Miracle-dz/UniFAR-FTUs huggingface dataset:
A detailed study of Andromeda XIX, an extreme local analogue of ultradiffuse galaxies

With a central surface brightness of μ0 = 29.3 mag arcsec−2, and half-light radius of $r_{\rm half}=3.1^{+0.9}_{-1.1}$ kpc, Andromeda XIX (And XIX) is an extremely diffuse satellite of Andromeda. We present spectra for ∼100 red giant branch stars in this galaxy, plus 16 stars in a nearby stellar stream. With this exquisite data set, we re-derive the properties of And XIX, measuring a systemic velocity of 〈vr〉 = −109.0 ± 1.6 km s−1 and a velocity dispersion of $\sigma _{vr} = 7.8^{+1.7}_{-1.5}$ km s−1 (higher than derived in our previous work). We marginally detect a velocity gradient along the major axis of $\frac{{\rm d} v}{{\rm d}\chi }= -2.1\pm 1.8~{\rm km\, s}^{-1}$ kpc−1. We find its mass-to-light ratio is higher than galaxies of comparable stellar mass (${}[M/L]_{\rm half} = 278^{+146}_{-198}\, \mathrm{M}_\odot /\mathrm{L}_\odot$), but its dynamics place it in a halo with a similar total mass to these galaxies. This could suggest that And XIX is a ‘puffed up’ dwarf galaxy, whose properties have been altered by tidal processes, similar to its Milky Way counterpart, Antlia II. For the nearby stream, we measure $v_r=-279.2\pm 3.7~{\rm km\, s}^{-1}$, and $\sigma _v=13.8^{+3.5}_{-2.6}~{\rm km\, s}^{-1}$. We measure its metallicity, and find it to be more metal rich than And XIX, implying that the two features are unrelated. Finally, And XIX’s dynamical and structural properties imply it is a local analogue to ultradiffuse galaxies (UDGs). Its complex dynamics suggest that the masses of distant UDGs measured from velocity dispersions alone should be carefully interpreted.
Inhomogeneous cosmology in an anisotropic Universe

With the era of precision cosmology upon us, and upcoming surveys expected to further improve the precision of our observations below the percent level, ensuring the accuracy of our theoretical cosmological model is of the utmost importance. Current tensions between our observations and predictions from the standard cosmological model have sparked curiosity in extending the model to include new physics. Although, some suggestions include simply accounting for aspects of our Universe that are ignored in the standard model. One example acknowledges the fact that our Universe contains significant density contrasts on small scales; in the form of galaxies, galaxy clusters, filaments, and voids. This small-scale structure is smoothed out in the standard model, by assuming large-scale homogeneity of the matter distribution, which could have a measurable effect due to the nonlinearity of Einstein's equations. This backreaction of small-scale structures on the large-scale dynamics has been suggested to explain the measured accelerating expansion rate of the Universe. Current standard cosmological simulations ignore the effects of General Relativity by assuming purely Newtonian dynamics. In this thesis, we take the first steps towards quantifying the backreaction of small-scale structures by performing cosmological simulations that solve Einstein's equations directly. Simulations like these will allow us to quantify potentially important effects on our observations that could become measurable as the precision of these observations increases into the future.
Solubility of Nitrogen in Methane, Ethane, and Mixtures of Methane and Ethane at Titan-Like Conditions: A Molecular Dynamics Study

We have studied the temperature dependence of the solubility of nitrogen in methane, ethane, and mixtures of methane and ethane using vapor-liquid equilibrium simulations of binary and ternary mixtures of nitrogen, methane and ethane for a range of temperatures between 90K and 110K at a pressure of 1.5 atm, thermodynamic conditions that may exist on the Saturn's giant moon, Titan. We find that --(i) the solubility of nitrogen in both methane and ethane decreases with increasing temperature; (ii) the solubility of nitrogen in methane is much larger compared to that in ethane at low temperatures, (iii) solubility of nitrogen in a ternary mixture of methane, ethane, and nitrogen increases upon increasing mole-fraction of methane. Our results are in quantitative agreement with the recent experimental measurement of the solubility of nitrogen in methane, ethane, and a mixture of methane and ethane. Furthermore, we find a strong temperature-dependent surface adsorption of nitrogen at the nitrogen-hydrocarbon interface, previously unknown. We have also investigated surface tension of the gas-liquid interface and find that it decreases upon decreasing temperature. Moreover, we find that the interfacial layer of adsorbed nitrogen and ethane show a preferential orientational ordering at the interface.
Exploring the spectral information content in the LAMOST medium-resolution survey (MRS)

