Miracle-dz/UniFAR-FTUs huggingface dataset:
Mathematical Analysis of N-R Algorithm for Experimental Implementation of SHEPWM Control on Single-phase Inverter

This paper presents a simulation and experimental validation of H-bridge inverter with the implementation of a Newton-Raphson algorithm for selective harmonics elimination. Contributions are made in interfacing and programming of Arduino ATmega328P microcontroller and use it to control of single-phase inverter by selective harmonic elimination technique. Through this work, we have given a general analytical description of the total harmonic distortion and present the method of application of the Newton-Raphson algorithm for selective harmonic elimination. MATLAB Simulink used for simulation and Arduino IDE software was used to program the Arduino board. The validity of the simulation model is verified by experimental results, the inverter was realized and the algorithm control is tested, results of the simulation and realization are compared, they show the efficiency of the system analysis and demonstrate the ability of Arduino to control the inverter and to generate complex signals for the electrical systems control.
Methods and Experiences for Developing Abstractions for Data-intensive, Scientific Applications

Developing software for scientific applications that require the integration of diverse types of computing, instruments, and data present challenges that are distinct from commercial software. These applications require scale, and the need to integrate various programming and computational models with evolving and heterogeneous infrastructure. Pervasive and effective abstractions for distributed infrastructures are thus critical; however, the process of developing abstractions for scientific applications and infrastructures is not well understood. While theory-based approaches for system development are suited for well-defined, closed environments, they have severe limitations for designing abstractions for scientific systems and applications. The design science research (DSR) method provides the basis for designing practical systems that can handle real-world complexities at all levels. In contrast to theory-centric approaches, DSR emphasizes both practical relevance and knowledge creation by building and rigorously evaluating all artifacts. We show how DSR provides a well-defined framework for developing abstractions and middleware systems for distributed systems. Specifically, we address the critical problem of distributed resource management on heterogeneous infrastructure over a dynamic range of scales, a challenge that currently limits many scientific applications. We use the pilot-abstraction, a widely used resource management abstraction for high-performance, high throughput, big data, and streaming applications, as a case study for evaluating the DSR activities. For this purpose, we analyze the research process and artifacts produced during the design and evaluation of the pilot-abstraction. We find DSR provides a concise framework for iteratively designing and evaluating systems. Finally, we capture our experiences and formulate different lessons learned.
Second Order Optimization Made Practical

Optimization in machine learning, both theoretical and applied, is presently dominated by first-order gradient methods such as stochastic gradient descent. Second-order optimization methods that involve second-order derivatives and/or second-order statistics of the data have become far less prevalent despite strong theoretical properties, due to their prohibitive computation, memory and communication costs. In an attempt to bridge this gap between theoretical and practical optimization, we present a proof-of-concept distributed system implementation of a second-order preconditioned method (specifically, a variant of full-matrix Adagrad), that along with a few yet critical algorithmic and numerical improvements, provides significant practical gains in convergence on state-of-the-art deep models and gives rise to actual wall-time improvements in practice compared to conventional first-order methods. Our design effectively utilizes the prevalent heterogeneous hardware architecture for training deep models which consists of a multicore CPU coupled with multiple accelerator units. We demonstrate superior performance on very large learning problems in machine translation where our distributed implementation runs considerably faster than existing gradient-based methods.
NeuroQuery, comprehensive meta-analysis of human brain mapping

Reaching a global view of brain organization requires assembling evidence on widely different mental processes and mechanisms. The variety of human neuroscience concepts and terminology poses a fundamental challenge to relating brain imaging results across the scientific literature. Existing meta-analysis methods perform statistical tests on sets of publications associated with a particular concept. Thus, large-scale meta-analyses only tackle single terms that occur frequently. We propose a new paradigm, focusing on prediction rather than inference. Our multivariate model predicts the spatial distribution of neurological observations, given text describing an experiment, cognitive process, or disease. This approach handles text of arbitrary length and terms that are too rare for standard meta-analysis. We capture the relationships and neural correlates of 7547 neuroscience terms across 13 459 neuroimaging publications. The resulting meta-analytic tool, neuroquery.org, can ground hypothesis generation and data-analysis priors on a comprehensive view of published findings on the brain.
Evidence for radio and X-ray auroral emissions from the magnetic B-type star ρ Oph A

