Miracle-dz/UniFAR-FTUs huggingface dataset:
Team QCRI-MIT at SemEval-2019 Task 4: Propaganda Analysis Meets Hyperpartisan News Detection

We describe our submission to SemEval-2019 Task 4 on Hyperpartisan News Detection. We rely on a variety of engineered features originally used to detect propaganda. This is based on the assumption that biased messages are propagandistic and promote a particular political cause or viewpoint. In particular, we trained a logistic regression model with features ranging from simple bag of words to vocabulary richness and text readability. Our system achieved 72.9% accuracy on the manually annotated testset, and 60.8% on the test data that was obtained with distant supervision. Additional experiments showed that significant performance gains can be achieved with better feature pre-processing.
Polarization of changing-look quasars

If the disappearance of the broad emission lines observed in changing-look quasars originates from the obscuration of the quasar core by dusty clouds moving in the torus, high linear optical polarization would be expected in those objects. We then measured the rest-frame UV-blue linear polarization of a sample of 13 changing-look quasars, 7 of them being in a type 1.9-2 state. For all quasars but one the polarization degree is lower than 1%. This suggests that the disappearance of the broad emission lines cannot be attributed to dust obscuration, and supports the scenario in which changes of look are caused by a change in the rate of accretion onto the supermassive black hole. Such low polarization degrees also indicate that these quasars are seen under inclinations close to the system axis. One type 1.9-2 quasar in our sample shows a high polarization degree of 6.8%. While this polarization could be ascribed to obscuration by a moving dusty cloud, we argue that this is unlikely given the very long time needed for a cloud from the torus to eclipse the broad emission line region of that object. We propose that the high polarization is due to the echo of a past bright phase seen in polar-scattered light. This interpretation raises the possibility that broad emission lines observed in the polarized light of some type 2 active galactic nuclei can be echoes of past type 1 phases and not evidence of hidden broad emission line regions.
Cross-Corpora Evaluation and Analysis of Grammatical Error Correction Models — Is Single-Corpus Evaluation Enough?

This study explores the necessity of performing cross-corpora evaluation for grammatical error correction (GEC) models. GEC models have been previously evaluated based on a single commonly applied corpus: the CoNLL-2014 benchmark. However, the evaluation remains incomplete because the task difficulty varies depending on the test corpus and conditions such as the proficiency levels of the writers and essay topics. To overcome this limitation, we evaluate the performance of several GEC models, including NMT-based (LSTM, CNN, and transformer) and an SMT-based model, against various learner corpora (CoNLL-2013, CoNLL-2014, FCE, JFLEG, ICNALE, and KJ). Evaluation results reveal that the models’ rankings considerably vary depending on the corpus, indicating that single-corpus evaluation is insufficient for GEC models.
PHD Thesis: Existence, Uniqueness & Explicit Bounds for Scattering By Rough Surfaces

We study 4 problems in the area of scattering of time harmonic acoustic or electromagnetic waves by unbounded rough surfaces/inhomogeneous layers. Specifically we study: i) a boundary value problem (BVP) for the Helmholtz equation, in both 2 and 3 dimensions, modelling scattering of time harmonic waves due to a source that lies within a finite distance of the boundary and which decays along the boundary, by a layer of spatially varying refractive index above an unbounded rough surface on which the field vanishes; ii) a BVP for the Helmholtz equation with an impedance boundary condition, in 2 and 3 dimensions, modelling the scattering of time harmonic acoustic waves due to a source that lies within a finite distance of the boundary and which decays along the boundary, by an unbounded rough impedance surface; iii) a problem of scattering of time harmonic waves by a layer of spatially varying refractive index at the interface between semi-infinite half-spaces of fixed positive refractive index (the waves arising due to a source that lies within a finite distance of the layer and which decays along the layer); iv) a BVP for the Helmholtz equation with a Dirichlet boundary condition, in 3 dimensions, modelling the scattering of time harmonic acoustic waves due to a point source, by an unbounded, rough, sound soft surface. We study i), ii) and iii) by variational methods; via analysis of equivalent variational formulations we prove these problems to be well-posed under various restrictions on the surface (must be Lipschitz), the frequency, the rate of change of the refractive index etc. We study iv) via a Brakhage-Werner type integral equation formulation, based on an ansatz for the solution as a combined single- and double-layer potential. We establish that when the rough surface is the graph of a Lipschitz function, the problem is well-posed for arbitrary frequency.
3D Radiation Hydrodynamics of a Dynamical Torus

