Miracle-dz/UniFAR-FTUs huggingface dataset:
Regional patterns of microsatellite diversity in Ethiopian tetraploid wheat accessions

This study was conducted to assess regional patterns of diversity of Ethiopian tetraploid wheat accessions and to identify areas of diversity that can be used as source of new germplasm for developing high yielding and stable varieties. A collection of 133 Ethiopian tetraploid wheat accessions and eight introduced cultivars was analysed using 29 wheat microsatellite markers. A total of 383 alleles were detected with an average value of 13.14 alleles per locus. Relatively more alleles were observed in the B genome than in the A genome. Gene diversity indices ranged from 0.08 to 0.95, with a mean value of 0.72. Accessions collected from the same region were pooled and the number of alleles and gene diversity were calculated over the 29 simple sequence repeats for each region. Higher numbers of alleles were detected in the Shewa region (8.72), followed by Tigray (5.86) and Hararghe (5.76). The highest average gene diversity value was found in Shewa (0.65), followed by Gondar (0.64). No significant correlation was observed between geographic distance and genetic distance. Out of 383 different alleles detected, 93 (24.4%) were observed to be region-specific. Region-specific alleles were found across all chromosomes except for Xgwm752, Xgwm155 and Xgwm148. Genetic similarity coefficients were estimated for all the possible 55 pairs of regional comparisons and they ranged from 0.16 to 0.52, with a mean value of 0.50. All provinces were differentiated in the UPGMA cluster diagram.
Do All Good Actors Look The Same? Exploring News Veracity Detection Across The U.S. and The U.K

A major concern with text-based news veracity detection methods is that they may not generalize across countries and cultures. In this short paper, we explicitly test news veracity models across news data from the United States and the United Kingdom, demonstrating there is reason for concern of generalizabilty. Through a series of testing scenarios, we show that text-based classifiers perform poorly when trained on one country's news data and tested on another. Furthermore, these same models have trouble classifying unseen, unreliable news sources. In conclusion, we discuss implications of these results and avenues for future work.
Gauss Coordinates vs Currents for the Yangian Doubles of the Classical Types

We consider relations between Gauss coordinates of $T$-operators for the Yangian doubles of the classical types corresponding to the algebras $\mathfrak{g}$ of $A$, $B$, $C$ and $D$ series and the current generators of these algebras. These relations are important for the applications in the $\mathfrak{g}$-invariant integrable models and construction of the Bethe vectors in these models.
Deep neural networks for inverse problems with pseudodifferential operators: an application to limited-angle tomography

We propose a novel convolutional neural network (CNN), called $\Psi$DONet, designed for learning pseudodifferential operators ($\Psi$DOs) in the context of linear inverse problems. Our starting point is the Iterative Soft Thresholding Algorithm (ISTA), a well-known algorithm to solve sparsity-promoting minimization problems. We show that, under rather general assumptions on the forward operator, the unfolded iterations of ISTA can be interpreted as the successive layers of a CNN, which in turn provides fairly general network architectures that, for a specific choice of the parameters involved, allow to reproduce ISTA, or a perturbation of ISTA for which we can bound the coefficients of the filters. Our case study is the limited-angle X-ray transform and its application to limited-angle computed tomography (LA-CT). In particular, we prove that, in the case of LA-CT, the operations of upscaling, downscaling and convolution, which characterize our $\Psi$DONet and most deep learning schemes, can be exactly determined by combining the convolutional nature of the limited angle X-ray transform and basic properties defining an orthogonal wavelet system. We test two different implementations of $\Psi$DONet on simulated data from limited-angle geometry, generated from the ellipse data set. Both implementations provide equally good and noteworthy preliminary results, showing the potential of the approach we propose and paving the way to applying the same idea to other convolutional operators which are $\Psi$DOs or Fourier integral operators.
Efficacy of Trapping and Shooting in Removing Breeding Brown-Headed Cowbirds

