Miracle-dz/UniFAR-FTUs huggingface dataset:
On Inference of Overlapping Coefficients in Two Inverse Lomax Populations

Overlapping coefficient is a direct measure of similarity between two distributions which is recently becoming very useful. This paper investigates estimation for some well-known measures of overlap, namely Matusita's measure $\rho$, Weitzman's measure $\Delta$ and $\Lambda$ based on Kullback-Leibler. Two estimation methods considered in this study are point estimation and Bayesian approach. Two Inverse Lomax populations with different shape parameters are considered. The bias and mean square error properties of the estimators are studied through a simulation study and a real data example.
A deep learning approach to cosmological dark energy models

We propose a novel deep learning tool in order to study the evolution of dark energy models. The aim is to combine two architectures: the Recurrent Neural Networks (RNN) and the Bayesian Neural Networks (BNN), we named this full network as RNN+BNN . The first one is capable of learning complex sequential information to classify objects like supernovae and use the light-curves directly to learn information from the sequence of observations. Since RNN is not capable to calculate the uncertainties, BNN emerges as a solution for problems in deep learning like, for example, the overfitting. For the trainings we use measurements of the distance modulus μ(z), such as those provided by Pantheon Supernovae Type Ia. In view of our results, the reported approach turns out to be a first promising step on how we can train a new neural network that can compute their own confidence regions for specific cosmological data. It is worth stressing that the new technique allows to reduce the computational load of expensive codes for dark energy models and probe the necessity of modified dark energy models at large redshifts for a supernovae trained sampler.
Orbital scattering by random interactions with extended substructures

This paper presents N-body and stochastic models that describe the motion of tracer particles in a potential that contains a large population of extended substructures. Fluctuations of the gravitational field induce a random walk of orbital velocities that is fully specified by drift and diffusion coefficients. In the impulse and local approximations, the coefficients are computed analytically from the number density, mass, size, and relative velocity of substructures without arbitrary cuts in forces or impact parameters. The resulting Coulomb logarithm attains a well-defined geometrical meaning, ln (Λ) = ln (D/c), where D/c is the ratio between the average separation and the individual size of substructures. Direct-force and Monte Carlo N-body experiments show excellent agreement with the theory if substructures are sufficiently extended (c/D ≳ 10−3) and not spatially overlapping (c/D ≲ 10−1). However, close encounters with point-like objects (c/D ≪ 10−3) induce a heavy-tailed, non-Gaussian distribution of high-energy impulses that cannot be described with Brownian statistics. In the point-mass limit (c/D ≈ 0), the median Coulomb logarithm measured from N-body models deviates from the theoretical relation, converging towards a maximum value 〈ln (Λ)〉 ≈ 8.2 independently of the mass and relative velocity of nearby substructures.
Behavioural case linkage: Linking residential burglary offences in New Zealand

Thesis title: Behavioural case linkage: Linking residential burglary offences in New Zealand Author: Martin Weeks This thesis aims to replicate and extend prior research on behavioural case linkage from the United Kingdom and Finland, using a sample of residential burglaries committed in New Zealand. Eighty-two solved residential burglaries, committed by 47 serial burglary offenders in Napier, New Zealand, are sampled from the New Zealand Police National Intelligence Application (NIA) database. Prior research using behavioural case linkage for residential burglary has found support for the usefulness of crime scene behaviours, inter-crime distance and temporal proximity to accurately predict offences committed by the same offender. Inter-crime distance has consistently shown higher degrees of accuracy in determining whether two crimes are linked to the same offender. Using the methodology followed by previous researchers, 41 linked crime pairs (two offences committed by the same offender) and 41 unlinked crime pairs (two offences committed by different offenders) are created. Three behavioural domains of crime scene behaviours, inter-crime distance and temporal proximity of offences committed by the same offender are compared with offences committed by different offenders. Logistic regression analysis and receiver operating curve (ROC) analysis is used to determine the ability of the three behavioural domains to accurately predict whether offences are linked or not. Similar to prior studies, all three behavioural domains showed moderate predictive ability in reliably determining the linked status of crime pairs. Contrary to prior studies inter-crime distance was found to be the least accurate predictor in
Correlation and path analysis of agronomic and morphological traits in maize 1

