Miracle-dz/UniFAR-FTUs huggingface dataset:
Mass varying neutrinos with different quintessence potentials

The mass-varying neutrino scenario is analyzed for three trial quintessence potentials (Ferreira-Joyce, inverse exponential, and thawing oscillating). The neutrino mass is generated via Yukawa coupling to the scalar field which represents dark energy. The inverse exponential and oscillating potentials are shown to successfully generate the neutrino masses in the range m ∼ 10-2-10-3 eV and to yield the current dark energy density in the regime of the late-time acceleration of the Universe. Depending on the choice of potentials, the acceleration could occur in two different regimes: (1) the regime of instability, and (2) the stable regime. The first regime of instability is after the Universe underwent a first-order transition and is rolling toward the new stable vacuum. The imaginary sound velocity c2 s < 0 in this regime implies growing fluctuations of the neutrino density (clustering). In the second regime, the Universe smoothly changes its stable states via a continuous transition. Since c2 s > 0, the neutrino density is stable. For all cases the predicted late-time acceleration of the Universe is asymptotically very close to that of the ΛCDM model. Further extensions of the theory to modify the neutrino sector of the Standard Model and to incorporate inflation are also discussed. It is also shown that in the stable regimes where the neutrino mass is given by the minimum of the thermodynamic potential, the tree-level dynamics of the scalar field is robust with respect to one-loop bosonic and fermionic corrections to the potential.
Design and Realization of a Multi-Cell-Switch-Mode Power Amplifier Employing a Digital Poly-Phase-Pulse-Width-Modulator

This paper presents a multi-cell switch-mode power amplifier formed by a series connection of six Hbridge switching cells. The phase-shifted gating signals of the switching cells are generated using a digital poly-phase-PWM implemented by application of a low-cost CPLD. The generation of the isolated DC link voltages of the switching cells is performed by six series-resonant half-bridge DC/DC converters. The advantages and drawbacks of the circuit topology and the proposed PWM scheme are discussed based on measurements gained from a realized 400V/5A prototype system. There, especially the impact of the delay of the digital modulator on the amplifiers dynamic behavior is analyzed. Fig.1: Basic circuit topology of the realized multi-cell switch-mode power amplifier based on a series connection of H-bridge switching-cells (N = 6 cells); the autonomous cells are controlled based on interleaved poly-phase pulse width modulation. switching frequency: fS = 25kHz (per cell) number of cells: fN = 6 H-cells
Contribution of starburst nuclei to the diffuse gamma-ray and neutrino flux

In nuclei of starburst galaxies (SBGs), the combination of an enhanced rate of supernova explosions and a high gas density suggests that cosmic rays (CRs) can be efficiently produced, and that most of them lose their energy before escaping these regions, resulting in a large flux of secondary products, including neutrinos. Although the flux inferred from an individual starburst region is expected to be well below the sensitivity of current neutrino telescopes, such sources may provide a substantial contribution to the diffuse neutrino flux measured by IceCube. Here, we compute the gamma-ray and neutrino flux due to SBGs based on a physical model of CR transport in a starburst nucleus, and accounting for the redshift evolution of the number density of starburst sources as inferred from recent measurements of the star formation rate. The model accounts for gamma-ray absorption both inside the sources and in the intergalactic medium. The latter process is responsible for electromagnetic cascades, which also contribute to the diffuse gamma-ray background at lower energies. The conditions for acceleration of CR protons up to energies exceeding $\sim 10 \, \rm PeV$ in starburst regions, necessary for the production of PeV neutrinos, are investigated in a critical way. We show that starburst nuclei can account for the diffuse neutrino flux above $\sim 200 \, \rm TeV$, thereby producing $\lesssim 40 {\rm { per\, cent}}$ of the extragalactic diffuse gamma-ray background. Below $\sim 200 \, \rm TeV$, the flux from starburst appears to be somewhat lower than the observed one, where both the Galactic contribution and the flux of atmospheric neutrinos may account for the difference.
Fast multigrid solution of high-order accurate multi-phase Stokes problems

A fast multigrid solver is presented for high-order accurate Stokes problems discretised by local discontinuous Galerkin (LDG) methods. The multigrid algorithm consists of a simple V-cycle, using an element-wise block Gauss-Seidel smoother. The efficacy of this approach depends on the pressure penalty stabilisation parameter underlying the LDG framework---provided the parameter is suitably chosen, numerical experiment shows that: (i) for steady-state Stokes problems, the convergence rate of the multigrid solver can match that of classical geometric multigrid methods for Poisson problems; (ii) for unsteady Stokes problems, the convergence rate further accelerates as the effective Reynolds number is increased. An extensive range of two- and three-dimensional test problems demonstrates the solver performance as well as high-order accuracy---these include cases with periodic, Dirichlet, and stress boundary conditions; variable-viscosity and multi-phase embedded interface problems containing density and viscosity discontinuities several orders in magnitude; and test cases with curved geometries using semi-unstructured meshes.
Contextual Recurrent Units for Cloze-style Reading Comprehension

