Miracle-dz/UniFAR-FTUs huggingface dataset:
COMPONENTES DE RENDIMENTO E PRODUTIVIDADE DO FEIJOEIRO COMUM SUBMETIDO À INOCULAÇÃO E CO-INOCULAÇÃO COM ESTIRPES DE Rhizobium tropici E Azospirillum brasilense

O objetivo do trabalho foi avaliar a influencia da inoculacao e co-inoculacao com bacterias do genero Rhizobium e Azospirillum sobre os componentes de producao e produtividade do feijoeiro comum. O experimento foi realizado no ano de agricola de 2015/2016 em delineamento de blocos casualizados com 5 repeticoes e 4 tratamentos: T1= Testemunha (sem inoculacao); T2= Inoculacao com Rhizobium tropici ; T3= Inoculacao com Azospirillum brasilense e T4= Co-inoculacao com Rhizobium tropici + Azospirillum brasilense. Os componentes de producao avaliados foram: altura de plantas (cm), diâmetro do colmo (cm), numero de vagens por planta, numero de graos por vagem, massa de cem graos (g) e produtividade (kg ha -1 ). Os tratamentos com inoculacao apresentaram plantas maiores e maior numero de vagens por planta do que a testemunha. A inoculacao, independente do microrganismo utilizado, aumentou a altura das plantas e o numero de vagens por planta. A co-inoculacao com Rhizobium tropici + Azospirillum brasilense apresentou a maior massa de cem graos (25,33 g) e a maior produtividade (2448,45 kg ha -1 ).
Cover 3-uniform hypergraphs by vertex-disjoint tight paths

Let $H$ be an $n$-vertex 3-graph such that every pair of vertices is in at least $n/3+o(n)$ edges. We show that $H$ contains two vertex-disjoint tight paths whose union covers the vertex set of $H$. The quantity two here is best possible and the degree condition is asymptotically best possible.
A Neural-embedded Choice Model: TasteNet-MNL Modeling Taste Heterogeneity with Flexibility and Interpretability

Discrete choice models (DCMs) and neural networks (NNs) can complement each other. We propose a neural network embedded choice model - TasteNet-MNL, to improve the flexibility in modeling taste heterogeneity while keeping model interpretability. The hybrid model consists of a TasteNet module: a feed-forward neural network that learns taste parameters as flexible functions of individual characteristics; and a choice module: a multinomial logit model (MNL) with manually specified utility. TasteNet and MNL are fully integrated and jointly estimated. By embedding a neural network into a DCM, we exploit a neural network's function approximation capacity to reduce specification bias. Through special structure and parameter constraints, we incorporate expert knowledge to regularize the neural network and maintain interpretability. On synthetic data, we show that TasteNet-MNL can recover the underlying non-linear utility function, and provide predictions and interpretations as accurate as the true model; while examples of logit or random coefficient logit models with misspecified utility functions result in large parameter bias and low predictability. In the case study of Swissmetro mode choice, TasteNet-MNL outperforms benchmarking MNLs' predictability; and discovers a wider spectrum of taste variations within the population, and higher values of time on average. This study takes an initial step towards developing a framework to combine theory-based and data-driven approaches for discrete choice modeling.
Seismic Evaluation and Retrofit of 230-kV Porcelain Transformer Bushings

Static and earthquake-simulator testing of two 230-kV, 3000A, Type-U transformer bushings manufactured in the mid 1980s by the General Electric (GE) Company was undertaken to evaluate both the seismic performance of bushings known to be vulnerable to damage from moderate earthquake shaking, and the efficacy of simple retrofit details. Both bushings survived earthquake shaking compatible with the IEEE spectrum for High Level qualification when mounted on a rigid frame. When mounted on a flexible frame, one 230-kV bushing was unable to sustain the High Level qualification shaking without oil leakage and slip of the porcelain units. Two retrofit details utilizing two semicircular ring plates and epoxy filler were prepared by utility representatives with the objective of limiting slip of the UPPER-1 porcelain unit over the gasket connection. The second detail, Ring-2, permitted the 230-kV bushing to sustain substantially larger accelerations and displacements than those of the unretrofitted bushing and eliminated the permanent slip of the UPPER-1 porcelain unit over the gasket connection for extreme earthquake shaking.
Multi-layer perceptron error surfaces: visualization, structure and modelling

