Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of Coronal Holes on CME Deflections: Numerical Study

The understanding of the causes that produce the deflection of coronal mass ejections (CMEs) is essential for the space weather forecast. We study the effects on CME trajectories produced by the different properties of a coronal hole (CH), close to the ejection area. We perform ideal magnetohydrodynamics simulations emulating the early rising of the CME in the presence of a CH. We find that, the stronger the magnetic field and the wider the CH area, the larger the CME deflection. The deflection reduces as the CH moves away from the ejection region. To quantify the deflection we propose a dimensionless parameter that depends on the CH properties. We show that a minimum magnetic energy region, responsible for the deflection, is associated with the presence of the CH near the CME. We establish a relationship between the CH properties, the location of the minimum magnetic region, and the CME deflection.
Origin and evolution of the Galactic inventories of interstellar dust and its composition

Interstellar dust is a significant component of matter in the galaxies. The dust owns its origin and reprocessing in a wide range of astrophysical environments. In order to understand the origin and evolution of the distinct types of interstellar dust grains, we have attempted a comprehensive correlated study of the thermodynamics condensation of dust grains in distinct stellar environments with the Galactic chemical evolution of the Milky Way Galaxy. The Galaxy is evolved in terms of elemental evolution resulting from stellar nucleosynthetic contributions of several generations of stars. Based on the elemental composition of the evolving Galaxy, the relative abundances of the major constituents of interstellar dust are assessed. The major aim is to redistribute the various condensable elements at any epoch during the evolution of the Galaxy into various grain constituents and understand their abundance evolution based on a mass-balance formalism. We also performed thermodynamical equilibrium condensation calculations to understand the stellar origin of various grain constituents that could carry the isotopic signatures of the various stellar nucleosynthetic sources. This is perhaps a novel attempt to estimate the bulk dust mass budget in the evolving Galaxy. The normalized mass of the Galactic dust is predicted to decrease with the increase in distance from the Galactic centre. It increases over time. The supernovae SN Ia are predicted as the most prominent sources of Fe-dust mass, the supernovae SN II+Ib/c produces oxides and silicate dust mass, and the AGB stars contributes to carbonaceous dust mass.
Geometric linearization of theories for incompressible elastic materials and applications

We derive geometrically linearized theories for incompressible materials from nonlinear elasticity theory in the small displacement regime. Our nonlinear stored energy densities may vary on the same (small) length scale as the typical displacements. This allows for applications to multiwell energies as, e.g., encountered in martensitic phases of shape memory alloys and models for nematic elastomers. Under natural assumptions on the asymptotic behavior of such densities we prove Gamma-convergence of the properly rescaled nonlinear energy functionals to the relaxation of an effective model. The resulting limiting theory is geometrically linearized in the sense that it acts on infinitesimal displacements rather than finite deformations, but will in general still have a limiting stored energy density that depends in a nonlinear way on the infinitesimal strains. Our results, in particular, establish a rigorous link of existing finite and infinitesimal theories for incompressible nematic elastomers.
Optical spectral characterization of the TeV extreme blazar 2WHSP J073326.7+515354

The emission from the relativistic jets in blazars usually outshines their host galaxies, challenging the determination of their distances and the characterization of the stellar population. The situation becomes more favorable in the case of the extreme blazars (EHBLs), for which the bulk of the emission of the relativistic jets is emitted at higher energies, unveiling the optical emission from the host galaxy. The distance determination is fundamental for the study of the intrinsic characteristics of the blazars, especially to estimate the intrinsic gamma-ray spectra distorted due to the interaction with the Extragalactic Background Light. In this work we report on the properties of 2WHSP~J073326.7+515354 host galaxy in the optical band, which is one of the few EHBLs detected at TeV energies. We present the first measurement of the distance of the source, $\mathrm{z}=0.06504\pm0.00002$ (velocity dispersion $\sigma=237 \pm 9\,\mathrm{km s^{-1}}$). We also perform a detailed study of the stellar population of its host galaxy. We find that the mass-weighted mean stellar age is $11.72\pm0.06\,\mathrm{Gyr}$ and the mean metallicity $[M/H]=0.159 \pm 0.016$. In addition, a morphological study of the host galaxy is also carried out. The surface brightness distribution is modelled by a composition of a dominant classical bulge ($R_e=3.77\pm1\arcsec $ or equivalently 4.74~kpc) plus an unresolved source which corresponds to the active nucleus. The black hole mass is estimated using both the mass relation with the velocity dispersion and the absolute magnitude from the bulge yielding comparable results: $(4.8\pm0.9)\times10^8\,M_{\odot}$ and $(3.7\pm1.0)\times10^8\,M_{\odot}$, respectively.
Adaptive Techniques in Practical Quantum Key Distribution

