Miracle-dz/UniFAR-FTUs huggingface dataset:
A Global Solution to a Slim Accretion Disk with Radiation-driven Outflows

The thickness of a slim disk is determined by the balance between the radiation force and the vertical component of the gravity of the black hole (BH). Vertical gravity is found to increase with the disk height, and it will decrease with the disk height if the disk thickness is above a critical value, which implies that gas at the disk surface may be driven into outflows by radiation force when the disk thickness surpasses the critical value. In this work, we derive a global solution to a slim disk with radiation-driven outflows. We find that the outflows are driven from the disk surface if the mass accretion rate is m ˙ ≳ 1.78 − 1.91 ( m ˙ = M ˙ / M ˙ Edd and M ˙ Edd = L Edd / 0.1 c 2 ) depending on the BH mass, while the outflows are suppressed when the mass accretion rate is lower than this critical value. The mass accretion rate decreases with the decreasing radius in the disk with outflows, and the rate of the gas swallowed by the BH is always limited to m ˙ in ∼ 1.78 − 1.91 even if the mass accretion rate at the outer edge of the disk is very high. This may set constraints on the massive BH growth through accretion in the early universe. Due to the presence of outflows, there is an upper limit on the radiation flux of the disk, which leads to saturation of the continuum spectra of the disk with outflows at the high-energy band. This may be tested by the observations of quasars or/and BH X-ray binaries.
Spatial scales and locality of magnetic helicity

Context. Magnetic helicity is approximately conserved in resistive magnetohydrodynamic models. It quantifies the entanglement of the magnetic field within the plasma. The transport and removal of helicity is crucial in both dynamo development in the solar interior and active region evolution in the solar corona. This transport typically leads to highly inhomogeneous distributions of entanglement. Aims. There exists no consistent systematic means of decomposing helicity over varying spatial scales and in localised regions. Spectral helicity decompositions can be used in periodic domains and is fruitful for the analysis of homogeneous phenomena. This paper aims to develop methods for analysing the evolution of magnetic field topology in non-homogeneous systems. Methods. The method of multi-resolution wavelet decomposition is applied to the magnetic field. It is demonstrated how this decomposition can further be applied to various quantities associated with magnetic helicity, including the field line helicity. We use a geometrical definition of helicity, which allows these quantities to be calculated for fields with arbitrary boundary conditions. Results. It is shown that the multi-resolution decomposition of helicity has the crucial property of local additivity. We demonstrate a general linear energy-topology conservation law, which significantly generalises the two-point correlation decomposition used in the analysis of homogeneous turbulence and periodic fields. The localisation property of the wavelet representation is shown to characterise inhomogeneous distributions, which a Fourier representation cannot. Using an analytic representation of a resistive braided field relaxation, we demonstrate a clear correlation between the variations in energy at various length scales and the variations in helicity at the same spatial scales. Its application to helicity flows in a surface flux transport model show how various contributions to the global helicity input from active region field evolution and polar field development are naturally separated by this representation. Conclusions. The multi-resolution wavelet decomposition can be used to analyse the evolution of helicity in magnetic fields in a manner which is consistently additive. This method has the advantage over more established spectral methods in that it clearly characterises the inhomogeneous nature of helicity flows where spectral methods cannot. Further, its applicability in aperiodic models significantly increases the range of potential applications.
Comprehensive models of novae at metallicity Z = 0.02 and Z = 10−4

Novae are the observational manifestations of thermonuclear runaways on the surface of accreting white dwarfs (WDs). Although novae are an ubiquitous phenomenon, their properties at low metallicity are not well understood. Using the publicly-available stellar evolution code Modules for Experiments in Stellar Astrophysics (mesa), we model the evolution of accreting carbon–oxygen WDs and consider models that accrete matter with metallicity Z = 0.02 or 10−4. We consider both models without mixing and with matter enriched by CO-elements assuming that mixing occurs in the process of accretion (with mixing fraction 0.25). We present and contrast ignition mass, ejected mass, recurrence period, and maximum luminosity of novae for different WD masses and accretion rates for these metallicities and mixing cases. We find that models with Z = 0.02 have ignition masses and recurrence periods smaller than models with low Z, while the ejected mass and maximum luminosity are larger. Retention efficiency during novae outbursts decreases with increasing metallicity. In our implementation, inclusion of mixing at the H/He interface reduces accreted mass, ejected mass, and recurrence period as compared to the no-mixing case, while the maximum luminosity becomes larger. Retention efficiency is significantly reduced, becoming negative in most of our models. For ease of use, we provide a tabular summary of our results.
Walling up Backdoors in Intrusion Detection Systems

