Miracle-dz/UniFAR-FTUs huggingface dataset:
Transfer Learning Approach for Arabic Offensive Language Detection System - BERT-Based Model

Developing a system to detect online offensive language is very important to the health and the security of online users. Studies have shown that cyberhate, online harassment and other misuses of technology are on the rise, particularly during the global Coronavirus pandemic in 2020. According to the latest report by the Anti-Defamation League (ADL), 35% of online users reported online harassment related to their identity-based characteristics, which is a 3% increase over 2019. Applying advanced techniques from the Natural Language Processing (NLP) field to support the development of an online hate-free community is a critical task for social justice. Transfer learning enhances the performance of the classifier by allowing the transfer of knowledge from one domain or one dataset to others that have not been seen before, thus, supporting the classifier to be more generalizable. In our study, we apply the principles of transfer learning cross multiple Arabic offensive language datasets to compare the effects on system performance. This study aims at investigating the effects of fine-tuning and training Bidirectional Encoder Representations from Transformers (BERT) model on multiple Arabic offensive language datasets individually and testing it using other datasets individually. Our experiment starts with a comparison among multiple BERT models to guide the selection of the main model that is used for our study. The study also investigates the effects of concatenating all datasets to be used for fine-tuning and training BERT model. Our results demonstrate the limited effects of transfer learning on the performance of the classifiers, particularly for highly dialectic comments.
Cloud point extraction of trace cyanide from environmental waters for indirect flame atomic absorption spectrometric determination

A method has been developed for indirect determination of cyanide in environmental waters based on cloud point extraction (CPE), preconcentration and determination by flame atomic absorption spectrometry (FAAS). The method was based on reduction of Cu(II) to Cu(I) in the presence of cyanide and complexation of the produced Cu(I)(CN)2− with gallocyanin (GC+) as an ion-pairing reagent at pH 4.0, followed by its extraction into polyethyleneglycol mono-p-nonylphenylether (Ponpe 7.5). Selectivity was improved with the use of suitable masking agents. Various factors influencing separation and preconcentration of cyanide have been investigated, and conditions were optimized, allowing determination of cyanide in the range of up to 1.2 mg L−1 with a detection limit of 0.00045 mg L−1. The method has been applied to the determination of cyanide in environmental waters, the results being in agreement with those obtained by a reference method.
Robust Policy Gradient against Strong Data Corruption

We study the problem of robust reinforcement learning under adversarial corruption on both rewards and transitions. Our attack model assumes an \textit{adaptive} adversary who can arbitrarily corrupt the reward and transition at every step within an episode, for at most $\epsilon$-fraction of the learning episodes. Our attack model is strictly stronger than those considered in prior works. Our first result shows that no algorithm can find a better than $O(\epsilon)$-optimal policy under our attack model. Next, we show that surprisingly the natural policy gradient (NPG) method retains a natural robustness property if the reward corruption is bounded, and can find an $O(\sqrt{\epsilon})$-optimal policy. Consequently, we develop a Filtered Policy Gradient (FPG) algorithm that can tolerate even unbounded reward corruption and can find an $O(\epsilon^{1/4})$-optimal policy. We emphasize that FPG is the first that can achieve a meaningful learning guarantee when a constant fraction of episodes are corrupted. Complimentary to the theoretical results, we show that a neural implementation of FPG achieves strong robust learning performance on the MuJoCo continuous control benchmarks.
Conformal symplectic Weinstein conjecture and non-squeezing

We study here, from the Gromov-Witten theory point of view, some aspects of rigidity of locally conformally symplectic manifolds, or $\lcs$ manifolds for short, which are a natural generalization of both contact and symplectic manifolds. In particular, we give a first known analogue of the classical Gromov non-squeezing in lcs geometry. Another possible version of non-squeezing related to contact non-squeezing is also discussed. In a different direction we study Gromov-Witten theory of the $\lcs$ manifold $C \times S ^{1} $ induced by a contact form $\lambda$ on $C$, and show that the extended Gromov-Witten invariant counting certain charged elliptic curves in $C \times S ^{1} $ is identified with the extended classical Fuller index of the Reeb vector field $R ^{\lambda} $, by extended we mean that these invariants can be $\pm \infty$-valued. Partly inspired by this, we conjecture existence of certain 1-d curves we call Reeb curves in certain $\lcs$ manifolds, which we call conformal symplectic Weinstein conjecture, and this is a direct extension of the classical Weinstein conjecture. Also using Gromov-Witten theory, we show that the CSW conjecture holds for a $C ^{3} $- neighborhood of the induced lcs form on $C \times S ^{1} $, for $C$ a contact manifold with contact form whose Reeb flow has non-zero extended Fuller index, e.g. $S ^{2k+1} $ with standard contact form, for which this index is $\pm \infty$. We also show that in some cases the failure of this conjecture implies existence of sky catastrophes for families of holomorphic curves in a $\lcs$ manifold. No examples of the latter phenomenon are known to exist, even in the un-tamed almost complex world, this phenomenon if it exists, would be analogous to sky catastrophes in dynamical systems discovered by Fuller.
Approximation Methods for Geometric Regulation

