Miracle-dz/UniFAR-FTUs huggingface dataset:
Heating of the Intergalactic Medium by Hydrogen Reionization

During reionization, the intergalactic medium is heated impulsively by supersonic ionization fronts (I-fronts). The peak gas temperatures behind the I-fronts, , are a key uncertainty in models of the thermal history after reionization. Here we use high-resolution radiative transfer (RT) simulations to study the parameter space of . We show that is only mildly sensitive to the spectrum of incident radiation over most of the parameter space, with temperatures set primarily by I-front speeds. We also explore what current models of reionization predict for by measuring I-front speeds in cosmological RT simulations. We find that the post-I-front temperatures evolve toward hotter values as reionization progresses. Temperatures of = 17,000–22,000 K are typical during the first half of reionization, but = 25,000–30,000 K may be achieved near the end of this process if I-front speeds reach ∼104 km s−1, as found in our simulations. Shorter reionization epochs lead to hotter . We discuss implications for z > 5 Lyα forest observations, which potentially include sight lines through hot, recently reionized patches of the universe. Interpolation tables from our parameter space study are made publicly available, along with a simple fit for the dependence of on the I-front speed.
Exploring the nature and synchronicity of early cluster formation in the Large Magellanic Cloud – IV. Evidence for multiple populations in Hodge 11 and NGC 2210

We present a multiple population search in two old Large Magellanic Cloud (LMC) Globular clusters, Hodge 11 and NGC 2210. This work uses data from the Advanced Camera for Surveys and Wide Field Camera 3 on the Hubble Space Telescope from programme GO-14164 in Cycle 23. Both of these clusters exhibit a broadened main sequence with the second population representing (20 ± ∼5) per cent for NGC 2210 and (30 ± ∼5) per cent for Hodge 11. In both clusters, the smaller population is redder than the primary population, suggesting CNO variations. Hodge 11 also displays a bluer second population in the horizontal branch, which is evidence for helium enhancement. However, even though NGC 2210 shows similarities to Hodge 11 in the main sequence, there does not appear to be a second population on NGC 2210’s horizontal branch. This is the first photometric evidence that ancient LMC Globular clusters exhibit multiple stellar populations.
Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning

Machine learning (ML) has progressed rapidly during the past decade and the major factor that drives such development is the unprecedented large-scale data. As data generation is a continuous process, this leads to ML model owners updating their models frequently with newly-collected data in an online learning scenario. In consequence, if an ML model is queried with the same set of data samples at two different points in time, it will provide different results. In this paper, we investigate whether the change in the output of a black-box ML model before and after being updated can leak information of the dataset used to perform the update, namely the updating set. This constitutes a new attack surface against black-box ML models and such information leakage may compromise the intellectual property and data privacy of the ML model owner. We propose four attacks following an encoder-decoder formulation, which allows inferring diverse information of the updating set. Our new attacks are facilitated by state-of-the-art deep learning techniques. In particular, we propose a hybrid generative model (CBM-GAN) that is based on generative adversarial networks (GANs) but includes a reconstructive loss that allows reconstructing accurate samples. Our experiments show that the proposed attacks achieve strong performance.
Analysis of Land Surface Temperature Evolution Based on Regional Road Scope

To balance sustainable road construction and environmental impact, intrinsic relationships between regional road networks and land surface temperature (LST) along the roads must be examined. In this study, we hypothesize that expanding road networks can affect the LST within a given region. Thus, we determined the influence of varying road grades on the surrounding LST. Using an improved single-window algorithm with reasonably adjusted water vapor parameters, LST data for the relevant areas were extracted from Landsat 8 data. Analyzing these data revealed that, in the improved single-window algorithm, the MOD05 L2 water vapor parameter could obtain results, indicating that road grades are positively correlated with LST changes when road network mileage is increased. In addition, we found that LST was increased by highways and low-grade roads over distances of 180 m and 150 m compared with the surrounding area, respectively. Furthermore, LST was affected by road interchanges over a distance of 300 m and by intersections formed by low-grade roads over 150 m; both have a linear nonuniform influence on the propagation models. Finally, we determined that different highways in the LST radiation range can interact to form an LST concentration zone, resulting in further LST increases.
Ultrafast control of spin interactions in honeycomb antiferromagnetic insulators