Abstract Trapping often is used to mitigate brood parasitism by brown-headed cowbirds (Molothrus ater). The efficacy of trapping to remove locally breeding cowbirds has not been compared to shooting, an alternative removal method. We used cluster analysis to group female cowbirds trapped and shot at Fort Hood, Texas, USA, during the 2003 and 2004 breeding seasons as potential local breeders (PLBs) or migrants based on their color, mass, ovarian development, and wing chord. The PLBs were paler and smaller, generally fitting the description of M. a. obscurus, the expected race of locally breeding cowbirds. We detected enlarged ovaries in 0% of migrants (n = 1,634) and 18% of PLBs (n = 959). We compared the efficiency (proportion of PLBs removed) and effectiveness (total no. of PLBs removed) of trapping and shooting. Monthly shooting efficiency was high (>90%) and always greater than that for trapping. Trapping efficiency was temporally variable but greatest in March (50%). Despite its inefficiency, trapping removed at least 8-fold more PLBs during March and April than shooting. Effectiveness of trapping and shooting was similar in May. Shooting removed 3-fold more PLBs in June than trapping. The cost of removing a single PLB by trapping in March and April was <$4 but increased to $153 by June. Cost of removing a single PLB by shooting ranged from $14–19 during all months except March, when few breeding cowbirds were shot. Knowledge of the efficacy of cowbird removal techniques over the breeding season will allow managers to strategically apply control to enhance program benefits.
SOT-MRAM based Sigmoidal Neuron for Neuromorphic Architectures

In this paper, the intrinsic physical characteristics of spin-orbit torque (SOT) magnetoresistive random-access memory (MRAM) devices are leveraged to realize sigmoidal neurons in neuromorphic architectures. Performance comparisons with the previous power- and area-efficient sigmoidal neuron circuits exhibit 74x and 12x reduction in power-area-product values for the proposed SOT-MRAM based neuron. To verify the functionally of the proposed neuron within larger scale designs, we have implemented a circuit realization of a 784x16x10 SOT-MRAM based multiplayer perceptron (MLP) for MNIST pattern recognition application using SPICE circuit simulation tool. The results obtained exhibit that the proposed SOT-MRAM based MLP can achieve accuracies comparable to an ideal binarized MLP architecture implemented on GPU, while realizing orders of magnitude increase in processing speed.
Application of Magnetosomes in Magnetic Hyperthermia

Nanoparticles, specifically magnetosomes, synthesized in nature by magnetotactic bacteria, are very promising to be usedin magnetic hyperthermia in cancer treatment. In this work, using the solution of the stochastic Landau–Lifshitz equation, we calculate the specific absorption rate (SAR) in an alternating (AC) magnetic field of assemblies of magnetosome chains depending on the particle size D, the distance between particles in a chain a, and the angle of the applied magnetic field with respect to the chain axis. The dependence of SAR on the a/D ratio is shown to have a bell-shaped form with a pronounced maximum. For a dilute oriented chain assembly with optimally chosen a/D ratio, a strong magneto-dipole interaction between the chain particles leads to an almost rectangular hysteresis loop, and to large SAR values in the order of 400–450 W/g at moderate frequencies f = 300 kHz and small magnetic field amplitudes H0 = 50–100 Oe. The maximum SAR value only weakly depends on the diameter of the nanoparticles and the length of the chain. However, a significant decrease in SAR occurs in a dense chain assembly due to the strong magneto-dipole interaction of nanoparticles of different chains.
Less is More: Robust and Novel Features for Malicious Domain Detection

Malicious domains are increasingly common and pose a severe cybersecurity threat. Specifically, many types of current cyber attacks use URLs for attack communications (e.g., C\&C, phishing, and spear-phishing). Despite the continuous progress in detecting these attacks, many alarming problems remain open, such as the weak spots of the defense mechanisms. Since machine learning has become one of the most prominent methods of malware detection, A robust feature selection mechanism is proposed that results in malicious domain detection models that are resistant to evasion attacks. This mechanism exhibits high performance based on empirical data. This paper makes two main contributions: First, it provides an analysis of robust feature selection based on widely used features in the literature. Note that even though the feature set dimensional space is reduced by half (from nine to four features), the performance of the classifier is still improved (an increase in the model's F1-score from 92.92\% to 95.81\%). Second, it introduces novel features that are robust to the adversary's manipulation. Based on an extensive evaluation of the different feature sets and commonly used classification models, this paper shows that models that are based on robust features are resistant to malicious perturbations, and at the same time useful for classifying non-manipulated data.
New Constraints on the Origin of Surface Brightness Profile Breaks of Disk Galaxies from MaNGA