The efficiency of selection can be broadened for certain traits using estimative of genetic parameters, which are fundamental for plant breeding. The estimative of genetic parameters allows identifying the nature of the action of genes involved in the control of quantitative traits and evaluates the efficiency of different breeding strategies to obtain genetic gains. Therefore, this study aimed to verify the variability and correlation between morphological and agronomic traits in synthetic maize populations, in order to practice indirect selection. Thirteen populations were evaluated in field experiments at JaboticabalSP and Campo Alegre de Goiás-GO, using randomized block design, for lodging (LOD), culm breakage (CB), plant height (PH), ear height (EH) and grain yield (GY). The 13 populations were also sown in 1 kg-plastic bags under black shade cloth, using a randomized complete block design with nine replications, for evaluation of the morphological traits: main root length (MRL), root fresh matter (RFM), root dry matter (RDM), average root diameter (ARD), root surface area (RSA), root tissue density (RTD) and shoots dry matter (SDM). The trait GY exhibited genetic variability enough to be effective if used for selection. The selection on the morphological traits is indicated on RDM, due to the ease in obtaining data and its accuracy, high correlation with all morphological traits and association with GY. The simultaneous selection based on RFM and RDM, for gains in GY, can be performed. The highest direct effect on GY was identified in LOD and CB, being indicated for indirect selection.
The mechanism behind the novel fibrosis recently described in NASH in HEPATOLOGY 2011 and Elsewhere

The recent flurry of exciting reports on the potential antifibrotic benefit of pentoxifylline (PTX) in nonalcoholic steatohepatitis (NASH) has encouraged me to write this letter to the Editor of HEPATOLOGY to include some earlier reports in this area of hepatology research. We were the first to report that plateletderived growth factor (PDGF) played a role in experimental hepatic fibrosis and describe its role in human fibrosis. PTX blocked fibrosis via an effect on PDGF, by inhibiting phosphorylation of c-Jun on serine 73. Our results in NASH and hepatitis C virus (HCV) showed that ribavirin, but not interferon, inhibited fibrosis, and that this effect was mediated by the block of phosphorylation of c-Jun on serine 73, resulting in decreased synthesis of collagen and decreased hepatic stellate cell proliferation. PTX decreased NASH sera-stimulated Fibrogenic Stimulation Index (FSI; our patented diagnostic test) and c-Jun phosphorylation as assessed by H-thymidine incorporation and Western analysis, respectively. Our recent data indicate that PTX decreased the FSI, and that the FSI correlates well with the METAVIR fibrosis score in HCV patients and may be predictive of fibrosis in this cohort.
Mullins-Sekerka as the Wasserstein flow of the perimeter

We prove the convergence of an implicit time discretization for the Mullins-Sekerka equation proposed in [F. Otto, Arch. Rational Mech. Anal. 141 (1998) 63-103]. Our simple argument shows that the limit satisfies the equation in a distributional sense as well as an optimal energy-dissipation relation. The proof combines simple arguments from optimal transport, gradient flows & minimizing movements, and basic geometric measure theory.
The Central Star of NGC 2346 as a Clue to Binary Evolution through the Common Envelope Phase

We present an analysis of the binary central star of the planetary nebula NGC 2346 based on archival data from the International Ultraviolet Explorer, and new low- and high-resolution optical spectra (3700–7300 Å). By including in the spectral analysis the contribution of both stellar and nebular continuum, we reconciled long-time discrepant UV and optical diagnostics and derive E(B–V) = 0.18 ± 0.01. We reclassified the companion star as A5IV by analyzing the wings of the Balmer absorption lines in the high-resolution (R = 67,000) optical spectra. Using the distance to the nebula of 1400 pc from Gaia DR2, we constructed a photoionization model based on abundances and line intensities derived from the low-resolution optical spectra, and obtained a temperature of Teff = 130,000 K and a luminosity of L = 170 L⊙ for the ionizing star, consistent with the UV continuum. This analysis allows us to better characterize the binary system’s evolution. We conclude that the progenitor star of NGC 2346 has experienced a common envelope phase, in which the companion star has accreted mass and evolved off the main-sequence.
A new look at random projections of the cube and general product measures

