Miracle-dz/UniFAR-FTUs huggingface dataset:
Models of the in Situ Formation of Detected Extrasolar Giant Planets

We present numerical simulations of the formation of the planetary companions to 47 UMa, CrB, and 51 Peg. They are assumed to have formed in situ according to the basic model that a core formed rst by accretion of solid particles, then later it captured substantial amounts of gas from the protoplanetary disk. In most of the calculations we prescribe a constant accretion rate for the solid core. The evolution of the gaseous envelope is calculated according to the following assumptions: (1) it is in quasi-hydrostatic equilibrium, (2) the gas accretion rate is determined by the requirement that the outer radius of the planet is the place at which the thermal velocity of the gas allows it to reach the boundary of the planet's Hill sphere, (3) the gas accretion rate is limited, moreover, by the prescribed maximum rate at which the nebula can supply the gas, and (4) the growth of the planet stops once it obtains approximately the minimummass determined from radial velocity measurements (in one case the planet is allowed to grow to twice this limit). Calculations are carried out through an initial phase during which solid accretion dominates, past the point of crossover when the masses of solid and gaseous material are equal, through the phase of rapid gas accretion, and into the nal phase of contraction and cooling at constant mass. Alternative calculations are presented for the case of 47 UMa in which the solid accretion rate is calculated, not assumed, and the dissolution of planetesimals within the gaseous envelope is considered. In all cases there is a short phase of high luminosity (10 3 10 2 L ) associated with rapid gas accretion. The height and duration of this peak depend on uncertain model parameters. The conclusion is reached that in situ formation of all of these companions is possible under some circumstances. However, it is more likely that orbital migration was an important component of the evolution, at least for the planets around CrB and 51 Peg. 2
Generalized 2-local isometries of spaces of continuously differentiable functions

Abstract Let C(n) (I) denote the Banach space of n-times continuously differentiable functions on I = [0, 1], equipped with the norm ||f||n = max { |f(0)|, |fʹ(0)|, … , |f(n–1)(0)|, ||f(n)||∞} (f ) ∈ C(n) (I)), where ||·||∞ is the supremum norm. We call a map T : C(n) (I) → C(n)(I) a 2-local real-linear isometry if for each pair f, g in C(n)(I), there exists a surjective real-linear isometry Tf,g : C(n)(I) → C(n)(I) such that T(f) = Tf,g(f) and T(g) = Tf,g(g). In this paper we show that every 2-local real-linear isometry of C(n)(I) is a surjective real-linear isometry. Moreover, a complete description of such maps is presented.
The association between glomerular filtration rate and stroke in hypertensive patients in rural areas of China

Objectives: The present study is to explore the association between estimated glomerular filtration rate (eGFR) and stroke in hypertensive patients in rural areas of China. Methods: The prospective study was based on 3711 hypertensive patients who were aged at least 35 years, free from cardiovascular diseases, and had serum creatinine at baseline. eGFR was estimated using the Chronic Kidney Disease Epidemiology Collaboration equation. Results: During a median follow-up of 4.9 years, 176 first-ever strokes (98 ischemic, 75 hemorrhagic, and three were unspecified strokes) occurred. We found no independent association between eGFR and risk of hemorrhagic stroke. In contrast, with decreasing eGFR, the risk of total and ischemic stroke strongly increased; the sex and age-adjusted hazard ratios for overall and ischemic stroke were 3.34 [95% confidence interval (CI) 1.61–6.93] and 3.79 (95% CI 1.33–10.81) for patients with eGFR below 60 versus eGFR at least 90 ml/min per 1.73 m2, respectively. In addition, patients with an eGFR of 60–90 relative to eGFR at least 90 ml/min per 1.73 m2 had hazard ratios of 2.31 (95% CI 1.53–3.51) and 3.24 (95% CI 1.76–6.00) for overall and ischemic stroke after adjustment for sex and age, respectively. Adjustment for other cardiovascular factors only slightly attenuated the associations. Conclusions: The patients with decreased eGFR, also included those with eGFR range between 60 and 90 ml/min per 1.73 m2, had an independently increased risk of overall and ischemic stroke in hypertensive patients in rural areas of China.
Learning to generalize Dispatching rules on the Job Shop Scheduling