In an effort to probe the origin of surface brightness profile (SBP) breaks widely observed in nearby disk galaxies, we carry out a comparative study of stellar population profiles of 635 disk galaxies selected from the Mapping Nearby Galaxies at Apache Point Observatory spectroscopic survey. We classify our galaxies into single exponential (Ti), down-bending (Tii), and up-bending (Tiii) SBP types and derive their spin parameters and radial profiles of age/metallicity-sensitive spectral features. Most Tii (Tiii) galaxies have down-bending (up-bending) star formation rate (SFR) radial profiles, implying that abrupt radial changes of SFR intensities contribute to the formation of both Tii and Tiii breaks. Nevertheless, a comparison between our galaxies and simulations suggests that stellar migration plays a significant role in weakening down-bending Σ⋆ profile breaks. While there is a correlation between the break strengths of SBPs and age/metallicity-sensitive spectral features for Tii galaxies, no such correlation is found for Tiii galaxies, indicating that stellar migration may not play a major role in shaping Tiii breaks, as is also evidenced by a good correspondence between the break strengths of Σ⋆ and SBPs of Tiii galaxies. We do not find evidence for galaxy spin being a relevant parameter for forming different SBP types, nor do we find significant differences between the asymmetries of galaxies with different SBP types, suggesting that environmental disturbances or satellite accretion in the recent past do not significantly influence the break formation. By dividing our sample into early and late morphological types, we find that galaxies with different SBP types follow nearly the same tight stellar mass– relation, which makes the hypothesis that stellar migration alone can transform SBP types from Tii to Ti and then to Tiii highly unlikely.
Bayesian Neural Networks

In recent times, neural networks have become a powerful tool for the analysis of complex and abstract data models. However, their introduction intrinsically increases our uncertainty about which features of the analysis are model-related and which are due to the neural network. This means that predictions by neural networks have biases which cannot be trivially distinguished from being due to the true nature of the creation and observation of data or not. In order to attempt to address such issues we discuss Bayesian neural networks: neural networks where the uncertainty due to the network can be characterised. In particular, we present the Bayesian statistical framework which allows us to categorise uncertainty in terms of the ingrained randomness of observing certain data and the uncertainty from our lack of knowledge about how data can be created and observed. In presenting such techniques we show how errors in prediction by neural networks can be obtained in principle, and provide the two favoured methods for characterising these errors. We will also describe how both of these methods have substantial pitfalls when put into practice, highlighting the need for other statistical techniques to truly be able to do inference when using neural networks.
Stochastic Recursive Momentum Method for Non-Convex Compositional Optimization

We propose a novel stochastic optimization algorithm called STOchastic Recursive Momentum for Compositional (STORM-Compositional) optimization that minimizes the composition of expectations of two stochastic functions, the latter being an optimization problem arising in various important machine learning applications. By introducing the momentum term in the compositional gradient updates, STORM-Compositional operates the stochastic recursive variance-reduced compositional gradients in an exponential-moving average way. This leads to an $O(\varepsilon^{-3})$ complexity upper bound for STORM-Compositional, that matches the best known complexity bounds in previously announced compositional optimization algorithms. At the same time, STORM-Compositional is a single loop algorithm that avoids the typical alternative tuning between large and small batch sizes, as well as recording of checkpoint gradients, that persist in variance-reduced stochastic gradient methods. This allows considerably simpler parameter tuning in numerical experiments, which demonstrates the superiority of STORM-Compositional over other stochastic compositional optimization algorithms.
Optimal regularity of stable solutions to nonlinear equations involving the p-Laplacian