A strong law of large numbers for $d$-dimensional random projections of the $n$-dimensional cube is derived. It shows that with respect to the Hausdorff distance a properly normalized random projection of $[-1,1]^n$ onto $\mathbb{R}^d$ almost surely converges to a centered $d$-dimensional Euclidean ball of radius $\sqrt{2/\pi}$, as $n\to\infty$. For every point inside this ball we determine the asymptotic number of vertices and the volume of the part of the cube projected `close' to this point. Moreover, large deviations for random projections of general product measures are studied. Let $\nu^{\otimes n}$ be the $n$-fold product measure of a Borel probability measure $\nu$ on $\mathbb{R}$, and let $I$ be uniformly distributed on the Stiefel manifold of orthogonal $d$-frames in $\mathbb{R}^n$. It is shown that the sequence of random measures $\nu^{\otimes n}\circ(n^{-1/2}I^*)^{-1}$, $n\in\mathbb{N}$, satisfies a large deviations principle with probability $1$. The rate function is explicitly identified in terms of the moment generating function of $\nu$. At the heart of the proofs lies a transition trick which allows to replace the uniform projection by the Gaussian one. A number of concrete examples are discussed as well, including the uniform distributions on the cube $[-1,1]^n$ and the discrete cube $\{-1,1\}^n$ as a special cases.
The Large-scale Ionization Cones in the Galaxy

There is compelling evidence for a highly energetic Seyfert explosion (1056–57 erg) that occurred in the Galactic center a few million years ago. The clearest indications are the X-ray/γ-ray “10 kpc bubbles” identified by the ROSAT and Fermi satellites. In an earlier paper, we suggested another manifestation of this nuclear activity, i.e., elevated Hα emission along a section of the Magellanic Stream due to a burst (or flare) of ionizing radiation from Sgr A*. We now provide further evidence for a powerful flare event: UV absorption line ratios (in particular / , Si iv/Si ii) observed by the Hubble Space Telescope reveal that some Magellanic Stream clouds toward both galactic poles are highly ionized by a source capable of producing ionization energies up to at least 50 eV. We show how these are clouds caught in a beam of bipolar, radiative “ionization cones” from a Seyfert nucleus associated with Sgr A*. In our model, the biconic axis is tilted by about 15° from the south Galactic pole with an opening angle of roughly 60°. For the Magellanic Stream at such large Galactic distances (D ≳ 75 kpc), nuclear activity is a plausible explanation for all of the observed signatures: elevated Hα emission and H ionization fraction (xe ≳ 0.5), enhanced / and Si iv/Si ii ratios, and high and Si iv column densities. Wind-driven “shock cones” are ruled out because the Fermi bubbles lose their momentum and energy to the Galactic corona long before reaching the Magellanic Stream. Our time-dependent Galactic ionization model (stellar populations, hot coronal gas, cloud–halo interaction) is too weak to explain the Magellanic Stream’s ionization. Instead, the nuclear flare event must have had a radiative UV luminosity close to the Eddington limit (fE ≈ 0.1–1). Our time-dependent Seyfert flare models adequately explain the observations and indicate that the Seyfert flare event took place To = 3.5 ± 1 Myr ago. The timing estimates are consistent with the mechanical timescales needed to explain the X-ray/γ-ray bubbles in leptonic jet/wind models (≈2–8 Myr).
NuSTAR Observations of the Unidentified INTEGRAL Sources: Constraints on the Galactic Population of HMXBs