Although high-resolution stellar spectra allow us to derive precise stellar labels (effective temperature, metallicity, surface gravity, elemental abundances, etc.) based on resolved atomic lines and molecular bands, low-resolution spectra have been proved to be competitive in determiningmany stellar labels at comparable precision. It is useful to consider the spectral information content when assessing the capability of a stellar spectrum in deriving precise stellar labels. In this work, we quantify the information content brought by the LAMOST-II medium-resolution spectroscopic survey (MRS) using the gradient spectra as well as the coefficients-of-dependence (CODs). In general, the wavelength coverage of the MRS well constrains the stellar labels but the sensitivities of different stellar labels vary with spectral types and metallicity of the stars of interest. Consequently, this affects the performance of the stellar label determination from the MRS spectra. By applying the SLAM method to the synthetic spectra which mimic the MRS data, we find that the precision of the fundamental stellar parameters Teff, log g and [M/H] are better when combining both the blue and red bands of the MRS. This is especially important for warm stars because the Hα line located in the red part plays a more important role in determining the effective temperature for warm stars. With blue and red parts together, we are able to reach similar performance to the low-resolution spectra except for warm stars. However, at [M/H] ∼ −2.0 dex, the uncertainties of fundamental stellar labels estimated from MRS are substantially larger than that from low-resolution spectra. We also tested the uncertainties of Teff, log g and [M/H] From MRS data induced from the radial velocity mismatch and find that a mismatch of about 1 km s−1, which is typical for LAMOST MRS data, would not significantly affect the stellar label estimates. Finally, reference precision limits are calculated using synthetic gradient spectra, according to which we expect abundances of at least 17 elements to be measured precisely from MRS spectra.
Erd\H{o}s-Szekeres theorem for multidimensional arrays

The classical Erdős-Szekeres theorem dating back almost a hundred years states that any sequence of $(n-1)^2+1$ distinct real numbers contains a monotone subsequence of length $n$. This theorem has been generalised to higher dimensions in a variety of ways but perhaps the most natural one was proposed by Fishburn and Graham more than 25 years ago. They defined the concept of a monotone and a lex-monotone array and asked how large an array one needs in order to be able to find a monotone or a lex-monotone subarray of size $n \times \ldots \times n$. Fishburn and Graham obtained Ackerman-type bounds in both cases. We significantly improve these results. Regardless of the dimension we obtain at most a triple exponential bound in $n$ in the monotone case and a quadruple exponential one in the lex-monotone case.
Driving classical Wolf-Rayet winds: A Γ- and Z-dependent mass-loss

Classical Wolf-Rayet (WR) stars are at a crucial evolutionary stage for constraining the fates of massive stars. The feedback of these hot, hydrogen-depleted stars dominates their surrounding by tremendous injections of ionizing radiation and kinetic energy. The strength of a WR wind decides the eventual mass of its remnant, likely a massive black hole. However, despite their major influence and importance for gravitational wave detection statistics, WR winds are particularly poorly understood. In this paper, we introduce the first set of hydrodynamically consistent stellar atmosphere models for classical WR stars of both the carbon (C) and nitrogen (N) sequence, i.e. WC and WN stars, as a function of stellar luminosity-to-mass ratio (or Eddington Gamma), and metallicity. We demonstrate the inapplicability of the CAK wind theory for classical WR stars and confirm earlier findings that their winds are launched at the (hot) iron (Fe) opacity peak. For log Z/Z⊙ > −2, Fe is also the main accelerator throughout the wind. Contrasting previous claims of a sharp lower mass-loss limit for WR stars, we obtain a smooth transition to optically thin winds. Furthermore, we find a strong dependence of the mass-loss rates on Eddington Γ, both at solar and sub-solar metallicity. Increases in WC carbon and oxygen abundances turn out to slightly reduce the predicted mass-loss rates. Calculations at subsolar metallicities indicate that below the metallicity of the SMC, WR mass-loss rates decrease much faster than previously assumed, potentially allowing for high black hole masses even in the local universe.
A Robust Pavement Mapping System Based on Normal-Constrained Stereo Visual Odometry