This paper introduces a Reinforcement Learning approach to better generalize heuristic dispatching rules on the Job-shop Scheduling Problem (JSP). Current models on the JSP do not focus on generalization, although, as we show in this work, this is key to learning better heuristics on the problem. A well-known technique to improve generalization is to learn on increasingly complex instances using Curriculum Learning (CL). However, as many works in the literature indicate, this technique might suffer from catastrophic forgetting when transferring the learned skills between different problem sizes. To address this issue, we introduce a novel Adversarial Curriculum Learning (ACL) strategy, which dynamically adjusts the difﬁculty level during the learning process to revisit the worst-performing instances. This work also presents a deep learning model to solve the JSP, which is equivariant w.r. t. the job deﬁnition and size-agnostic. Conducted experiments on Taillard’s and Demirkol’s instances show that the presented approach signiﬁcantly improves the current state-of-the-art models on the JSP. It reduces the average optimality gap from 19.35% to 10.46% on Taillard’s instances and from 38.43% to 18.85% on Demirkol’s instances. Our implementation is available online 2 .
Testing for Heavy Tails using Empirical Distribution Shape: The Tail Ratio Test

We look at goodness-of-fit tests for heavy tailed distributions. As these distributions are found in numerous fields, adequate goodness-offit testing helps to correctly model them. We inspect the power of four different tests to find the best one: the Kolmogorov-Smirnov test, the Anderson-Darling test, the Estimated Score test and the Tail Ratio test. The latter is developed in this paper. We use simulation to find critical values of these tests and use simulation once again to find their power. Our results show that the Tail Ratio test has the highest power of the four tests.
Don’t fear the unlabelled: Safe semi-supervised learning via simple debiasing

Semi-supervised learning (SSL) provides an effective means of leveraging un-1 labelled data to improve a model’s performance. Even though the domain has 2 received a considerable amount of attention in the past years, most methods present 3 the common drawback of lacking theoretical guarantees. Our starting point is to 4 notice that the estimate of the risk that most discriminative SSL methods minimise 5 is biased, even asymptotically. This bias impedes the use of standard statistical 6 learning theory and can hurt empirical performance. We propose a simple way of 7 removing the bias. Our debiasing approach is straightforward to implement and 8 applicable to most deep SSL methods. We provide simple theoretical guarantees on 9 the trustworthiness of these modified methods, without having to rely on the strong 10 assumptions on the data distribution that SSL theory usually requires. In particular, 11 we provide generalisation error bounds for the proposed methods. We evaluate 12 debiased versions of different existing SSL methods, such as the Pseudo-label 13 method and Fixmatch, and show that debiasing can compete with classic deep SSL 14 techniques in various settings by providing better calibrated models. Additionally, 15 we provide a theoretical explanation of the intuition of the popular SSL methods. 16
Tailoring incoming shear and turbulence profiles for lab‐scale wind turbines

An active grid is used to generate a variety of turbulent shear profiles in a wind tunnel. The vertical bars are set to flap through varying angles across the test section producing a variation in the perceived solidity, resulting in a mean shear. The horizontal bars are used in a fully random operational mode to set the background turbulence level. It is demonstrated that mean velocity profiles with approximately the same shear can be produced with different turbulence intensities and local turbulent Reynolds numbers based on the Taylor microscale, λ. Conversely, it is also demonstrated that flows can be produced with similar turbulence intensity profiles but different mean shear. It is confirmed that the length scales and dynamics, the latter being assessed through the velocity spectra and probability density functions, do not vary significantly across the investigation domain. Such flows are of particular relevance for studies investigating the effect of in-flow conditions on obstacles where these studies wish to decouple the effects of turbulence intensity and mean shear, a feat previously unattainable in experimental facilities. Given that the power output of wind turbines is known to be a function of both mean shear and turbulence intensity, the experimental methodology presented herein is invaluable to the wind turbine model testing community who, at present, cannot exert such control authority over the in-flow conditions.
Exploring Derivatives by Means of GeoGebra

