Miracle-dz/UniFAR-FTUs huggingface dataset:
Fake News Spreader Detection on Twitter using Character N-Grams

The authors of fake news often use facts from verified news sources and mix them with misinformation to create confusion and provoke unrest among the readers. The spread of fake news can thereby have serious implications on our society. They can sway political elections, push down the stock price or crush reputations of corporations or public figures. Several websites have taken on the mission of checking rumors and allegations, but are often not fast enough to check the content of all the news being disseminated. Especially social media websites have offered an easy platform for the fast propagation of information. Towards limiting fake news from being propagated among social media users, the task of this year's PAN 2020 challenge lays the focus on the fake news spreaders. The aim of the task is to determine whether it is possible to discriminate authors that have shared fake news in the past from those that have never done it. In this notebook, we describe our profiling system for the fake news detection task on Twitter. For this, we conduct different feature extraction techniques and learning experiments from a multilingual perspective, namely English and Spanish. Our final submitted systems use character n-grams as features in combination with a linear SVM for English and Logistic Regression for the Spanish language. Our submitted models achieve an overall accuracy of 73% and 79% on the English and Spanish official test set, respectively. Our experiments show that it is difficult to differentiate solidly fake news spreaders on Twitter from users who share credible information leaving room for further investigations. Our model ranked 3rd out of 72 competitors.
Conformal bi-slant submersions

We study conformal bi-slant submersions from almost Hermitian manifolds onto Riemannian manifolds as a generalized of conformal anti-invariant, conformal semi-invariant, conformal semi-slant, conformal slant and conformal hemi-slant submersions. We investigated the integrability of distributions and obtain necessary and sufficient conditions for the maps to have totally geodesic fibers. Also we studied the total geodesicity of such maps.
A Survey on Semantic Parsing from the perspective of Compositionality

Different from previous surveys in semantic parsing (Kamath and Das, 2018) and knowledge base question answering(KBQA)(Chakraborty et al., 2019; Zhu et al., 2019; Hoffner et al., 2017) we try to takes a different perspective on the study of semantic parsing. Specifically, we will focus on (a)meaning composition from syntactical structure(Partee, 1975), and (b) the ability of semantic parsers to handle lexical variation given the context of a knowledge base (KB). In the following section after an introduction of the field of semantic parsing and its uses in KBQA, we will describe meaning representation using grammar formalism CCG (Steedman, 1996). We will discuss semantic composition using formal languages in Section 2. In section 3 we will consider systems that uses formal languages e.g. $\lambda$-calculus (Steedman, 1996), $\lambda$-DCS (Liang, 2013). Section 4 and 5 consider semantic parser using structured-language for logical form. Section 6 is on different benchmark datasets ComplexQuestions (Bao et al.,2016) and GraphQuestions (Su et al., 2016) that can be used to evaluate semantic parser on their ability to answer complex questions that are highly compositional in nature.
Sequential Reinforced 360-Degree Video Adaptive Streaming With Cross-User Attentive Network

In the tile-based 360-degree video streaming, predicting user’s future viewpoints and developing adaptive bitrate (ABR) algorithms are essential for optimizing user’s quality of experience (QoE). Traditional single-user based viewpoint prediction methods fail to achieve good performance in long-term prediction, and the recently proposed reinforcement learning (RL) based ABR schemes applied in traditional video streaming can not be directly applied in the tile-based 360-degree video streaming due to the exponential action space. Therefore, we propose a sequential reinforced 360-degree video streaming scheme with cross-user attentive network. Firstly, considering different users may have the similar viewing preference on the same video, we propose a cross-user attentive network (CUAN), boosting the performance of long-term viewpoint prediction by selectively utilizing cross-user information. Secondly, we propose a sequential RL-based (360SRL) ABR approach, transforming action space size of each decision step from exponential to linear via introducing a sequential decision structure. We evaluate the proposed CUAN and 360SRL using trace-driven experiments and experimental results demonstrate that CUAN and 360SRL outperform existing viewpoint prediction and ABR approaches with a noticeable margin.
Framework for Designing Filters of Spectral Graph Convolutional Neural Networks in the Context of Regularization Theory