The NuSTAR Legacy program titled Unidentified INTEGRAL Sources targeted faint hard X-ray sources revealed by INTEGRAL in the Galactic plane in order to provide conclusive identification of their nature and insights on the population of faint hard X-ray sources. The NuSTAR and Swift X-Ray Telescope observations obtained in 2015–2017 contributed to the successful identification of five persistent sources. Here, we report on the spectral and variability analyses that helped to consolidate the classifications of IGR J10447–6027, IGR J16181–5407, and IGR J20569+4940 as active galactic nuclei and IGR J17402–3656 as an intermediate polar. An optical spectrum of the blazar IGR J20569+4940 is also presented. Combining these results with successful identifications of other such faint and persistent INTEGRAL sources reported in the literature, we investigate possible implications for the population of persistent high-mass X-ray binaries (HMXBs) below the identification completion limit of the INTEGRAL survey. The current trend hints at a deficit of persistent HMXBs below F17–60 keV = 10−11 erg cm−2 s−1, but additional efforts dedicated to classifying faint hard X-ray sources are needed before we can draw solid conclusions.
GraphZoom: A multi-level spectral approach for accurate and scalable graph embedding

Graph embedding techniques have been increasingly deployed in a multitude of different applications that involve learning on non-Euclidean data. However, existing graph embedding models either fail to incorporate node attribute information during training or suffer from node attribute noise, which compromises the accuracy. Moreover, very few of them scale to large graphs due to their high computational complexity and memory usage. In this paper we propose GraphZoom, a multi-level framework for improving both accuracy and scalability of unsupervised graph embedding algorithms. GraphZoom first performs graph fusion to generate a new graph that effectively encodes the topology of the original graph and the node attribute information. This fused graph is then repeatedly coarsened into much smaller graphs by merging nodes with high spectral similarities. GraphZoom allows any existing embedding methods to be applied to the coarsened graph, before it progressively refine the embeddings obtained at the coarsest level to increasingly finer graphs. We have evaluated our approach on a number of popular graph datasets for both transductive and inductive tasks. Our experiments show that GraphZoom can substantially increase the classification accuracy and significantly accelerate the entire graph embedding process by up to 40.8x, when compared to the state-of-the-art unsupervised embedding methods.
Nonvolatile Ferroelectric Field Control of the Anomalous Hall Effect in 
BiFeO3/SrRuO3
 Bilayer

In this work, the BiFeO3 (BFO)/SrRuO3 (SRO) heterostructure was fabricated and the anomalous Hall effect (AHE) was investigated the in BFO/SRO. It is found the nonmonotonic anomalous Hall resistivity behavior in BFO/SRO is originated from the inhomogeneous SRO layer instead of the topological Hall effect. It is surprised that the AHE in BFO/SRO structure can be manipulated by ferroelectric polarization of BFO. Moreover, an inhomogeneous phenomenological model has been applied on those structure. Furthermore, the modification of band structure in SRO under ferroelectric polarization was discussed by first principle calculation. The ferroelectric-manipulated AHE suggests a new pathway to realize nonvolatile, reversible and low energy-consuming voltage-controlled spintronic devices.
EVM-CNN: Real-Time Contactless Heart Rate Estimation From Facial Video

With the increase in health consciousness, noninvasive body monitoring has aroused interest among researchers. As one of the most important pieces of physiological information, researchers have remotely estimated the heart rate (HR) from facial videos in recent years. Although progress has been made over the past few years, there are still some limitations, like the processing time increasing with accuracy and the lack of comprehensive and challenging datasets for use and comparison. Recently, it was shown that HR information can be extracted from facial videos by spatial decomposition and temporal filtering. Inspired by this, a new framework is introduced in this paper to remotely estimate the HR under realistic conditions by combining spatial and temporal filtering and a convolutional neural network. Our proposed approach shows better performance compared with the benchmark on the MMSE-HR dataset in terms of both the average HR estimation and short-time HR estimation. High consistency in short-time HR estimation is observed between our method and the ground truth.
Deep Hyperedges: a Framework for Transductive and Inductive Learning on Hypergraphs