The paper aims to explain how GeoGebra can be used in a differential calculus course to explore the Derivative concepts by providing dynamic-visualizations of the concept. Design research methodology was used in this research by designing an instructional design (hypothetical learning trajectories) in the first phase and conducting the teaching experiment in the second phase. The data collected during the experiment consist of video recordings of the classroom activities, observations, interviews, and students written work. In the third phase of the design research, the data were analyzed retrospectively by comparing the actual learning process and the hypothetical learning trajectory. The results show that the dynamic feature of GeoGebra offers the possibility of zooming in on a graph corresponds to taking infinitesimal when a secant line transforms into a tangent line. This builds a foundation for the understanding of the definition of derivative intuitively.
Molecular gas in z~6 quasar host galaxies

We investigate the molecular gas content of z~6 quasar host galaxies using the IRAM / Northern Extended Millimeter Array. We target the 3mm dust continuum, and the line emission from CO(6-5), CO(7-6), [CI]2-1 in 10 infra-red-luminous quasars that have been previously studied in their 1mm dust continuum and [CII] line emission. We detect CO(7-6) at various degrees of significance in all the targeted sources, thus doubling the number of such detections in z~6 quasars. The 3mm to 1mm flux density ratios are consistent with a modified black body spectrum with a dust temperature $T_{dust}$~47 K and an optical depth $\tau_{\nu}$=0.2 at the [CII] frequency. Our study provides us with four independent ways to estimate the molecular gas mass, $M_{H2}$, in the targeted quasars. This allows us to set constraints on various parameters used in the derivation of molecular gas mass estimates, such as the mass per luminosity ratios $\alpha_{CO}$ and $\alpha_{[CII]}$, the gas-to-dust ratio $\delta_{g/d}$, and the carbon abundance [C]/H2. Leveraging either on the dust, CO, [CI], or [CII] emission yields mass estimates of the entire sample in the range $M_{H2}$~$10^{10}$ to $10^{11}$ M$_{\odot}$. We compare the observed luminosities of dust, [CII], [CI], and CO(7-6) with predictions from photo-dissociation and X-ray dominated regions. We find that the former provide better model fits to our data, assuming that the bulk of the emission arises from dense ($n_H>10^4$ cm$^{-3}$) clouds with a column density $N_{H}$~$10^{23}$ cm$^{-2}$, exposed to a radiation field with intensity $G_0$~$10^3$ (in Habing units). Our analysis reiterates the presence of massive reservoirs of molecular gas fueling star formation and nuclear accretion in $z$~6 quasar host galaxies. It also highlights the power of combined 3mm and 1mm observations for quantitative studies of the dense gas content in massive galaxies at cosmic dawn.
Hybrid Recommender System based on Autoencoders

Proficient Recommender Systems heavily rely on Matrix Factorization (MF) techniques. MF aims at reconstructing a matrix of ratings from an incomplete and noisy initial matrix; this prediction is then used to build the actual recommendation. Simultaneously, Neural Networks (NN) met tremendous successes in the last decades but few attempts have been made to perform recommendation with autoencoders. In this paper, we gather the best practice from the literature to achieve this goal. We first highlight the link between these autoencoder based approaches and MF. Then, we refine the training approach of autoencoders to handle incomplete data. Second, we design an end-to-end system which handles external information. Finally, we empirically evaluate these approaches on the MovieLens and Douban dataset.
Weather Impact on Heat-Related Illness in a Tropical City State, Singapore

In this article we propose a novel hurdle negative binomial (HNB) regression combined with a distributed lag nonlinear model (DLNM) to model weather factors’ impact on heat related illness (HRI) in Singapore. AIC criterion is adopted to help select proper combination of weather variables and check their lagged effect as well as nonlinear effect. The process of model selection and validation is demonstrated. It is observed that the predicted occurrence rate is close to the observed one. The proposed combined model can be used to predict HRI cases for mitigating HRI occurrences and provide inputs for related public health policy considering climate change impact.
Influence of samarium substitution on the ferroelectricity of Bi4Ti3O12 ceramic