In these notes we collect some results from several of the authors' works in order to make available a single source and show how the approximate geometric methods for regulation have been developed, and how the control design strategy has evolved from the theoretical methods, involving the regulator equations, to what we now call the regularized controller. In between these two extremes we developed, in a series of works, a fairly rigorous analysis of the regularization scheme leading to the regularized dynamic regulator equations and an iterative scheme that produces very accurate tracking and disturbance rejection control laws. In our most recent work we have extended dynamic regulator equations to what we now refer to as the regularized controller. This new formulation has only recently being applied to examples including linear and nonlinear delay equations.
Graph-Based Equilibrium Metrics for Dynamic Supply–Demand Systems With Applications to Ride-sourcing Platforms

Abstract How to dynamically measure the local-to-global spatio-temporal coherence between demand and supply networks is a fundamental task for ride-sourcing platforms, such as DiDi. Such coherence measurement is critically important for the quantification of the market efficiency and the comparison of different platform policies, such as dispatching. The aim of this paper is to introduce a graph-based equilibrium metric (GEM) to quantify the distance between demand and supply networks based on a weighted graph structure. We formulate GEM as the optimal objective value of an unbalanced optimal transport problem, which can be formulated as an equivalent linear programming and efficiently solved. We examine how the GEM can help solve three operational tasks of ride-sourcing platforms. The first one is that GEM achieves up to 70.6% reduction in root-mean-square error over the second-best distance measurement for the prediction accuracy of order answer rate. The second one is that the use of GEM for designing order dispatching policy increases drivers’ revenue for more than 1%, representing a huge improvement in number. The third one is that GEM can serve as an endpoint for comparing different platform policies in AB test. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
Modeling 3D Surface Manifolds with a Locally Conditioned Atlas

Recently proposed 3D object reconstruction methods represent a mesh with an atlas - a set of planar patches approximating the surface. However, their application in a real-world scenario is limited since the surfaces of reconstructed objects contain discontinuities, which degrades the quality of the final mesh. This is mainly caused by independent processing of individual patches, and in this work, we postulate to mitigate this limitation by preserving local consistency around patch vertices. To that end, we introduce a Locally Conditioned Atlas (LoCondA), a framework for representing a 3D object hierarchically in a generative model. Firstly, the model maps a point cloud of an object into a sphere. Secondly, by leveraging a spherical prior, we enforce the mapping to be locally consistent on the sphere and on the target object. This way, we can sample a mesh quad on that sphere and project it back onto the object's manifold. With LoCondA, we can produce topologically diverse objects while maintaining quads to be stitched together. We show that the proposed approach provides structurally coherent reconstructions while producing meshes of quality comparable to the competitors.
On the variation of bi‐periodic waves in the transverse direction

Weakly nonlinear, bi‐periodic patterns of waves that propagate in the x ‐direction with amplitude variation in the y ‐direction are generated in a laboratory. The amplitude variation in the y ‐direction is studied within the framework of the vector (vNLSE) and scalar (sNLSE) nonlinear Schrödinger equations using the uniform‐amplitude, Stokes‐like solution of the vNLSE and the Jacobi elliptic sine function solution of the sNLSE. The wavetrains are generated using the Stokes‐like solution of vNLSE; however, a comparison of both predictions shows that while they both do a reasonably good job of predicting the observed amplitude variation in y , the comparison with the elliptic function solution of the sNLSE has significantly less error when the ratio of y ‐wavenumber to the two‐dimensional wavenumber is less than about 0.25. For ratios between about 0.25 and 0.30 (the limit of the experiments), the two models have comparable errors. When the ratio is less than about 0.17, agreement with the vNLSE solution requires a third‐harmonic term in the y ‐direction, obtained from a Stokes‐type expansion of interacting, symmetric wavetrains. There is no evidence of instability growth in the x ‐direction, consistent with the work of Segur and colleagues, who showed that dissipation stabilizes the modulational instability. Finally, there is some extra amplitude variation in y , which is examined via a qualitative stability calculation that allows symmetry breaking in that direction.
High-Performance Large-Scale Image Recognition Without Normalization