We present new ATCA multiwavelength radio measurements (range 2.1-21.2 GHz) of the early-type magnetic star rho OphA, performed in 2019 March during three different observing sessions. These new ATCA observations evidence a clear rotational modulation of the stellar radio emission and the detection of coherent auroral radio emission from rho OphA at 2.1 GHz. We collected high-resolution optical spectra of rho OphA acquired by several instruments over a time span of about 10 yr. We also report new magnetic field measurements of rho OphA that, together with the radio light curves and the temporal variation of the equivalent width of the He I line (lambda = 5015 angstrom), were used to constrain the rotation period and the stellar magnetic field geometry. The above results have been used to model the stellar radio emission, modelling that allowed us to constrain the physical condition of rho OphA's magnetosphere. Past XMM-Newton measurements showed periodic X-ray pulses from. OphA. We correlate the X-ray light curve with themagnetic field geometry of rho OphA. The already published XMM-Newton data have been re-analysed showing that the X-ray spectra of rho OphA are compatible with the presence of a non-thermal X-ray component. We discuss a scenario where the emission phenomena occurring at the extremes of the electromagnetic spectrum, radio and X-ray, are directly induced by the same plasma process. We interpret the observed X-ray and radio features of rho OphA as having an auroral origin.
Complete Endomorphisms in Computer Vision.

Correspondences between k-tuples of points are key in multiple view geometry and motion analysis. Regular transformations are posed by homographies between two projective planes that serves as structural models for images. Such transformations can not include degenerate situations. Fundamental or essential matrices expand homographies with structural information by using degenerate bilinear maps. The projectivization of the endomorphisms of a three-dimensional vector space includes all of them. Hence, they are able to explain a wider range of eventually degenerate transformations between arbitrary pairs of views. To include these degenerate situations, this paper introduces a completion of bilinear maps between spaces given by an equivariant compactification of regular transformations. This completion is extensible to the varieties of fundamental and essential matrices, where most methods based on regular transformations fail. The construction of complete endomorphisms manages degenerate projection maps using a simultaneous action on source and target spaces. In such way, this mathematical construction provides a robust framework to relate corresponding views in multiple view geometry.
Transformer Hawkes Process

Modern data acquisition routinely produce massive amounts of event sequence data in various domains, such as social media, healthcare, and financial markets. These data often exhibit complicated short-term and long-term temporal dependencies. However, most of the existing recurrent neural network based point process models fail to capture such dependencies, and yield unreliable prediction performance. To address this issue, we propose a Transformer Hawkes Process (THP) model, which leverages the self-attention mechanism to capture long-term dependencies and meanwhile enjoys computational efficiency. Numerical experiments on various datasets show that THP outperforms existing models in terms of both likelihood and event prediction accuracy by a notable margin. Moreover, THP is quite general and can incorporate additional structural knowledge. We provide a concrete example, where THP achieves improved prediction performance for learning multiple point processes when incorporating their relational information.
Deep Sigma Point Processes

We introduce Deep Sigma Point Processes, a class of parametric models inspired by the compositional structure of Deep Gaussian Processes (DGPs). Deep Sigma Point Processes (DSPPs) retain many of the attractive features of (variational) DGPs, including mini-batch training and predictive uncertainty that is controlled by kernel basis functions. Importantly, since DSPPs admit a simple maximum likelihood inference procedure, the resulting predictive distributions are not degraded by any posterior approximations. In an extensive empirical comparison on univariate and multivariate regression tasks we find that the resulting predictive distributions are significantly better calibrated than those obtained with other probabilistic methods for scalable regression, including variational DGPs--often by as much as a nat per datapoint.
Refinement of Unsupervised Cross-Lingual Word Embeddings

Cross-lingual word embeddings aim to bridge the gap between high-resource and low-resource languages by allowing to learn multilingual word representations even without using any direct bilingual signal. The lion's share of the methods are projection-based approaches that map pre-trained embeddings into a shared latent space. These methods are mostly based on the orthogonal transformation, which assumes language vector spaces to be isomorphic. However, this criterion does not necessarily hold, especially for morphologically-rich languages. In this paper, we propose a self-supervised method to refine the alignment of unsupervised bilingual word embeddings. The proposed model moves vectors of words and their corresponding translations closer to each other as well as enforces length- and center-invariance, thus allowing to better align cross-lingual embeddings. The experimental results demonstrate the effectiveness of our approach, as in most cases it outperforms state-of-the-art methods in a bilingual lexicon induction task.
Three steps mixing for general random walks on the hypercube at criticality

We introduce a general class of random walks on the $N$-hypercube, study cut-off for the mixing time, and provide several types of representation for the transition probabilities. We observe that for a sub-class of these processes with long range (i.e. non-local) there exists a critical value of the range that allows an "almost-perfect" mixing in at most three steps. In other words, the total variation distance between the three steps transition and the stationary distribution decreases geometrically in $N$, which is the dimension of the hypercube. In some cases, the walk mixes almost-perfectly in exactly two steps. Notice that a well-known result (Theorem 1 in Diaconis and Shahshahani (1986)) shows that there exist no random walk on Abelian groups (such as the hypercube) which mixes perfectly in exactly two steps.
Analyzing Brain Networks in Population Neuroscience: A Case for the Bayesian Philosophy