Light enables ultrafast, direct and nonthermal control of the exchange and Dzyaloshinskii-Moriya interactions. We consider two-dimensional honeycomb lattices described by the Kane-Mele-Hubbard model at half filling and in the strongly correlated limit, i.e., the Mott insulator phase of a canted antiferromagnet. Based on Floquet theory, we demonstrate that by changing the amplitude and frequency of polarized laser pulses, one can tune the amplitudes and signs of and even the ratio between the exchange and Dzyaloshinskii-Moriya spin interactions. Furthermore, the renormalizations of the spin interactions are independent of helicity. Our results pave the way for ultrafast optical spin manipulation in recently discovered two-dimensional magnetic materials.
fairseq: A Fast, Extensible Toolkit for Sequence Modeling

fairseq is an open-source sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling, and other text generation tasks. The toolkit is based on PyTorch and supports distributed training across multiple GPUs and machines. We also support fast mixed-precision training and inference on modern GPUs. A demo video can be found at https://www.youtube. com/watch? v=OtgDdWtHvto
Governmental Ownership of Voluntary Sustainability Information Disclosure in an Emerging Economy: Evidence from Vietnam

Emerging economies have increasingly paid attention to sustainability issues in the business circle. However, few studies have explored what facilitates sustainability information disclosure. This study examines how corporate governance mechanisms, particularly government ownership, affect sustainability disclosure in an emerging economy—Vietnam. By combining related research streams, including stakeholder theory, institutional perspective, and principal–agent theory, we present a hypothesis on the effect of corporate governance on sustainability reporting. The logistic regression analysis and analysis of variance on 2678 Vietnamese sample firm-years from 2010 through 2016 indicate that government ownership is negatively associated with voluntary environmental and social information disclosure. Additionally, they demonstrate that ownership concentration tends to lower non-financial information disclosure, while individual largest shareholder has a positive effect. These findings provide managers and policymakers with theoretical and practical implications to encourage firms in emerging Asian economies such as Vietnam to adopt sustainability activities and disclose social information.
A constrained transport method for the solution of the resistive relativistic MHD equations

We describe a novel Godunov-type numerical method for solving the equations of resistive relativistic magnetohydrodynamics. In the proposed approach, the spatial components of both magnetic and electric fields are located at zone interfaces and are evolved using the constrained transport formalism. Direct application of Stokes' theorem to Faraday's and Ampere's laws ensures that the resulting discretization is divergence-free for the magnetic field and charge-conserving for the electric field. Hydrodynamic variables retain, instead, the usual zone-centred representation commonly adopted in finite-volume schemes. Temporal discretization is based on Runge-Kutta implicit-explicit (IMEX) schemes in order to resolve the temporal scale disparity introduced by the stiff source term in Ampere's law. The implicit step is accomplished by means of an improved and more efficient Newton-Broyden multidimensional root-finding algorithm. The explicit step relies on a multidimensional Riemann solver to compute the line-averaged electric and magnetic fields at zone edges and it employs a one-dimensional Riemann solver at zone interfaces to update zone-centred hydrodynamic quantities. For the latter, we introduce a five-wave solver based on the frozen limit of the relaxation system whereby the solution to the Riemann problem can be decomposed into an outer Maxwell solver and an inner hydrodynamic solver. A number of numerical benchmarks demonstrate that our method is superior in stability and robustness to the more popular charge-conserving divergence cleaning approach where both primary electric and magnetic fields are zone-centered. In addition, the employment of a less diffusive Riemann solver noticeably improves the accuracy of the computations.
A comparison principle for random walk on dynamical percolation

We consider the model of random walk on dynamical percolation introduced by Peres, Stauffer and Steif (2015). We obtain comparison results for this model for hitting and mixing times and for the spectral-gap and log-Sobolev constant with the corresponding quantities for simple random walk on the underlying graph $G$, for general graphs. When $G$ is the torus $\mathbb{Z}_n^d$, we recover the results of Peres et al. and we also extend them to the critical case. We also obtain bounds in the cases where $G$ is a transitive graph of moderate growth and also when it is the hypercube.
An Efficient Network Intrusion Detection System Based on Feature Selection and Ensemble Classifier

Since Internet is so popular and prevailing in human life, countering cyber threats, especially attack detection, is a challenging area of research in the field of cyber security. Intrusion detection systems (IDSs) are essential entities in a network topology aiming to safeguard the integrity and availability of sensitive assets in the protected systems. Although many supervised and unsupervised learning approaches from the field of machine learning and pattern recognition have been used to increase the efficacy of IDSs, it is still a problem to deal with lots of redundant and irrelevant features in high-dimension datasets for network anomaly detection. To this end, we propose a novel methodology combining the benefits of correlation-based feature selection(CFS) and bat algorithm(BA) with an ensemble classifier based on C4.5, Random Forest(RF), and Forest by Penalizing Attributes(Forest PA), which can be able to classify both common and rare types of attacks with high accuracy and efficiency. The experimental results, using a novel intrusion detection dataset, namely CIC-IDS2017, reveal that our CFS-BA-Ensemble method is able to contribute more critical features and significantly outperforms individual approaches, achieving high accuracy and low false alarm rate. Moreover, compared with the majority of the existing state-of-the-art and legacy techniques, our approach exhibits better performance under several classification metrics in the context of classification accuracy, f-measure, attack detection rate, and false alarm rate.
Single metal-poor ultra compact dwarf galaxy at one kiloparsec distance from the low-mass elliptical galaxy FCC 47