We have developed a new dynamical model of the torus region in active galactic nuclei (AGNs), using a three-dimensional radiation hydrodynamics algorithm. These new simulations have the specific aim to explore the role of radiatively driven outflows, which is hotly debated in current literature as a possible explanation for the observed IR emission from the polar regions of AGNs. In this first paper, we only consider radiative effects induced by the primary radiation from the AGN. The simulations generate a disk and outflow structure that qualitatively agrees with observations, although the outflow is radial rather than polar, likely due to the lack of radiation pressure from hot dust. We find cutoffs between the wind and disk at gas temperatures of 1000 K and dust temperatures of 100 K, producing kinematic signatures that can be used for interpretation of high-resolution IR observations. We also produce line emission maps to aid in the interpretation of recent ALMA observations and future James Webb Space Telescope observations. We investigate a number of simulation parameters and find that the anisotropy of the radiation field is equally important to the Eddington factor, despite the anisotropy often being assumed to have a single, sometimes arbitrary form in many previous works. We also find that supernovae can have a small but significant impact, but only at extremely high star formation rates.
Space-time approximation of stochastic $p$-Laplace systems

We consider systems of stochastic evolutionary equations of the $p$-Laplace type. We establish convergence rates for a finite-element based space-time approximation, where the error is measured in a suitable quasi-norm. Under natural regularity assumptions on the solution, our main result provides linear convergence in space and convergence of order $\alpha$ in time for all $\alpha\in(0,\frac{1}{2})$. The key ingredient of our analysis is a random time-grid, which allows us to compensate for the lack of time regularity.
Policy Gradient Search: Online Planning and Expert Iteration without Search Trees

Monte Carlo Tree Search (MCTS) algorithms perform simulation-based search to improve policies online. During search, the simulation policy is adapted to explore the most promising lines of play. MCTS has been used by state-of-the-art programs for many problems, however a disadvantage to MCTS is that it estimates the values of states with Monte Carlo averages, stored in a search tree; this does not scale to games with very high branching factors. We propose an alternative simulation-based search method, Policy Gradient Search (PGS), which adapts a neural network simulation policy online via policy gradient updates, avoiding the need for a search tree. In Hex, PGS achieves comparable performance to MCTS, and an agent trained using Expert Iteration with PGS was able defeat MoHex 2.0, the strongest open-source Hex agent, in 9x9 Hex.
Impact of Perceived Value on Customer Satisfaction and Continuance Intention of Bicycle Sharing Service

Bicycle sharing service has been identified as one of the most significant applications in the sharing economy, yet few studies have explored its value stream from a customer perception perspective. Drawing upon value creation theoretical framework, this study proposes a research model to examine the impact mechanism of four value streams on customer satisfaction and continuance intention of bicycle sharing service. An empirical study was conducted and 293 valid data was collected from users of two leading bicycle-sharing platforms in China. Structural equation modeling technique was used to examine the research model. The empirical results suggest that emotional value is the most significant antecedent of customer satisfaction and continuance intention, followed by functional value and economic value. While environmental value has a positive but weaker influence on customer satisfaction. A post-hoc analysis further suggests that gender difference exists on the association between the four value streams and customer satisfaction.
Towards Generalized Speech Enhancement with Generative Adversarial Networks