Abstract We consider the equation - Δ p ⁢ u = f ⁢ ( u ) {-\Delta_{p}u=f(u)} in a smooth bounded domain of ℝ n {\mathbb{R}^{n}} , where Δ p {\Delta_{p}} is the p-Laplace operator. Explicit examples of unbounded stable energy solutions are known if n ≥ p + 4 ⁢ p p - 1 {n\geq p+\frac{4p}{p-1}} . Instead, when n < p + 4 ⁢ p p - 1 {n<p+\frac{4p}{p-1}} , stable solutions have been proved to be bounded only in the radial case or under strong assumptions on f. In this article we solve a long-standing open problem: we prove an interior C α {C^{\alpha}} bound for stable solutions which holds for every nonnegative f ∈ C 1 {f\in C^{1}} whenever p ≥ 2 {p\geq 2} and the optimal condition n < p + 4 ⁢ p p - 1 {n<p+\frac{4p}{p-1}} holds. When p ∈ ( 1 , 2 ) {p\in(1,2)} , we obtain the same result under the nonsharp assumption n < 5 ⁢ p {n<5p} . These interior estimates lead to the boundedness of stable and extremal solutions to the associated Dirichlet problem when the domain is strictly convex. Our work extends to the p-Laplacian some of the recent results of Figalli, Ros-Oton, Serra, and the first author for the classical Laplacian, which have established the regularity of stable solutions when p = 2 {p=2} in the optimal range n < 10 {n<10} .
DO SOCIAL NETWORKING SITES PRIVACY POLICIES DIFFER? A LINGUISTIC ANALYSIS OF THE TEN MOST POPULAR SOCIAL NETWORKING SITES

Social networking sites such as Facebook and LinkedIn are used by 65% of the US population and nearly 2 billion people worldwide. A significant amount of personal information is shared on these sites. As a result the privacy policies that these sites follow is critical. Our study reviews the privacy policies of the top ten social networking sites and reviews them from a linguistic perspective using LIWC (Linguistic and Word Count) analysis. A detailed analysis by site and comparisons across sites is performed. We found significant differences in several linguistic measures including Analytic, Clout, Tone, and use of pronouns. The Implications of these differences and other key findings are discussed.
Quantification of the South African Lockdown Regimes, for the SARS-CoV-2 Pandemic, and the Levels of Immunity They Require to Work

This research quantifies the various South African lockdown regimes, for the SARS-CoV-2 pandemic, in terms of the basic reproduction number, r0. It further calculates the levels of immunity required for these selfsame lockdown regimes to begin to work, then predicts their perceived values, should infections have been underestimated by a factor of 10. The first, level-5 lockdown was a valiant attempt to contain the highly infectious, SARS-CoV-2 virus, based on a limited knowledge. Its basic reproduction number (r0 = 1.93) never came anywhere close to the requirement of being less than unity. Obviously, it could be anticipated that the same would apply for subsequent, lower levels of lockdown. The basic reproduction number for the level-3 lockdown was found to be 2.34 and that of the level-4 lockdown, 1.69. The suggestion is therefore that the level-4 lockdown might have been marginally 'smarter' than the 'harder', level-5 lockdown, although its basic reproduction number may merely reflect an adjustment by the public to the new normal, or the ever-present error associated with data sets, in general. The pandemic's basic reproduction number was calculated to be 3.16, in the Swedish context. The lockdowns therefore served to ensure that the medical system was not overwhelmed, bought it valuable time to prepare and provided useful data. The lockdowns nonetheless failed significantly in meeting any objective to curtail the pandemic.
GPU-based parallel simulations of the Gatenby-Gawlinski model with anisotropic, heterogeneous acid diffusion

We introduce a variant of the Gatenby-Gawlinski model for acid-mediated tumor invasion, accounting for anisotropic and heterogeneous diffusion of the lactic acid across the surrounding healthy tissues. Numerical simulations are performed for two-dimensional data by employing finite volume schemes on staggered Cartesian grids, and parallel implementation through the modern CUDA GPUs technology is considered. The effectiveness of such approach is proven by reproducing biologically relevant results like the formation of propagating fronts and the emergence of an interstitial gap between normal and cancerous cells, which is driven by the pH lowering strategy and depends significantly on the diffusion rates. By means of a performance analysis of the serial and parallel execution protocols, we infer that exploiting highly parallel GPU-based computing devices allows to rehabilitate finite volume schemes on regularly-shaped meshes, together with explicit time discretization, for complex applications to interface diffusion problems of invasive processes.
Object-Independent Human-to-Robot Handovers Using Real Time Robotic Vision

