Miracle-dz/UniFAR-FTUs huggingface dataset:
Slight compressibility and sensitivity to changes in Poisson's ratio

Finite element simulations of rubbers and biological soft tissue usually assume that the material being deformed is slightly compressible. It is shown here that, in shearing deformations, the corresponding normal stress distribution can exhibit extreme sensitivity to changes in Poisson's ratio. These changes can even lead to a reversal of the usual Poynting effect. Therefore, the usual practice of arbitrarily choosing a value of Poisson's ratio when numerically modelling rubbers and soft tissue will, almost certainly, lead to a significant difference between the simulated and actual normal stresses in a sheared block because of the difference between the assumed and actual value of Poisson's ratio. The worrying conclusion is that simulations based on arbitrarily specifying Poisson's ratio close to 1∕2 cannot accurately predict the normal stress distribution even for the simplest of shearing deformations. It is shown analytically that this sensitivity is caused by the small volume changes, which inevitably accompany all deformations of rubber‐like materials. To minimise these effects, great care should be exercised to accurately determine Poisson's ratio before simulations begin. Copyright © 2011 John Wiley & Sons, Ltd.
HotFlip: White-Box Adversarial Examples for Text Classification

We propose an efficient method to generate white-box adversarial examples to trick a character-level neural classifier. We find that only a few manipulations are needed to greatly decrease the accuracy. Our method relies on an atomic flip operation, which swaps one token for another, based on the gradients of the one-hot input vectors. Due to efficiency of our method, we can perform adversarial training which makes the model more robust to attacks at test time. With the use of a few semantics-preserving constraints, we demonstrate that HotFlip can be adapted to attack a word-level classifier as well.
An Epidemiological Study on the Prevalence of use of Temporary Methods of Contraception among Married Women

: Introduction Unwanted pregnancies are a major cause of unwanted child births and of maternal and infant deaths. Aims and Objectives: This epidemiological survey was conducted among 200 married women to assess the prevalence of use of temporary methods of contraception and the association of selected socio demographic factors with its usage. Methodology: A house to house survey was conducted and data was recorded on a pre-designed and pre-tested questionnaire following an interview technique. Results: The study revealed that 81.5 % (163) women were using either of the temporary contraceptive method. There were 17% (34) women who were not using any of the method though they wanted to regulate their fertility. A highly significant association (p<0.001) was observed between age, socioeconomic status, duration of married life and number of living children with usage. Conclusion: Lack of knowledge, fear of side effects, spouse and mother in law’s disapproval were the reasons related to non-usage of contraceptive methods.
Mixture of Salix Genotypes Promotes Root Colonization With Dark Septate Endophytes and Changes P Cycling in the Mycorrhizosphere

The roots of Salix spp. can be colonized by two types of mycorrhizal fungi (ectomycorrhizal and arbuscular) and furthermore by dark-septate endophytes. The fungal root colonization is affected by the plant genotype, soil properties and their interactions. However, the impact of host diversity accomplished by mixing different Salix genotypes within the site on root-associated fungi and P-mobilization in the field is not known. It can be hypothesized that mixing of genotypes with strong eco-physiological differences changes the diversity and abundance of root-associated fungi and P-mobilization in the mycorrhizosphere based on different root characteristics. To test this hypothesis, we have studied the mixture of two fundamentally eco-physiologically different Salix genotypes (S. dasyclados cv. ‘Loden’ and S. schwerinii × S. viminalis cv. ‘Tora’) compared to plots with pure genotypes in a randomized block design in a field experiment in Northern Germany. We assessed the abundance of mycorrhizal colonization, fungal diversity, fine root density in the soil and activities of hydrolytic enzymes involved in P-mobilization in the mycorrhizosphere in autumn and following spring after three vegetation periods. Mycorrhizal and endophytic diversity was low under all Salix treatments with Laccaria tortilis being the dominating ectomyorrhizal fungal species, and Cadophora and Paraphaeosphaeria spp. being the most common endophytic fungi. Interspecific root competition increased richness and root colonization by endophytic fungi (four taxa in the mixture vs. one found in the pure host genotype cultures) more than by ectomycorrhizal fungi and increased the activities of hydrolytic soil enzymes involved in the P-mineralization (acid phosphatase and β-glucosidase) in mixed stands. The data suggest selective promotion of endophytic root colonization and changed competition for nutrients by mixture of Salix genotypes.
GenDR: A Generic Deep Realizer with Complex Lexicalization