Recurrent Neural Networks (RNN) are known as powerful models for handling sequential data, and especially widely utilized in various natural language processing tasks. In this paper, we propose Contextual Recurrent Units (CRU) for enhancing local contextual representations in neural networks. The proposed CRU injects convolutional neural networks (CNN) into the recurrent units to enhance the ability to model the local context and reducing word ambiguities even in bi-directional RNNs. We tested our CRU model on sentence-level and document-level modeling NLP tasks: sentiment classification and reading comprehension. Experimental results show that the proposed CRU model could give significant improvements over traditional CNN or RNN models, including bidirectional conditions, as well as various state-of-the-art systems on both tasks, showing its promising future of extensibility to other NLP tasks as well.
An Algorithm for Learning Tactical Behavior from Observation

This paper describes the components and mechanics of an algorithm that learns context-transition logic for a Context-Based Reasoning [1] model of some tactical behavior. The purpose of the algorithm is to automatically generate a knowledge-base for context-transitions given a sequence of observations of an expert performing a tactical behavior requiring ‘high-level’ decision-making steps. The knowledge that will be extracted with this algorithm will be ‘high-level’ in that it will identify situations where the expert has chosen to alter his lower-level course of action or behavior. The situations and goals requiring these lower-level behaviors are considered to be contexts that, when combined along with the overall goals of the expert’s behavior (his mission) and the logic that defines when context transitions are necessary (the context-transition logic), represent the complete behavior known by the expert and desired by an autonomous agent tasked to perform that behavior. Assuming that these lower-level behaviors are known and that their corresponding contexts have been encoded, the algorithm proposed will complete the knowledge necessary for an autonomous CxBR model of the behavior, provided that the behavior is well-defined. For this research, a well-defined behavior (or mission) is achieved with a clear description of goals and a logical partitioning of the behavior into contexts. Because of this, the mission goals for the CxBR model must be defined so that they are consistent with the goals assumed by the expert to be observed. Conversely, the context-partitioning must be performed by a knowledge-engineer without any correspondence or interaction with the expert, as the only connection required between the paradigm and the expert’s behavior is that the low-level behaviors coded into contexts by the knowledge-engineer be similar in execution to those employed by the expert. The more similar these lowlevel behaviors can be, the more closely-tied the CxBR model’s performance will be to the learning algorithm’s ability to generate good context-transition logic. The expert must also be clear on the behavior he is to perform, and must also be in an environment in which he can operate normally and without hesitation. Finally, the observational system must be situated so it has the most direct access to the stimuli on the expert without impeding him. Testing the algorithm requires that all learning takes place within a simulation so that the observing system can have identical perspective of the scenario as the expert.
Supplemental Material: Orientation-Aware Semantic Segmentation on Icosahedron Spheres

We include the pseudo code of our main CNN operators, applied to the icosahedron mesh components, denoted {Ci}i=0. Note, many operations will be a direct result of a combination of these operators (i.e. Pyramid Pooling Layers [4]). First we detail padding in Algorithm 1. Our orientation-aware hexagonal convolutions with arc-based interpolations for north-alignment are given in Algorithm 2. Algorithm 3 presents up-sampling. We emphasize, convolutions with kernel size 1, pooling, batch normalization, non-linearities and biases are directly computed on the spherical components without padding, through standard unchanged CNN operators.
Unconditional uniqueness of higher order nonlinear Schrödinger equations

We show the existence of weak solutions in the extended sense of the Cauchy problem for the cubic fourth order nonlinear Schrödinger equation with the initial data u0 ∈ X, where X∈{M2,qs(ℝ),Hσ(T),Hs1(ℝ)+Hs2(T)}\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$X \in \{M_{2,q}^s(\mathbb{R}),\,{H^\sigma}(\mathbb{T}),\,{H^{{s_1}}}(\mathbb{R}) + {H^{{s_2}}}(\mathbb{T})\}$$\end{document} and q ∈ [1, 2], s ⩾ 0, or σ ⩾ 0, or s2 ⩾ s1 ⩾ 0. Moreover, if M2,qs(ℝ) ↪ L3(ℝ), or if σ⩾16\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma \geqslant {1 \over 6}$$\end{document}, or if s1⩾16\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${s_1} \geqslant {1 \over 6}$$\end{document} and s2>12\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${s_2} > {1 \over 2}$$\end{document} we how that the Cauchy problem is unconditionally wellposed in X. Similar results hold true for all higher order nonlinear Schrödinger equations and mixed order NLS due to a factorization property of the corresponding phase factors. For the proof we employ the normal form reduction via the differentiation by parts technique and build upon our previous work.
Towards Pose-Invariant Lip-Reading