Interest in poisoning attacks and backdoors recently resurfaced for Deep Learning (DL) applications. Several successful defense mechanisms have been recently proposed for Convolutional Neural Networks (CNNs), for example in the context of autonomous driving. We show that visualization approaches can aid in identifying a backdoor independent of the used classifier. Surprisingly, we find that common defense mechanisms fail utterly to remove backdoors in DL for Intrusion Detection Systems (IDSs). Finally, we devise pruning-based approaches to remove backdoors for Decision Trees (DTs) and Random Forests (RFs) and demonstrate their effectiveness for two different network security datasets.
A goodness‐of‐fit test for the functional linear model with functional response

The functional linear model with functional response (FLMFR) is one of the most fundamental models to assess the relation between two functional random variables. In this article, we propose a novel goodness‐of‐fit test for the FLMFR against a general, unspecified, alternative. The test statistic is formulated in terms of a Cramér–von Mises norm over a doubly projected empirical process which, using geometrical arguments, yields an easy‐to‐compute weighted quadratic norm. A resampling procedure calibrates the test through a wild bootstrap on the residuals and the use of convenient computational procedures. As a sideways contribution, and since the statistic requires a reliable estimator of the FLMFR, we discuss and compare several regularized estimators, providing a new one specifically convenient for our test. The finite sample behavior of the test is illustrated via a simulation study. Also, the new proposal is compared with previous significance tests. Two novel real data sets illustrate the application of the new test.
Wide-field Survey of Dwarf Satellite Systems around 10 Hosts in the Local Volume

We present the results of an extensive search for dwarf satellite galaxies around 10 primary host galaxies in the Local Volume (LV, D < 12 Mpc) using archival CFHT/MegaCam imaging data. The hosts span a wide range in properties, with stellar masses ranging from that of the Large Magellanic Cloud to ∼3 times that of the Milky Way. The surveyed hosts are: NGC 1023, NGC 1156, NGC 2903, NGC 4258, NGC 4565, NGC 4631, NGC 5023, M51, M64, and M104. We detect satellite candidates using a consistent semi-automated detection algorithm that is optimized for the detection of low surface brightness objects. Depending on the host, our completeness limit is Mg ∼ −8 to −10 (assuming the distance of the host). We detect objects with surface brightness down to mag arcsec−2 at ≳90% completeness. The survey areas of the six best-surveyed hosts cover most of the inner projected area, which will roughly double the number of massive LV hosts surveyed at this level of area and luminosity completeness, once distances are measured for the candidates. The number of detected candidates range from 1 around M64 to 33 around NGC 4258. In total, 155 candidates are found, of which 93 are new. While we defer an analysis of the satellite luminosity functions of the hosts until distance information is available for the candidates, we do show that the candidates are primarily red, spheroidal systems with properties roughly consistent with known satellites in the Local Group.
Student Performance Prediction with Optimum Multilabel Ensemble Model

Abstract One of the important measures of quality of education is the performance of students in academic settings. Nowadays, abundant data is stored in educational institutions about students which can help to discover insight on how students are learning and to improve their performance ahead of time using data mining techniques. In this paper, we developed a student performance prediction model that predicts the performance of high school students for the next semester for five courses. We modeled our prediction system as a multi-label classification task and used support vector machine (SVM), Random Forest (RF), K-nearest Neighbors (KNN), and Multi-layer perceptron (MLP) as base-classifiers to train our model. We further improved the performance of the prediction model using a state-of-the-art partitioning scheme to divide the label space into smaller spaces and used Label Powerset (LP) transformation method to transform each labelset into a multi-class classification task. The proposed model achieved better performance in terms of different evaluation metrics when compared to other multi-label learning tasks such as binary relevance and classifier chains.
Stacking models for nearly optimal link prediction in complex networks

