Miracle-dz/UniFAR-FTUs huggingface dataset:
INDÚSTRIA 4.0: UM DESAFIO COMPETITIVO INDUSTRY 4.0: A CHALLENGE OF COMPETITION INDUSTRIA 4.0: UN DESAFÍO COMPETITIVO

Introduction: The value creation in industry in developed countries is being driven by the fourth stage of industrialization, denominated by Industry 4.0. The new industrial revolution will be motivated by next-generation information technologies such as Internet of Things (IoT), cloud computing, Big Data and data analysis, robotics, mobile computing, simulation and modelling, cyber-physical systems, among others. This opens new horizons for industry, but the challenges are countless creating difficulties for companies in the adoption of these technologies. Objectives: To make a powerful and deep literature revision pursuing a technical analysis of the Industry 4.0 requirements. Further. Methods: We will address the main risks and challenges associated with IoT and define the regional attractiveness measures as growth drivers that leaders must put in place to appeal for companies chasing 4.0. Results: IoT joins the digital world and the physical world being considered the next generation network or the future Internet. It allows to give life and communication capacity either to living beiings or to inanimate objects. IoT's intervention in Industry 4.0 im extreme, with a continuous interconnection of the digital and physical domain. Conclusions: Portugal's growth prospects will increasingly depend on policies that enable the economy to compete successfully and create new income opportunities. At the moment, there are structural bottlenecks that continue to curb growth and exacerbate vulnerabilities. Solving some of these problems will now lay the foundation for solid growth in the coming years, but this calls for a renewal of the impetus for structural reforms. Industry 4.0, can contribute significantly to reducing regional asymmetries. But in the longer term, skills will need to be improved to foster development and reduce the high levels of such inequalities.
Materialization Trade-offs for Feature Transfer from Deep CNNs for Multimodal Data Analytics

Deep convolutional neural networks (CNNs) achieve nearhuman accuracy on many image understanding tasks. Thus they are now increasingly used to integrate images with structured data for multimodal analytics applications. Since training deep CNNs from scratch is expensive, transfer learning has become popular: using a pre-trained CNN, one “reads off” a certain layer of CNN features to represent images and combines them with other features for a downstream ML task. Since no single layer will always offer best accuracy in general, such feature transfer requires comparing many layers. The current dominant approach to this process on top of scalable analytics systems such as Spark using deep learning toolkits such as TensorFlow is fraught with inefficiency due to redundant CNN inference and the potential for system crashes due to mismanaged memory. We present Vista, the first data system to mitigate such issues by elevating the feature transfer workload to a declarative level and formalizing the data model of CNN inference. Vista enables automated optimization of feature materialization trade-offs, distributed memory management, and system configuration. Real-world experiments show that apart from enabling seamless feature transfer, Vista substantially improves system reliability and reduces runtimes by up to 90%. ACM Reference Format: Supun Nakandala Arun Kumar. 2018. Materialization Tradeoffs for Feature Transfer from Deep CNNs for Multimodal Data Analytics. In Proceedings of ACM Conference (Conference’17). ACM, New York, NY, USA, 18 pages. https://doi.org/10.1145/nnnnnnn. nnnnnnn Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACMmust be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. Conference’17, July 2017, Washington, DC, USA © 2018 Association for Computing Machinery. ACM ISBN 978-x-xxxx-xxxx-x/YY/MM. $15.00 https://doi.org/10.1145/nnnnnnn.nnnnnnn Convolutional+Pooling+ReLU Layers Fully Connected Layers Output Low-level Features Mid-level Features High-level Features Input Image features from a specified layer Structured Features Multimodal Feature Set Concatenate (A) CNN Inference Brand Tags Price Brand Tags Price Image Features Downstream ML Model (B) CNN Feature Transfer for Multimodal Analytics Figure 1: (A) Simplified illustration of a typical deep CNN and its hierarchy of learned features (based on [68]). (B) Illustration ofCNN feature transfer for multimodal analytics.
Nutrient Metabolism Whole Wheat and Triticale Flours with Differing Viscosities Stimulate Cecal Fermentations and Lower Plasma and Hepatic Lipids in Rats 1