Graph convolutional neural networks (GCNNs) have been widely used in graph learning. It has been observed that the smoothness functional on graphs can be defined in terms of the graph Laplacian. This fact points out in the direction of using Laplacian in deriving regularization operators on graphs and its consequent use with spectral GCNN filter designs. In this work, we explore the regularization properties of graph Laplacian and proposed a generalized framework for regularized filter designs in spectral GCNNs. We found that the filters used in many state-of-the-art GCNNs can be derived as a special case of the framework we developed. We designed new filters that are associated with well-defined regularization behavior and tested their performance on semi-supervised node classification tasks. Their performance was found to be superior to that of the other state-of-the-art techniques.
A Comparative Study of Deep Learning Loss Functions for Multi-Label Remote Sensing Image Classification

This paper analyzes and compares different deep learning loss functions in the framework of multi-label remote sensing (RS) image scene classification problems. We consider seven loss functions: 1) cross-entropy loss; 2) focal loss; 3) weighted cross-entropy loss; 4) Hamming loss; 5) Huber loss; 6) ranking loss; and 7) sparseMax loss. All the considered loss functions are analyzed for the first time in RS. After a theoretical analysis, an experimental analysis is carried out to compare the considered loss functions in terms of their: 1) overall accuracy; 2) class imbalance awareness (for which the number of samples associated to each class significantly varies); 3) convexibility and differentiability; and 4) learning efficiency (i.e., convergence speed). On the basis of our analysis, some guidelines are derived for a proper selection of a loss function in multi-label RS scene classification problems.
Bartonella spp. in Rats and Zoonoses

Bartonella spp. were detected in rats (Rattus norvegicus) trapped in downtown Los Angeles, California, USA. Of 200 rats tested, putative human pathogens, B. rochalimae and B. tribocorum were found in 37 (18.5%) and 115 (57.5%) rats, respectively. These bacteria among rodents in a densely populated urban area are a public health concern. B artonella spp. are vector-borne bacteria associated with an increasing array of emerging zoonotic infections in humans and animals (1). Some Bartonella spp. are widely distributed among small mammals in the United States and potentially cause human health concerns because these bacteria may be associated with human diseases (2). However, limited surveys have been conducted to identify infectious agents involved in zoonotic infections in rodents within urban areas of the United States (3). Norway rats (Rattus norvegicus) have been shown to harbor several Bartonella species, including B. tribocorum, B. elizabethae, B. rattimassiliensis, B. phoceensis, B. queenslandensis, and a strain closely related to B. rochalimae (3-6), of which B. elizabethae, B. rochalimae, and B. tribocorum were implicated in human diseases (7-11). Our study had 3 purposes. The fi rst purpose was to investigate prevalence of Bartonella spp. infections in rodent populations in downtown Los Angeles, California, USA. The second purpose was to evaluate genetic diversity of Bartonella spp. in blood of urban rats by analyzing variations of the citrate synthase (gltA) gene. The third purpose was to compare rates of detection of Bartonella spp. infections in rats between culture and molecular assays. The Study
Discovery of a Fast-expanding Shell in the Inside-out Born-again Planetary Nebula HuBi 1 through High-dispersion Integral Field Spectroscopy

HuBi 1 has been proposed to be member of the rare class of born-again planetary nebulae (PNe), i.e., its central star experienced a very late thermal pulse and ejected highly processed material at high speeds inside the old hydrogen-rich PN. In this Letter we present GTC MEGARA integral field spectroscopic observations of the innermost regions of HuBi 1 at high spectral resolution ≃16 km s−1 and multi-epoch subarcsecond images obtained ≃12 yr apart. The analysis of these data indicates that the inner regions of HuBi 1 were ejected ≃200 yr ago and expand at velocities ≃300 km s−1, in excellent agreement with the born-again scenario. The unprecedented tomographic capabilities of the GTC MEGARA high-dispersion observations used here reveal that the ejecta in HuBi 1 has a shell-like structure, in contrast to the disrupted disk and jet morphology of the ejecta in other born-again PNe.
The Most Ordinary Formation of the Most Unusual Double Black Hole Merger

