Cross-lingual Text Reuse Detection Using Translation Plus Monolingual Analysis for English-Urdu Language Pair

Cross-Lingual Text Reuse Detection (CLTRD) has recently attracted the attention of the research community due to a large amount of digital text readily available for reuse in multiple languages through online digital repositories. In addition, efficient machine translation systems are freely and readily available to translate text from one language into another, which makes it quite easy to reuse text across languages, and consequently difficult to detect it. In the literature, the most prominent and widely used approach for CLTRD is Translation plus Monolingual Analysis (T+MA). To detect CLTR for English-Urdu language pair, T+MA has been used with lexical approaches, namely, N-gram Overlap, Longest Common Subsequence, and Greedy String Tiling. This clearly shows that T+MA has not been thoroughly explored for the English-Urdu language pair. To fulfill this gap, this study presents an in-depth and detailed comparison of 26 approaches that are based on T+MA. These approaches include semantic similarity approaches (semantic tagger based approaches, WordNet-based approaches), probabilistic approach (Kullback-Leibler distance approach), monolingual word embedding-based approaches siamese recurrent architecture, and monolingual sentence transformer-based approaches for English-Urdu language pair. The evaluation was carried out using the CLEU benchmark corpus, both for the binary and the ternary classification tasks. Our extensive experimentation shows that our proposed approach that is a combination of 26 approaches obtained an F 1 score of 0.77 and 0.61 for the binary and ternary classification tasks, respectively, and outperformed the previously reported approaches [ 41 ] ( F 1 = 0.73) for the binary and ( F 1 = 0.55) for the ternary classification tasks) on the CLEU corpus.

Empirical Evaluation of Shallow and Deep Learning Classifiers for Arabic Sentiment Analysis

This work presents a detailed comparison of the performance of deep learning models such as convolutional neural networks, long short-term memory, gated recurrent units, their hybrids, and a selection of shallow learning classifiers for sentiment analysis of Arabic reviews. Additionally, the comparison includes state-of-the-art models such as the transformer architecture and the araBERT pre-trained model. The datasets used in this study are multi-dialect Arabic hotel and book review datasets, which are some of the largest publicly available datasets for Arabic reviews. Results showed deep learning outperforming shallow learning for binary and multi-label classification, in contrast with the results of similar work reported in the literature. This discrepancy in outcome was caused by dataset size as we found it to be proportional to the performance of deep learning models. The performance of deep and shallow learning techniques was analyzed in terms of accuracy and F1 score. The best performing shallow learning technique was Random Forest followed by Decision Tree, and AdaBoost. The deep learning models performed similarly using a default embedding layer, while the transformer model performed best when augmented with araBERT.

The phase diagrams of beryllium and magnesium oxide at megabar pressures

We perform ab initio simulations of beryllium (Be) and magnesium oxide (MgO) at megabar pressures and compare their structural and thermodynamic properties. We make a detailed comparison of our two recently derived phase diagrams of Be [Wu et al., Phys. Rev. B 104, 014103 (2021)] and MgO [Soubiran and Militzer, Phys. Rev. Lett. 125, 175701 (2020)] using the thermodynamic integration technique, as they exhibit striking similarities regarding their shape. We explore whether the Lindemann criterion can explain the melting temperatures of these materials through the calculation of the Debye temperature at high pressure. From our free energy calculations, we obtained a melting curve for Be that is well represented by the fit Tm(P) = 1564K*[1 + P/(15.8037 GPa)]^0.414 , and a melting line of MgO, which can be well reproduced by the fit Tm(P) = 3010K*(1 + P/a)^(1/c) with a = 10.5797 GPa and c = 2.8683 for the B1 phase and a = 26.1163 GPa and c = 2.2426 for the B2 phase. Both materials exhibit negative Clapeyron slopes on the boundaries between the two solid phases that are strongly affected by anharmonic effects, which also influences the location of the solid-solid-liquid triple point. We find that the quasi-harmonic approximation underestimates the stability range of the low-pressure phases, namely hcp for Be and B1 for MgO. We also compute the phonon dispersion relations at low and high pressure for each of the phases of these materials, and also explore how the phonon density of states is modified by temperature. Finally, we derive secondary shock Hugoniot curves in addition to the principal Hugoniot curve for both materials, and study their offsets in pressure between solid and liquid branches.

Primary and Secondary Negative Symptoms in Schizophrenia

The negative symptoms of schizophrenia include volitional (motivational) impairment manifesting as avolition, anhedonia, social withdrawal, and emotional disorders such as alogia and affective flattening. Negative symptoms worsen patients' quality of life and functioning. From the diagnostic point of view, it is important to differentiate between primary negative symptoms, which are regarded as an integral dimension of schizophrenia, and secondary negative symptoms occurring as a result of positive symptoms, comorbid depression, side effects of antipsychotics, substance abuse, or social isolation. If secondary negative symptoms overlap with primary negative symptoms, it can create a false clinical impression of worsening deficit symptoms and disease progression, which leads to the choice of incorrect therapeutic strategy with excessive dopamine blocker loading. Different longitudinal trajectories of primary and secondary negative symptoms in different schizophrenia stages are proposed as an important additional discriminating factor. This review and position paper focuses primarily on clinical aspects of negative symptoms in schizophrenia, their definition, phenomenology, factor structure, and classification. It covers the historical and modern concepts of the paradigm of positive and negative symptoms in schizophrenia, as well as a detailed comparison of the assessment tools and psychometric tests used for the evaluation of negative symptoms.

