Miracle-dz/UniFAR-FTUs huggingface dataset:
NEW ASPECTS CONCERNING THE THERMAL BEHAVIOUR OF A VISCOUS COUPLING

The present paper deals with the performances of multidisk viscous coupling submitted to a sudden change in rotational velocity, from naught to nominal velocity. Because of the particular destination of these couplings, which are widely used for automotive transmissions, their running is not long enough to reach the steady-state thermal regime, so that the traditional, steady-state, method of calculus does not give correct information about the performances of the coupling. The mathematical model presented in this work allows us to calculate the bidimensional temperature field in viscous coupling. As the model is based on a system of coupled nonlinear equations, it requires the use of the finite element method.
Preliminary Synthesis and Characterization of Mesoporous Nanocrystalline Zirconia

A novel method to prepare mesoporous nano-zirconia was developed. The synthesis was carried out in the presence of PEO surfactants via a solid-state reaction. The materials exhibit a strong diraction peak at low 2 angle and their nitrogen adsorption/desorption isotherms are typical of type IV with H1 hysteresis loops. The pore structure imaged by TEM can be described as wormhole domains. The tetragonal zirconia nanocrystals are uniform in size (around 1.5 nm) and their mesopores focus on around 4.6 nm. The zirconia nanocrystal growth is tentatively postulated to be the result of an aggregation mechanism. This study also reveals that the PEO surfactants can interact with the Zr-O-Zr framework to reinforce the thermal stability of zirconia. The ratio of NaOH to ZrOCl2, crystallization and calcination temperature play an important role in the synthesis of mesoporous nano-zirconia.
Landau Theory of Biaxial Nematic Liquid Crystals

Abstract The orientational order of molecules in a liquid crystal is traditionally described by spatial or temporal averages of functions that are quadratic in the direction cosines of molecular axes with respect to laboratory fixed axes. The free energy describing the nematic phase must be independent of the choice of coordinate systems and therefore rotationally invariant with respect to both laboratory and molecular frames. A Landau theory for biaxial and uniaxial nematics based on six fundamental invariants is developed which is properly rotationally invariant. Four possible nematic phases, two uniaxial and two biaxial, result. NMR experiments should be able to identify which of these actually occur in specific materials.
A Log-normal Distribution-based Exposure Assessment Method for Unbalanced Data

Dear Sir Lyles et al. (1997a) recently presented a method for the collection, analysis, and interpretation of occupational exposure data. This method was specifically designed for assessing compliance relative to a longterm exposure limit. In this and related articles (Rappaport et al., 1995; Lyles et al., 1997b) they interpret legal and authoritative occupational exposure limits (OELs) for chronic disease agents as long-term limits; that is, as upper limits for the long-term, lifetime average exposure of each employee. Furthermore, they recommend that their method be used to determine 'whether or not the overall exposure distribution for an entire group of workers is in compliance with specified regulatory guidelines [emphasis added]'. While it is important to expand the toolbox of statistical methods available to industrial hygienists, a candidate method must meet several criteria in order to make it into my toolbox. The method must be consistent with the OEL, it must be accessible, and it must be efficient and effective. That is to say, it cannot be based upon a faulty interpretation of the OEL, the calculations and interpretation of results do not require excessive angst and aggravation, it must yield decisions with a minimum expenditure of resources, and there is a reasonable expectation that the decisions are correct. Given these criteria, I have several reservations regarding the Lyles et al. statistical method and interpretation of exposure limits. I also include several comments regarding the specifics of their ANOVAbased method and suggestions for simplifying two steps in the method.
Low-Fat Foods

