Miracle-dz/UniFAR-FTUs huggingface dataset:
Topic, Sentiment and Impact Analysis: COVID19 Information Seeking on Social Media

When people notice something unusual, they discuss it on social media. They leave traces of their emotions via text expressions. A systematic collection, analysis, and interpretation of social media data across time and space can give insights on local outbreaks, mental health, and social issues. Such timely insights can help in developing strategies and resources with an appropriate and efficient response. This study analysed a large Spatio-temporal tweet dataset of the Australian sphere related to COVID19. The methodology included a volume analysis, dynamic topic modelling, sentiment detection, and semantic brand score to obtain an insight on the COVID19 pandemic outbreak and public discussion in different states and cities of Australia over time. The obtained insights are compared with independently observed phenomena such as government reported instances.
Drift Estimation of the Threshold Ornstein-Uhlenbeck Process From Continuous and Discrete Observations

We refer by threshold Ornstein-Uhlenbeck to a continuous-time threshold autoregressive process. It follows the Ornstein-Uhlenbeck dynamics when above or below a fixed level, yet at this level (threshold) its coefficients can be discontinuous. We discuss (quasi)-maximum likelihood estimation of the drift parameters, both assuming continuous and discrete time observations. In the ergodic case, we derive consistency and speed of convergence of these estimators in long time and high frequency. Based on these results, we develop a heuristic test for the presence of a threshold in the dynamics. Finally, we apply these statistical tools to short-term US interest rates modeling.
Estimation of the black hole spin in LMC X-1 using AstroSat

LMC X-1, a persistent, rapidly rotating, extra-galactic, black hole X-ray binary (BHXB) discovered in 1969, has always been observed in its high soft state. Unlike many other BHXBs, the black hole mass, source distance and binary orbital inclination are well established. In this work, we report the results of simultaneous broadband spectral studies of LMC X-1 carried out using the data from Soft X-ray Telescope and Large Area X-ray Proportional Counter aboard AstroSat as observed on 2016 November 26$^{th}$ and 2017 August 28$^{th}$. The combined spectrum was modelled with a multicolour blackbody emission (diskbb), a Gaussian along with a Comptonization component (simpl) in the energy range 0.7$-$30.0 keV. The spectral analysis revealed that the source was in its high soft state ($\Gamma$=2.67$^{+0.24}_{-0.24}$ and $\Gamma$=2.12$^{+0.19}_{-0.20}$) with a hot disc (kT$_{in}$=0.86$^{+0.01}_{-0.01}$ and kT$_{in}$=0.87$^{+0.02}_{-0.02}$). Thermal disc emission was fit with a relativistic model (kerrbb) and spin of the black hole was estimated to be 0.93$^{+0.01}_{-0.01}$ and 0.93$^{+0.04}_{-0.03}$ (statistical errors) for the two Epochs through X-ray continuum-fitting, which agrees with the previous results.
A Functional Analysis of the Complex Call of the Frog Physalaemus pustulosus

Males of Physalaemus pustulosus are capable of producing two sounds simultaneously. All other known cases of complex frog calls are instances of the different sounds being produced serially. Comparison of call analyses and laryngeal morphology among Physalaemus gracilis, P. olfersi and P. pustulosus leads to the conclusion that the two sounds are produced by separate vibrating structures in the larynx. Lateral passageways bypassing the median free edges of the vocal cords in all three of these species are described, a feature not previously reported for any anuran larynx. A mechanism is suggested that integrates the available knowledge into a hypothesis of call production in P. pustulosus.
When Distributed Ledger Technology meets Internet of Things - Benefits and Challenges

There is a growing interest from both the academia and industry to employ distributed ledger technology in the Internet-of-Things domain for addressing security-related and performance challenges. Distributed ledger technology enables non-trusted entities to communicate and reach consensus in a fully distributed manner through a cryptographically secure and immutable ledger. However, significant challenges arise mainly related to transaction processing speed and user privacy. This work explores the interplay between Internet-of-Things and distributed ledger technology, analysing the fundamental characteristics of this technology and discussing the related benefits and challenges.
Prospects for nuclear spin hyperpolarization of molecular samples using nitrogen-vacancy centers in diamond

