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Acknowledged as one of the most successful online cooperative projects in human society, Wikipedia has obtained rapid growth in recent years and desires continuously to expand content and disseminate knowledge values for everyone globally. The shortage of volunteers brings to Wikipedia many issues, including developing content for over 300 languages at the present. Therefore, the benefit that machines can automatically generate content to reduce human efforts on Wikipedia language projects could be considerable. In this paper, we propose our mapping process for the task of converting Wikidata statements to natural language text (WS2T) for Wikipedia projects at the sentence level. The main step is to organize statements, represented as a group of quadruples and triples, and then to map them to corresponding sentences in English Wikipedia. We evaluate the output corpus in various aspects: sentence structure analysis, noise filtering, and relationships between sentence components based on word embedding models. The results are helpful not only for the data-to-text generation task but also for other relevant works in the field., Comment: 29 pages

The use of multilevel information carriers, also known as qudits, is a promising path for exploring scalability of quantum computing devices. Here we present a proof-of-principle realization of a quantum processor register that uses optically-addressed $^{171}$Yb$^{+}$ ion qudits in a linear trap. The rich level structure of $^{171}$Yb$^{+}$ ions allows using the Zeeman sublevels of the quadrupole clock transition at 435.5 nm for efficient and robust qudit encoding. We demonstrate the realization of the universal set of gates consisting of single-qudit rotations and a two-qudit Molmer-Sorensen operation with a two-ququart system, which is formally equivalent to a universal gate-based four-qubit processor. Our results paves a way towards further studies of more efficient implementations of quantum algorithms with trapped-ion-based processors and, specifically, exploring properties of $^{171}$Yb$^{+}$ ion qudits., Comment: 11 pages, 5 figures; corrected acknowledgments

Let $C$ be the classical middle third Cantor set. It is well known that $C+C = [0,2]$ (Steinhaus, 1917). (Here $+$ denotes the Minkowski sum.) Let $U$ be the set of $z \in [0,2]$ which have a unique representation as $z = x + y$ with $x, y \in C$ (the set of uniqueness). It isn't difficult to show that $\dim_H U = \log(2) / \log(3)$ and $U$ essentially looks like $2C$. Assuming $0,n-1 \in A \subset \{0,1,\dots,n-1\}$, define $C_A = C_{A,n}$ as the linear Cantor set which the attractor of the iterated function system \[ \{ x \mapsto (x + a) / n: a \in A \}. \] We consider various properties of such linear Cantor sets. Our main focus will be on the structure of $C_{A,n}+C_{A,n}$ depending on $n$ and $A$ as well as the properties of the set of uniqueness $U_A$., Comment: Added some additional relevant references. Emphasized that almost all z \in C_A + C_A have a continuum of representations as z = x + y with x, y \in C_A. Added the observation that dim_H(U_A) < 1 for trivial reasons

This paper portrays the method of UAV magnetometry survey data interpolation. The method accommodates the fact that this kind of data has a spatial distribution of the samples along a series of straight lines (similar to maritime tacks), which is a prominent characteristic of many kinds of UAV surveys. The interpolation relies on the very basic Nearest Neighbours algorithm, although augmented with a Machine Learning approach. Such an approach enables the error of less than 5 percent by intelligently adjusting the Nearest Neighbour algorithm parameters. The method was pilot tested on geomagnetic data with Borok Geomagnetic Observatory UAV aeromagnetic survey data.

First Rosenblatt's theorem about omnipotence of shallow networks states that elementary perceptrons can solve any classification problem if there are no discrepancies in the training set. Minsky and Papert considered elementary perceptrons with restrictions on the neural inputs: a bounded number of connections or a relatively small diameter of the receptive field for each neuron at the hidden layer. They proved that under these constraints, an elementary perceptron cannot solve some problems, such as the connectivity of input images or the parity of pixels in them. In this note, we demonstrated first Rosenblatt's theorem at work, showed how an elementary perceptron can solve a version of the travel maze problem, and analysed the complexity of that solution. We constructed also a deep network algorithm for the same problem. It is much more efficient. The shallow network uses an exponentially large number of neurons on the hidden layer (Rosenblatt's $A$-elements), whereas for the deep network the second order polynomial complexity is sufficient. We demonstrated that for the same complex problem deep network can be much smaller and reveal a heuristic behind this effect.