Pavement condition is crucial for civil infrastructure maintenance. This task usually requires efficient road damage localization, which can be accomplished by the visual odometry system embedded in unmanned aerial vehicles (UAVs), However, the state-of-the-art visual odometry and mapping methods suffer from large drift under the degeneration of the scene structure. To alleviate this issue, we integrate normal constraints into the visual odometry process, which greatly helps to avoid large drift. By parameterizing the normal vector on the tangential plane, the normal factors are coupled with traditional reprojection factors in the pose optimization procedure. The experimental results demonstrate the effectiveness of the proposed system. The overall absolute trajectory error is improved by approximately 20%, which indicates that the estimated trajectory is much more accurate than that obtained using other state-of-the-art methods.
The exceptional zero phenomenon for elliptic units

The exceptional zero phenomenon has been widely studied in the realm of $p$-adic $L$-functions, where the starting point lies in the foundational work of Mazur, Tate and Teitelbaum. This phenomenon also appears in the study of Euler systems, which comes as no surprise given the interaction between these two settings. When this occurs, one is led to study higher order derivatives of the Euler system in order to extract the arithmetic information which is usually encoded in the explicit reciprocity laws. In this work, we focus on the elliptic units of an imaginary quadratic field and study this exceptional zero phenomenon, proving an explicit formula relating the logarithm of a {\it derived} elliptic unit either to special values of Katz's two variable $p$-adic $L$-function or to its derivatives. Further, we interpret this fact in terms of an $\mathcal L$-invariant, and relate this result to other approaches to the exceptional zero phenomenon concerning Heegner points and Beilinson--Flach elements.
Joint quantile regression for spatial data

Linear quantile regression is a powerful tool to investigate how predictors may affect a response heterogeneously across different quantile levels. Unfortunately, existing approaches find it extremely difficult to adjust for any dependency between observation units, largely because such methods are not based upon a fully generative model of the data. For analysing spatially indexed data, we address this difficulty by generalizing the joint quantile regression model of Yang and Tokdar (Journal of the American Statistical Association, 2017, 112(519), 1107–1120) and characterizing spatial dependence via a Gaussian or t‐copula process on the underlying quantile levels of the observation units. A Bayesian semiparametric approach is introduced to perform inference of model parameters and carry out spatial quantile smoothing. An effective model comparison criteria is provided, particularly for selecting between different model specifications of tail heaviness and tail dependence. Extensive simulation studies and two real applications to particulate matter concentration and wildfire risk are presented to illustrate substantial gains in inference quality, prediction accuracy and uncertainty quantification over existing alternatives.
Adaptive BEM with convergence rates

This paper presents adaptive boundary element methods for positive, negative, as well as zero order operator equations, together with proofs that they converge at certain rates. The convergence rates are quasi-optimal in a certain sense under mild assumptions that are analogous to what is typically assumed in the theory of adaptive finite element methods. In particular, no saturationtype assumption is used. The main ingredients of the proof that constitute new findings are some results on a posteriori error estimates for boundary element methods, and an inverse-type inequality involving boundary integral operators on locally refined finite element spaces.
THE Z-HOMOTOPY FIXED POINTS OF Cn SPECTRA WITH APPLICATIONS TO NORMS OF MUR