The Multi-Layer Perceptron (MLP) is one of the most widely applied and researched Artificial Neural Network model. MLP networks are normally applied to performing supervised learning tasks, which involve iterative training methods to adjust the connection weights within the network. This is commonly formulated as a multivariate non-linear optimization problem over a very high-dimensional space of possible weight configurations. Analogous to the field of mathematical optimization, training an MLP is often described as the search of an error surface for a weight vector which gives the smallest possible error value. Although this presents a useful notion of the training process, there are many problems associated with using the error surface to understand the behaviour of learning algorithms and the properties of MLP mappings themselves. Because of the high-dimensionality of the system, many existing methods of analysis are not well-suited to this problem. Visualizing and describing the error surface are also nontrivial and problematic. These problems are specific to complex systems such as neural networks, which contain large numbers of adjustable parameters, and the investigation of such systems in this way is largely a developing area of research. In this thesis, the concept of the error surface is explored using three related methods. Firstly, Principal Component Analysis (PCA) is proposed as a method for visualizing the learning trajectory followed by an algorithm on the error surface. It is found that PCA provides an effective method for performing such a visualization, as well as providing an indication of the significance of individual weights to the training process. Secondly, sampling methods are used to explore the error surface and to measure certain properties of the error surface, providing the necessary data for an intuitive description of the error surface. A number of practical MLP error surfaces are found to contain a high degree of ultrametric structure, in common with other known configuration spaces of complex systems. Thirdly, a class of global optimization algorithms is also developed, which is focused on the construction and evolution of a model of the error surface (or search spa ce) as an integral part of the optimization process. The relationships between this algorithm class, the Population-Based Incremental Learning algorithm, evolutionary algorithms and cooperative search are discussed. The work provides important practical techniques for exploration of the error surfaces of MLP networks. These techniques can be used to examine the dynamics of different training algorithms, the complexity of MLP mappings and an intuitive description of the nature of the error surface. The configuration spaces of other complex systems are also amenable to many of these techniques. Finally, the algorithmic framework provides a powerful paradigm for visualization of the optimization process and the development of parallel coupled optimization algorithms which apply knowledge of the error surface to solving the optimization problem.
Multi-Reference Entropy Model for Learned Image Compression

Recently, learned image compression has achieved remarkable performance. Entropy model, which accurately estimates the distribution of latent representation, plays an important role in boosting rate distortion performance. Most entropy models capture correlations in one dimension. However, there are channel-wise, local and global spatial correlations in latent representation. To address this issue, we propose multi-reference entropy models MEM and MEM + to capture channel, local and global spatial contexts. We divide latent representation into slices. When decoding current slice, we use previously decoded slices as contexts and use attention map of previously decoded slice to predict global correlations in current slice. To capture local contexts, we propose enhanced checkerboard context capturing to avoid performance degradation while retaining two-pass decoding. Based on MEM and MEM + , we propose image compression models MLIC and MLIC + . Extensive experimental evaluations have shown that our MLIC and MLIC + achieve state-of-the-art performance and they reduce BD-rate by 9 . 77% and 13 . 09% on Kodak dataset over VVC when measured in PSNR.
Towards a high performing UV-A sensor based on Silicon Carbide and hydrogenated Silicon Nitride absorbing layers

Exposure to ultraviolet (UV) radiation is a major risk factor for most skin cancers. The sun is our primary natural source of UV radiation. The strength of the sun's ultraviolet radiation is expressed as Solar UV Index (UVI). UV-A (320–400 nm) and UV-B (290–320 nm) rays mostly contribute to UVI. UV-B is typically the most destructive form of UV radiation because it has enough energy to cause photochemical damage to cellular DNA. Also overexposure to UV-A rays, although these are less energetic than UV-B photons, has been associated with toughening of the skin, suppression of the immune system, and cataract formation. The use of preventive measures to decrease sunlight UV radiation absorption is fundamental to reduce acute and irreversible health diseases to skin, eyes and immune system. In this perspective UV sensors able to monitor in a monolithic and compact chip the UV Index and relative UV-A and UV-B components of solar spectrum can play a relevant role for prevention, especially in view of the integration of these detectors in close at hand portable devices. Here we present the preliminary results obtained on our UV-A sensor technology based on the use of hydrogenated Silicon Nitride (SiN:H) thin passivating layers deposited on the surface of thin continuous metal film Ni2Si/4H-SiC Schottky detectors, already used for UV-Index monitoring. The first UV-A detector prototypes exhibit a very low leakage current density of about 0.2 pA/mm2 and a peak responsivity value of 0.027 A/W at 330 nm, both measured at 0V bias.
Cooperative Training of Deep Aggregation Networks for RGB-D Action Recognition