Whole flours from oat, rye or barley effectively modify digestive fermentation and lipid metabolism, whereas the effectiveness of whole wheat flour has not been established. To address this question, cecal digestion, short-chain fatty acid (SCFA) metabolism and cholesterol metabolism were investigated in four groups of rats fed the following semipurified diets differing in their carbohydrate source: a control diet (purified wheat starch) and three whole cereal flour diets [Valoris wheat (Wv), Soissons wheat (Ws), or Carnac triticale (Tc)]. Wv is particularly viscous and rich in arabinoxylans, and Tc is richer in hemicellulose than wheat. Compared with controls, rats fed the whole-flour diets had enlarged ceca and a moderate acidification of the bulk pH (;6.4). In these rats, the cecal SCFA pool size was enhanced (P , 0.05), and the SCFA molar ratio reflected propionic/butyric acid–rich fermentations, especially in those fed Tc. The portal SCFA concentrations reflected the rise of the acetic and propionic acid pools in the cecum, whereas portal butyric acid remained relatively low, probably reflecting extensive metabolism by the cecal wall. The fecal excretion of total steroids (bile acids 1 sterols) was markedly enhanced by all of the whole-flour diets, with Wv (178%) . Tc (164%) . Ws (147%). In parallel, there was a significant plasma cholesterol–lowering effect for rats fed Wv (227%) and Tc (232%) and a plasma triglyceride– lowering effect (approximately 240%) in all rats fed whole-flour diets (P , 0.05). This effect was observed mainly for triglyceride-rich lipoprotein-cholesterol, whereas HDL cholesterol was unaffected. These results indicate that whole wheat flours can strikingly affect cecal SCFA, especially butyrate, and are effective plasma cholesterol– lowering agents. J. Nutr. 131: 1770–1776, 2001.
32 Neural Computation Theories of Learning q

This postulate was experimentally confirmed in the hippocampus, where high-frequency stimulation (HFS) of a presynaptic neuron causes long-term potentiation (LTP) in the synapses connecting it to the postsynaptic neurons (Bliss and Lomo, 1973). LTP takes place only if the postsynaptic cell is also active and sufficiently depolarized (Kelso et al., 1986). In many brain areas, this is due to the N-methyl-D-aspartate (NMDA) type of glutamate receptor, which opens when glutamate is bound to the receptor and the postsynaptic cell is sufficiently depolarized at the same time. Hebb’s rule has served as the starting point for studying the learning capabilities of artificial neural networks (ANN) and for the theoretical analysis and computational modeling of biological neural systems (Hertz et al., 1991). The architecture of an ANN determines its behavior and learning capabilities. The architecture of a network is defined by the connections among the artificial neural units and the function that each unit performs on its inputs. Two general classes of network models have feedforward and recurrent architectures. The simplest feedforward network has one layer of input units and one layer of output units (Fig. 1, left). All connections are unidirectional and project from the input units to the output units. Perceptron is an example of such a simple feedforward network (Rosenblatt, 1958). It can learn to classify patterns from examples. It turned out that perceptron can only classify patterns that are linearly separable – that is, if the positive patterns can be separated from all negative patterns by a plane in the space of input patterns. More powerful multilayer feedforward networks can discriminate patterns that are not linearly separable. In a multilayer feedforward network, the “hidden” layers of units between the input and output layers allow more flexibility in learning features (Rumelhart et al., 1986). Multilayer feedforward networks can solve some difficult problems (Rumelhart and McClelland, 1986) and underlie current rapid development of the field of deep learning in machine learning (LeCun et al., 2015). In contrast to strictly feedforward network models, recurrent networks also have feedback connections among units in the network (Fig. 1, right). A simple recurrent network can have a uniform architecture such as all-to-all connectivity combined with symmetrical weights between units as in Hopfield network (Hopfield, 1982), or it can be a network with specific connections designed to model a particular biological system (Sporns, 2010).
Mathematical techniques in image processing

