Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolution of galactic planes of satellites in the eagle simulation

We study the formation of planes of dwarf galaxies around Milky Way (MW)-mass haloes in the eagle galaxy formation simulation. We focus on satellite systems similar to the one in the MW: spatially thin or with a large fraction of members orbiting in the same plane. To characterize the latter, we introduce a robust method to identify the subsets of satellites that have the most coplanar orbits. Out of the 11 MW classical dwarf satellites, 8 have highly clustered orbital planes whose poles are contained within a 22° opening angle centred around (l, b) = (182°, −2°). This configuration stands out when compared to both isotropic and typical ΛCDM satellite distributions. Purely flattened satellite systems are short-lived chance associations and persist for less than $1\, \rm {Gyr}$. In contrast, satellite subsets that share roughly the same orbital plane are longer lived, with half of the MW-like systems being at least $4\, \rm {Gyr}$ old. On average, satellite systems were flatter in the past, with a minimum in their minor-to-major axes ratio about $9\, \rm {Gyr}$ ago, which is the typical infall time of the classical satellites. MW-like satellite distributions have on average always been flatter than the overall population of satellites in MW-mass haloes and, in particular, they correspond to systems with a high degree of anisotropic accretion of satellites. We also show that torques induced by the aspherical mass distribution of the host halo channel some satellite orbits into the host’s equatorial plane, enhancing the fraction of satellites with coplanar orbits. In fact, the orbital poles of coplanar satellites are tightly aligned with the minor axis of the host halo.
IT Impact on Innovation at the Individual and Group Level - A Literature Review

Information technology (IT) is broadly recognized as an important element that supports innovation within organizations, however there has been relatively little integration of research in Information Systems on this topic. In this literature review, we examine and synthesize studies on the effects of IT in supporting innovation at the individual and group levels of analysis published in the past ten years in the leading Information Systems journals. We find that although innovation is inherently done by individuals and groups, there have been relatively few studies that examined how technology affects the innovation process and outcomes at the individual or group level. Further, much of the extant research is narrowly focused on incremental innovation. Through synthesis of the extant research, we identify opportunities for future research on the role of technology in innovation.
Increased expression level of ANGPTL8 in White Adipose tissue under Chronic Cold Treatment

Background It is well known that Angiopoietin like protein (ANGPTL) mainly 3, 4 and 8 play a major role in maintaining energy homeostasis by regulating lipoprotein lipase (LPL) activity, which is a key regulator of triglyceride (TG) metabolism. Our aim is to evaluate the level of ANGPTL3, 4 and 8 in mice maintained under cold conditions in the liver, brown adipose tissue (BAT), and white adipose tissue (WAT). Methods C57BL/6J mice were exposed to cold temperature at 4˚C over a period of ten days with food given ad libitum. Animal tissues were harvested at days 0, 1, 3, 5, and 10 (cold treatment groups, n = 10 in each group, control, n = 5). Liver, subcutaneous adipose tissue (SAT), and BAT were used to investigate the expression level of different genes. ANGPTL3, 4 and 8 genes expression were measured in liver. ANGPTL4, 8 and UCP1 were measured in SAT and BAT. Results ANGPTL 3 and 8 gene expression levels were significantly reduced in mice liver tissues after cold treatment (P-value < 0.05). However, the gene expression level of ANGPTL4 was not significantly changed. In BAT, ANGPTL8 expression was not changed after cold treatment while ANGPTL4 was significantly reduced (P-value < 0.05). ANGPTL4 level was also significantly reduced in SAT, whereas the gene expression level of ANGPTL8 showed over a 5-fold increase. Similarly, the UCP1 gene expression was also significantly increased in SAT. Additionally, protein overexpression of ANGPTL8 was further confirmed by immunohistochemistry after extended cold treatment. Conclusion Our data shows that ANGPTL proteins are inhibited in the liver and BAT under cold treatment. This agrees with other studies that showed that reduction in ANGPTL4 in BAT improved thermogenesis in response to acute cold exposure. However, in our study we also observed that ANGPTL8 is activated under these conditions in SAT. This suggests that it might be involved in the regulation of lipolysis as well as enhancing SAT browning.
Public Libraries as Anchor Institutions in Smart Communities: Current Practices and Future Development

Local governments around the world are exploring different strategies to become smarter: more efficient, sustainable, and highly interconnected. However, many actors outside government need to be involved too. For instance, public libraries have the potential to play a very important role in the development of smart and connected communities, due to their strong legitimacy in the eyes of citizens and the types of services and programs they are already providing, which in many cases go well beyond what could be considered traditional library services. This paper analyzes innovative practices in public libraries and argue that they already show the potential of public libraries to become catalysts for smart and connected communities. Together, these innovations represent an opportunity for governments and communities to engage and a new way to think about public libraries as very important actors in smart community initiatives.
The accretion disc-jet connection in blazars