ABSTRACT Bismuth titanate (BT-‘Bi4Ti3O12’) is one of the extensively studied materials of the Bismuth layered structure ferroelectric (BLSF) family for its large spontaneous polarization along c-axis. In the present investigation, we have synthesised SmxBi4-xTi3O12 (x = 0.37, 0.57, 0.72) ceramics using the conventional solid-state reaction method. The dielectric anomaly was observed near 450°C. Well-broad hysteresis loops were observed above room temperature up to 100°C. The coercive field (Ec) and consistent remanent polarization (Pr) values with temperature, compared to BT, indicates that the title compound has good promising ferroelectric-nature. This phenomenon is attributed to the easy recovery of defects (such as oxygen vacancies) accumulated at grain-interfaces, above room temperature, resulting in to well-hysteresis behaviour. The results were further confirmed with remanent polarization (2 Pr vs. temperature) plots.
The effect of corporate tax avoidance on the cost of equity

While prior studies have examined how investors perceive extreme forms of tax avoidance behavior such as tax sheltering and uncertain tax position (e.g., Hanlon and Slemrod 2009; Wilson 2009; Koester 2011; Hutchens and Rego 2012), there is little evidence on how investors perceive less extreme forms of tax avoidance. This study fills this void by examining the relation between firm’s cost of equity and corporate tax avoidance using three measures that capture less extreme forms of corporate tax avoidance: book-tax differences, permanent book-tax differences, and long-run cash effective tax rates. We find that less aggressive forms of corporate tax avoidance significantly reduces a firm’s cost of equity. Further analyses reveal that this effect is stronger for (i) firms with better outside monitoring, (ii) firms that likely realize higher marginal benefits from tax savings, and (iii) firms with better information quality. Our study presents large-sample results on how investors perceive less aggressive corporate tax avoidance and shows that tax planning is a value-enhancing activity for shareholders.
Integrating Multisource Block-Wise Missing Data in Model Selection

Abstract For multisource data, blocks of variable information from certain sources are likely missing. Existing methods for handling missing data do not take structures of block-wise missing data into consideration. In this article, we propose a multiple block-wise imputation (MBI) approach, which incorporates imputations based on both complete and incomplete observations. Specifically, for a given missing pattern group, the imputations in MBI incorporate more samples from groups with fewer observed variables in addition to the group with complete observations. We propose to construct estimating equations based on all available information, and integrate informative estimating functions to achieve efficient estimators. We show that the proposed method has estimation and model selection consistency under both fixed-dimensional and high-dimensional settings. Moreover, the proposed estimator is asymptotically more efficient than the estimator based on a single imputation from complete observations only. In addition, the proposed method is not restricted to missing completely at random. Numerical studies and ADNI data application confirm that the proposed method outperforms existing variable selection methods under various missing mechanisms. Supplementary materials for this article are available online.
Policy Gradients for Contextual Bandits

We study a generalized contextual-bandits problem, where there is a state that decides the distribution of contexts of arms and affects the immediate reward when choosing an arm. The problem applies to a wide range of realistic settings such as personalized recommender systems and natural language generations. We put forward a class of policies in which the marginal probability of choosing an arm (in expectation of other arms) in each state has a simple closed form and is differentiable. In particular, the gradient of this class of policies is in a succinct form, which is an expectation of the action-value multiplied by the gradient of the marginal probability over pairs of states and single contexts. These findings naturally lead to an algorithm, coined policy gradient for contextual bandits (PGCB). As a further theoretical guarantee, we show that the variance of PGCB is less than the standard policy gradients algorithm. We also derive the off-policy gradients, and evaluate PGCB on a toy dataset as well as a music recommender dataset. Experiments show that PGCB outperforms both classic contextual-bandits methods and policy gradient methods.
Restenosis in one lesion in patients with multilesion stenting occurs even when the companion lesion is free of restenosis