Quantum Key Distribution (QKD) can provide information-theoretically secure communications and is a strong candidate for the next generation of cryptography. However, in practice, the performance of QKD is limited by "practical imperfections" in realistic sources, channels, and detectors (such as multi-photon components or imperfect encoding from the sources, losses and misalignment in the channels, or dark counts in detectors). Addressing such practical imperfections is a crucial part of implementing QKD protocols with good performance in reality. There are two highly important future directions for QKD: (1) QKD over free space, which can allow secure communications between mobile platforms such as handheld systems, drones, planes, and even satellites, and (2) fibre-based QKD networks, which can simultaneously provide QKD service to numerous users at arbitrary locations. These directions are both highly promising, but so far they are limited by practical imperfections in the channels and devices, which pose huge challenges and limit their performance. In this thesis, we develop adaptive techniques with innovative protocol and algorithm design, as well as novel techniques such as machine learning, to address some of these key challenges, including (a) atmospheric turbulence in channels for free-space QKD, (b) asymmetric losses in channels for QKD network, and (c) efficient parameter optimization in real time, which is important for both free-space QKD and QKD networks. We believe that this work will pave the way to important implementations of free-space QKD and fibre-based QKD networks in the future.
The Creation and Detection of Deepfakes

Generative deep learning algorithms have progressed to a point where it is difficult to tell the difference between what is real and what is fake. In 2018, it was discovered how easy it is to use this technology for unethical and malicious applications, such as the spread of misinformation, impersonation of political leaders, and the defamation of innocent individuals. Since then, these “deepfakes” have advanced significantly. In this article, we explore the creation and detection of deepfakes and provide an in-depth view as to how these architectures work. The purpose of this survey is to provide the reader with a deeper understanding of (1) how deepfakes are created and detected, (2) the current trends and advancements in this domain, (3) the shortcomings of the current defense solutions, and (4) the areas that require further research and attention.
Extreme mass ratio inspirals with scalar hair

Stellar mass objects orbiting around supermassive black holes are primary targets for future gravitational wave detectors like LISA. However, in theories beyond general relativity, the corresponding waveform templates are still relatively poorly known. We propose a universal description for these systems which applies to any black hole with a non trivial scalar profile, or scalar hair. To this aim, we use the effective field theory recently introduced by Franciolini et al. to write the most general action for the perturbations of a spherically symmetric solution up to some given order in derivatives and/or number of fields. At any post-Newtonian order, the background metric and the relevant operators can be encoded in a limited number of parameters which are readily calculated in some given scalar tensor model, as we show with a couple of examples. In terms of such parameters, we obtain an analytic expression for the dissipated power in the odd sector by solving perturbatively the Regge-Wheeler equation in the presence of a point-particle source.
An Asymetric Cycle-Consistency Loss for Dealing with Many-to-One Mappings in Image Translation: A Study on Thigh MR Scans

Generative adversarial networks using a cycle-consistency loss facilitate unpaired training of image-translation models and thereby exhibit a very high potential in manifold medical applications. However, the fact that images in one domain potentially map to more than one image in another domain (e.g. in case of pathological changes) exhibits a major challenge for training the networks. In this work, we offer a solution to improve the training process in case of many-to-one mappings by modifying the cycle-consistency loss. We show formally and empirically that the proposed method improves the performance significantly without radically changing the architecture and without increasing the overall complexity. We evaluate our method on thigh MRI scans with the final goal of segmenting the muscle in fat-infiltrated patients' data.
SIGN: Scalable Inception Graph Neural Networks

Graph representation learning has recently been applied to a broad spectrum of problems ranging from computer graphics and chemistry to high energy physics and social media. The popularity of graph neural networks has sparked interest, both in academia and in industry, in developing methods that scale to very large graphs such as Facebook or Twitter social networks. In most of these approaches, the computational cost is alleviated by a sampling strategy retaining a subset of node neighbors or subgraphs at training time. In this paper we propose a new, efficient and scalable graph deep learning architecture which sidesteps the need for graph sampling by using graph convolutional filters of different size that are amenable to efficient precomputation, allowing extremely fast training and inference. Our architecture allows using different local graph operators (e.g. motif-induced adjacency matrices or Personalized Page Rank diffusion matrix) to best suit the task at hand. We conduct extensive experimental evaluation on various open benchmarks and show that our approach is competitive with other state-of-the-art architectures, while requiring a fraction of the training and inference time.
The Differential Entropy of Mixtures: New Bounds and Applications