Lip-reading models have been significantly improved recently thanks to powerful deep learning architectures. However, most works focused on frontal or near frontal views of the mouth. As a consequence, lip-reading performance seriously deteriorates in non-frontal mouth views. In this work, we present a framework for training pose-invariant lip-reading models on synthetic data instead of collecting and annotating non-frontal data which is costly and tedious. The proposed model significantly outperforms previous approaches on non-frontal views while retaining the superior performance on frontal and near frontal mouth views. Specifically, we propose to use a 3D Morphable Model (3DMM) to augment LRW, an existing large-scale but mostly frontal dataset, by generating synthetic facial data in arbitrary poses. The newly derived dataset, is used to train a state-of-the-art neural network for lip-reading. We conducted a cross-database experiment for isolated word recognition on the LRS2 dataset, and reported an absolute improvement of 2.55%. The benefit of the proposed approach becomes clearer in extreme poses where an absolute improvement of up to 20.64% over the baseline is achieved.
Decorated Protein Networks: Functional Nanomaterials with Tunable Domain Size

The implementation of natural and artificial proteins with designer properties and functionalities offers unparalleled opportunity for functional nanoarchitectures formed through self-assembly. However, to exploit the opportunities offered we require the ability to control protein assembly into the desired architecture while avoiding denaturation and therefore retaining protein functionality. Here we address this challenge with a model system of fluorescent proteins. Using techniques of self-assembly manipulation inspired by soft matter where interactions between components are controlled to yield the desired structure, we show that it is possible to assemble networks of proteins of one species which we can decorate with another, whose coverage we can tune. Consequently, the interfaces between domains of each component can also be tuned, with applications for example in energy transfer. Our model system of fluorescent proteins eGFP and mCherry retain their fluorescence throughout the assembly process, thus demonstrating that functionality is preserved.
A Comparison Of The KI67 Proliferation Index And CD117 & Cox-2 Expressions In Renal Carcinomas

The present study has three main aims; a) to investigate the relationship between the tumor grade (TG), type (TT) and size (TS) on the renal carcinoma cancer (RCC) types, and demographic features of the patients such as gender and age, b) to find out the correlation between KI67 proliferation index and COX-2 and CD117 on the RCC, and c) to find out whether the immunohistochemical (IHC) reagents can determine the prognosis of some renal tumor diseases.In this research, 50 cases with RCC in Dicle University Hospital (30 clear cell type, 8 multi-lobular type, 5 sarcomatoid type, 4 papillary type, 3 chromophobe type), and 1 with radical nephrectomy were investigated retrospectively.The TG was carried out using Fuhrman grading system (FGS) and IHC examination was carried out on KI67, COX2, and CD117.The results of the present study did not indicate that gender had any effects on tumor parameters, but CD117 and age had an effect on CD117 and COX2. The results indicated strong correlations between tumor parameters such as TT and TG and CD117, KI67, TG and TT and CD117, and COX2 and KI67.
Enhancement of Electroluminescence Intensity from Dye-Doped Poly(3-Alkylthiophene) Light Emitting Diode with Different Alkyl-Side-Chain Length

Electroluminescent (EL) diodes utilizing poly(3-alkylthiophene) (P3AT) containing fluorescent dyes have been fabricated and their marked enhancement of emission characteristics has been discussed. Enhancement of EL efficiency has been observed for diodes with P3AT doped with PBD (2-(4-biphenylyl)-5-(4- tert-butylphenyl)-1,3,4-oxadiazole), PPCP (1,2,3,4,5-pentaphenyl-1,3-cyclopentadiene) or BPPC (N,N'-bis(2,5-di- tert-butylphenyl)-3,4,9,10-perylenedicarboximide), and more than 500 times higher emission efficiency is obtained in PPCP-doped P3AT. Based on the result of thickness dependence of EL efficiency in PBD-doped P3AT, the layer thickness at which maximum efficiency is obtained is found to be smaller than that in undoped P3AT. The mechanism of emission enhancement by doping of dye molecules into P3AT with different alkyl-side-chain lengths has been discussed in terms of carrier trapping and the energy band diagram.
Part-Pose Guided Amur Tiger Re-Identification

In this paper, we present our solution to tiger re-identification (re-ID) in both the plain and the wild tracks in the 2019 Computer Vision for Wild life Conservation Challenge (CVWC2019). We introduce a novel part-pose guided framework for the tiger re-ID task, which consists of two part streams and one full stream based on the pose characteristics of tiger. Considering missing and inaccurate pose annotations, the two part streams are used as a regulator to guide the full stream in learning and aligning the local features in the training stage. We only use the learnt full stream for the tiger re-ID task in the inference stage. The proposed model has the advantage that despite requiring pose information at training time it is not needed during inference, so it is particularly suitable for tiger re-ID in the wild. Our proposed method outperforms the state-of-the-art and finished top in both the PlainID and WildID competitions at CVWC2019. The source of code will be public available at https://github.com/LcenArthas/CVWC2019-Amur-Tiger-Re-ID
Physical activity level and associated factors: an epidemiological study with elderly