We introduce a computationally tractable way to describe the Zhomotopy fixed points of a Cn-spectrum E, producing a genuine Cn spectrum EhnZ whose fixed and homotopy fixed points agree and are the Z-homotopy fixed points of E. These form a piece of a contravariant functor from the divisor poset of n to genuine Cn-spectra, and when E is an N∞-ring spectrum, this functor lifts to a functor of N∞-ring spectra. For spectra like the Real Johnson–Wilson theories or the norms of Real bordism, the slice spectral sequence provides a way to easily compute the RO(G)-graded homotopy groups of the spectrum EhnZ, giving the homotopy groups of the Z-homotopy fixed points. For the more general spectra in the contravariant functor, the slice spectral sequences interpolate between the one for the norm of Real bordism and the especially simple Z-homotopy fixed point case, giving us a family of new tools to simplify slice computations.
Journal of Experimental Psychology: Learning, Memory, and Cognition

Abstract: Previous studies have shown that speakers of languages such as German, Spanish and French reactivate the syntactic gender of the antecedent of a pronoun in order to license gender agreement. As syntactic gender information is assumed to be stored in the lexicon, this has motivated the claim that pronouns in these languages reactivate the lexical entry of their antecedent noun. In contrast, in languages without syntactic gender such as English, lexical retrieval might be unnecessary. Using eye-tracking while reading, we examined whether antecedent retrieval involves rapid semantic and phonological reactivation. We compared German and English. In German, we found early sensitivity to the semantic, but not to the phonological features of the pronoun's antecedent. In English, readers did not immediately show either semantic or phonological effects specific to coreference. We propose that early semantic facilitation arises due to syntactic gender reactivation, and that antecedent retrieval varies crosslinguistically depending on the type of information relevant to the grammar of each language.
Élaboration et évaluation d’un test de concordance de script pour mesurer le raisonnement clinique infirmier relatif à l’usage optimal des médicaments en centre d’hébergement

Development and evaluation of a script concordance test to measure nursing clinical reasoning regarding the optimal use of medication in long-term care facilities By Annie Gilbert Clinical Sciences Program (Nursing) Thesis presented to the Faculté de médecine et des sciences de la santé for a Masters degree (MSc.) in Clinical Sciences (nursing), Faculté de médecine et des sciences de la santé, Université de Sherbrooke, Sherbrooke, Québec, Canada, J1H 5N4 The administration of prescribed medication in long-term care facilities is a task that requires the nurse to solve complex problems in a context of uncertainty. It demands a rigorous clinical reasoning for which there is no valid and reliable measurement tool. The purpose of this study was to develop a script concordance test (SCT) to measure nursing clinical reasoning regarding the optimal use of medication in long-term care facilities and to document its validity and reliability. After optimization, the SCT was made up of 55 items divided in 20 clinical vignettes. It has been tested on nursing students (n = 76) and expert nurses (n = 10). The test was completed a second time (test re-test) by 35 students. The mean score obtained by the novice students is 55.6 ± 7.7 while the one obtained by the expert nurses is 63.7 ± 8.0. The applied statistical tests showed a significant difference in the average of the two groups (p < 0.02). The Cronbach alpha coefficient for the entire SCT is 0.65. An inter-class correlation coefficient (ICC) was calculated to assess the temporal stability of the measuring tool. The result is 0.618 (p = 0.000). The SCT developed as part of this study needs to be modified in order for it to be more reliable. The other statistical results demonstrate that the SCT is able to differentiate between levels of expertise. This study adds to previous studies that evaluated clinical reasoning and confirms the importance of following guidelines that rely on evidence-based data to develop a SCT.
Expression of Molecular Markers and Risk Categorization in Pre-neoplastic and Non-neoplastic Cervical Lesions