The Laser Interferometer Gravitational-Wave Observatory (LIGO)/Virgo Collaboration reported the detection of the most massive black hole–black hole (BH–BH) merger to date with component masses of 85M⊙ and 66M⊙ (GW190521). Motivated by recent observations of massive stars in the 30 Doradus cluster in the Large Magellanic Cloud (M⋆ ≳ 200M⊙; e.g., R136a) and employing newly estimated uncertainties on pulsational pair-instability mass loss (that allow for the possibility of forming BHs with mass up to MBH ∼ 90M⊙), we show that it is trivial to form such massive BH–BH mergers through the classical isolated binary evolution (with no assistance from either dynamical interactions or exotica). A binary consisting of two massive (180M⊙ + 150M⊙) Population II stars (metallicity: Z ≈ 0.0001) evolves through a stable Roche lobe overflow and common envelope episode. Both exposed stellar cores undergo direct core collapse and form massive BHs while avoiding pair-instability pulsation mass loss or total disruption. LIGO/Virgo observations show that the merger rate density of light BH–BH mergers (both components: MBH < 50M⊙) is of the order of 10–100Gpc−3yr−1, while GW190521 indicates that the rate of heavier mergers is 0.02–0.43Gpc−3yr−1. Our model (with standard assumptions about input physics), but extended to include 200M⊙ stars and allowing for the possibility of stellar cores collapsing to 90M⊙ BHs, produces the following rates: 63Gpc−3yr−1 for light BH–BH mergers and 0.04Gpc−3yr−1 for heavy BH–BH mergers. We do not claim that GW190521 was formed by an isolated binary, but it appears that such a possibility cannot be excluded.
What if Neural Networks had SVDs?

Various Neural Networks employ time-consuming matrix operations like matrix inversion. Many such matrix operations are faster to compute given the Singular Value Decomposition (SVD). Previous work allows using the SVD in Neural Networks without computing it. In theory, the techniques can speed up matrix operations, however, in practice, they are not fast enough. We present an algorithm that is fast enough to speed up several matrix operations. The algorithm increases the degree of parallelism of an underlying matrix multiplication $H\cdot X$ where $H$ is an orthogonal matrix represented by a product of Householder matrices. Code is available at this http URL .
Python-based reduced order quadrature building code for fast gravitational wave inference

The low frequency sensitivity on the orders of a few Hz in future gravitational-wave observatories will enable the detection of gravitational wave signals of very long duration. The runtime of parameter estimation with these long waveforms can be months even years, which make it impractical with existing Bayesian inference pipelines. Reduced order modelling and reduced order quadrature integration rules have recently been exploited as promising techniques that can greatly reduce parameter estimation computational costs. We describe a Python-based reduced order quadrature building code named PyROQ that builds the reduced order quadrature data needed to accelerate the parameter estimation of gravitational waves. The infrastructure of the code is also directly applicable to gravitational wave inference for space-borne gravitational-wave detectors such as the Laser Interferometer Space Antenna (LISA). In addition, the techniques are broadly applicable to other research fields where fast Bayesian analysis is necessary.
Towards Text-Based Machine Translation