In the last decades, many approaches and various algorithms are introduced to minimize the functional calculus of the Tixotrop model. These classical techniques are formulated in an iterative form, that will cost a lot of CPU time. To overcome this disadvantage, one can use the decomposition method which reduces the problem to a sequence sub-problems of a more manageable size. In this particular context, we introduce a method of correction subspace based on the decomposition methods for the minimization of the Tixotrop model. These methods allow us to divide the space of the initial problem into several subspaces, to solve sub-problems faster. Then the solution of the original problem is obtained from the sub-problemsones . The main di¢ culty in the resolution the tixotrop model by a domain decomposition technique has a non regular behavior of the borders, with discontinuities preserving. As the non regular model of the tixotrop is not adapted for the decomposition algorithm with matching and non matching to minimize the functional associated to the problem, after regularization; we provide several tests showing the e¤ectiveness of the algorithm for image restoration. Furthermore we present a comparison of these results with the same algorithm by the total variation method.
The Function of Rab 35 in evelop ent and isease

Rab35 mediates membrane trafficking between the plasma membrane and the early endosomes at the cell surface. Our understanding of the cellular function of Rab35 reveals its role in development and diseases. In the developmental context, Rab35 has been shown to play an important role in regulating epithelial polarity, lumen opening, myoblast fusion, intercalation of epithelium, myelination, neurite outgrowth, and oocyte meiotic maturation. Disruption of recycling endosome mediated by Rab35 has been linked to several neurological diseases, including Parkinson’s disease and Down syndrome. In addition, because Rab35 modulates cell migration through its interaction with various effectors, Rab35 plays an important role in cancers. Lastly, the Rab35-mediated recycling endosomal pathway and exocytosis is utilized by pathogens or hijacked by pathogens to promote their infection and survival. This review summarizes the function of Rab35 in endocytosis and focuses on the role of Rab35 in the context of development and diseases.
Genome-wide association study for refractive astigmatism reveals genetic co-determination with spherical equivalent refractive error

Citation for published version: Li, Q, Wojciechowski, R, Simpson, CL, Hysi, PG, Verhoeven, VJM, Ikram, MK, Höhn, R, Vitart, V, Hewitt, AW, Oexle, K, Mäkelä, K-M, MacGregor, S, Pirastu, M, Fan, Q, Cheng, C-Y, St Pourcain, B, McMahon, G, Kemp, JP, Northstone, K, Rahi, JS, Cumberland, PM, Martin, NG, Sanfilippo, PG, Lu, Y, Wang, YX, Hayward, C, Polašek, O, Campbell, H, Bencic, G, Wright, AF, Wedenoja, J, Zeller, T, Schillert, A, Mirshahi, A, Lackner, K, Yip, SP, Yap, MKH, Ried, JS, Gieger, C, Murgia, F, Wilson, JF, Fleck, B, Yazar, S, Vingerling, JR, Hofman, A, Uitterlinden, A, Rivadeneira, F, Amin, N, Karssen, L, Oostra, BA & The CREAM Consortium 2015, 'Genome-wide association study for refractive astigmatism reveals genetic codetermination with spherical equivalent refractive error: the CREAM consortium', Human Genetics, vol. 134, no. 2, pp. 131-146. https://doi.org/10.1007/s00439-014-1500-y
Do Consumers Hedge Against Future Preference Uncertainty ? Testing a Theory of Shopping and Consumption August 30 , 2017

This paper presents empirical evidence from two behavioral experiments and from multi-market panel data that together support a normative theory of multi-product choice and consumption. The experiments show that sets of hedonic products are chosen in a way that maximizes total expected future utility, given uncertainty about future preferences. The chosen set can therefore be interpreted as a utility-maximizing hedge. To maximize expected utility, products in the set must be consumed strategically. We find evidence of the strategic consumption pattern predicted by the theory, with consumers effectively preserving variety for future consumption occasions. A boundary condition for strategic consumption is also identified. Finally, analysis of yogurt purchases made by single-person households in two geographic markets shows that shoppers with lower consumption rates choose relatively more variety than shoppers with higher consumption rates do. Taken together, these results provide support for the normative theory of multi-product shopping and consumption.
TR-2007-10 May 29 , 2007 Bayesian Models for Accelerated Life Tests