Network connectivity fingerprints are among today’s best choices to obtain a faithful sampling of an individual’s brain and cognition. Widely available MRI scanners can provide rich information tapping into network recruitment and reconfiguration that now scales to hundreds and thousands of humans. Here we contemplate advantages of analyzing such connectome profiles using Bayesian strategies. These analysis techniques afford full probability estimates of the studied network coupling phenomena; provide analytical machinery to separate epistemological uncertainty and biological variability in a coherent manner; usher towards avenues to go beyond binary statements on existence vs. non-existence of an effect; and afford credibility estimates around all model parameters at play, which thus enable single-subject subjects predictions with rigorous uncertainty intervals. We illustrate the brittle boundary between healthy and diseased brain circuits by autism spectrum disorder as a recurring theme where, we argue, network-based approaches in neuroscience will require careful probabilistic answers.
Omp2b Porin Alteration in the Course of Evolution of Brucella spp.

The genus Brucella comprises major pathogenic species causing disease in livestock and humans, e.g. B. melitensis. In the past few years, the genus has been significantly expanded by the discovery of phylogenetically more distant lineages comprising strains from diverse wildlife animal species, including amphibians and fish. The strains represent several potential new species, with B. inopinata as solely named representative. Being genetically more distant between each other, relative to the “classical” Brucella species, they present distinct atypical phenotypes and surface antigens. Among surface protein antigens, the Omp2a and Omp2b porins display the highest diversity in the classical Brucella species. The genes coding for these proteins are closely linked in the Brucella genome and oriented in opposite directions. They share between 85 and 100% sequence identity depending on the Brucella species, biovar, or genotype. Only the omp2b gene copy has been shown to be expressed and genetic variation is extensively generated by gene conversion between the two copies. In this study, we analyzed the omp2 loci of the non-classical Brucella spp. Starting from two distinct ancestral genes, represented by Australian rodent strains and B. inopinata, a stepwise nucleotide reduction was observed in the omp2b gene copy. It consisted of a first reduction affecting the region encoding the surface L5 loop of the porin, previously shown to be critical in sugar permeability, followed by a nucleotide reduction in the surface L8 loop-encoding region. It resulted in a final omp2b gene size shared between two distinct clades of non-classical Brucella spp. (African bullfrog isolates) and the group of classical Brucella species. Further evolution led to complete homogenization of both omp2 gene copies in some Brucella species such as B. vulpis or B. papionis. The stepwise omp2b deletions seemed to be generated through recombination with the respective omp2a gene copy, presenting a conserved size among Brucella spp. , and may involve short direct DNA repeats. Successive Omp2b porin alteration correlated with increasing porin permeability in the course of evolution of Brucella spp. They possibly have adapted their porin to survive environmental conditions encountered and to reach their final status as intracellular pathogen.
Epistemic Modality: A Corpus-Based Analysis of Epistemic Markers in EU and Polish Judgments

Abstract The aim of this paper is to establish the repertoire and distribution of verbal and adverbial exponents of epistemic modality in English- and Polish-language judgments passed by the Court of Justice of the EU (CJEU) and non-translated judgments passed by the Supreme Court of Poland (SN). The study applies a model for categorizing exponents of epistemicity with regard to their (i) level (high-, medium- and low-level of certainty, necessity or possibility expressed by the markers; primary dimension), (ii) perspective (own vs. reported perspective), (iii) opinion (based either on facts or beliefs) and (iv) time (the embedding of epistemic markers in sentences relating to the past, present or future) (contextual dimensions). It examines the degree of intra-generic convergence of translated EU judgments and non-translated national judgments in terms of the employment of epistemic markers, as well as the degree of authoritativeness of judicial argumentation, and determines whether the frequent use of epistemic markers constitutes a generic feature of judgments. The research material consists of a parallel corpus of English- and Polish-language versions of 200 EU judgments and a corpus of 200 non-translated domestic judgments. The results point to the high salience and differing patterns of use of epistemic markers in both EU and national judgments. The frequent use of high-level epistemic markers boosts the authoritativeness of judicial reasoning.
Performance deterioration of GaN-based laser diode by V-pits in the upper waveguide layer