The power spectral density (PSD) of the X-ray emission variability from the accretion disc-corona region of black hole X-ray binaries and active galactic nuclei has a broken power law shape with a characteristic break time-scale. If the disc and the jet are connected, the jet variability may also contain a characteristic time-scale related to that of the disc-corona. Recent observations of the blazar Mrk 421 have confirmed the broken power law shape of the PSD of its jet X-ray variability. We model the time variability of a blazar, in which emitting particles are assumed to be accelerated by successive shock waves flowing down the jet with a varying inter-shock time-scale. We investigate the possible relation between the characteristic time-scales in the disc and jet variability based on the above model, along with mathematically and physically simulated disc variability. We find that both the PSD of the jet and disc variability may have a broken power law shape but the break time-scales are not related in general except only in systems with a small range of BH mass. The break in the jet and the disc PSD are connected to the interval between large amplitude outbursts in the jet (inter-shock time-scale) and to the viscous time-scale in the disc, respectively. In frequency bands where multiple emission processes are involved or emission is from lower energy particles, the break in the PSD may not be prominent enough for detection.
Astro 2020 Science White Paper: Joint Gravitational Wave and Electromagnetic Astronomy with LIGO and LSST in the 2020's

The blossoming field of joint gravitational wave and electromagnetic (GW-EM) astronomy is one of the most promising in astronomy. The first, and only, joint GW-EM event GW170817 provided remarkable science returns that still continue to this day. Continued growth in this field requires increasing the sample size of joint GW-EM detections. In this white paper, we outline the case for using some percentage of LSST survey time for dedicated target-of-opportunity follow up of GW triggers in order to efficiently and rapidly identify optical counterparts. We show that the timeline for the LSST science survey is well matched to the planned improvements to ground based GW detectors in the next decade. LSST will become particularly crucial in the later half of the 2020s as more and more distant GW sources are detected. Lastly, we highlight some of the key science goals that can be addressed by a large sample of joint GW-EM detections.
Perpetrators in League of Legends: Scale Development and Validation of Toxic Behavior

Toxic behavior (TB) – a form of releasing frustration and anger in a detrimental way – is a common phenomenon in online games. Despite its importance, a validated questionnaire measuring TB is yet missing. In this paper, we apply a comprehensive procedure for scale development by using two difference sources of items. In the first one, the item pool is adapted from an existing scale. In the second one, the act frequency approach is applied to generate a pool of items. We evaluated both scales based on survey data from 380 online gamers. Both instruments are juxtaposed based on their psychometric properties. The results indicate that the adapted scale performs better in the context of our study than the scale generated from the act frequency approach and is, thus, the preferable choice. With a validated measurement scale in place, we discuss how future research can benefit from the TB scale proposed here.
Young stellar populations in early-type dwarf galaxies

Aims. To understand the stellar population content of early-type dwarf galaxies (dEs) and their environmental dependence, we compare the slopes and intrinsic scatter of color–magnitude relations (CMRs) for three nearby clusters, Fornax, Virgo, and Coma. Additionally, we present and compare internal color profiles of these galaxies to identify central blue regions with younger stars. Methods. We use the imaging of the HST/ACS Fornax cluster in the magnitude range of −18.7 ≤ Mg′ ≤ −16.0 to derive magnitudes, colors, and color profiles, which we compare with literature measurements from the HST/ACS Virgo and Coma Cluster Survey. We take advantage of HST accuracy to investigate and parameterize the (g′−z′) color profiles of these dEs. Results. Based on analysis of the color profiles, we report on a large number of dEs with young stellar populations at their center in all three clusters. While for Virgo and Coma the number of blue-cored dEs is found to be 85%±2% and 53%±3%, respectively, for Fornax, we find that all galaxies have a blue core. We show that bluer cores reside in fainter dEs, similar to the trend seen in nucleated dEs. We find no correlation between the luminosity of the galaxy and the size of its blue core. Moreover, a comparison of the CMRs of the three clusters shows that the scatter in the CMR of Virgo is considerably larger than in the Fornax and Coma clusters. Presenting adaptive smoothing we show that the galaxies on the blue side of the CMR often show evidence for dust extinction, which strengthens the interpretation that the bluer colors are due to young stellar populations. We also find that outliers on the red side of the CMR are more compact than what is expected for their luminosity. We find several of these red outliers in Virgo, often close to more massive galaxies. No red outlying compact early-types are found in Fornax and Coma in this magnitude range while we find three in the Virgo cluster. We show that the CMR of the Fornax and Virgo clusters are slightly bluer than that of Coma. We suggest that the large number of outliers and larger scatter found for Virgo CMR are a result of the alternative assembly history of this cluster.
Background driving distribution functions and series representations for log-gamma selfdecomposable random variables