In this article we consider accelerated life test models where the effect of accelerated environments on failure behavior is described by time transformation functions. We present extensions of basic models by introducing hierarchical and dynamic strategies to allow for changes in the time transformation function with the accelerated environment. We develop Markov chain Monte Carlo methods to make Bayesian inferences from these models where no analytical forms are available for the posterior and predictive distributions of interest. We illustrate our approach with two examples and discuss what type of insights can be obtained from Bayesian analysis.
Investigating the Effect of Customer Incivility on Employee Incivility via Employee Burnout in the Hospitality Industry

INVESTIGATING THE EFFECT OF CUSTOMER INCIVILITY ON EMPLOYEE INCIVILITY VIA EMPLOYEE BURNOUT IN THE HOSPITALITY INDUSTRY Yi Wen Advisor: University of Guelph, 2018 Dr. HS Chris Choi This study investigates the relationships among customer incivility, employee incivility, and employee burnout in the hospitality industry. It further explores whether or not supervisory support, as a coping strategy, moderates the relationship between customer incivility and burnout. A survey questionnaire was distributed to 446 restaurant employees in Wuhan, Hubei Province, China. A structural equation model (SEM) was then applied for data analysis. The results demonstrate that (a) customer incivility is positively related to employee incivility in the hospitality industry, (b) burnout mediates the relationship between customer incivility and employee incivility, and (c) supervisory support plays an important moderating role between customer incivility and employee burnout. Based on these findings, theoretical and practical implications are provided, as well as the study’s limitations and recommendations for future studies.
A regularization interpretation of the proximal point method for weakly convex functions

Empirical evidence and theoretical results suggest that the proximal point method can be computed approximately and still converge faster than the corresponding gradient descent method, in both the stochastic and exact gradient case. In this article we provide a perspective on this result by interpreting the method as gradient descent on a regularized function. This perspective applies in the case of weakly convex functions where proofs of the faster rates are not available. Using this analysis we find the optimal value of the regularization parameter in terms of the weak convexity.
Study on ear number per plant in maize RIL populating under two nitrogen regimes

Maize (Zea mays L.) is one of the most important crops in the world. Quantitative trait locus (QTL) mapping must depend on statistical analysis on phenotypic data of traits in a segregation population. In this presented study, a recombinant inbred line (RIL) population derived from the cross Mo17 × Huangzao4 were used to investigate the trait ear number per plant (ENPP) under two nitrogen regimes. Based on the phenotypic data of the two parental inbred lines and RIL population in the trait ENPP, SPSS version 11.5 software was selected to perform descriptive statistics, analysis of variance and correlation analysis. The results could be further used for QTL identification for the agronomic trait ENPP in maize breeding program.
A Markov Decision Model to Optimize Hotel Room Occupancy under Stochastic Demand

Hotels continually face the challenge of planning and managing room occupancy within an environment of demand uncertainty. In this paper, an optimization method for allocating hotel rooms to occupants is proposed. The objective of the model is to optimally admit customers in order to maximize revenue. Adopting a Markov decision process approach, the states of a Markov chain represent possible states of demand for hotel room occupancy. The decision of whether or not to admit additional occupants is made using dynamic programming over a finite period planning horizon. The approach demonstrates the existence of an optimal statedependent admission policy for occupants as well as the corresponding hotel room revenue.
Mechanistic Study and Catalyst Development of Selective Carbon Monoxide

Please note that technical editing may introduce minor changes to the text and/or graphics, which may alter content. The journal’s standard Terms & Conditions and the Ethical guidelines still apply. In no event shall the Royal Society of Chemistry be held responsible for any errors or omissions in this Accepted Manuscript or any consequences arising from the use of any information it contains. Accepted Manuscript Catalysis Science & Technology
Global Model Push Pull Worker 1 Worker 2 Worker 3 Training Data Local Model PS 1 PS 2