We present an approach for safe, and object-independent human-to-robot handovers using real time robotic vision, and manipulation. We aim for general applicability with a generic object detector, a fast grasp selection algorithm, and by using a single gripper-mounted RGB-D camera, hence not relying on external sensors. The robot is controlled via visual servoing towards the object of interest. Putting a high emphasis on safety, we use two perception modules: human body part segmentation, and hand/finger segmentation. Pixels that are deemed to belong to the human are filtered out from candidate grasp poses, hence ensuring that the robot safely picks the object without colliding with the human partner. The grasp selection, and perception modules run concurrently in real-time, which allows monitoring of the progress. In experiments with 13 objects, the robot was able to successfully take the object from the human in 81.9% of the trials.
A generalized linear joint trained framework for semi-supervised leaning of sparse features

The elastic-net is among the most widely used types of regularization algorithms, commonly associated with the problem of supervised generalized linear model estimation via penalized maximum likelihood. Its nice properties originate from a combination of $\ell_1$ and $\ell_2$ norms, which endow this method with the ability to select variables taking into account the correlations between them. In the last few years, semi-supervised approaches, that use both labeled and unlabeled data, have become an important component in the statistical research. Despite this interest, however, few researches have investigated semi-supervised elastic-net extensions. This paper introduces a novel solution for semi-supervised learning of sparse features in the context of generalized linear model estimation: the generalized semi-supervised elastic-net (s2net), which extends the supervised elastic-net method, with a general mathematical formulation that covers, but is not limited to, both regression and classification problems. We develop a flexible and fast implementation for s2net in R, and its advantages are illustrated using both real and synthetic data sets.
A Bayesian approach to modelling multimessenger emission from blazars using lepto-hadronic kinetic equations

Abstract Blazar TXS 0506+056 is the main candidate for a coincident neutrino and gamma-ray flare event. In this paper, we compose a detailed kinetic lepto-hadronic emission model capable of producing a photon and neutrino spectrum given a set of parameters. Our model includes a range of large-scale geometries and both dynamical and steady-state injection models for electrons and protons. We link this model with a Markov Chain Monte Carlo sampler to obtain a powerful statistical tool that allows us to both fit the Spectral Energy Distribution and study the probability density functions and correlations of the parameters. Assuming a fiducial neutrino flux, we demonstrate how multi-messenger observations can be modelled jointly in a Bayesian framework. We find the best parameters for each of the variants of the model tested and report on their cross-correlations. Additionally, we confirm that reproducing the neutrino flux of TXS 0506+056 requires an extreme proton to electron ratio either in the local acceleration process or from external injection.
Tree-Projected Gradient Descent for Estimating Gradient-Sparse Parameters on Graphs

We study estimation of a gradient-sparse parameter vector $\boldsymbol{\theta}^* \in \mathbb{R}^p$, having strong gradient-sparsity $s^*:=\|\nabla_G \boldsymbol{\theta}^*\|_0$ on an underlying graph $G$. Given observations $Z_1,\ldots,Z_n$ and a smooth, convex loss function $\mathcal{L}$ for which $\boldsymbol{\theta}^*$ minimizes the population risk $\mathbb{E}[\mathcal{L}(\boldsymbol{\theta};Z_1,\ldots,Z_n)]$, we propose to estimate $\boldsymbol{\theta}^*$ by a projected gradient descent algorithm that iteratively and approximately projects gradient steps onto spaces of vectors having small gradient-sparsity over low-degree spanning trees of $G$. We show that, under suitable restricted strong convexity and smoothness assumptions for the loss, the resulting estimator achieves the squared-error risk $\frac{s^*}{n} \log (1+\frac{p}{s^*})$ up to a multiplicative constant that is independent of $G$. In contrast, previous polynomial-time algorithms have only been shown to achieve this guarantee in more specialized settings, or under additional assumptions for $G$ and/or the sparsity pattern of $\nabla_G \boldsymbol{\theta}^*$. As applications of our general framework, we apply our results to the examples of linear models and generalized linear models with random design.