This overview paper describes the first shared task on fake news detection in Urdu language. The task was posed as a binary classification task, in which the goal is to differentiate between real and fake news. We provided a dataset divided into 900 annotated news articles for training and 400 news articles for testing. The dataset contained news in five domains: (i) Health, (ii) Sports, (iii) Showbiz, (iv) Technology, and (v) Business. 42 teams from 6 different countries (India, China, Egypt, Germany, Pakistan, and the UK) registered for the task. 9 teams submitted their experimental results. The participants used various machine learning methods ranging from feature-based traditional machine learning to neural networks techniques. The best performing system achieved an F-score value of 0.90, showing that the BERT-based approach outperforms other machine learning techniques

This paper gives the overview of the first shared task at FIRE 2020 on fake news detection in the Urdu language. This is a binary classification task in which the goal is to identify fake news using a dataset composed of 900 annotated news articles for training and 400 news articles for testing. The dataset contains news in five domains: (i) Health, (ii) Sports, (iii) Showbiz, (iv) Technology, and (v) Business. 42 teams from 6 different countries (India, China, Egypt, Germany, Pakistan, and the UK) registered for the task. 9 teams submitted their experimental results. The participants used various machine learning methods ranging from feature-based traditional machine learning to neural network techniques. The best performing system achieved an F-score value of 0.90, showing that the BERT-based approach outperforms other machine learning classifiers., Comment: arXiv admin note: substantial text overlap with arXiv:2207.11893

With the growth of social media platform influence, the effect of their misuse becomes more and more impactful. The importance of automatic detection of threatening and abusive language can not be overestimated. However, most of the existing studies and state-of-the-art methods focus on English as the target language, with limited work on low- and medium-resource languages. In this paper, we present two shared tasks of abusive and threatening language detection for the Urdu language which has more than 170 million speakers worldwide. Both are posed as binary classification tasks where participating systems are required to classify tweets in Urdu into two classes, namely: (i) Abusive and Non-Abusive for the first task, and (ii) Threatening and Non-Threatening for the second. We present two manually annotated datasets containing tweets labelled as (i) Abusive and Non-Abusive, and (ii) Threatening and Non-Threatening. The abusive dataset contains 2400 annotated tweets in the train part and 1100 annotated tweets in the test part. The threatening dataset contains 6000 annotated tweets in the train part and 3950 annotated tweets in the test part. We also provide logistic regression and BERT-based baseline classifiers for both tasks. In this shared task, 21 teams from six countries registered for participation (India, Pakistan, China, Malaysia, United Arab Emirates, and Taiwan), 10 teams submitted their runs for Subtask A, which is Abusive Language Detection and 9 teams submitted their runs for Subtask B, which is Threatening Language detection, and seven teams submitted their technical reports. The best performing system achieved an F1-score value of 0.880 for Subtask A and 0.545 for Subtask B. For both subtasks, m-Bert based transformer model showed the best performance.

Both policy and research benefit from a better understanding of individuals' jobs. However, as large-scale administrative records are increasingly employed to represent labor market activity, new automatic methods to classify jobs will become necessary. We developed an automatic job offers classifier using a dataset collected from the largest job bank of Mexico known as Bumeran https://www.bumeran.com.mx/ Last visited: 19-01-2022.. We applied machine learning algorithms such as Support Vector Machines, Naive-Bayes, Logistic Regression, Random Forest, and deep learning Long-Short Term Memory (LSTM). Using these algorithms, we trained multi-class models to classify job offers in one of the 23 classes (not uniformly distributed): Sales, Administration, Call Center, Technology, Trades, Human Resources, Logistics, Marketing, Health, Gastronomy, Financing, Secretary, Production, Engineering, Education, Design, Legal, Construction, Insurance, Communication, Management, Foreign Trade, and Mining. We used the SMOTE, Geometric-SMOTE, and ADASYN synthetic oversampling algorithms to handle imbalanced classes. The proposed convolutional neural network architecture achieved the best results when applied the Geometric-SMOTE algorithm., Comment: 13 pages, 2 figures, 8th World Conference On Soft Computing

Given the current social distance restrictions across the world, most individuals now use social media as their major medium of communication. Millions of people suffering from mental diseases have been isolated due to this, and they are unable to get help in person. They have become more reliant on online venues to express themselves and seek advice on dealing with their mental disorders. According to the World health organization (WHO), approximately 450 million people are affected. Mental illnesses, such as depression, anxiety, etc., are immensely common and have affected an individuals' physical health. Recently Artificial Intelligence (AI) methods have been presented to help mental health providers, including psychiatrists and psychologists, in decision making based on patients' authentic information (e.g., medical records, behavioral data, social media utilization, etc.). AI innovations have demonstrated predominant execution in numerous real-world applications broadening from computer vision to healthcare. This study analyzes unstructured user data on the Reddit platform and classifies five common mental illnesses: depression, anxiety, bipolar disorder, ADHD, and PTSD. We trained traditional machine learning, deep learning, and transfer learning multi-class models to detect mental disorders of individuals. This effort will benefit the public health system by automating the detection process and informing appropriate authorities about people who require emergency assistance., Comment: 11 pages, 2 figures, 8th World Conference On Soft Computing

We report on a comprehensive experimental and theoretical study of Fe$_{1-x}$Rh$_{x}$Ge compounds, within the entire concentration range $x \in \left[0.0 - 1.0\right]$, using X-Ray diffraction, small-angle neutron scattering, magnetometry and theoretical calculations. While FeGe and RhGe are single phase helimagnet and unconventional superconductor, respectively, an internal splitting of the crystallographic and magnetic states is found for intermediate compositions $x \in \left[0.2 - 0.9\right]$. A theoretical analysis of the stability of the two detected phases, together with the experimental data, indicate that this splitting preserves a common space group and occurs within single crystallites. Despite their apparent similarity, these two phases however display different magnetic structures, with distinct ferro- and helimagnetic character.