From social networks to protein complexes to disease genomes to visual data, hypergraphs are everywhere. However, the scope of research studying deep learning on hypergraphs is still quite sparse and nascent, as there has not yet existed an effective, unified framework for using hyperedge and vertex embeddings jointly in the hypergraph context, despite a large body of prior work that has shown the utility of deep learning over graphs and sets. Building upon these recent advances, we propose \textit{Deep Hyperedges} (DHE), a modular framework that jointly uses contextual and permutation-invariant vertex membership properties of hyperedges in hypergraphs to perform classification and regression in transductive and inductive learning settings. In our experiments, we use a novel random walk procedure and show that our model achieves and, in most cases, surpasses state-of-the-art performance on benchmark datasets. Additionally, we study our framework's performance on a variety of diverse, non-standard hypergraph datasets and propose several avenues of future work to further enhance DHE.
Hierarchical stochastic neighbor embedding as a tool for visualizing the encoding capability of magnetic resonance fingerprinting dictionaries

In Magnetic Resonance Fingerprinting (MRF) the quality of the estimated parameter maps depends on the encoding capability of the variable flip angle train. In this work we show how the dimensionality reduction technique Hierarchical Stochastic Neighbor Embedding (HSNE) can be used to obtain insight into the encoding capability of different MRF sequences. Embedding high-dimensional MRF dictionaries into a lower-dimensional space and visualizing them with colors, being a surrogate for location in low-dimensional space, provides a comprehensive overview of particular dictionaries and, in addition, enables comparison of different sequences. Dictionaries for various sequences and sequence lengths were compared to each other, and the effect of transmit field variations on the encoding capability was assessed. Clear differences in encoding capability were observed between different sequences, and HSNE results accurately reflect those obtained from an MRF matching simulation.
Stack Pressure Considerations for Room‐Temperature All‐Solid‐State Lithium Metal Batteries

All‐solid‐state batteries are expected to enable batteries with high energy density with the use of lithium metal anodes. Although solid electrolytes are believed to be mechanically strong enough to prevent lithium dendrites from propagating, various reports today still show cell failure due to lithium dendrit growth at room temperature. While cell parameters such as current density, electrolyte porosity, and interfacial properties have been investigated, mechanical properties of lithium metal and the role of applied stack pressure on the shorting behavior are still poorly understood. Here, failure mechanisms of lithium metal are investigated in all‐solid‐state batteries as a function of stack pressure, and in situ characterization of the interfacial and morphological properties of the buried lithium is conducted in solid electrolytes. It is found that a low stack pressure of 5 MPa allows reliable plating and stripping in a lithium symmetric cell for more than 1000 h, and a Li | Li6PS5Cl | LiNi0.80Co0. 15Al0.05O2 full cell, plating more than 4 µm of lithium per charge, is able to cycle over 200 cycles at room temperature. These results suggest the possibility of enabling the lithium metal anode in all‐solid‐state batteries at reasonable stack pressures.
AND ENZYMATIC CHARACTERISTICS AS AFFECTED BY 7-YEAR NO TILLAGE UNDER MAIZE CROPPING IN A MEADOW BROWN

No tillage is being popularized for the rainfed maize production in Northeast China. In order to evaluate its effects on the nutrient contents and enzymatic characteristics in upland soils of Northeast China, surface (0-20 cm) meadow brown soil samples were collected from the plots under no tillage and conventional tillage in a 7-year field experiment under maize cropping in Shenyang, with the soil pH, contents of total C, N, P and S and available N, activities of αand β-galactosidase, αand β-glucosidase, urease, protease, phosphomonoesterase, phosphodiesterase, and arylsulphatase, and kinetic parameters of β-glucosidase, protease, phosphomonoesterase, phosphodiesterase, and arylsulphatase determined. Comparing with conventional tillage, no tillage increased the contents of soil total C, N, and S and available N, the activities of test enzymes, and the Vmax/Km of soil urease, protease, and phosphomonoesterase, but decreased the activity of soil α-galactosidase and the Vmax/Km of soil β-glucosidase significantly. All the results suggest that long term no tillage for the maize production on meadow brown soil of Northeast China could enhance soil nutrients storage and the turnover of soil N and P, but had definite negative effects on the transformation of soil C.