Fats are responsible for performing varied and important functions in the body, such as providing calories, essential fatty acids, and fat-soluble vitamins. They are considered very important among the ingredients and the sensory aspects of the functional properties of the food. They influence the melting point, consistency, and formation of crystals in the spreadability of many foods and are also responsible for flavor, aroma, creamy appearance, aeration, stability, and feeling of fullness after meals. However, the consumption of high amounts of fats and oils has often been associated with obesity and multiple chronic diseases. To reduce fat and caloric value of foods, we can reduce or eliminate fat from the formulation by increasing the amount of proteins, carbohydrates, fibers, and water. However, it is not so easy to treat with fat substitution in a food formulation. The crystallization behavior of lipids has important implications, especially in industrial processing of products whose physical characteristics (consistency and melting point) are affected by the crystal structure of fat, such as chocolate, margarine, and shortenings. Much of the knowledge about the crystal structure of the fat comes from studies performed on diffraction of x-rays. The crystal structure depends on the specific type of triacylglycerol (TAG) present, the composition and distribution of fatty acids, the purity of TAG, and the crystallization conditions (temperature, cooling rate, shear, and solvent). The ideal fat replacers should be a composite of recognized safety and health, which has all the functional and organoleptic properties with the benefit of significant calorie reduction. Fat food processors have been careful about developing and producing low-fat foods due to the problems it could generate in the production, such as an increased risk of unstable products and unconfident production parameters. Some fat foods can be considered as margarines, creams, chocolate products, ice creams, cakes, and some baked goods. © 2015 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The substitution of trans and saturated fatty acids should be considered also when creating a low-fat food. Saturated and trans fats refer to a group of fatty acids, each with its own properties and characteristics. Despite saturated fats’ potential health benefits, saturated fat has long been associated with increased risk of heart disease, stroke, and even cancer, as well trans fats. When designing a low-fat food as spread, for example, it is important to observe the fat composition, the quantity of liquid oil, because the oil phase needs to cover a higher amount of water droplets and the solid fats cause disappearance of smoothness. A combination of the right process parame‐ ters and fat composition can give a satisfactory fat food product. In this chapter, the possibilities of low-fat food creation are discussed.
Chemical flooding compositional simulator

A one-dimensional, compositional, chemical flood simulator, which enables calculation of oil recovery as a function of several major process variables, has been developed. The principal phenomenological relationships which must be input include phase behavior and interfacial tensions, as a function of electrolyte and surfactant concentrations, and polymer viscosity as a function of electrolyte and polymer concentration. The emphasis to date has been on the study of the process itself, especially the complex interactions which occur due to 2- and 3-phase behavior, interfacial tension, fractional flow, dispersion, adsorption, cation exchange, chemical slug size, and polymer transport. (12 refs.)
Impact of baking time and temperature on nutrient content and sensory quality of sweet potatoes

ABSTRACT Sweet potatoes are a valuable economic asset to Louisiana. The impact of baking time and temperature on cook loss, vitamin C content, total sugars content, and sensory quality of sweet potatoes were compared when baked at 325°F, 375°F, and 425°F for 60, 45, and 30 minutes respectively. The 325°F sample had a significantly lower vitamin C content and a significantly higher total sugars content compared to the other two samples. However, the 425°F sample had a significantly lower percent cook loss compared to the other two samples. The 425°F sample had a significantly lower cook loss and total sugars compared to the 325°F samples. Untrained consumer panelists perceived the 425°F sample to be significantly less tender and less liked than the other two samples. These results have important implications for chefs, the food industry, and nutritional feeding programs.
Direct Determination of Organic Carbon by Dry Combustion in Soils with Carbonates

Abstract In acid soils, where organic carbon (C) corresponds to total C, direct determination of organic C by dry combustion is possible, whereas in soils with carbonates also a separate measurement of inorganic C is required. In this case, direct quantification of organic C can be accomplished by the Walkley‐Black method, which is time‐consuming and involves greatly polluting by‐products. Hence, a method able to determine directly organic C by dry combustion is strongly needed for soils with carbonates. This study proposes such a method, after it was found to be highly reliable in calcareous soils of a Mediterranean island. The correction factor to use in the Walkley‐Black method to account for nonrecoverable C was calculated. It does not show any overall relationship with the contents of either organic C or inorganic C, and for all land uses examined in the island, it is not significantly different from the commonly suggested value 1.30.
Deep Adversarial Inconsistent Cognitive Sampling for Multi-view Progressive Subspace Clustering

Deep multiview clustering methods have achieved remarkable performance. However, all of them failed to consider the difficulty labels (uncertainty of ground truth for training samples) over multiview samples, which may result in a nonideal clustering network for getting stuck into poor local optima during the training process; worse still, the difficulty labels from the multiview samples are always inconsistent, and such a fact makes it even more challenging to handle. In this article, we propose a novel deep adversarial inconsistent cognitive sampling (DAICS) method for multiview progressive subspace clustering. A multiview binary classification (easy or difficult) loss and a feature similarity loss are proposed to jointly learn a binary classifier and a deep consistent feature embedding network, throughout an adversarial minimax game over difficulty labels of multiview consistent samples. We develop a multiview cognitive sampling strategy to select the input samples from easy to difficult for multiview clustering network training. However, the distributions of easy and difficult samples are mixed together, hence not trivial to achieve the goal. To resolve it, we define a sampling probability with a theoretical guarantee. Based on that, a golden section mechanism is further designed to generate a sample set boundary to progressively select the samples with varied difficulty labels via a gate unit, which is utilized to jointly learn a multiview common progressive subspace and clustering network for more efficient clustering. Experimental results on four real-world datasets demonstrate the superiority of DAICS over state-of-the-art methods.
Deep Learning-based Face Super-resolution: A Survey