Context. Photometric surveys of galaxy clusters have revealed a large number of ultra compact dwarfs (UCDs) around predominantly massive elliptical galaxies. Their origin is still debated as some UCDs are considered to be the remnant nuclei of stripped dwarf galaxies while others seem to mark the high-mass end of the star cluster population. Aims. We aim to characterize the properties of a UCD found at very close projected distance (rwproj = 1.1 kpc) from the centre of the low-mass (M ∼ 1010 M⊙) early-type galaxy FCC 47. This is a serendipitous discovery from MUSE adaptive optics science verification data. We explore the potential origin of this UCD as either a massive cluster or the remnant nucleus of a dissolved galaxy. Methods. We used archival Hubble Space Telescope data to study the photometric and structural properties of FCC 47-UCD1. In the MUSE data, the UCD is unresolved, but we used its spectrum to determine the radial velocity and metallicity. Results. The surface brightness of FCC 47-UCD1 is best described by a single King profile with low concentration C = Rt/Rc ∼ 10 and large effective radius (reff = 24 pc). Its integrated magnitude and blue colour (Mg = −10.55 mag, (g − z) = 1.46 mag) combined with a metallicity of [M/H] = −1.12 ± 0.10 dex and an age > 8 Gyr obtained from the full fitting of the MUSE spectrum suggests a stellar population mass of M* = 4.87 × 106 M⊙. The low S/N of the MUSE spectrum prevents detailed stellar population analysis. Due to the limited spectral resolution of MUSE, we can only give an upper limit on the velocity dispersion (σ < 17 km s−1), and consequently on its dynamical mass (Mdyn < 1.3 × 107 M⊙). Conclusions. The origin of the UCD cannot be constrained with certainty. The low metallicity, old age, and magnitude are consistent with a star cluster origin, whereas the extended size is consistent with an origin as the stripped nucleus of a dwarf galaxy with a initial stellar mass of a few 108 M⊙.
Geometry of small causal diamonds

The geometry of small causal diamonds in the absence of matter is considered, based on three distinct constructions that are common in the literature, namely the geodesic ball, Alexandrov interval and lightcone cut. The causal diamond geometry is studied perturbatively using Riemann normal coordinate expansion up to the leading order in both vacuum and non-vacuum. We provide a collection of results including the area of the codimension-two edge, the maximal hypersurface volume and their isoperimetric ratio for each construction. By solving the evolution equations of the optical quantities on the lightcone, we find that intriguingly only the lightcone cut construction yields an area deficit proportional to the Bel-Robinson superenergy density W in four dimensional spacetime, but such a direct connection fails to hold in any other dimension. We also compute the volume of the Alexandrov interval causal diamond in vacuum, which we believe is important but missing from the literature. Our work extends the earlier works on the causal diamond geometry by Gibbons and Solodukhin [1], and by Jacobson, Senovilla and Speranza [2]. Some potential applications of our results in mathematical general relativity and quantum gravity are also discussed.
On the Vulnerability of CNN Classifiers in EEG-Based BCIs

Deep learning has been successfully used in numerous applications because of its outstanding performance and the ability to avoid manual feature engineering. One such application is electroencephalogram (EEG)-based brain-computer interface (BCI), where multiple convolutional neural network (CNN) models have been proposed for EEG classification. However, it has been found that deep learning models can be easily fooled with adversarial examples, which are normal examples with small deliberate perturbations. This paper proposes an unsupervised fast gradient sign method (UFGSM) to attack three popular CNN classifiers in BCIs, and demonstrates its effectiveness. We also verify the transferability of adversarial examples in BCIs, which means we can perform attacks even without knowing the architecture and parameters of the target models, or the datasets they were trained on. To the best of our knowledge, this is the first study on the vulnerability of CNN classifiers in EEG-based BCIs, and hopefully will trigger more attention on the security of BCI systems.
Long-term Water Regime Studies of a Degraded Floating Fen in Hungary

