NAG, miRNA21, 124 (p<0.0001; R2=0.748). Conclusions: In patients with type 2 DM lncRNAs exert either deleterious or protective functions within glomeruli and PT. LncRNAs may contribute to DKD through modulating miRNAs expression and activities. This observation holds true independently of albuminuria and DKD stage.