A novel deep neural network training paradigm that exploits the conjoint information in multiple heterogeneous sources is proposed. Specifically, in a RGB-D based action recognition task, it cooperatively trains a single convolutional neural network (named c-ConvNet) on both RGB visual features and depth features, and deeply aggregates the two kinds of features for action recognition. Differently from the conventional ConvNet that learns the deep separable features for homogeneous modality-based classification with only one softmax loss function, the c-ConvNet enhances the discriminative power of the deeply learned features and weakens the undesired modality discrepancy by jointly optimizing a ranking loss and a softmax loss for both homogeneous and heterogeneous modalities. The ranking loss consists of intra-modality and cross-modality triplet losses, and it reduces both the intra-modality and cross-modality feature variations. Furthermore, the correlations between RGB and depth data are embedded in the c-ConvNet, and can be retrieved by either of the modalities and contribute to the recognition in the case even only one of the modalities is available. The proposed method was extensively evaluated on two large RGB-D action recognition datasets, ChaLearn LAP IsoGD and NTU RGB+D datasets, and one small dataset, SYSU 3D HOI, and achieved state-of-the-art results.
Power comparison of data depth-based nonparametric tests for testing equality of locations

ABSTRACT In the recent years, the notion of data depth has been used in nonparametric multivariate data analysis since it gives natural ‘centre-outward’ ordering of multivariate data points with respect to the given data cloud. In the literature, various nonparametric tests are developed for testing equality of location of two multivariate distributions based on data depth. Here, we define two nonparametric tests based on two different test statistic for testing equality of locations of two multivariate distributions. In the present work, we compare the performance of these tests with the tests developed by Li and Liu [New nonparametric tests of multivariate locations and scales using data depth. Statist Sci. 2004;(1):686–696] for testing equality of locations of two multivariate distributions. Comparison in terms of power is done for multivariate symmetric and skewed distributions using simulation for three popular depth functions. Application of tests to real life data is provided. Conclusion and recommendations are also provided.
Deterministic models of Southern Hemisphere circulation variability

Statistical models of atmospheric variability typically attempt to account for deterministic seasonal variations by constructing a long-term average for each day or month of the year. Year-to-year variability can then be treated as some form of stochastic process about this long-term average. In general, the stochastic processes are assumed to be statistically stationary (invariant under time translation). However, for a non-linear system such as the Earth’s atmosphere, multiple seasonal evolutions may be possible for the same external forcing. In the presence of such a multiplicity of solutions, the identification of a seasonal cycle with a long-term average may not be the optimal procedure. Previous research has suggested that multiple evolutions of the seasonal cycle of the Southern Hemisphere mid-latitude circulation may be possible. The central goal of this thesis is to build on this work and to present evidence for different seasonal evolutions of the Southern Hemisphere mid-latitude circulation. This evidence is initially presented by highlighting a low-frequency peak in an aspect of the Southern Hemisphere mid-latitude circulation that is viewed as a harmonic of the annual cycle (quasi-two year). Statistically stationary models of variability about a long-term average are argued to be unable to account for the presence of this harmonic. Following this, an alternative model of circulation variability is proposed that explicitly references various stages of the seasonal cycle in a deterministic manner. In particular, explicit reference is made to the downward shift and to the final breakdown of the stratospheric polar vortex. A re-interpretation of several previous results in the literature including Southern Annular Mode persistence timescales, Southern Hemisphere mid-latitude climate change and the semi-annual oscillation of the mid-latitude jet is subsequently presented using this alternative perspective.
Early and late outcomes following aortic root enlargement: A multicenter propensity score–matched cohort analysis

Objective: The safety and efﬁcacy of aortic root enlargement (ARE) at the time of aortic valve replacement (AVR) remains unknown. The objective of this multicenter study was to compare AVR with ARE to AVR for early and late mortality and secondary safety outcomes. Methods: Clinical and administrative databases in Ontario, Canada, were linked to obtain patients undergoing AVR with or without ARE from 2008 to 2017. Baseline characteristics were compared and 1:1 propensity score matching was performed to account for differences in baseline characteristics. Early outcomes were compared in the matched groups. Late mortality was compared using Kaplan-Meier survival curves and a Cox-proportional hazard model. Results: Sixteen thousand six hundred ﬁfty-six patients undergoing AVR in 11 Ontario institutions were reviewed. Patients who underwent ARE were younger, nonurgent, more likely to be men and had lower rates of hypertension, ischemic heart disease, and congestive heart failure. Propensity score matching yielded similar groups for comparison, with 809 pairs for AVR versus AVR with ARE. There was no difference in 30-day mortality between AVR with ARE versus AVR (2.0 % vs 2.1 % ; P ¼ 1.00). Rates of chest reopening for bleeding, permanent pacemaker implantation, and blood transfusions were similar. Late mortality over 8 years was similar between AVR with ARE and AVR ( P ¼ .45). In a sensitivity analysis, results were similar in 525 pairs comparing AVR with coronary artery bypass grafting and ARE to AVR with coronary artery bypass grafting, except that chest reopening for bleeding was higher with AVR with coronary artery bypass grafting and ARE (7.2 % vs 3.2 %
INSTABILITIES IN ELASTOMERS AND IN SOFT TISSUES