Significance Networks are a powerful tool for modeling complex biological and social systems. However, most networks are incomplete, and missing connections can negatively affect scientific analyses. Today, many algorithms can predict missing connections, but it is unknown how accuracy varies across algorithms and networks and whether link predictability varies across scientific domains. Analyzing 203 link prediction algorithms applied to 550 diverse real-world networks, we show that no predictor is best or worst overall. We then combine these many predictors into a single state-of-the-art algorithm that achieves nearly optimal performance on both synthetic networks with known optimality and real-world networks. Not all networks are equally predictable, however, and we find that social networks are easiest, while biological and technological networks are hardest. Most real-world networks are incompletely observed. Algorithms that can accurately predict which links are missing can dramatically speed up network data collection and improve network model validation. Many algorithms now exist for predicting missing links, given a partially observed network, but it has remained unknown whether a single best predictor exists, how link predictability varies across methods and networks from different domains, and how close to optimality current methods are. We answer these questions by systematically evaluating 203 individual link predictor algorithms, representing three popular families of methods, applied to a large corpus of 550 structurally diverse networks from six scientific domains. We first show that individual algorithms exhibit a broad diversity of prediction errors, such that no one predictor or family is best, or worst, across all realistic inputs. We then exploit this diversity using network-based metalearning to construct a series of “stacked” models that combine predictors into a single algorithm. Applied to a broad range of synthetic networks, for which we may analytically calculate optimal performance, these stacked models achieve optimal or nearly optimal levels of accuracy. Applied to real-world networks, stacked models are superior, but their accuracy varies strongly by domain, suggesting that link prediction may be fundamentally easier in social networks than in biological or technological networks. These results indicate that the state of the art for link prediction comes from combining individual algorithms, which can achieve nearly optimal predictions. We close with a brief discussion of limitations and opportunities for further improvements.
Iterative annealing mechanism explains the functions of the GroEL and RNA chaperones

Molecular chaperones are ATP‐consuming machines, which facilitate the folding of proteins and RNA molecules that are kinetically trapped in misfolded states. Unassisted folding occurs by the kinetic partitioning mechanism according to which folding to the native state, with low probability as well as misfolding to one of the many metastable states, with high probability, occur rapidly. GroEL is an all‐purpose stochastic machine that assists misfolded substrate proteins to fold. The RNA chaperones such as CYT‐19, which are ATP‐consuming enzymes, help the folding of ribozymes that get trapped in metastable states for long times. GroEL does not interact with the folded proteins but CYT‐19 disrupts both the folded and misfolded ribozymes. The structures of GroEL and RNA chaperones are strikingly different. Despite these differences, the iterative annealing mechanism (IAM) quantitatively explains all the available experimental data for assisted folding of proteins and ribozymes. Driven by ATP binding and hydrolysis and GroES binding, GroEL undergoes a catalytic cycle during which it samples three allosteric states, T (apo), R (ATP bound), and R″ (ADP bound). Analyses of the experimental data show that the efficiency of the GroEL–GroES machinery and mutants is determined by the resetting rate k R ″ → T, which is largest for the wild‐type (WT) GroEL. Generalized IAM accurately predicts the folding kinetics of Tetrahymena ribozyme and its variants. Chaperones maximize the product of the folding rate and the steady‐state native state fold by driving the substrates out of equilibrium. Neither the absolute yield nor the folding rate is optimized.
VVV-WIT-04: an extragalactic variable source caught by the VVV Survey