Находятся фоновые управляющие функции распределения (background driving distribution functions) для саморазложимых распределений (случайных величин). Для величин с распределением log-гамма и их фоновых управляющих величин получены представления в виде рядов. Обновляющая величина для K-распределения Бесселя идентифицирована как составная пуассоновская величина.
Bounded Rational Decision-Making from Elementary Computations That Reduce Uncertainty

In its most basic form, decision-making can be viewed as a computational process that progressively eliminates alternatives, thereby reducing uncertainty. Such processes are generally costly, meaning that the amount of uncertainty that can be reduced is limited by the amount of available computational resources. Here, we introduce the notion of elementary computation based on a fundamental principle for probability transfers that reduce uncertainty. Elementary computations can be considered as the inverse of Pigou–Dalton transfers applied to probability distributions, closely related to the concepts of majorization, T-transforms, and generalized entropies that induce a preorder on the space of probability distributions. Consequently, we can define resource cost functions that are order-preserving and therefore monotonic with respect to the uncertainty reduction. This leads to a comprehensive notion of decision-making processes with limited resources. Along the way, we prove several new results on majorization theory, as well as on entropy and divergence measures.
Informal statistical models and modeling

ABSTRACT Growing scholarship on the pedagogical applications of statistical modeling is currently taking place to create adaptations of this practice to introduce novices to statistics. These are intended to promote novices’ reasoning, and are typically void of formal mathematical procedures and calculations. In this article, we define the potential product that novices can create as they engage in such informal implementations, Informal Statistical Models (ISM), as purposeful representations – with both deterministic and stochastic components – of how observed variability in data is generated. This definition reflects the potential connection between informal constructs that novices and young students can create and the formal view of statistical models, thereby suggesting how informal insights may be later cultivated into more mature understandings. We illustrate the usefulness of this definition by providing an in-depth account of the reasoning that a pair of sixth-grade students employed through their informal statistical modeling process. The findings illustrate a four-episode sequence where students created and developed an initial deterministic model before extending it with an informal stochastic component. This shows how starting with deterministic models can be a valuable precursor to develop initial ISMs.
Average Sensitivity of Graph Algorithms

In modern applications of graphs algorithms, where the graphs of interest are large and dynamic, it is unrealistic to assume that an input representation contains the full information of a graph being studied. Hence, it is desirable to use algorithms that, even when only a (large) subgraph is available, output solutions that are close to the solutions output when the whole graph is available. We formalize this idea by introducing the notion of average sensitivity of graph algorithms, which is the average earth mover's distance between the output distributions of an algorithm on a graph and its subgraph obtained by removing an edge, where the average is over the edges removed and the distance between two outputs is the Hamming distance. In this work, we initiate a systematic study of average sensitivity. After deriving basic properties of average sensitivity such as composition, we provide efficient approximation algorithms with low average sensitivities for concrete graph problems, including the minimum spanning forest problem, the global minimum cut problem, the minimum $s$-$t$ cut problem, and the maximum matching problem. In addition, we prove that the average sensitivity of our global minimum cut algorithm is almost optimal, by showing a nearly matching lower bound. We also show that every algorithm for the 2-coloring problem has average sensitivity linear in the number of vertices. One of the main ideas involved in designing our algorithms with low average sensitivity is the following fact; if the presence of a vertex or an edge in the solution output by an algorithm can be decided locally, then the algorithm has a low average sensitivity, allowing us to reuse the analyses of known sublinear-time algorithms and local computation algorithms (LCAs). Using this connection, we show that every LCA for 2-coloring has linear query complexity, thereby answering an open question.
A Many Objective Optimization Approach for Transfer Learning in EEG Classification

In Brain-Computer Interfacing (BCI), due to inter-subject non-stationarities of electroencephalogram (EEG), classifiers are trained and tested using EEG from the same subject. When physical disabilities bottleneck the natural modality of performing a task, acquisition of ample training data is difficult which practically obstructs classifier training. Previous works have tackled this problem by generalizing the feature space amongst multiple subjects including the test subject. This work aims at knowledge transfer to classify EEG of the target subject using a classifier trained with the EEG of another unit source subject. A many-objective optimization framework is proposed where optimal weights are obtained for projecting features in another dimension such that single source-trained target EEG classification performance is maximized with the modified features. To validate the approach, motor imagery tasks from the BCI Competition III Dataset IVa are classified using power spectral density based features and linear support vector machine. Several performance metrics, improvement in accuracy, sensitivity to the dimension of the projected space, assess the efficacy of the proposed approach. Addressing single-source training promotes independent living of differently-abled individuals by reducing assistance from others. The proposed approach eliminates the requirement of EEG from multiple source subjects and is applicable to any existing feature extractors and classifiers. Source code is available at this http URL.
Attentive Mimicking: Better Word Embeddings by Attending to Informative Contexts