Batch normalization is a key component of most image classification models, but it has many undesirable properties stemming from its dependence on the batch size and interactions between examples. Although recent work has succeeded in training deep ResNets without normalization layers, these models do not match the test accuracies of the best batch-normalized networks, and are often unstable for large learning rates or strong data augmentations. In this work, we develop an adaptive gradient clipping technique which overcomes these instabilities, and design a significantly improved class of Normalizer-Free ResNets. Our smaller models match the test accuracy of an EfficientNet-B7 on ImageNet while being up to 8.7x faster to train, and our largest models attain a new state-of-the-art top-1 accuracy of 86.5%. In addition, Normalizer-Free models attain significantly better performance than their batch-normalized counterparts when finetuning on ImageNet after large-scale pre-training on a dataset of 300 million labeled images, with our best models obtaining an accuracy of 89.2%. Our code is available at https://github.com/deepmind/ deepmind-research/tree/master/nfnets
Tidal Circularization of Binaries by Resonance Locking. I. The Importance of the Pre-main Sequence

Although tidal dissipation in binary stars has been studied for over a century, theoretical predictions have yet to match the observed properties of binary populations. This work quantitatively examines the recent proposal of tidal circularization by resonance locking, where tidal dissipation arises from resonances between the star’s natural oscillation frequencies and harmonics of the orbital frequency, and where resonances are “locked” for an extended period of time due to concurrent stellar evolution. We focus on tidal resonances with axi-symmetric gravity modes, and examine binaries with primary masses from one to two solar masses. We find that orbital evolution via resonance locking occurs primarily during the star’s pre-main-sequence phase, with the main-sequence phase contributing negligibly. Resonance locking, ignoring nonlinearity, can circularize binaries with peri-center distances out to ∼10 stellar radii, corresponding to circular periods of ∼4–6 days. However, we find resonantly excited gravity modes will become nonlinear in stellar cores, which prevents them from reaching their full, linear amplitudes. We estimate that such a “saturated resonance lock” reduces the circularization period by about a third, but resonance locking remains much more effective than the cumulative actions of equilibrium tides. In a companion paper, we examine recent binary data to compare against theory.
Quasi-stationary distributions of multi-dimensional diffusion processes

The present paper is devoted to the investigation of the long term behavior of a class of singular multi-dimensional diffusion processes that get absorbed in finite time with probability one. Our focus is on the analysis of quasi-stationary distributions (QSDs), which describe the long term behavior of the system conditioned on not being absorbed. Under natural Lyapunov conditions, we construct a QSD and prove the sharp exponential convergence to this QSD for compactly supported initial distributions. Under stronger Lyapunov conditions ensuring that the diffusion process comes down from infinity, we show the uniqueness of a QSD and the exponential convergence to the QSD for all initial distributions. Our results can be seen as the multi-dimensional generalization of Cattiaux et al (Ann. Prob. 2009) as well as the complement to Hening and Nguyen (Ann. Appl. Prob. 2018) which looks at the long term behavior of multi-dimensional diffusions that can only become extinct asymptotically. The centerpiece of our approach concerns a uniformly elliptic operator that we relate to the generator, or the Fokker-Planck operator, associated to the diffusion process. This operator only has singular coefficients in its zeroth-order terms and can be handled more easily than the generator. For this operator, we establish the discreteness of its spectrum, its principal spectral theory, the stochastic representation of the semigroup generated by it, and the global regularity for the associated parabolic equation. We show how our results can be applied to most ecological models, among which cooperative, competitive, and predator-prey Lotka-Volterra systems.
EvoSplit: An evolutionary approach to split a multi-label data set into disjoint subsets

This paper presents a new evolutionary approach, EvoSplit, for the distribution of multi-label data sets into disjoint subsets for supervised machine learning. Currently, data set providers either divide a data set randomly or using iterative stratification, a method that aims to maintain the label (or label pair) distribution of the original data set into the different subsets. Following the same aim, this paper first introduces a single-objective evolutionary approach that tries to obtain a split that maximizes the similarity between those distributions independently. Second, a new multi-objective evolutionary algorithm is presented to maximize the similarity considering simultaneously both distributions (labels and label pairs). Both approaches are validated using well-known multi-label data sets as well as large image data sets currently used in computer vision and machine learning applications. EvoSplit improves the splitting of a data set in comparison to the iterative stratification following different measures: Label Distribution, Label Pair Distribution, Examples Distribution, folds and fold-label pairs with zero positive examples.
Separation and Determination of Clenbuterol, Cimaterol and Salbutamol by Capillary Electrophoresis with Amperometric Detection

Capillary electrophoresis with amperometric detection was applied to determine some β2-agonists, such as clenbuterol, cimaterol and salbutamol in this paper. The working electrode used was a 0.3 mm diameter carbon disk electrode. In this work, the pH 6.0–6.4 borax-potassium dihydrogen phosphate was used as running buffer (150 mmol/L), 10 kV as the separation voltage and 1.05 V (vs. Ag/AgCl, 3 mol/L KCl) as the detection potential. Under the optimum conditions, the analytes were baseline separated within 16 min. Linear range for cimaterol, clenbuterol and salbutamol was 1.0–2000, 2.0–2000 and 1.0–2000 ng/mL, respectively. The detection limits (define as 3σ/k) were 0.5, 1.0 and 0.4 ng/mL for cimaterol, clenbuterol and salbutamol, respectively. The developed method has been applied to determine these three analytes in feed and urine by standard addition. The mean recoveries for these three analytes ranged from 89.0% to 102.0%.
Differentiable Generative Phonology