Deep learning (DL) training jobs bring some unique challenges to existing cluster managers, such as unpredictable training times, an all-or-nothing execution model, and inflexibility in GPU sharing. Our analysis of a large GPU cluster in production shows that existing big data schedulers cause long queueing delays and low overall performance. We present Tiresias, a GPU cluster manager tailored for distributed DL training jobs, which efficiently schedules and places DL jobs to reduce their job completion times (JCTs). Given that a DL job’s execution time is often unpredictable, we propose two scheduling algorithms – Discretized TwoDimensional Gittins index relies on partial information and Discretized Two-Dimensional LAS is information-agnostic – that aim to minimize the average JCT. Additionally, we describe when the consolidated placement constraint can be relaxed, and present a placement algorithm to leverage these observations without any user input. Experiments on the Michigan ConFlux cluster with 60 P100 GPUs and largescale trace-driven simulations show that Tiresias improves the average JCT by up to 5.5× over an Apache YARN-based resource manager used in production. More importantly, Tiresias’s performance is comparable to that of solutions assuming perfect knowledge.
CORPORATE GOVERNANCE AS A PREDICTOR OF LIQUIDITY MANAGEMENT

This paper is based on a novel theoretical model derived by the researchers to explain the patterns of corporate governance, fi rms’ fi nancial policies and liquidity position. A deductive approach has been adopted to reconcile and examine the model. The study has concluded that corporate governance is a signifi cant variable in determining the liquidity position of fi rms. In this way, corporate governance becomes a crucial determinant of the liquidity management. This study also indicates that capital structure and the patterns of ownership play important roles in the determination of corporate governance of an institution. This is a value addition study in the existing literature in a sense that corporate governance is an exogenous as well as an endogenous variable in the model. The issue of liquidity crisis in the form of circularity debt, exist in the Energy sector of Pakistan, must be readdressed with the view point of corporate governance rather than from the aspect of fi nancial management.
FUTURE OF ARTIFICIAL INTELLIGENCE : TREATS AND POSSIBILITY

In this article, author wants to focus on problem that are important and urgent in the sense of their solutions are needed before to the artificial intelligence explosion. Some technical problems of human – robots cooperation in the field of artificial Intelligence are searched. The research needed to reduce the risks of the machine intelligence revolution; author will propose his vision of threats and possibility. Author tried to show some examples of Independence from humans of artificial Intelligence systems and humans needs of it. Author want to show: people strongly needs to find their place in coexist with other form of life that will be much smarter than people and will be more dangerous, more usefully for humans according to their smart.
Habitual physical activity in children : the role of genes and the environment 1 – 3

Background: Understanding the factors that contribute to physical inactivity in children is important because sedentary behavior strongly relates to metabolic disorders such as obesity and diabetes. Objective: We aimed to quantify the genetic and environmental influences on physical activity energy expenditure (PAEE) in 100 sex-concordant dizygotic (n 38) and monozygotic (n 62) twin
Formative Assessment of Meaningful Learning in IS Education Using Concept Mapping

Information Systems (IS) education needs to focus on meaningful learning because it is essential in cultivating students’ problem solving and critical thinking skills. In formative assessment of the meaningful learning, we need to provide feedback to guide and enhance learning. In this study, we propose a conceptual model of meaningful learning. The model justifies the values of Concept Mapping (CM) as a formative assessment tool because of its effective dual role as both assessment artifact and communication artifact. The model suggests four potential feedback focal areas for effective feedback. We conducted preliminary experiments to validate CM’s utility as a communication tool. The CMs constructed by the students provide new lens for instructors to gauge students’ meaningful learning, and, more importantly, to provide detailed and precise feedback on students’ learning effectiveness. The major contribution is the adaptation of a widely used thinking tool for meaningful learning and its assessment in IS education, which is validated by models based on learning theories and cognitive science.