The increasing use of world wide networking facilities for international information exchange and service provision demands for specific applications of machine translation (MT) tools which are beyond their traditional tasks. The new areas of application of MT will have to be centered around various human-to-human communication media taking into account the different cultures inherent in natural language. Thus, MT has to aim for cross-cultural communication when being used as an information and service provider between different natural languages on the information super-highways. Most of the information electronically exchanged or accessed deals with a specialized vocabulary (terminology). In this paper we will argue for terminology-based MT and discuss the various knowledge sources, which will also ensure an appropriate basis for text-based MT. We have restricted our work to the domain of telecommunications, in particular that of satellite communication
Sequential Linearization Method for Bound-Constrained Mathematical Programs with Complementarity Constraints

We propose an algorithm for solving bound-constrained mathematical programs with complementarity constraints on the variables. Each iteration of the algorithm involves solving a linear program with complementarity constraints in order to obtain an estimate of the active set. The algorithm enforces descent on the objective function to promote global convergence to B-stationary points. We provide a convergence analysis and preliminary numerical results on a range of test problems. We also study the effect of fixing the active constraints in a bound-constrained quadratic program that can be solved on each iteration in order to obtain fast convergence.
Denoising convolutional neural networks for photoacoustic microscopy

Photoacoustic imaging is a new imaging technology in recent years, which combines the advantages of high resolution and rich contrast of optical imaging with the advantages of high penetration depth of acoustic imaging. Photoacoustic imaging has been widely used in biomedical fields, such as brain imaging, tumor detection and so on. The signal-to-noise ratio (SNR) of image signals in photoacoustic imaging is generally low due to the limitation of laser pulse energy, electromagnetic interference in the external environment and system noise. In order to solve the problem of low SNR of photoacoustic images, we use feedforward denoising convolutional neural network to further process the obtained images, so as to obtain higher SNR images and improve image quality. We use Python language to manage the referenced Python external library through Anaconda, and build a feedforward noise-reducing convolutional neural network on Pycharm platform.We first processed and segmated a training set containing 400 images, and then used it for network training. Finally, we tested it with a series of cerebrovascular photoacoustic microscopy images.The results show that the peak signal-to-noise ratio (PSNR) of the image increases significantly before and after denoising.The experimental results verify that the feed-forward noise reduction convolutional neural network can effectively improve the quality of photoacoustic microscopic images, which provides a good foundation for the subsequent biomedical research.
Network Analysis of the 2016 Presidential Campaign Tweets

We applied complex network analysis to ~27,000 tweets posted by the 2016 presidential election's principal participants in the USA. We identified the stages of the election campaigns and the recurring topics addressed by the candidates. Finally, we revealed the leader-follower relationships between the candidates. We conclude that Secretary Hillary Clinton's Twitter performance was subordinate to that of Donald Trump, which may have been one factor that led to her electoral defeat.
Dynamic sparsity on dynamic regression models

In the present work, we consider variable selection and shrinkage for the Gaussian dynamic linear regression within a Bayesian framework. In particular, we propose a novel method that allows for time-varying sparsity, based on an extension of spike-and-slab priors for dynamic models. This is done by assigning appropriate Markov switching priors for the time-varying coefficients' variances, extending the previous work of Ishwaran and Rao (2005). Furthermore, we investigate different priors, including the common Inverted gamma prior for the process variances, and other mixture prior distributions such as Gamma priors for both the spike and the slab, which leads to a mixture of Normal-Gammas priors (Griffin ad Brown, 2010) for the coefficients. In this sense, our prior can be view as a dynamic variable selection prior which induces either smoothness (through the slab) or shrinkage towards zero (through the spike) at each time point. The MCMC method used for posterior computation uses Markov latent variables that can assume binary regimes at each time point to generate the coefficients' variances. In that way, our model is a dynamic mixture model, thus, we could use the algorithm of Gerlach et al (2000) to generate the latent processes without conditioning on the states. Finally, our approach is exemplified through simulated examples and a real data application.
Deep discriminant analysis for task-dependent compact network search