The speech enhancement task usually consists of removing additive noise or reverberation that partially mask spoken utterances, affecting their intelligibility. However, little attention is drawn to other, perhaps more aggressive signal distortions like clipping, chunk elimination, or frequency-band removal. Such distortions can have a large impact not only on intelligibility, but also on naturalness or even speaker identity, and require of careful signal reconstruction. In this work, we give full consideration to this generalized speech enhancement task, and show it can be tackled with a time-domain generative adversarial network (GAN). In particular, we extend a previous GAN-based speech enhancement system to deal with mixtures of four types of aggressive distortions. Firstly, we propose the addition of an adversarial acoustic regression loss that promotes a richer feature extraction at the discriminator. Secondly, we also make use of a two-step adversarial training schedule, acting as a warm up-and-fine-tune sequence. Both objective and subjective evaluations show that these two additions bring improved speech reconstructions that better match the original speaker identity and naturalness.
On Near-cloaking for Linear Elasticity

We make precise some results on the cloaking of displacement fields in linear elasticity. In the spirit of transformation media theory, the transformed governing equations in Cosserat and Willis frameworks are shown to be equivalent to certain high contrast small defect problems for the usual Navier equations. We discuss near-cloaking for elasticity systems via a regularized transform and perform numerical experiments to illustrate our near-cloaking results. We also study the sharpness of the estimates from [H. Ammari, H. Kang, K. Kim and H. Lee, J. Diff. Eq. 254, 4446-4464 (2013)], wherein the convergence of the solutions to the transmission problems is investigated, when the Lame parameters in the inclusion tend to extreme values. Both soft and hard inclusion limits are studied and we also touch upon the finite frequency case. Finally, we propose an approximate isotropic cloak algorithm for a symmetrized Cosserat cloak.
DISCREPANCY OF A CONVEX SET WITH ZERO CURVATURE AT ONE POINT

Let $\Omega \subset \mathbb{R}^{d}$ be a convex body with everywhere positive curvature except at the origin and with the boundary $\partial \Omega$ as the graph of the function $y=|x|^{\gamma}$ in a neighborhood of the origin with $\gamma \geq 2$. We consider the $L^{p}$ norm of the discrepancy with respect to translations and rotations of a dilated copy of the set $\Omega$.
The Relationship between Periodontitis and Oral Health Literacy among the Older People in Thailand

OBJECTIVE: The purpose of this study was to evaluate the relationship between periodontitis and oral health literacy among the older population in Thailand. MATERIAL & METHODS: This cross-sectional study was performed between July 1 and September 30, 2020, in Panare district, Pattani province, Thailand. The inclusion criteria were older individuals more than 60 years of age who had at least six remaining teeth. Information on sociodemographic characteristics and oral health-related behaviors were collected using a self-reported questionnaire. Oral health literacy was categorized using the Thai version of the Health Literacy in Dentistry scale (HeLD‐Th). A trained examiner performed clinical periodontal examinations. The data were analyzed using the Mann-Whitney U test, Fisher's exact test, and binary logistic regression analysis. RESULTS: A total of 216 independently living older adults participated and completed the study protocol. The initial analyses indicated significant associations between severe periodontitis and low oral health literacy (p = 0.029) and insufficient toothbrushing duration (p < 0.001). However, in multivariate analysis, only toothbrushing duration showed significant association (p = 0.003). CONCLUSIONS: Oral health literacy interventions and oral hygiene practices for improving periodontal health status among the Thai older adults are necessary.
Neo: A Learned Query Optimizer

Query optimization is one of the most challenging problems in database systems. Despite the progress made over the past decades, query optimizers remain extremely complex components that require a great deal of hand-tuning for specific workloads and datasets. Motivated by this shortcoming and inspired by recent advances in applying machine learning to data management challenges, we introduce Neo ( Neural Optimizer ), a novel learning-based query optimizer that relies on deep neural networks to generate query executions plans. Neo bootstraps its query optimization model from existing optimizers and continues to learn from incoming queries, building upon its successes and learning from its failures. Furthermore, Neo naturally adapts to underlying data patterns and is robust to estimation errors. Experimental results demonstrate that Neo, even when bootstrapped from a simple optimizer like PostgreSQL, can learn a model that offers similar performance to state-of-the-art commercial optimizers, and in some cases even surpass them.
Institutional Logics and Digital Collective Action at Amnesty International - the Decoder Initiative