A Comprehensive Evaluation of Internet-of-Things Platforms

Internet of things (IoT) has attracted researchers in recent years as it has a great potential to solve many emerging problems. An IoT platform is missioned to operate as a horizontal key element for serving various vertical IoT domains such as structure monitoring, smart agriculture, healthcare, miner safety monitoring, smart home, and healthcare. In this chapter, the authors propose a comprehensive analysis of IoT platforms to evaluate their capabilities. The selected metrics (features) to investigate the IoT platforms are “ability to serve different domains,” “ability to handle different data formats,” “ability to process unlimited size of data from various context,” “ability to convert unstructured data to structured data,” and “ability to produce complex reports.” These metrics are chosen by considering the reporting capabilities of various IoT platforms, big data concepts, and domain-related issues. The authors provide a detailed comparison derived from the metric analysis to show the advantages and drawbacks of IoT platforms.

Configuring methods for deadlock-free routing

This paper discusses the problem of tracking of deadlock-free routes. A brief overview of existing software tools providing this functionality is given. A complete overview of the proposed software for building routes for given SpaceWire onboard networks is presented. The paper discusses the application of different existing methods for the choosing of the best route from the list of the deadlock-free routes. A brief overview of the methods for of choosing the best route according to the provided criteria is given. A new method for choosing of the best route and its modification is proposed. Authors provide the result of the methods application and the detailed comparison.

X-ray absorption spectroscopy and actinide electrochemistry: a setup dedicated to radioactive samples applied to neptunium chemistry

A spectroelectrochemical setup has been developed to investigate radioactive elements in small volumes (0.7 to 2 ml) under oxidation–reduction (redox) controlled conditions by X-ray absorption spectroscopy (XAS). The cell design is presented together with in situ XAS measurements performed during neptunium redox reactions. Cycling experiments on the NpO2 2+/NpO2 + redox couple were applied to qualify the cell electrodynamics using XANES measurements and its ability to probe modifications in the neptunyl hydration shell in a 1 mol l−1 HNO3 solution. The XAS results are in agreement with previous structural studies and the NpO2 2+/NpO2 + standard potential, determined using Nernst methods, is consistent with measurements based on other techniques. Subsequently, the NpO2 +, NpO2 2+ and Np4+ ion structures in solution were stabilized and measured using EXAFS. The resulting fit parameters are again compared with other results from the literature and with theoretical models in order to evaluate how this spectroelectrochemistry experiment succeeds or fails to stabilize the oxidation states of actinides. The experiment succeeded in: (i) implementing a robust and safe XAS device to investigate unstable radioactive species, (ii) evaluate in a reproducible manner the NpO2 2+/NpO2 + standard potential under dilute conditions and (iii) clarify mechanistic aspects of the actinyl hydration sphere in solution. In contrast, a detailed comparison of EXAFS fit parameters shows that this method is less appropriate than the majority of the previously reported chemical methods for the stabilization of the Np4+ ion.

COSMOS2020: A Panchromatic View of the Universe to z ∼ 10 from Two Complementary Catalogs

The Cosmic Evolution Survey (COSMOS) has become a cornerstone of extragalactic astronomy. Since the last public catalog in 2015, a wealth of new imaging and spectroscopic data have been collected in the COSMOS field. This paper describes the collection, processing, and analysis of these new imaging data to produce a new reference photometric redshift catalog. Source detection and multiwavelength photometry are performed for 1.7 million sources across the 2 deg2 of the COSMOS field, ∼966,000 of which are measured with all available broadband data using both traditional aperture photometric methods and a new profile-fitting photometric extraction tool, The Farmer, which we have developed. A detailed comparison of the two resulting photometric catalogs is presented. Photometric redshifts are computed for all sources in each catalog utilizing two independent photometric redshift codes. Finally, a comparison is made between the performance of the photometric methodologies and of the redshift codes to demonstrate an exceptional degree of self-consistency in the resulting photometric redshifts. The i < 21 sources have subpercent photometric redshift accuracy and even the faintest sources at 25 < i < 27 reach a precision of 5%. Finally, these results are discussed in the context of previous, current, and future surveys in the COSMOS field. Compared to COSMOS2015, it reaches the same photometric redshift precision at almost one magnitude deeper. Both photometric catalogs and their photometric redshift solutions and physical parameters will be made available through the usual astronomical archive systems (ESO Phase 3, IPAC-IRSA, and CDS).

A Vertically Resolved MSE Framework Highlights the Role of the Boundary Layer in Convective Self-Aggregation

Convective self-aggregation refers to a phenomenon in which random convection can self-organize into large-scale clusters over an ocean surface with uniform temperature in cloud-resolving models. Previous literature studies convective aggregation primarily by analyzing vertically integrated (VI) moist static energy (MSE) variance. That is the global MSE variance, including both the local MSE variance at a given altitude and the covariance of MSE anomalies between different altitudes. Here we present a vertically resolved (VR) MSE framework that focuses on the local MSE variance to study convective self-aggregation. Using a cloud-resolving simulation, we show that the development of self-aggregation is associated with an increase of local MSE variance, and that the diabatic and adiabatic generation of the MSE variance is mainly dominated by the boundary layer (BL, the lowest 2 km). The results agree with recent numerical simulation results and the available potential energy analyses showing that the BL plays a key role in the development of self-aggregation. Additionally, we find that the lower free troposphere (2 - 4 km) also generates significant MSE variance in the first 15 days. We further present a detailed comparison between the global and local MSE variance frameworks in their mathematical formulation and diagnostic results, highlighting their differences.