Most of today's popular deep architectures are hand-engineered for general purpose applications. However, this design procedure usually leads to massive redundant, useless, or even harmful features for specific tasks. Such unnecessarily high complexities render deep nets impractical for many real-world applications, especially those without powerful GPU support. In this paper, we attempt to derive task-dependent compact models from a deep discriminant analysis perspective. We propose an iterative and proactive approach for classification tasks which alternates between (1) a pushing step, with an objective to simultaneously maximize class separation, penalize co-variances, and push deep discriminants into alignment with a compact set of neurons, and (2) a pruning step, which discards less useful or even interfering neurons. Deconvolution is adopted to reverse `unimportant' filters' effects and recover useful contributing sources. A simple network growing strategy based on the basic Inception module is proposed for challenging tasks requiring larger capacity than what the base net can offer. Experiments on the MNIST, CIFAR10, and ImageNet datasets demonstrate our approach's efficacy. On ImageNet, by pushing and pruning our grown Inception-88 model, we achieve better-performing models than smaller deep Inception nets grown, residual nets, and famous compact nets at similar sizes. We also show that our grown deep Inception nets (without hard-coded dimension alignment) can beat residual nets of similar complexities.
Two-Hop Multi-UAV Relay Network Optimization with Directional Antennas

In this paper, we consider the multi-UAV deployment problem for a two-hop relaying system. For a better network performance, UAVs carry directional antennas that are modeled by a realistic radiation pattern. The goal is to maximize the minimum user rates, and therefore achieve fairness in the network. We propose an iterative algorithm to optimize the TDMA scheduling in both hops, UAV trajectories, antenna beamwidths, and transmit power of the base station and relays. Simulation results show the throughput improvement as a result of optimizing the directional antenna radiation patterns. In addition, we derive the optimal power allocation, which combined with the beamwidth optimization yields to a much better performance.
Sampled-data in Space Control of Scalar Semilinear Parabolic and Hyperbolic Systems

The paper describes a novel method of sampled-data in space (spatial variable) control of scalar semilinear systems of parabolic and hyperbolic type with unknown parameters and distributed disturbances. A finite set of sampled-data in the spatial variable measurements is available. The control law depends on the function which depends on the spatial variable and on a finite set of state measurements. A special choice of this function can affect on some properties of the closed-loop system. In particular, the paper describes the examples of this function that provides reduced control costs in comparison with some other control methods. The exponential stability of the closed-loop system and robustness with respect to unknown parameters and disturbances is proposed in terms of linear matrix inequalities (LMIs). The simulations confirm theoretical results and show the efficiency of the proposed control law compared with some existing ones.
Characterization of Leucyl -Trna Synthetase From Homo Sapiens and Escherichia Coli in Aminoacylation, Amino Acid Editing and Interdomain Interactions

Nawaz, M. H. , Pang Y. L. J., Hsu, J. L. and Martinis, S. A. Molecular and functional dissection of a putative RNA binding region of yeast mitochondrial leucyl-tRNA synthetase, 21 International tRNA workshop, Bangalore, India, December 2, 2005. Pang Y. L. J. and Martinis, S. A. Paradigm shift for the amino acid editing mechanism of human cytoplasmic leucyl-tRNA synthetase, 23 International tRNA workshop, Aveiro, Portugal, January 28, 2010
A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

Modern radar systems have high requirements in terms of accuracy, robustness and real-time capability when operating on increasingly complex electromagnetic environments. Traditional radar signal processing (RSP) methods have shown some limitations when meeting such requirements, particularly in matters of target classification. With the rapid development of machine learning (ML), especially deep learning, radar researchers have started integrating these new methods when solving RSP-related problems. This paper aims at helping researchers and practitioners to better understand the application of ML techniques to RSP-related problems by providing a comprehensive, structured and reasoned literature overview of ML-based RSP techniques. This work is amply introduced by providing general elements of ML-based RSP and by stating the motivations behind them. The main applications of ML-based RSP are then analysed and structured based on the application field. This paper then concludes with a series of open questions and proposed research directions, in order to indicate current gaps and potential future solutions and trends.