Historical trends in water management and recent climatic variations put wetlands in the Carpathian basin under strong pressure and led to their degradation. The lack of extended site specific environmental data series inhibits the understanding of long-term eco-hydrological processes. This undermines the success of restoration and/or management efforts. As a precedent we analyzed a recently degraded Hungarian lowland wetland, the Nyárjas fen in order to identify the main cause of its drying. Our method is based on one-dimensional simulations of a variably saturated soil column representing the dominant hydrological conditions of the wetland. To properly define the necessary soil hydraulic parameters, soil sampling, laboratory measurements and inverse modelling were carried out. The hydrological simulations for the 1961-2010 period clearly suggest that (i) the wetland degraded due to a temporal unfavorable combination of regional groundwater depletion and decreased precipitation, (ii) and could not recover afterwards despite the improvement of hydrological conditions. The ecological water demand of the Nyárjas fen can be explicitly expressed in terms of groundwater level. However, water availability is a necessary, but not sufficient criteria of good habitat status. The elaborated methodology provides the basis of bottom-up type environmental water demand estimation on a regional/national scale.
UHop: An Unrestricted-Hop Relation Extraction Framework for Knowledge-Based Question Answering

In relation extraction for knowledge-based question answering, searching from one entity to another entity via a single relation is called “one hop”. In related work, an exhaustive search from all one-hop relations, two-hop relations, and so on to the max-hop relations in the knowledge graph is necessary but expensive. Therefore, the number of hops is generally restricted to two or three. In this paper, we propose UHop, an unrestricted-hop framework which relaxes this restriction by use of a transition-based search framework to replace the relation-chain-based search one. We conduct experiments on conventional 1- and 2-hop questions as well as lengthy questions, including datasets such as WebQSP, PathQuestion, and Grid World. Results show that the proposed framework enables the ability to halt, works well with state-of-the-art models, achieves competitive performance without exhaustive searches, and opens the performance gap for long relation paths.
Microscopic understanding of magnetic interactions in bilayer 
CrI3

We performed the detailed microscopic analysis of the inter-layer magnetic couplings for bilayer CrI$_3$. As the first step toward understanding the recent experimental observations and utilizing them for device applications, we estimated magnetic force response as well as total energy. Various van der Waals functionals equivocally point to the ferromagnetic ground state for the low-temperature structured bilayer CrI$_3$ which is further confirmed independently by magnetic force response calculations. The calculated orbital-dependent magnetic forces clearly show that $e_g$-$t_{2g}$ interaction is the key to stabilize this ferromagnetic order. By suppressing this ferromagnetic interaction and enhancing antiferromagnetic orbital channels of $e_g$-$e_g$ and $t_{2g}$-$t_{2g}$, one can realize the desirable antiferromagnetic order. We showed that high-temperature monoclinic stacking can be the case. Our results provide unique information and insight to understand the magnetism of multi-layer CrI$_3$ paving the way to utilize it for applications.
A DUPLA JORNADA DO ATLETA UNIVERSITÁRIO: PERSPECTIVAS PARA A CONCILIAÇÃO ENTRE ESTUDOS E TREINOS NA UNIVERSIDADE DE BRASÍLIA

O objetivo do presente estudo foi apresentar e analisar o perfil dos estudantes atletas da Universidade de Brasilia (UnB) que representaram a instituicao em competicoes universitarias entre 2017 e 2018 mediante utilizacao de um questionario estruturado de autoaplicacao com amostra definida em 76 individuos. A analise exploratoria foi conduzida por meio de estatistica descritiva no software SPSS e da analise textual por meio do software IRaMuTeQ. Os resultados apresentam o exito na politica de representacao promovida pela UnB e relativo sucesso na conciliacao da dupla jornada dos estudantes atletas, apesar de a maioria dos atletas terem alcancando resultados nacionais e internacionais. Apontamos a necessidade de aprofundamento do tema a nivel universitario e a urgente organizacao de estatutos institucionais que qualifiquem o esporte de representacao e de elite na UnB.
Building Density Level of Urban Slum Area in Jakarta

Currently, the number of urban residents is increasing and some of the urban population live in slum areas. Therefore, identifying the characteristics of slum areas has become crucial. This study aimed to identify more specific slum locations in Jakarta through the pattern of building densities analysis between case studies of neighborhoods association (RT) in 15 hamlets (RW) that classified as heavy slums category. This study also attempted to determine the relation between building density levels in the slum area and Jakarta spatial detail planning. This study engaged the Cluster and Outlier Analysis (Anselin Local Moran's I) method. This study also observed socio-economic factors of citizen census data based the Dasawisma Census of Family Welfare Empowerment in 2019. The result shows that slum locations that had direct neighbors towards areas which was designated at spatial detail planning as industrial zones and ware housing areas as well as office, trade and service zones, obtained a higher level of building density compared to slum locations that secured neighbors to areas that were designated as housing zones. High economic opportunities provided attraction and affected the growth of slum locations. The results also reveal that slum areas were not a concentrated population with low income and/or low education. Applying cluster and outlier modeling of building density levels of urban slums in Jakarta based on RT cluster level could reveal more specific slum locations and could identify factors that influence the differences of building density levels.