We report the discovery of VVV-WIT-04, a near-infrared (near-IR) variable source towards the Galactic disc located ∼0.2 arcsec apart from the position of the radio source PMN J1515−5559. The object was found serendipitously in the near-IR data of the ESO public survey VISTA Variables in the Vía Láctea (VVV). Our analysis is based on variability, multicolour, and proper motion data from VVV and VVV eXtended surveys, complemented with archive data at longer wavelengths. We suggest that VVV-WIT-04 has an extragalactic origin as the near-IR counterpart of PMN J1515−5559. The Ks-band light curve of VVV-WIT-04 is highly variable and consistent with that of an optically violent variable quasar. The variability in the near-IR can be interpreted as the redshifted optical variability. Residuals to the proper motion vary with the magnitude suggesting contamination by a blended source. Alternative scenarios, including a transient event such as a nova or supernova, or even a binary microlensing event, are not in agreement with the available data.
Refined $\alpha$-Divergence Variational Inference via Rejection Sampling

We present an approximate inference method, based on a synergistic combination of R\'enyi $\alpha$-divergence variational inference (RDVI) and rejection sampling (RS). RDVI is based on minimization of R\'enyi $\alpha$-divergence $D_\alpha(p||q)$ between the true distribution $p(x)$ and a variational approximation $q(x)$; RS draws samples from a distribution $p(x) = \tilde{p}(x)/Z_{p}$ using a proposal $q(x)$, s.t. $Mq(x) \geq \tilde{p}(x), \forall x$. Our inference method is based on a crucial observation that $D_\infty(p||q)$ equals $\log M(\theta)$ where $M(\theta)$ is the optimal value of the RS constant for a given proposal $q_\theta(x)$. This enables us to develop a \emph{two-stage} hybrid inference algorithm. Stage-1 performs RDVI to learn $q_\theta$ by minimizing an estimator of $D_\alpha(p||q)$, and uses the learned $q_\theta$ to find an (approximately) optimal $\tilde{M}(\theta)$. Stage-2 performs RS using the constant $\tilde{M}(\theta)$ to improve the approximate distribution $q_\theta$ and obtain a sample-based approximation. We prove that this two-stage method allows us to learn considerably more accurate approximations of the target distribution as compared to RDVI. We demonstrate our method's efficacy via several experiments on synthetic and real datasets.
SocialNLP EmotionX 2019 Challenge Overview: Predicting Emotions in Spoken Dialogues and Chats

We present an overview of the EmotionX 2019 Challenge, held at the 7th International Workshop on Natural Language Processing for Social Media (SocialNLP), in conjunction with IJCAI 2019. The challenge entailed predicting emotions in spoken and chat-based dialogues using augmented EmotionLines datasets. EmotionLines contains two distinct datasets: the first includes excerpts from a US-based TV sitcom episode scripts (Friends) and the second contains online chats (EmotionPush). A total of thirty-six teams registered to participate in the challenge. Eleven of the teams successfully submitted their predictions performance evaluation. The top-scoring team achieved a micro-F1 score of 81.5% for the spoken-based dialogues (Friends) and 79.5% for the chat-based dialogues (EmotionPush).
The clustering of the SDSS-IV extended Baryon Oscillation Spectroscopic Survey DR14 LRG sample: structure growth rate measurement from the anisotropic LRG correlation function in the redshift range 0.6 < z < 1.0

We analyse the anisotropic clustering of the Sloan Digital Sky Survey-IV Extended Baryon Oscillation Spectroscopic Survey (eBOSS) Luminous Red Galaxy Data Release 14 (DR14) sample combined with Baryon Oscillation Spectroscopic Survey (BOSS) CMASS sample of galaxies in the redshift range 0.6 < z < 1.0, which consists of 80 118 galaxies from eBOSS and 46 439 galaxies from the BOSS-CMASS sample. The eBOSS-CMASS Luminous Red Galaxy sample has a sky coverage of 1844 deg2, with an effective volume of 0.9 Gpc3. The analysis was made in configuration space using a Legendre multipole expansion. The Redshift Space Distortion signal is modelled as a combination of the Convolution Lagrangian Perturbation Model and the Gaussian Streaming Model. We constrain the logarithmic growth of structure times the amplitude of dark matter density fluctuations, f(zeff)σ8(zeff) = 0.454 ± 0.134, and the Alcock-Paczynski dilation scales which constraints the angular diameter distance $D_A(z_{\mathrm{ eff}})=1466.5 \pm 133.2 (r_s/r_s^{\rm fid})$ and $H(z_{\rm eff})=105.8 \pm 15.7 (r_s^{\rm fid}/r_s) \mathrm{km\, s^{-1}\, Mpc^{-1}}$, where rs is the sound horizon at the end of the baryon drag epoch and $r_s^{\rm fid}$ is its value in the fiducial cosmology at an effective redshift zeff = 0.72. These results are in full agreement with the current Λ-Cold Dark Matter (Λ-CDM) cosmological model inferred from Planck measurements. This study is the first eBOSS LRG full-shape analysis i.e. including Redshift Space Distortions simultaneously with the Alcock-Paczynski effect and the Baryon Acoustic Oscillation scale.
The NLTE Analyses of Carbon Emission Lines in the Atmospheres of O- and B-type Stars