Biological soft tissues exhibit elastic properties which can be dramatically different from rubber-type materials (elastomers). To gain a better understanding of the role of constitutive relationships in determining material responses under loads we compare three different types of instabilities (two in compression, one in extension) in hyperelasticity for various forms of strain energy functions typically used for elastomers and for soft tissues. Surprisingly, we find that the strain-hardening property of soft tissues does not always stabilize the material. In particular we show that the stability analyses for a compressed half-space and for a compressed spherical thick shell can lead to opposite conclusions: a soft tissue material is more stable than an elastomer in the former case and less stable in the latter case.
A pr 2 01 8 An Accelerated Directional Derivative Method for Smooth Stochastic Convex Optimization

We consider smooth stochastic convex optimization problems in the context of algorithms which are based on directional derivatives of the objective function. This context can be considered as an intermediate one between derivativefree optimization and gradient-based optimization. We assume that at any given point and for any given direction, a stochastic approximation for the directional derivative of the objective function at this point and in this direction is available with some additive noise. The noise is assumed to be of an unknown nature, but bounded in the absolute value. We underline that we consider directional derivatives in any direction, as opposed to coordinate descent methods which use only derivatives in coordinate directions. For this setting, we propose a non-accelerated and an accelerated directional derivative method and provide their complexity bounds. Despite that our algorithms do not use gradient information, our non-accelerated algorithm has a complexity bound which is, up to a factor logarithmic in problem dimension, similar to the complexity bound of gradient-based algorithms. Our accelerated algorithm has a complexity bound which coincides with the complexity bound of the accelerated gradient-based algorithm up to a factor of square root of the problem dimension, whereas for existing directional derivative methods this factor is of the order of problem dimension. We also extend these results to strongly convex problems. Finally, we consider derivative-free optimization as a particular case of directional derivative optimization with noise in the directional derivative and obtain complexity bounds for non-accelerated and accelerated derivative-free methods. Complexity bounds for these algorithms inherit the gain in the dimension dependent factors from our directional derivative methods.
Multi-Particle Effects in Strong-Field Quantum Electrodynamics

In this work we study Nonlinear Compton Scattering, one of the most fundamental processes of Quantum Electrodynamics in a strong electromagnetic field background. One can imagine Nonlinear Compton Scattering as a process where a free electron scatters with a laser field, and while doing so it generates radiation. With the imminent inauguration of some Petawatt laser facilities, Nonlinear Compton Scattering will be routinely tested, especially in order to understand the dynamics of plasmas interacting with strong fields. Despite its promising applications, this process has always been studied for single particles with a well-defined initial momentum. We will show which effects arise when initially one or multiple electrons are in a wave packet state; in particular, we will be interested in which modifications are brought about by quantum effects, and which instead have a classical explanation. Moreover, in the multi-particle case, we will focus on the coherence of the emitted radiation, and calculate how quantum effects limit the frequencies at which the radiation can be coherent even at a regime typically associated with Classical Electrodynamics.
The location of domestic and foreign production affiliates by French multinational firms

In this paper we combine two traditions in the analysis of firms’ location patterns. One led by trade economists who try to understand why do firms invest abroad, and another one led by urban/regional economists, who frequently use patterns of inter-regional or inter-city choices to estimate agglomeration economies. We contribute to the trade-motivated set of papers on location choices by adding the domestic country in the choice set, while accounting for firm’s heterogeneity in the choices. Our econometric results using French firm-level data show an important ‘‘home bias” in manufacturing investment decisions. A crucial finding, which bridges with our contribution to the agglomeration literature, is that the spatial clustering of affiliates belonging to the same industrial group accounts for the lion’s share of this home bias. 2010 Elsevier Inc. All rights reserved.