Abstract The effect of V-pits in the upper waveguide (UWG) on device performance of GaN-based laser diodes (LDs) has been studied. Experimental results demonstrate that in comparison with the LDs with u-In0.017Ga0. 983N/u-GaN multiple UWG or u-In0.017Ga0. 983N one, the LDs with a single u-GaN UWG has the best device performance. They have a smaller threshold current density, and a larger and more stable output optical power. The lowest threshold current density is as low as 1.3 kA/cm2, and the optical power reaches to 2.77 W. Furthermore, atomic force microscopy suggests that the deterioration of device performance of former kinds of devices may be attributed to the increase of V-pits’ size and quantity in the undoped-In0.017Ga0. 983N UWG layer, and these V-pits could introduce more nonradiative recombination centers and exacerbate the inhomogeneity of injection current. Moreover, theoretical calculation results indicate that the increase of leakage current and optical loss are additional reasons for the device performance deterioration, which may be caused by a reduction of the potential barrier height for electrons in the quantum wells and by an increased background electron concentration in UWG.
Green synthesis of TiO2 nanoparticles from Syzygium cumini extract for photo-catalytic removal of lead (Pb) in explosive industrial wastewater

Abstract Green synthesis is a simple, non-toxic, economical and eco-friendly approach for the synthesis of nanoparticles. In the present work, nanoparticles of titanium dioxide (TiO2 NPs) were synthesized using an aqueous solution of Syzygium cumini leaf extract as a capping agent. These green synthesized TiO2 NPs were further evaluated for photo catalytic removal of lead from industrial wastewater. Obtained nanoparticles were characterized using: high-resolution scanning electron microscopy (HRSEM), high-resolution transmission electron microscopy (HRTEM), X-ray energy dispersive spectroscopy (EDS), Fourier transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD), dynamic light scattering (DLS) and Brunauer-Emmett-Teller (BET). Obtained results revealed that synthesized TiO2 NPs possess spherical morphology with anatase phase with a large BET surface area of 105 m2/g. Photo catalytic studies of TiO2 NPs for lead removal from explosive wastewater were performed in a self-designed reactor. Inductive coupled plasma spectroscopy (ICP) was used to determine the lead concentration. Obtained results witnessed 75.5% removal in chemical oxygen demand (COD) and 82.53% removal in lead (Pb2+). This application of green TiO2 NPs is being explored for the first time.
The Impact of R&D Expenditure on Productivity in the Manufacturing Industry in Poland

Abstract EU strategy documents, including the Europe 2020 Strategy, emphasise the priority role of industry in the growth of EU economies. The aim of the presented research is to measure and assess the impact of expenditure on research and development (R&D) activities on the competitiveness of manufacturing divisions in Poland. Labour productivity, expressed as (1) a quotient of gross value added and employment, and (2) a quotient of sold production and employment in the analysed manufacturing divisions, was adopted as the measure of competitiveness. The empirical part of the paper is based on data published by the Central Statistical Office. The analysis covers the period 2009-2017 and provides a contribution to determining the role and importance of research and development as well as innovative activities in shaping competitive advantages of manufacturing enterprises. The obtained results for panel models confirm that R&D expenditure is a secondary factor in changes occurring in the Polish manufacturing industry.
Single-particle perovskite lasers: from material properties to cavity design

Abstract Last decade, halide perovskites demonstrate high potential for efficient, tunable, and cheap photonic sources. Recently, single-particle perovskite lasers of various compositions and shapes with all dimensions close or smaller than the emitted wavelengths were demonstrated experimentally in a broad range of temperatures. In this review, we aim to cover not only the recent progress in the single-particle perovskite lasers but also provide a comprehensive analysis on strategies to achieve the most compact perovskite lasers with the best working parameters.
Kalasha affricates: An acoustic analysis of place contrasts

Abstract Kalasha (Northwestern Indo-Aryan, spoken in Pakistan) exhibits a complex set of ten affricate phonemes, which is exceedingly rare among the world’s languages and not representative of the broader South Asian context. This paper presents results of an acoustic analysis of place contrasts (dental, retroflex, and alveolopalatal) in affricates of four laryngeal specifications (voiceless unaspirated, voiceless aspirated, non-breathy voiced, and breathy voiced). These consonants were produced by four male speakers of Kalasha in a variety of phonetic contexts, resulting in a sample of close to 700 affricate tokens. A series of acoustic analyses of the data revealed that place contrasts in Kalasha affricates are distinguished robustly by both burst/frication spectra and formant transitions, but not by duration, which correlates more with laryngeal features. Place distinctions are somewhat diminished for voiced affricates but are largely unaffected by aspiration and syllable position. Most of these results are consistent with what is known about comparable (yet laryngeally simpler) place contrasts in other languages outside of South Asia. However, some of them are unique and may reflect the typological uniqueness and complexity of Kalasha’s affricate system.