Mixture distributions are extensively used as a modeling tool in diverse areas from machine learning to communications engineering to physics, and obtaining bounds on the entropy of mixture distributions is of fundamental importance in many of these applications. This article provides sharp bounds on the entropy concavity deficit, which is the difference between the differential entropy of the mixture and the weighted sum of differential entropies of constituent components. Toward establishing lower and upper bounds on the concavity deficit, results that are of importance in their own right are obtained. In order to obtain nontrivial upper bounds, properties of the skew-divergence are developed and notions of “skew” $f$ -divergences are introduced; a reverse Pinsker inequality and a bound on Jensen-Shannon divergence are obtained along the way. Complementary lower bounds are derived with special attention paid to the case that corresponds to independent summation of a continuous and a discrete random variable. Several applications of the bounds are delineated, including to mutual information of additive noise channels, thermodynamics of computation, and functional inequalities.
Sparse Convolutional Beamforming for 3-D Ultrafast Ultrasound Imaging

Real-time 3-D ultrasound (US) provides a complete visualization of inner body organs and blood vasculature, crucial for diagnosis and treatment of diverse diseases. However, 3-D systems require massive hardware due to the huge number of transducer elements and consequent data size. This increases cost significantly and limit both frame rate and image quality, thus preventing the 3-D US from being common practice in clinics worldwide. A recent study presented a technique called sparse convolutional beamforming algorithm (SCOBA), which obtains improved image quality while allowing notable element reduction in the context of 2-D focused imaging. In this article, we build upon previous work and introduce a nonlinear beamformer for 3-D imaging, called COBA-3D, consisting of 2-D spatial convolution of the in-phase and quadrature received signals. The proposed technique considers diverging-wave transmission and achieves improved image resolution and contrast compared with standard delay-and-sum beamforming while enabling a high frame rate. Incorporating 2-D sparse arrays into our method creates SCOBA-3D: a sparse beamformer that offers significant element reduction and, thus, allows performing 3-D imaging with the resources typically available for 2-D setups. To create 2-D thinned arrays, we present a scalable and systematic way to design 2-D fractal sparse arrays. The proposed framework paves the way for affordable ultrafast US devices that perform high-quality 3-D imaging, as demonstrated using phantom and ex-vivo data.
Revisiting the Context Window for Cross-lingual Word Embeddings

Existing approaches to mapping-based cross-lingual word embeddings are based on the assumption that the source and target embedding spaces are structurally similar. The structures of embedding spaces largely depend on the co-occurrence statistics of each word, which the choice of context window determines. Despite this obvious connection between the context window and mapping-based cross-lingual embeddings, their relationship has been underexplored in prior work. In this work, we provide a thorough evaluation, in various languages, domains, and tasks, of bilingual embeddings trained with different context windows. The highlight of our findings is that increasing the size of both the source and target window sizes improves the performance of bilingual lexicon induction, especially the performance on frequent nouns.
Personalized automatic sleep staging with single-night data: a pilot study with Kullback–Leibler divergence regularization

Objective: Brain waves vary between people. This work aims to improve automatic sleep staging for longitudinal sleep monitoring via personalization of algorithms based on individual characteristics extracted from sleep data recorded during the first night. Approach: As data from a single night are very small, thereby making model training difficult, we propose a Kullback–Leibler (KL) divergence regularized transfer learning approach to address this problem. We employ the pretrained SeqSleepNet (i.e. the subject independent model) as a starting point and finetune it with the single-night personalization data to derive the personalized model. This is done by adding the KL divergence between the output of the subject independent model and it of the personalized model to the loss function during finetuning. In effect, KL-divergence regularization prevents the personalized model from overfitting to the single-night data and straying too far away from the subject independent model. Main results: Experimental results on the Sleep-EDF Expanded database consisting of 75 subjects show that sleep staging personalization with single-night data is possible with help of the proposed KL-divergence regularization. On average, we achieve a personalized sleep staging accuracy of 79.6%, a Cohen’s kappa of 0.706, a macro F1-score of 73.0%, a sensitivity of 71.8%, and a specificity of 94.2%. Significance: We find both that the approach is robust against overfitting and that it improves the accuracy by 4.5 percentage points compared to the baseline method without personalization and 2.2 percentage points compared to it with personalization but without regularization.
Analytic Simplification of Neural Network Based Intra-Prediction Modes For Video Compression

With the increasing demand for video content at higher resolutions, it is evermore critical to find ways to limit the complexity of video encoding tasks in order to reduce costs, power consumption and environmental impact of video services. In the last few years, algorithms based on Neural Networks (NN) have been shown to benefit many conventional video coding modules. But while such techniques can considerably improve the compression efficiency, they usually are very computationally intensive. It is highly beneficial to simplify models learnt by NN so that meaningful insights can be exploited with the goal of deriving less complex solutions. This paper presents two ways to derive simplified intra-prediction from learnt models, and shows that these streamlined techniques can lead to efficient compression solutions.
COVID-19 Chest CT Image Segmentation - A Deep Convolutional Neural Network Solution