As social movement organizations (SMOs) enter the platform economy to transform their processes and scale their impact, they have to reckon their traditional logic with the emerging logic of the platform organization. In this paper we examine the Decoder initiative at Amnesty International. The Decoder initiative introduced the global SMO into the uncharted territory of online microtasking of political activism. The platform allowed rapid scaling of repetitive unstructured data tasks to generate large quantities of standardized data, leveraging the work of thousands previously unaffiliated digital supporters around the world. While partially contrasting, we trace and examine the coexistence of the SMO and digital platform logics. We conclude with implications for theory and practice.
Revisiting Metastable Dark Energy and Tensions in the Estimation of Cosmological Parameters

We investigate constraints on some key cosmological parameters by confronting metastable dark energy (DE) models with different combinations of the most recent cosmological observations. Along with the standard ΛCDM model, two phenomenological metastable DE models are considered: (i) DE decays exponentially, (ii) DE decays into dark matter. We find that: (1) when considering the most recent supernovae and BAO data, and assuming a fiducial ΛCDM model, the inconsistency in the estimated value of the parameter obtained by either including or excluding Planck cosmic microwave background (CMB) data becomes very much substantial and points to a clear tension; (2) although the two metastable DE models that we study provide greater flexibility in fitting the data, and they indeed fit the supernovae (SNe) Ia+BAO data substantially better than ΛCDM, they are not able to alleviate this tension significantly when CMB data are included; (3) while local measurements of the Hubble constant are significantly higher relative to the estimated value of H0 in our models (obtained by fitting to SNe Ia and BAO data), the situation seems to be rather complicated with hints of inconsistency among different observational data sets (CMB, SNe Ia+BAO, and local H0 measurements). Our results indicate that we might not be able to remove the current tensions among different cosmological observations by considering simple modifications of the standard model or by introducing minimal DE models. A complicated form of expansion history, different systematics in different data and/or a nonconventional model of the early universe might be responsible for these tensions.
Radiation pattern and outflow geometry: a new probe of black hole spin?

We explore the impact of the central black hole (BH) spin on the large-scale properties of the host galaxy, by considering radiative feedback. The BH spin determines the radiation pattern from the accretion disc, which directly imprints on the geometry of the radiation-driven outflows. We show that for low BH spins, the emission is vertically focused, giving rise to polar/prolate outflows; while for high BH spins, the radiation pattern is more isotropic, leading to quasi-spherical/oblate outflows. Reversing the argument, we can potentially deduce the spin of the central BH from the observed morphology of galactic outflows. In principle, this may provide a novel way of constraining the central BH spin from galaxy-scale observations. Indeed, the BH spin can have significant macroscopic effects on galactic scales, ultimately shaping the large-scale feedback and the resulting obscuration.
Tracial moment problems on hypercubes

In this paper we introduce the "tracial $K$-moment problem" and the "sequential matrix-valued $K$-moment problem" and show the equivalence of the solvability of these problems. Using a Haviland's theorem for matrix polynomials, we solve these $K$-moment problems for the case where $K$ is the hypercube $[-1,1]^n$.
DeepSEED: 3D Squeeze-and-Excitation Encoder-Decoder ConvNets for Pulmonary Nodule Detection

Pulmonary nodule detection plays an important role in lung cancer screening with low-dose computed tomography (CT) scans. Although promising performance has been achieved by deep learning based nodule detection methods, it remains challenging to build nodule detection networks with good generalization performance due to unbalanced positive and negative samples. In order to overcome this problem and further improve state-of-the-art region proposal network methods, we develop a novel deep 3D convolutional neural network with an Encoder-Decoder structure for pulmonary nodule detection. Particularly, we utilize a dynamically scaled cross entropy loss to reduce the false positive rate and compensate the significant data imbalance problem. We adopt the squeeze-and-excitation structure to learn effective image features and fully utilize channel inter-dependency. We have validated our method based on publicly available CT scans from LIDC/IDRI dataset and its subset LUNA16 with thinner slices. Ablation studies and experimental results have demonstrated that our method could outperform state-of-the-art nodule detection methods by a large margin, with an average FROC score of 86.2% on LUNA16, and an average FROC score of 77.3% on LIDC/IDRI dataset when trained on LUNA16 only.
Cohomological induction and uniform measure equivalence