The B20-type cobalt germanide CoGe was investigated by measuring the specific heat, resistivity, and $^{59}$Co nuclear magnetic resonance (NMR). We observed a phase transition at $T_Q=13.7$ K, evidenced by a very narrow peak of the specific heat and sharp changes of the nuclear spin-spin ($T_2^{-1}$) and spin-lattice ($T_1^{-1}$) relaxation rates. The fact that the entropy release is extremely small and the Knight shift is almost independent of temperature down to low temperatures as anticipated in a paramagnetic metal indicates that the $T_Q$ transition is of non-magnetic origin. In addition, we detected a crossover scale $T_0\sim30$ K below which the resistivity and the NMR linewidth increase, and $T_1^{-1}$ is progressively distributed in space, that is, a static and dynamical spatial inhomogeneity develops. While the order parameter for the $T_Q$ transition remains an open question, a group-theoretical analysis suggests that the finite electric quadrupole density arising from the low local site symmetry at cobalt sites could drive the crystal symmetry lowering from the P2$_1$3 symmetry that is commensurate to the R3 symmetry with an incommensurate wavevector, which fairly well accounts for the $T_Q$ transition. The quadrupole-order-driven commensurate-incommensurate phase transition may be another remarkable phenomenon arising from the structural chirality inherent in the noncentrosymmetric B20 family., Comment: 9 pages including appendix; 5 figures; published in Physical Review B

The DIRAC experiment at CERN investigated in the reaction $\rm{p}(24~\rm{GeV}/c) + Ni$ the particle pairs $K^+K^-, \pi^+ \pi^-$ and $p \bar{p}$ with relative momentum $Q$ in the pair system less than 100 MeV/c. Because of background influence studies, DIRAC explored three subsamples of $K^+K^-$ pairs, obtained by subtracting -- using time-of-flight (TOF) technique -- background from initial $Q$ distributions with $K^+K^-$ sample fractions more than 70\%, 50\% and 30\%. The corresponding pair distributions in $Q$ and in its longitudinal projection $Q_L$ were analyzed first in a Coulomb model, which takes into account only Coulomb final state interaction (FSI) and assuming point-like pair production. This Coulomb model analysis leads to a $K^+K^-$ yield increase of about four at $Q_L=0.5$ MeV/c compared to 100 MeV/c. In order to study contributions from strong interaction, a second more sophisticated model was applied, considering besides Coulomb FSI also strong FSI via the resonances $f_0(980)$ and $a_0(980)$ and a variable distance $r^*$ between the produced $K$ mesons. This analysis was based on three different parameter sets for the pair production. For the 70\% subsample and with best parameters, $3680\pm 370$ $K^+K^-$ pairs was found to be compared to $3900\pm 410$ $K^+K^-$ extracted by means of the Coulomb model. Knowing the efficiency of the TOF cut for background suppression, the total number of detected $K^+K^-$ pairs was evaluated to be around $40000\pm 10\%$, which agrees with the result from the 30\% subsample. The $K^+K^-$ pair number in the 50\% subsample differs from the two other values by about three standard deviations, confirming -- as discussed in the paper -- that experimental data in this subsample is less reliable.

Resin-impregnated high-field Nb3Sn type of accelerator magnets are known to require extensive training campaigns and even may exhibit performance-limiting defects after thermal or electromagnetic cycling. In order to efficiently explore technological solutions for this behaviour and assess a wide variety of impregnation material combinations and surface treatments, the BOnding eXperiment (BOX) sample was developed. BOX provides a short-sample test platform featuring magnet-relevant Lorentz forces and exhibits associated training. Here we report on the comparative behaviour of BOX samples comprising the same Nb3Sn Rutherford cable but impregnated either with common resins used in high-field magnets, or with less conventional paraffin wax. Remarkably, the two paraffin wax-impregnated BOX samples reached their critical current without training and are also resilient to thermal and mechanical cycling. These rather encouraging results strongly contrast to those obtained with resin impregnated samples, which show the characteristic extensive training and at best barely reach their critical current value., Comment: 7 pages, 6 figures, To be published in IOP's Superconductor Science and Technology Journal (SuST)