We read with interest the article of Hoffmann et al. [1] on the lesion-to-lesion relationship in the process of restenosis. The authors confirmed the interrelation between multiple stented lesions with respect to restenosis as previously described in a large population undergoing coronary stent implantation [2]. Although we really enjoyed reading this article, we would like to draw your attention in a clear misinterpretation of the results we have published almost 3 years ago [2]. Hoffmann et al. [1] wrote in their discussion section that for any given lesion, restenosis in the companion lesion was the only independent risk factor of restenosis found in our study. The interpretation of our findings would have been facilitated simply by looking at figure 1 of our article [2], which clearly shows that there are several patient-, lesion-, and procedure-related factors with an independent impact on restenosis, and that restenosis in the companion lesion (defined as intrapatient correlation in our article) is only one of these factors. We believe that the discussion deserves the same accuracy with which Hoffmann et al. [1] performed the analysis.
Implicit Object Mapping With Noisy Data

Modelling individual objects in a scene as Neural Radiance Fields (NeRFs) provides an alternative geometric scene representation that may benefit downstream robotics tasks such as scene understanding and object manipulation. However, we identify three challenges to using real-world training data collected by a robot to train a NeRF: (i) The camera trajectories are constrained, and full visual coverage is not guaranteed - especially when obstructions to the objects of interest are present; (ii) the poses associated with the images are noisy due to odometry or localization noise; (iii) the objects are not easily isolated from the background. This paper evaluates the extent to which above factors degrade the quality of the learnt implicit object representation. We introduce a pipeline that decomposes a scene into multiple individual object-NeRFs, using noisy object instance masks and bounding boxes, and evaluate the sensitivity of this pipeline with respect to noisy poses, instance masks, and the number of training images. We uncover that the sensitivity to noisy instance masks can be partially alleviated with depth supervision and quantify the importance of including the camera extrinsics in the NeRF optimisation process.
Nonequilibrium dynamics of the localization-delocalization transition in non-Hermitian Aubry-Andr´e model

In this paper, we investigate the driven dynamics of the localization transition in the non-Hermitian Aubry-Andr´e model with the periodic boundary condition. Depending on the strength of the quasi-periodic potential λ , this model undergoes a localization-delocalization phase transition. We ﬁnd that the localization length ξ satisﬁes ξ ∼ ε − ν with ε being the distance from the critical point and ν = 1 being a universal critical exponent independent of the non-Hermitian parameter. In addition, from the ﬁnite-size scaling of the energy gap between the ground state and the ﬁrst excited state, we determine the dynamic exponent z as z = 2. The critical exponent of the inverse participation ratio (IPR) for n th eigenstate is also determined as s = 0 . 1197. By changing ε linearly to cross the critical point, we ﬁnd that the driven dynamics can be described by the Kibble-Zurek scaling (KZS). Moreover, we show that the KZS with the same set of the exponents can be generalized to the localization phase transitions in the excited states.
The power of moral words: Understanding framing effects in extreme Dictator games using sentiment analysis and moral judgments

Recent work shows that people are not solely motivated by the economic consequences of the available actions, but they also have moral preferences for “doing the right thing”, independently of its economic consequences. Here we add to this literature with two experiments. In Study 1 (N = 567) we implement an extreme dictator game in which dictators either get $0.50 and another person gets nothing, or the other way around (i.e., the other person gets $0.50 and the dictator gets nothing). We experimentally manipulate the words describing the available actions using six words, from very negative (e.g., stealing) to very positive (e.g., donating) connotations. Our hypothesis is that people are reluctant to make actions described using words with a negative connotation, and are eager to make actions described using words with a positive connotation, independently of their economic consequences. As predicted, we find that the connotation of the word has a U-shaped effect on pro-sociality. Moreover, we show that the overall pattern of results, the U-shape, but not its details, can be explained using a technique from Computational Linguistics known as Sentiment Analysis. In Study 2 (N = 413, pre-registered) we make a step forward and we collect the self-reported moral judgment and feeling associated to each of the six words used in Study 1. We show that the rate of pro-sociality in Study 1 can be predicted from the moral judgments and the feelings in Study 2 via Krupka & Weber’s utility function. In sum, our findings provide additional evidence for the existence and relevance of moral preferences, confirm the usefulness of Krupka & Weber’s utility function, and suggest that building bridges from computational linguistics to behavioral science can contribute to our understanding of human