Abstract Introduction: This study is relevant insofar since it provides information on the elements that interfere in the level of physical activity of the elderly that attend the Fitness zone. Objective: Identifying the level of physical activity and its associated factors related to the elderly who usually go to third age fitness centers in Maringá/PR. Method: A cross-sectional study was carried out with 970 elderly users of the Fitness zone of the municipality of Maringá, state of Paraná, Brazil. A sociodemographic questionnaire and the International Physical Activity Questionnaire (IPAQ) was used. The data were analyzed by the Pearson’s Chi-squared, the Binary Logistic Regression and the Hosmer-Lemeshow test (p < 0,05). Results: The data indicates that being married (p = 0,047) and having completed higher education (p = 0,001) is significantly associated with higher physical activity level. The lower use of medication (p = 0,008), the excellent health perception (p = 0,037), and no history of near-falls (p = 0,038) were associated with the physical activity practice. The subjects who had no history of near-falls in the last six months and who did not have osteoporosis were 1.671 [95% CI = 1.009-2.613] and 1.891 [95% CI = 1.008-2.915] times more likely to be active/very active when compared to the elderly who had near-falls in the last semester and who had osteoporosis. Conclusion: It was concluded that sociodemographic variables and health conditions are associated to the physical activity level in the elderly. Further, elderly who reported the absence of a history of near falls and osteoporosis have more chance to be physically active.
Probabilistic Record Matching for Entity Resolution Using Markov Logic Networks

Entity resolution (ER) is a problem in identifying objects referring to the same real-world entity into a single representation. In the context of the database, ER is also known as record linkage to determine records that refer to the same entities in which statistical probabilistic approach of this type of ER is called probabilistic record linkage (PRL). In addition, PRL has been used for various ER problems, including derivatives that use machine learning as an improvement. However, this probabilistic approach has one problem in ER for dealing with missing data that commonly occur in unreliable datasets. Such unreliable data can lead to more uncertainty and can reduce the quality of the final result. This paper discusses an alternative approach of PRL using a Markov logic networks (MLN) to infer the matching of record pairs in unreliable datasets, especially for datasets with a high rate of missing data. The proposed approach was inspired by a model of matching dependencies (MDS) that has been formally introduced to address unreliable datasets. Experimentation on real-world datasets taken from State Islamic University of Maulana Malik Ibrahim Malang Indonesia was done with 0.977 accuracy approaching 0.986 in the previous method.
Environmental Sustainability And The Framing Of Formula E Motor Racing In UK And Flemish Newspapers

Developed in cooperation with the Fédération Internationale d’Automobile (FIA) as motor sport’s governing body, the fully electric racing series Formula E represents itself as a driving force in making the motor sport and automotive industries more environmentally sustainable (hereafter: ES). However, the question remains whether such ES efforts are picked up on by the media, and more specifically newspapers that are still considered a benchmark for in-depth and reflective journalism, despite a dramatic rise of online and social media coverage of sport. Combining a quantitative content analysis with a qualitative framing analysis, this article identified, compared and contrasted frames, and the significance of ES herein, in a range of UK and Flemish quality and popular newspapers. Results indicate that, although ES did not constitute a frame in itself, it was significantly part of other frames that represented Formula E both positively and negatively. Additionally, results uncover distinct differences in reporting on (ES in) Formula between UK and Flemish newspapers. This article suggests such differences are related to reasons of a historical and regional nature and subsequently affect the representation of ES in Formula E.
Automatically Testing String Solvers

SMT solvers are at the basis of many applications, such as program verification, program synthesis, and test case generation. For all these applications to provide reliable results, SMT solvers must answer queries correctly. However, since they are complex, highly-optimized software systems, ensuring their correctness is challenging. In particular, state-of-the-art testing techniques do not reliably detect when an SMT solver is unsound. In this paper, we present an automatic approach for generating test cases that reveal soundness errors in the implementations of string solvers, as well as potential completeness and performance issues. We synthesize input formulas that are satisfiable or unsatisfiable by construction and use this ground truth as test oracle. We automatically apply satisfiability-preserving transformations to generate increasingly-complex formulas, which allows us to detect many errors with simple inputs and, thus, facilitates debugging. The experimental evaluation shows that our technique effectively reveals bugs in the implementation of widely-used SMT solvers and applies also to other types of solvers, such as automata-based solvers. We focus on strings here, but our approach carries over to other theories and their combinations.