Worldwide, cervical cancer is the fourth most common cancer in women [1]. It is preceded by many stages of pre-neoplastic lesions classified as Atypical squamous cells of undetermined significance (ASC-US) / Atypical squamous cells cannot exclude HSIL (ASC-H), Low-grade squamous intraepithelial lesion (LSIL), and High-grade squamous intraepithelial lesion (HSIL) (Bethesda, 2001). However, a large proportion of cases of epithelial malignancy arise from apparently normal cell and also from the LSIL where as many HSILs remain static for many years [2]. In this study, many important markers and epidemiological parameters Pap smears were examined to look for high risk cases of cervical non-malignant lesions. Methods: A total of 60 cases and controls were selected by colposcopic examination and smears were collected. Slides were used for immunohistochemical staining for four markers: p53, Ki-67, PCNA and CEA and PAP stain. All experimentally generated data were analyzed by Epi Info (TM) 3.5. 3. Results: Expression of PCNA, p53, Ki-67 and CEA were significantly associated with both in Epithelial Cell Abnormalities (ECA) and Negative for Intraepithelial Lesion or Malignancy (NILM). Among HSIL, LSIL and ASCUS, HSIL was more common in Muslim women in comparison to Hindus. Whereas inflammatory lesions like RCC and RCCI were more common in Hindu population. Muslim women have a more normal Cytopathology smear than Hindus. Conclusions: Both the NILM and ECA cases carry risk for cancer conversion. Women with High grade Squamous Intraepithelial lesion or HSIL with PCNA, p53, Ki-67 and CEA strong positivity may have a higher risk of developing cervical cancer.
2 Smoothed Polynomial Time and Smoothed-P 2 . 1

Smoothed analysis is a new way of analyzing algorithms introduced by Spielman and Teng (J. ACM, 2004). Classical methods like worst-case or average-case analysis have accompanying complexity classes, like P and Avg-P, respectively. While worst-case or average-case analysis give us a means to talk about the running time of a particular algorithm, complexity classes allows us to talk about the inherent difficulty of problems. Smoothed analysis is a hybrid of worst-case and average-case analysis and compensates some of their drawbacks. Despite its success for the analysis of single algorithms and problems, there is no embedding of smoothed analysis into computational complexity theory, which is necessary to classify problems according to their intrinsic difficulty. We propose a framework for smoothed complexity theory, define the relevant classes, and prove some first hardness results (of bounded halting and tiling) and tractability results (binary optimization problems, graph coloring, satisfiability). Furthermore, we discuss extensions and shortcomings of our model and relate it to semi-random models.
Saturation and forward jets in proton-lead collisions at the LHC

We investigate the forward-jet energy spectrum within the Color Glass Condensate framework at 5 TeV center-of-mass energy. In particular, we focus on the kinematic range covered by the CMS-CASTOR calorimeter. We show that our saturation-model calculations are compatible with the CASTOR measurements and that to optimally reproduce the data, effects of multi-parton interactions need to be included. We predict a significant nuclear suppression - reaching down to 50% at the lowest considered jet energies $E_{\rm jet} \sim 500 \, {\rm GeV}$.
Mechanic ’ s Hand : A Clinical Diagnostic Aid in Dermatomyositis

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Development and experimentation of a deep learning system for convolutional and recurrent neural networks

At present, there are very few deep learning toolkits focused on the task of Handwritten Text Recognition (HTR). HTR refers to the problem of recognizing a sequence of characters in an input image. For this reason, we have decided to build PyLaia, a toolkit for performing handwritten text document analysis experiments. PyLaia is flexible, open-source, device-agnostic, and can be used to express a wide variety of experiments, including training and inference over Convolutional and Recurrent based deep Neural Network models. It has been used for conducting research over the IAM and RIMES datasets. PyLaia is also a successor to Laia, written in Lua. This work describes the implementation of the system that we have built using PyTorch as the basis for our toolkit. The software is extensible and easily configurable and provides a rich set of functional layers with a particular focus on HTR. Additionally, we also describe the implementation of our custom model architecture which combines convolutional and recurrent layers to compete with current state-of-the-art architectures in the field. A wide array of experiments have been carried out to validate our implementation. The experiments presented achieve improved results over those done using the toolkit’s predecessor. We also compare the impact of several features such as the usage of a model which allows inputs of variable height or the use of dropout. PyLaia is mantained as an open-source package under the MIT license and is available at https://github.com/jpuigcerver/PyLaia