We present a model atom for C i–C ii–C iii–C iv using the most up-to-date atomic data and evaluated the non-local thermodynamic equilibrium line formation in classical 1D atmospheric models of O-B-type stars. Our models predict the emission lines of C ii 9903 Å and 18,535 Å to appear at effective temperature Teff ≥ 17,500 K, those of C ii 6151 Å and 6461 Å to appear at Teff > 25,000 K, and those of C iii 5695, 6728–44 and 9701–17 Å to appear at Teff ≥ 35,000 K (log g = 4.0). Emission occurs in the lines of minority species, where the photoionization-recombination mechanism provides a depopulation of the lower levels to a greater extent than the upper levels. For C ii 9903 and 18535 Å, the upper levels are mainly populated from the C iii reservoir through the Rydberg states. For C iii 5695 and 6728–44 Å, the lower levels are depopulated due to photon losses in UV transitions at 885, 1308, and 1426–28 Å, which become optically thin in the photosphere. We analyzed the lines of C i, C ii, C iii, and C iv for twenty-two O-B-type stars with temperature range between 15,800 ≤Teff ≤ 38,000 K. Abundances from emission lines of C i, C ii, and C iii are in agreement with those from absorption ones for most of the stars. We obtained log ϵC = 8.36 ± 0.08 from twenty B-type stars, that is in line with the present-day Cosmic Abundance Standard. The obtained carbon abundances in 15 Mon and HD 42088 from emission and absorption lines are 8.27 ± 0.11 and 8.31 ± 0.11, respectively.
Estimation of Wasserstein distances in the Spiked Transport Model

We propose a new statistical model, the spiked transport model, which formalizes the assumption that two probability distributions differ only on a low-dimensional subspace. We study the minimax rate of estimation for the Wasserstein distance under this model and show that this low-dimensional structure can be exploited to avoid the curse of dimensionality. As a byproduct of our minimax analysis, we establish a lower bound showing that, in the absence of such structure, the plug-in estimator is nearly rate-optimal for estimating the Wasserstein distance in high dimension. We also give evidence for a statistical-computational gap and conjecture that any computationally efficient estimator is bound to suffer from the curse of dimensionality.
Embedding globular clusters in dark matter minihaloes solves the cusp–core and timing problems in the Fornax dwarf galaxy

We use a fully GPU N-body code to demonstrate that dark matter (DM) minihaloes, as a new component of globular clusters (GCs), resolve both the timing and cusp–core problems in Fornax if the (five or six) GCs were recently accreted (≤3 Gyr ago) by Fornax. Under these assumptions, infall of these GCs does not occur and no star clusters form in the centre of Fornax in accordance with observations. We find that crossings of GCss that have DM minihaloes near the Fornax centre induce a cusp-to-core transition of the DM halo and hence resolve the cusp–core problem in this dwarf galaxy. The DM core size depends on the frequency of GC crossings. Our simulations clearly demonstrate also that between the passages, the DM halo can regenerate its cusp. Moreover, our models are in good agreement with constraints on the DM masses of GCs as our clusters lose a large fraction of their initial DM minihaloes. These results provide circumstantial evidence for the universal existence of DM haloes in GCs.