After initial proof-of-principle demonstrations, optically pumped nitrogen-vacancy (NV) centers in diamond have been proposed as a noninvasive platform to achieve hyperpolarization of nuclear spins in molecular samples over macroscopic volumes and enhance the sensitivity in nuclear magnetic resonance (NMR) experiments. In this work we model the process of polarization of external samples by NV centers and theoretically evaluate their performance in a range of scenarios. We find that average nuclear spin polarizations exceeding 10% can in principle be generated over macroscopic sample volumes $(\ensuremath{\gtrsim}\ensuremath{\mu}\mathrm{l})$ with a careful engineering of the system's geometry to maximize the diamond-sample contact area. The fabrication requirements and other practical challenges are discussed. We then explore the possibility of exploiting local polarization enhancements in nano/micro-NMR experiments based on NV centers. For micro-NMR we find that modest signal enhancements over thermal polarization (by 1--2 orders of magnitude) can in essence be achieved with existing technology, with larger enhancements achievable via microstructuring of the sample/substrate interface. However, there is generally no benefit for nano-NMR where the detection of statistical polarization provides the largest signal-to-noise ratio. This work will guide future experimental efforts to integrate NV-based hyperpolarization to NMR systems.
Counter-Unmanned Aircraft System(s) (C-UAS): State of the Art, Challenges, and Future Trends

Unmanned aircraft systems (UAS), or unmanned aerial vehicles, often referred to as drones, have been experiencing healthy growth in the United States and around the world. The positive uses of UAS have the potential to save lives, increase safety and efficiency, and enable more effective science and engineering research. However, UAS are subject to threats stemming from increasing reliance on computer and communication technologies, which place public safety, national security, and individual privacy at risk. To promote safe, secure, and privacy-respecting UAS operations, there is an urgent need for innovative technologies for detecting, tracking, identifying, and mitigating UAS. A Counter-UAS (C-UAS) system is defined as a system or device capable of lawfully and safely disabling, disrupting, or seizing control of an unmanned aircraft or UAS. Over the past five years, significant research efforts have been made to detect, and mitigate UAS: detection technologies are based on acoustic, vision, passive radio frequency, radar, and data fusion; and mitigation technologies include physical capture or jamming. In this tutorial, we provide a comprehensive survey of existing literature in the area of C-UAS, identify the challenges in countering unauthorized or unsafe UAS, and evaluate the trends of detection and mitigation for protecting against UAS-based threats. The objective of this tutorial is to present a systematic introduction of C-UAS technologies, thus fostering a research community committed to the safe integration of UAS into the airspace system.
The Fornax Deep Survey data release 1

We present the first data release of the Fornax Deep Survey (FDS), an imaging survey using using the wide-field imager OmegaCAM mounted on the VST in the SDSS u', g', r', and i'-bands covering the Fornax Galaxy Cluster and the infalling Fornax A Group. FDS is a joint project between NOVA (previously called FOCUS - PI: R. F. Peletier) and INAF (as part of VEGAS - PIs: M. Capaccioli and E. Iodice). With exposure times of about 9 hours over an area of ~28 square degrees, this survey is a legacy dataset for studies of members of the Fornax Galaxy Cluster and the infalling Fornax A Group down to a surface brightness limit of ~28 mag/arcsec^2 (1-sigma surface brightness over a 1 arcsecond^2 area) and opens a new parameter regime to investigate the role of the cluster environment in shaping the properties of its galaxy population. After the Virgo cluster,Fornax is the second nearest galaxy cluster to us, and with its different mass and evolutionary state, it provides a valuable comparison that makes it possible to understand the various evolutionary effects on galaxies and galaxy clusters. Details about the survey can be found in A. Venhola, R. F. Peletier, E. Laurikainen et al., 2018, A&A 620, 165. In this release, 181 Gb of (compressed) fits files reduced using the system are present. Catalogues with the complete sample of sources including dwarf galaxies part of the cluster, globular clusters, and background galaxies will be provided in forthcoming releases. The data products are available via the ESO Science Portal at https://archive.eso. org/scienceportal/home? publ_date=2020-08-26
POISSON STRUCTURES ON MODULI SPACES OF HIGGS BUNDLES OVER STACKY CURVES