Face super-resolution (FSR), also known as face hallucination, which is aimed at enhancing the resolution of low-resolution (LR) face images to generate high-resolution face images, is a domain-specific image super-resolution problem. Recently, FSR has received considerable attention and witnessed dazzling advances with the development of deep learning techniques. To date, few summaries of the studies on the deep learning-based FSR are available. In this survey, we present a comprehensive review of deep learning-based FSR methods in a systematic manner. First, we summarize the problem formulation of FSR and introduce popular assessment metrics and loss functions. Second, we elaborate on the facial characteristics and popular datasets used in FSR. Third, we roughly categorize existing methods according to the utilization of facial characteristics. In each category, we start with a general description of design principles, present an overview of representative approaches, and then discuss the pros and cons among them. Fourth, we evaluate the performance of some state-of-the-art methods. Fifth, joint FSR and other tasks, and FSR-related applications are roughly introduced. Finally, we envision the prospects of further technological advancement in this field.
Selection of Optimal Parameters in the Fast K-Word Proximity Search Based on Multi-component Key Indexes

Proximity full-text search is commonly implemented in contemporary full-text search systems. Let us assume that the search query is a list of words. It is natural to consider a document as relevant if the queried words are near each other in the document. The proximity factor is even more significant for the case where the query consists of frequently occurring words. Proximity full-text search requires the storage of information for every occurrence in documents of every word that the user can search. For every occurrence of every word in a document, we employ additional indexes to store information about nearby words, that is, the words that occur in the document at distances from the given word of less than or equal to the MaxDistance parameter. We showed in previous works that these indexes can be used to improve the average query execution time by up to 130 times for queries that consist of words occurring with high-frequency. In this paper, we consider how both the search performance and the search quality depend on the value of MaxDistance and other parameters. Well-known GOV2 text collection is used in the experiments for reproducibility of the results. We propose a new index schema after the analysis of the results of the experiments. This is a pre-print of a contribution published in Supplementary Proceedings of the XXII International Conference on Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2020), Voronezh, Russia, October 13-16, 2020, P. 336-350, published by CEUR Workshop Proceedings. The final authenticated version is available online at: http://ceur-ws.org/Vol-2790/
Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing

We introduce Trankit, a light-weight Transformer-based Toolkit for multilingual Natural Language Processing (NLP). It provides a trainable pipeline for fundamental NLP tasks over 100 languages, and 90 pretrained pipelines for 56 languages. Built on a state-of-the-art pretrained language model, Trankit significantly outperforms prior multilingual NLP pipelines over sentence segmentation, part-of-speech tagging, morphological feature tagging, and dependency parsing while maintaining competitive performance for tokenization, multi-word token expansion, and lemmatization over 90 Universal Dependencies treebanks. Despite the use of a large pretrained transformer, our toolkit is still efficient in memory usage and speed. This is achieved by our novel plug-and-play mechanism with Adapters where a multilingual pretrained transformer is shared across pipelines for different languages. Our toolkit along with pretrained models and code are publicly available at: https://github.com/nlp-uoregon/trankit. A demo website for our toolkit is also available at: http://nlp.uoregon. edu/trankit. Finally, we create a demo video for Trankit at: https://youtu.be/q0KGP3zGjGc.
Modelling wind speed with a univariate probability distribution depending on two baseline functions

Characterizing the wind speed distribution properly is essential for the satisfactory production of potential energy in wind farms, being the mixture models usually employed in the description of such data. However, some mixture models commonly have the undesirable property of non-identifiability. In this work, we present an alternative distribution which is able to fit the wind speed data decently. The new model, called Normal-Weibull-Weibull, is identifiable and its cumulative distribution function is written as a composition of two baseline functions. We discuss structural properties of the class that generates the proposed model, such as the linear representation of the probability density function, moments and moment generating function. We perform a Monte Carlo simulation study to investigate the behavior of the maximum likelihood estimates of the parameters. Finally, we present applications of the new distribution for modelling wind speed data measured in five different cities of the Northeastern Region of Brazil.
Effects of cathode materials and arc current on optimal bias of a cathodic arc through a magnetic duct