Learning high-quality embeddings for rare words is a hard problem because of sparse context information. Mimicking (Pinter et al., 2017) has been proposed as a solution: given embeddings learned by a standard algorithm, a model is first trained to reproduce embeddings of frequent words from their surface form and then used to compute embeddings for rare words. In this paper, we introduce attentive mimicking: the mimicking model is given access not only to a word’s surface form, but also to all available contexts and learns to attend to the most informative and reliable contexts for computing an embedding. In an evaluation on four tasks, we show that attentive mimicking outperforms previous work for both rare and medium-frequency words. Thus, compared to previous work, attentive mimicking improves embeddings for a much larger part of the vocabulary, including the medium-frequency range.
Three-Dimensional Numerical Study of the Effect of Protective Barrier on the Dispersion of the Contaminant in a Building

The finite volume method and potential-vorticity vector formalism in their three-dimensional form were used to numerically study the impact of an adiabatic and impermeable vertical barrier on the dispersion of a local aero-contaminant due to the double-diffusive Rayleigh–Benard convection inside a cubic container. Different governing parameters such as the Rayleigh number, buoyancy ratio and barrier height were analyzed for Le = 1.2 and Pr = 0.7, representing an air-contaminant mixture. The potential-vector-vorticity formalism in the three-dimensional form allowed the elimination of the pressure terms appearing in the Navier–Stokes equations. It was found that the heat and mass transfer as well as the effectiveness of the barrier in reducing contaminant dispersion are strongly influenced by the buoyancy ratio, the barrier size and the Rayleigh number. In addition, the barrier effectiveness is more than 70% for a height of half the building height.
Big Three Dragons: A z = 7.15 Lyman-break galaxy detected in [O iii] 88 μm, [C ii] 158 μm, and dust continuum with ALMA

We present new ALMA observations and physical properties of a Lyman break galaxy at z = 7.15. Our target, B14-65666, has a bright ultra-violet (UV) absolute magnitude, MUV ≈ −22.4, and has been spectroscopically identified in Lyα with a small rest-frame equivalent width of ≈4 Å. A previous Hubble Space TElescope (HST) image has shown that the target is composed of two spatially separated clumps in the rest-frame UV. With ALMA, we have newly detected spatially resolved [O iii] 88 μm, [C ii] 158 μm, and their underlying dust continuum emission. In the whole system of B14-65666, the [O iii] and [C ii] lines have consistent redshifts of 7.1520 ± 0.0003, and the [O iii] luminosity, (34.4 ± 4.1) × 108 L⊙, is about three times higher than the [C ii] luminosity, (11.0 ± 1.4) × 108 L⊙. With our two continuum flux densities, the dust temperature is constrained to be Td ≈ 50–60 K under the assumption of a dust emissivity index of βd = 2.0–1.5, leading to a large total infrared luminosity of LTIR ≈ 1 × 1012 L⊙. Owing to our high spatial resolution data, we show that the [O iii] and [C ii] emission can be spatially decomposed into two clumps associated with the two rest-frame UV clumps whose spectra are kinematically separated by ≈200 km s−1. We also find these two clumps have comparable UV, infrared, [O iii], and [C ii] luminosities. Based on these results, we argue that B14-65666 is a starburst galaxy induced by a major merger. The merger interpretation is also supported by the large specific star formation rate (defined as the star formation rate per unit stellar mass), sSFR $= 260^{+119}_{-57}\:$Gyr−1, inferred from our SED fitting. Probably, a strong UV radiation field caused by intense star formation contributes to its high dust temperature and the [O iii]-to-[C ii] luminosity ratio.
Data Mining Classification Techniques for Diabetes Prediction

Diabetes may be predicted and prevented by exploring critical diabetes characteristics by computational data extraction methods. This study proposed a system biology approach to the pathogenic process to identify essential biomarkers as drug targets. The fact that disease recognition and investigation require many details, data mining plays a critical role in healthcare. This study aims to evaluate the efficiency of the methods used that are based on classification. Besides, the researchers have highlighted the most widely employed techniques and the strategies with the best precision. Many analyses include multiple Machine Learning algorithms for various disease assessments and predictions to improve overall issues. The detection and prediction of diseases is an aspect of classification and prediction. This paper estimates diabetes by its key features and also categorizes the relations between conflicting elements. The recursive random forest removal function provided a significant feature range. Random Forest Classifier investigated the diabetes estimate. RF offers 75,7813 greater precisions than Support Vector Machine (SVM). and may assist medical professionals in making care decisions.