We construct a general cohomological induction isomorphism from a uniform measure equivalence of locally compact, second countable, unimodular groups which, as a special case, yields that the graded cohomology rings of quasi-isometric, connected, simply connected, nilpotent Lie groups are isomorphic. This unifies results of Shalom and Sauer and also provides new insight into the quasi-isometry classification problem for low dimensional nilpotent Lie groups.
A p‐adic analogue of Siegel's theorem on sums of squares

Siegel proved that every totally positive element of a number field K is the sum of four squares, so in particular the Pythagoras number is uniformly bounded across number fields. The p‐adic Kochen operator provides a p‐adic analogue of squaring, and a certain localisation of the ring generated by this operator consists of precisely the totally p‐integral elements of K. We use this to formulate and prove a p‐adic analogue of Siegel's theorem, by introducing the p‐Pythagoras number of a general field, and showing that this number is uniformly bounded across number fields. We also generally study fields with finite p‐Pythagoras number and show that the growth of the p‐Pythagoras number in finite extensions is bounded.
Interactive effect of silicon and mycorrhizal inoculation on growth, yield and water productivity of rice under water-deficit stress

Abstract Drought is a major abiotic stress limiting growth and productivity of field and horticultural crops. Exogenous application of silicon (Si) and arbuscular mycorrhizal fungi (AMF) inoculation has the potential to alleviate the adverse effect of drought stress on plant. A factorial experiment consisting of four monosilicic acid (MSA) doses (0, 75, 150 and 300 kg ha−1), two levels of AMF inoculation (inoculation of AMF [+AMF] and without inoculation of AMF [–AMF]) and three soil moisture regimes (100%, 75% and 50% field capacity [FC]) was conducted to evaluate the effect of Si and AMF inoculation on growth, yield and water productivity of rice under water-deficit stress. The soil moisture regimes of 75% and 50% FC indicate a 25% and 50% depletion of the maximum amount of water held in the soil, respectively, whereas 100% FC refers to the maximum amount of water held in the soil after gravitational water drainage stops. An overall reduction in growth and yield of rice was observed with decreasing soil moisture regime; however, application of 300 kg MSA ha−1 increased shoot dry matter by 28% compared with 0 kg MSA ha−1 under limited soil moisture availability of 75% and 50% FC. Similarly, grain yield was increased by 37% and 39% at 300 kg MSA ha−1 compared with the control in the –AMF and + AMF plants, respectively, under moderate soil moisture level of 75% FC. The corresponding increase at 50% FC was 52% and 55%, respectively. The present results demonstrated a synergistic effect of exogenous application of MSA at 300 kg ha−1 (60 kg ha−1 soluble Si) and AMF inoculation on rice under water-deficit stress.
EvoCreeper: Automated Black-Box Model Generation for Smart TV Applications

Smart TVs are coming to dominate the television market. This accompanied by an increase in the use of the smart TV applications (apps). Due to the increasing demand, developers need modeling techniques to analyze these apps and assess their comprehensiveness, completeness, and quality. In this paper, we present an automated strategy for generating models of smart TV apps based on a black-box reverse engineering. The strategy can be used to cumulatively construct a model for a given app by exploring the user interface in a manner consistent with the use of a remote control device and extracting the runtime information. The strategy is based on capturing the states of the user interface to create a model during runtime without any knowledge of the internal structure of the app. We have implemented our strategy in a tool called EvoCreeper. The evaluation results show that our strategy can automatically generate unique states and a comprehensive model that represents the real user interactions with an app using a remote control device. The models thus generated can be used to assess the quality and completeness of smart TV apps in various contexts, such as the control of other consumer electronics in smart houses.