A negatively biased collecting plate was used to collect the ion current of the cathodic arc plasma transported through a curved magnetic duct. The optimal duct bias at which the duct has the maximum efficiency for plasma transport was measured for C, Ti, Mo, and W plasmas as a function of the arc current and guiding magnetic field. The optimal bias decreased with the magnetic field and was almost steady when the field was above 400 G. The optimal bias at 400 G and above increased with the arc current for C plasma but the opposite relationship was observed for Ti, Mo, and W plasmas. The effects of the plasma density, ion mass, ion kinetic energy, and magnetic field on the optimal bias are discussed.
Accuracy and Architecture Studies of Residual Neural Network solving Ordinary Differential Equations

In this paper we consider utilizing a residual neural network (ResNet) to solve ordinary differential equations. Stochastic gradient descent method is applied to obtain the optimal parameter set of weights and biases of the network. We apply forward Euler, Runge-Kutta2 and Runge-Kutta4 finite difference methods to generate three sets of targets training the ResNet and carry out the target study. The well trained ResNet behaves just as its counterpart of the corresponding one-step finite difference method. In particular, we carry out (1) the architecture study in terms of number of hidden layers and neurons per layer to find the optimal ResNet structure; (2) the target study to verify the ResNet solver behaves as accurate as its finite difference method counterpart; (3) solution trajectory simulation. Even the ResNet solver looks like and is implemented in a way similar to forward Euler scheme, its accuracy can be as high as any one step method. A sequence of numerical examples are presented to demonstrate the performance of the ResNet solver.
Analysis of an abstract mixed formulation for viscoelastic problems

This study provides an abstract framework to analyze mixed formulations in viscoelasticity, in the classic saddle point form. Standard hypothesis for mixed methods are adapted to the Volterra type equations in order to obtain stability of the proposed problem. Error estimates are derived for suitable finite element spaces. We apply the developed theory to a bending moment formulation for a linear viscoelastic Timoshenko beam and for the Laplace operator with memory terms. For both problems we report numerical results to asses the performance of the methods.
Deep Diffusion Processes for Active Learning of Hyperspectral Images

A method for active learning of hyperspectral images (HSI) is proposed, which combines deep learning with diffusion processes on graphs. A deep variational autoencoder extracts smoothed, denoised features from a high-dimensional HSI, which are then used to make labeling queries based on graph diffusion processes. The proposed method combines the robust representations of deep learning with the mathematical tractability of diffusion geometry, and leads to strong performance on real HSI.
FlashP: An Analytical Pipeline for Real-time Forecasting of Time-Series Relational Data

Interactive response time is important in analytical pipelines for users to explore a sufficient number of possibilities and make informed business decisions. We consider a forecasting pipeline with large volumes of high-dimensional time series data. Real-time forecasting can be conducted in two steps. First, we specify the part of data to be focused on and the measure to be predicted by slicing, dicing, and aggregating the data. Second, a forecasting model is trained on the aggregated results to predict the trend of the specified measure. While there are a number of forecasting models available, the first step is the performance bottleneck. A natural idea is to utilize sampling to obtain approximate aggregations in real time as the input to train the forecasting model. Our scalable real-time forecasting system FlashP (Flash Prediction) is built based on this idea, with two major challenges to be resolved in this paper: first, we need to figure out how approximate aggregations affect the fitting of forecasting models, and forecasting results; and second, accordingly, what sampling algorithms we should use to obtain these approximate aggregations and how large the samples are. We introduce a new sampling scheme, called GSW sampling, and analyze error bounds for estimating aggregations using GSW samples. We introduce how to construct compact GSW samples with the existence of multiple measures to be analyzed. We conduct experiments to evaluate our solution its alternatives on real data.
How Much Automation Does a Data Scientist Want?

Data science and machine learning (DS/ML) are at the heart of the recent advancements of many Artificial Intelligence (AI) applications. There is an active research thread in AI, \autoai, that aims to develop systems for automating end-to-end the DS/ML Lifecycle. However, do DS and ML workers really want to automate their DS/ML workflow? To answer this question, we first synthesize a human-centered AutoML framework with 6 User Role/Personas, 10 Stages and 43 Sub-Tasks, 5 Levels of Automation, and 5 Types of Explanation, through reviewing research literature and marketing reports. Secondly, we use the framework to guide the design of an online survey study with 217 DS/ML workers who had varying degrees of experience, and different user roles"matching"to our 6 roles/personas. We found that different user personas participated in distinct stages of the lifecycle -- but not all stages. Their desired levels of automation and types of explanation for AutoML also varied significantly depending on the DS/ML stage and the user persona. Based on the survey results, we argue there is no rationale from user needs for complete automation of the end-to-end DS/ML lifecycle. We propose new next steps for user-controlled DS/ML automation.