The goal of generative phonology, as formulated by Chomsky and Halle (1968), is to specify a formal system that explains the set of attested phonological strings in a language. Traditionally, a collection of rules (or constraints, in the case of optimality theory) and underlying forms (UF) are posited to work in tandem to generate phonological strings. However, the degree of abstraction of UFs with respect to their concrete realizations is contentious. As the main contribution of our work, we implement the phonological generative system as a neural model differentiable end-to-end, rather than as a set of rules or constraints. Contrary to traditional phonology, in our model, UFs are continuous vectors in $\mathbb{R}^d$, rather than discrete strings. As a consequence, UFs are discovered automatically rather than posited by linguists, and the model can scale to the size of a realistic vocabulary. Moreover, we compare several modes of the generative process, contemplating: i) the presence or absence of an underlying representation in between morphemes and surface forms (SFs); and ii) the conditional dependence or independence of UFs with respect to SFs. We evaluate the ability of each mode to predict attested phonological strings on 2 datasets covering 5 and 28 languages, respectively. The results corroborate two tenets of generative phonology, viz. the necessity for UFs and their independence from SFs. In general, our neural model of generative phonology learns both UFs and SFs automatically and on a large-scale.
Lenient Regret and Good-Action Identification in Gaussian Process Bandits

In this paper, we study the problem of Gaussian process (GP) bandits under relaxed optimization criteria stating that any function value above a certain threshold is"good enough". On the theoretical side, we study various {\em lenient regret} notions in which all near-optimal actions incur zero penalty, and provide upper bounds on the lenient regret for GP-UCB and an elimination algorithm, circumventing the usual $O(\sqrt{T})$ term (with time horizon $T$) resulting from zooming extremely close towards the function maximum. In addition, we complement these upper bounds with algorithm-independent lower bounds. On the practical side, we consider the problem of finding a single"good action"according to a known pre-specified threshold, and introduce several good-action identification algorithms that exploit knowledge of the threshold. We experimentally find that such algorithms can often find a good action faster than standard optimization-based approaches.
The Kinematics of z ≳ 6 Quasar Host Galaxies

We explore the kinematics of 27 z ≳ 6 quasar host galaxies observed in [C ii] 158 μm ([C ii]) emission with the Atacama Large Millimeter/submillimeter Array at a resolution of ≈025. We find that nine of the galaxies show disturbed [C ii] emission, due to either a close companion galaxy or a recent merger. Ten galaxies have smooth velocity gradients consistent with the emission arising from a gaseous disk. The remaining eight quasar host galaxies show no velocity gradient, suggesting that the gas in these systems is dispersion dominated. All galaxies show high velocity dispersions with a mean of 129 ± 10 km s−1. To provide an estimate of the dynamical mass within twice the half-light radius of the quasar host galaxy, we model the kinematics of the [C ii] emission line using our publicly available kinematic fitting code, qubefit. This results in a mean dynamical mass of 5.0 ± 0.8( ± 3.5) × 1010 M ⊙. Comparison between the dynamical mass and the mass of the supermassive black hole reveals that the sample falls above the locally derived bulge mass–black hole mass relation at 2. 4σ significance. This result is robust even if we account for the large systematic uncertainties. Using several different estimators for the molecular mass, we estimate a gas mass fraction of >10%, indicating that gas makes up a large fraction of the baryonic mass of z ≳ 6 quasar host galaxies. Finally, we speculate that the large variety in [C ii] kinematics is an indication that gas accretion onto z ≳ 6 supermassive black holes is not caused by a single precipitating factor.
Adversarially robust deepfake media detection using fused convolutional neural network predictions

Deepfakes are synthetically generated images, videos or audios, which fraudsters use to manipulate legitimate information. Current deepfake detection systems struggle against unseen data. To address this, we employ three different deep Convolutional Neural Network (CNN) models, (1) VGG16, (2) InceptionV3, and (3) XceptionNet to classify fake and real images extracted from videos. We also constructed a fusion of the deep CNN models to improve the robustness and generalisation capability. The proposed technique outperforms state-of-the-art models with 96.5% accuracy, when tested on publicly available DeepFake Detection Challenge (DFDC) test data, comprising of 400 videos. The fusion model achieves 99% accuracy on lower quality DeepFake-TIMIT dataset videos and 91.88% on higher quality DeepFake-TIMIT videos. In addition to this, we prove that prediction fusion is more robust against adversarial attacks. If one model is compromised by an adversarial attack, the prediction fusion does not let it affect the overall classification.