We demonstrate the construction of Poisson structures via Lie algebroids on moduli spaces of stable Higgs bundles over stacky curves. Considering specific cases of such Deligne-Mumford stacks, as well as of twistings, provides various important examples of moduli spaces that can be equipped with a Poisson structure. Special attention is given to moduli spaces of parabolic Higgs bundles and of Higgs bundles over root stacks.
Center-to-limb Variation of the Inverse Evershed Flow

We present the properties of the inverse Evershed flow (IEF) based on the center-to-limb variation of the plasma speed and loop geometry of chromospheric superpenumbral fibrils in eleven sunspots that were located at a wide range of heliocentric angles from 12° to 79°. The observations were acquired at the Dunn Solar Telescope in the spectral line of Hα at 656 nm to determine chromospheric flows and the photospheric Si i line at 1082.7 nm to estimate the photospheric umbral magnetic field strength. All sunspots display opposite line-of-sight (LOS) velocities on the limb and center side with a distinct shock signature near the outer penumbral edge. We developed a simplified flexible sunspot model assuming axisymmetry and prescribing the radial flow speed profile at a known loop geometry to replicate the observed two-dimensional IEF patterns under different viewing angles. The simulated flow maps match the observations for chromospheric loops with 10–20 Mm length starting at 0.8–1.1 sunspot radii, an apex height of 1–3 Mm, and a flow speed of 2–9 km s−1. We find on average a good agreement of the simulated velocities and the observations on elliptical annuli around the sunspot. Individual IEF channels show a significant range of variation in their properties and reach maximal LOS speeds of up to 12 km s−1. Upwards or downwards directed flows do not show a change of sign in the LOS velocities for heliocentric angles above 30°. Our results are consistent with the IEF being caused by a siphon flow mechanism driving a flow at about sonic speed along elevated loops with a flattened top in the chromosphere.
Electric Vehicle – Wireless Charging-Discharging Lane Decentralized Peer-to-Peer Energy Trading

This paper investigates the problem of bidirectional energy exchange between electric vehicles (EVs) and road lanes embedded with wireless power transfer technologies called wireless charging-discharging lanes (WCDLs). As such, EVs could provide better services to the grid, especially for balancing the energy supply-demand, while bringing convenience for EV users, because no cables and EV stops are needed. To enable this EV–WCDL energy exchange, a novel decentralized peer-to-peer (P2P) trading mechanism is proposed, in which EVs directly negotiate with a WCDL to reach consensus on the energy price and amounts to be traded. Those energy price and amounts are solutions of an optimization problem aiming at optimizing private cost functions of EVs and WCDL. The negotiation process between EVs and WCDL is secured by a privacy-preserving consensus protocol. Further, to assure successful trading with desired energy price and amounts, an analytical and systematic method is proposed to select cost function parameters by EVs and WCDL in a decentralized manner. Simulations are then carried out to validate the developed theoretical results, which confirm the effectiveness and scalability of the proposed algorithm.
Genetic Characterization of Insulin Growth Factor-1 and Its Receptor Genes in Egyptian Buffalo (Bubalus bubalis L.)

Aim: The somatotropic axis (SA) comprises genes associated with economically important quantitative traits in livestock like mammary and muscle growth as well as carcass traits. Insulin growth factor -1 (IGF-1) and its receptor ( IGF-1R) are two important genes belonging to the SA. The aim of this study was to evaluate the genetic polymorphism ofIGF1/SnaBI andIGF-1R/TaqIrestriction sites in Egyptian buffalo. Methodology :Genomic DNA was extracted from blood samples of 100 healthy buffaloes maintained at the Mahlet Mussa and ElGmeasa herds from 2010 to 2012. PCR was performed using primers flanking a 250 -bp fragment of the regulatory region of the buffalo IGF-1 gene and a616-bp fragment of theIGF-1R gene encompassing 51-bp from exon 12, 479-bp from intron 12 and 86bp from exon 13. The PCR-amplified fragments were digested withSnaBI (IGF-1) and TaqI (IGF-1R), electrophoresed and analyzed on agarose gels stained with ethi dium bromide.The two amplified fragments were also sequenced and aligned with published sequences. Results:All buffaloes investigated in this study were genotyped BB (i.e., negative for the SnaBI restriction site at position 224 ^225 (TAC^GTA) of theIGF-1regulatory region) and Research Article
On the Reliability of the PNU for Source Camera Identification Tasks