A novel coronavirus disease 2019 (COVID-19) was detected and has spread rapidly across various countries around the world since the end of the year 2019, Computed Tomography (CT) images have been used as a crucial alternative to the time-consuming RT-PCR test. However, pure manual segmentation of CT images faces a serious challenge with the increase of suspected cases, resulting in urgent requirements for accurate and automatic segmentation of COVID-19 infections. Unfortunately, since the imaging characteristics of the COVID-19 infection are diverse and similar to the backgrounds, existing medical image segmentation methods cannot achieve satisfactory performance. In this work, we try to establish a new deep convolutional neural network tailored for segmenting the chest CT images with COVID-19 infections. We firstly maintain a large and new chest CT image dataset consisting of 165,667 annotated chest CT images from 861 patients with confirmed COVID-19. Inspired by the observation that the boundary of the infected lung can be enhanced by adjusting the global intensity, in the proposed deep CNN, we introduce a feature variation block which adaptively adjusts the global properties of the features for segmenting COVID-19 infection. The proposed FV block can enhance the capability of feature representation effectively and adaptively for diverse cases. We fuse features at different scales by proposing Progressive Atrous Spatial Pyramid Pooling to handle the sophisticated infection areas with diverse appearance and shapes. We conducted experiments on the data collected in China and Germany and show that the proposed deep CNN can produce impressive performance effectively.
A Review of Methods for Estimating Algorithmic Complexity: Options, Challenges, and New Directions

Some established and also novel techniques in the field of applications of algorithmic (Kolmogorov) complexity currently co-exist for the first time and are here reviewed, ranging from dominant ones such as statistical lossless compression to newer approaches that advance, complement and also pose new challenges and may exhibit their own limitations. Evidence suggesting that these different methods complement each other for different regimes is presented and despite their many challenges, some of these methods can be better motivated by and better grounded in the principles of algorithmic information theory. It will be explained how different approaches to algorithmic complexity can explore the relaxation of different necessary and sufficient conditions in their pursuit of numerical applicability, with some of these approaches entailing greater risks than others in exchange for greater relevance. We conclude with a discussion of possible directions that may or should be taken into consideration to advance the field and encourage methodological innovation, but more importantly, to contribute to scientific discovery. This paper also serves as a rebuttal of claims made in a previously published minireview by another author, and offers an alternative account.
Assessment of knowledge and attitude and practice towards migraine prevention and treatment among general population in Saudi Arabia

Background: migraine is considered as one of the most common chronic neurological disorder with a high prevalence among adults worldwide. Objective : this study aimed to detect the knowledge and attitude and practice towards migraine prevention and treatment among general population in Kingdom of Saudi Arabia (KSA). Methods: this was a descriptive cross sectional questionnaire study that was conducted at Saudi Arabia, from April to July 2018 among a random sample consisted of 600 adults in distributed in different parts of KSA. Results: the awareness level among the respondents was inadequate among most of the subjects regarding, the gender effect, the triggering factors of migraine and the prophylactic treatment.The attitude and the level of practice were also improper among most of the subjects as most of them don’t consider migraine as a disorder that needs consultant and the majority would take medication without consultation. Conclusion: the KAP level regarding migraine prevention and treatment was in adequate among most of the included subjects however the majority of included subjects were highly educated.
A Significantly Neutral Intergalactic Medium Around the Luminous z = 7 Quasar J0252–0503

Luminous z ≥ 7 quasars provide direct probes of the evolution of supermassive black holes (SMBHs) and the intergalactic medium (IGM) during the epoch of reionization (EoR). The Lyα damping wing absorption imprinted by neutral hydrogen in the IGM can be detected in a single EoR quasar spectrum, allowing the measurement of the IGM neutral fraction toward that line of sight. However, damping wing features have only been detected in two z > 7 quasars in previous studies. In this paper, we present new high-quality optical and near-infrared spectroscopy of the z = 7.00 quasar DES J025216.64–050331.8 obtained with Keck/Near-Infrared Echellette Spectrometer and Gemini/GMOS. By using the Mg ii single-epoch virial method, we find that it hosts a SMBH accreting at an Eddington ratio of λEdd = 0.7 ± 0.1, consistent with the values seen in other luminous z ∼ 7 quasars. Furthermore, the Lyα region of the spectrum exhibits a strong damping wing absorption feature. The lack of associated metal absorption in the quasar spectrum indicates that this absorption is imprinted by a neutral IGM. Using a state-of-the-art model developed by Davies et al., we measure a volume-averaged neutral hydrogen fraction at z = 7 of within 68% (95%) confidence intervals when marginalizing over quasar lifetimes of . This is the highest IGM neutral fraction yet measured using reionization-era quasar spectra.