The PNU is an essential and reliable tool to perform SCI and, during the years, became a standard de-facto for this task in the forensic field. In this paper, we show that, although strategies exist that aim to cancel, modify, replace the PNU traces in a digital camera image, it is still possible, through our experimental method, to find residual traces of the noise produced by the sensor used to shoot the photo. Furthermore, we show that is possible to inject the PNU of a different camera in a target image and trace it back to the source camera, but only under the condition that the new camera is of the same model of the original one used to take the target image. Both cameras must fall within our availability. For completeness, we carried out 2 experiments and, rather than using the popular public reference dataset, CASIA TIDE, we preferred to introduce a dataset that does not present any kind of statistical artifacts. A preliminary experiment on a small dataset of smartphones showed that the injection of PNU from a different device makes it impossible to identify the source camera correctly. For a second experiment, we built a large dataset of images taken with the same model DSLR. We extracted a denoised version of each image, injected each one with the RN of all the cameras in the dataset and compared all with a RP from each camera. The results of the experiments, clearly, show that either in the denoised images and the injected ones is possible to find residual traces of the original camera PNU. The combined results of the experiments show that, even in theory is possible to remove or replace the \ac{PNU} from an image, this process can be, easily, detected and is possible, under some hard conditions, confirming the robustness of the \ac{PNU} under this type of attacks.
A Higher Order Parsimony Method to Reduce Long-Branch Attraction

Results are presented for the problem of reconstructing a phylogenetic tree for a quartet of four species from DNA strings for the species. For quartets, a notion of order of approximation is deﬁned. It is shown that choosing the topology for a given quartet by maximum parsimony (MP) is of order 2. A new method of order 3 is described. This method, called higher order parsimony (HOP), gives improved results on artiﬁcial data of nucleotides, greatly reducing the effect of long-branch attraction. Comparisons are made with MP, maximum likelihood (ML), and Hadamard conjugation on many artiﬁcial data sets chosen such that long-branch attraction would be a problem. Both HOP and ML are much more accurate than MP. The accuracies of HOP and ML appear comparable, with ML somewhat more accurate when the model used to generate substitutions closely matches that assumed by ML, and transitions dominate over transversions. When the model for substitutions differs substantially from that assumed by ML, then HOP may outperform ML, suggesting that HOP is more model-independent. The required computer time for HOP is, moreover, much shorter than that for ML, giving HOP a major practical advantage. HOP works best when the alphabet corresponds to sequences of nucleotides; hence, a modiﬁcation is also presented that applies when the alphabet corresponds to amino acid residues.
The Exact Low Energy Effective action of $\mathcal{N}=2$ SYM via Seiberg-Witten Theory and Localisation

In the mid 1990's Seiberg and Witten determined the exact low energy effective action of $\mathcal{N}=2$ supersymmetric Yang-Mills theory with gauge group $SU(2)$. Later, in the early 2000's Nekrasov calculated this action directly using localisation techniques. This work serves as an introduction to the area, developing both approaches and reconciling their results.
Assessing cosmic acceleration with the Alcock–Paczyński effect in the SDSS-IV quasar catalogue

The geometry of the Universe may be probed using the Alcock-Paczynski (AP) effect, in which the observed redshift size of a spherical distribution of sources relative to its angular size varies according to the assumed cosmological model. Past applications of this effect have been limited, however, by a paucity of suitable sources and mitigating astrophysical factors, such as internal redshift-space distortions and poorly known source evolution. In this Letter, we introduce a new test based on the AP effect that avoids the use of spatially bound systems, relying instead on sub-samples of quasars at redshifts z < 1.5 in the Sloan Digital Sky Survey IV, with a possible extension to higher redshifts and improved precision when this catalog is expanded by upcoming surveys. We here use this method to probe the redshift-dependent expansion rate in three pertinent Friedmann-Lemaitre-Robertson-Walker (FLRW) cosmologies: LCDM, which predicts a transition from deceleration to acceleration at z ~ 0.7; Einstein-de Sitter, in which the Universe is always decelerating; and the R_h=ct universe, which expands at a constant rate. LCDM is consistent with these data, but R_h=ct is favoured overall.
Hierarchical Deep Learning Ensemble to Automate the Classification of Breast Cancer Pathology Reports by ICD-O Topography

Like most global cancer registries, the National Cancer Registry in South Africa employs expert human coders to label pathology reports using appropriate International Classification of Disease for Oncology (ICD-O) codes spanning 42 different cancer types. The annotation is extensive for the large volume of cancer pathology reports the registry receives annually from public and private sector institutions. This manual process, coupled with other challenges results in a significant 4-year lag in reporting of annual cancer statistics in South Africa. We present a hierarchical deep learning ensemble method incorporating state of the art convolutional neural network models for the automatic labelling of 2201 de-identified, free text pathology reports, with appropriate ICD-O breast cancer topography codes across 8 classes. Our results show an improvement in primary site classification over the state of the art CNN model by greater than 14% for F1 micro and 55% for F1 macro scores. We demonstrate that the hierarchical deep learning ensemble improves on state-of-the-art models for ICD-O topography classification in comparison to a flat multiclass model for predicting ICD-O topography codes for pathology reports.
Geometric compression of invariant manifolds in neural networks

We study how neural networks compress uninformative input space in models where data lie in d dimensions, but the labels of which only vary within a linear manifold of dimension d ∥ < d. We show that for a one-hidden-layer network initialized with infinitesimal weights (i.e. in the feature learning regime) trained with gradient descent, the first layer of weights evolves to become nearly insensitive to the d ⊥ = d − d ∥ uninformative directions. These are effectively compressed by a factor λ∼p , where p is the size of the training set. We quantify the benefit of such a compression on the test error ϵ. For large initialization of the weights (the lazy training regime), no compression occurs and for regular boundaries separating labels we find that ϵ ∼ p −β , with β Lazy = d/(3d − 2). Compression improves the learning curves so that β Feature = (2d − 1)/(3d − 2) if d ∥ = 1 and β Feature = (d + d ⊥/2)/(3d − 2) if d ∥ > 1. We test these predictions for a stripe model where boundaries are parallel interfaces (d ∥ = 1) as well as for a cylindrical boundary (d ∥ = 2). Next, we show that compression shapes the neural tangent kernel (NTK) evolution in time, so that its top eigenvectors become more informative and display a larger projection on the labels. Consequently, kernel learning with the frozen NTK at the end of training outperforms the initial NTK. We confirm these predictions both for a one-hidden-layer fully connected network trained on the stripe model and for a 16-layer convolutional neural network trained on the Modified National Institute of Standards and Technology database (MNIST), for which we also find β Feature > β Lazy. The great similarities found in these two cases support the idea that compression is central to the training of MNIST, and puts forward kernel principal component analysis on the evolving NTK as a useful diagnostic of compression in deep networks.
Morphological differentiation between two populations of the Shemaya, Chalcalburnus chalcoides: a geometrical morphometric approach

Abstract Morphological differences (shape and size) among two populations of Shemaya (Danube Bleak), Chalcalburnus chalcoides,were studied by the geometric methods of shape PCA, thin-plate splines and multivariate analysis of partial warp scores. Measurements of Cartesian coordinates of 15 landmarks were obtained from 66 specimens from the Rivers Haraz and Shirud. For each specimen, centroid sizes were computed; sexual dimorphism and variation in size among sites were tested using t-test. Sexes differed significantly in size (P<0.001), with females being larger than males. Populations had significant size differences (P<0.001), with specimens from the River Shirud being larger than those from the River Haraz. Body shapes were significantly different between sexes (P<0.001) and populations (P<0.001), but interaction of sexes at sites were insignificant (P=358). TPS analysis showed a sexual dimorphism of shape, in particular the abdominal area, which appeared to be more enlarged in the females of the two populations. The canonical vitiate analysis CVA based on partial warps correctly identified all female specimens and 95% of male specimens at both locations. The results show thast the difference in body shape between the two populations is probably due to habitat influence (water flow speed).