We present a generic deep realizer called GenDR, which takes as input an abstract semantic representation of predicate-argument relations, and produces corresponding syntactic dependency structures in English, French, Lithuanian and Persian, with the possibility to fairly easily add more languages. It is generic in that it is designed to operate across a wide range of languages and applications, given the appropriate lexical resources. The focus is on the lexicalization of multiword expressions, with built-in rules to handle thousands of different cross-linguistic patterns of collocations (intensiﬁers, support verbs, causatives, etc.), and on rich paraphrasing, with the ability to produce many syntactically and lexically varied outputs from the same input. The system runs on a graph transducer, MATE (Bohnet et al., 2000; Bohnet and Wanner, 2010), and its grammar design is directly borrowed from MARQUIS (Lareau and Wanner, 2007; Wanner et al., 2009; Wanner et al., 2010), which we have trimmed down to its core and built upon. The grammar and demo dictionaries are distributed under a CC-BY-SA licence (http://bit.ly/2x8xGVO). This paper explains the design of the grammar, how multiword expressions (especially collocations) are dealt with, and how the syntactic structure is derived from the relative communicative salience of the meanings involved.
Predictors of Problematic Internet and mobile phone usage in adolescents

espanolThis study uses an innovative statistical strategy to test the role of certain variables as predictors of problematic Internet and mobile phone usage among adolescents in Spain and in the United Kingdom . A paperand- pencil questionnaire was used, with socio-demographics and patterns of technology usage as variables, and two tests were administered: the Problematic Internet Entertainment Use Scale for Adolescents (PIEUSA) and the Mobile Phone Problem Use Scale for Adolescents (MPPUSA). The overall sample size was 2228 high school students aged between 11 and 18 from Barcelona and London. PIEUSA and MPPUSA scores were transformed into normed scores, and both were then dichotomized according to three statistical criteria as cut-off points (i.e., median, 80th percentile, and extreme scores below the 25th percentile and above the 75th percentile) in order to establish the relationship between the variables above and the excessive use of the Internet or mobile phones, using a binary logistic regression. The results show that the best predictive model for both technologies includes socio-demographic variables as predictors of extreme scores for excessive Internet and mobile phone usage, with good sensitivity, specificity and classification accuracy, as well as a notable capacity for discrimination according to the receiveroperating characteristic curve. Implications of these findings are discussed. catalaAquest estudi utilitza una innovadora estrategia estadistica per investigar el paper predictiu que tenen algunes variables en l�us problematic d�Internet i els telefons mobils entre els adolescents a Espanya i el Regne Unit. Es va aplicar una enquesta amb factors sociodemografics i patrons d�us de la tecnologia, alhora que es van administrar dues proves: l�Escala de l�Us Problematic d�Internet entre Adolescents (PIEUSA), i l�Escala de l�Us Problematic dels Telefons Mobils entre Adolescents (MPPUSA). La mida total de la mostra era de 2228 estudiants d�institut de Barcelona i Londres amb edats entre els 11 i els 18 anys. Les puntuacions en la PIEUSA i la MPPUSA es van transformar en puntuacions normades, i despres es van dicotomitzar utilitzant tres criteris estadistics per determinar els punts de tall (es a dir, la mitjana, el percentil 80 i les puntuacions extremes per sota del percentil 25 i per sobre el percentil 75), per tal d�establir la relacio entre els variables esmentades i l�us excessiu de l�Internet o dels telefons mobils, a traves d�una regressio logistica binaria. Els resultats indiquen que el millor model predictiu per a ambdos tipus de tecnologia inclou variables sociodemografiques com a predictors de puntuacions extremes en l�us excessiu d�ambues tecnologies, amb un bon grau de sensibilitat, especificitat i precisio de la classificacio, a mes d�una bona capacitat discriminatoria segons la corba ROC. Es comenten les implicacions d�aquestes troballes.
Joint Learning of Sense and Word Embeddings

Methods for learning lower-dimensional representations (embeddings) of words using unlabelled data have received a renewed interested due to their myriad success in various Natural Language Processing (NLP) tasks. However, despite their success, a common deﬁciency associated with most word embedding learning methods is that they learn a single representation for a word, ignoring the different senses of that word (polysemy). To address the polysemy problem, we propose a method that jointly learns sense-aware word embeddings using both unlabelled and sense-tagged text corpora. In particular, our proposed method can learn both word and sense embeddings by efﬁciently exploiting both types of resources. Our quantitative and qualitative experimental results using unlabelled text corpus with (a) manually annotated word senses, and (b) pseudo annotated senses demonstrate that the proposed method can correctly learn the multiple senses of an ambiguous word. Moreover, the word embeddings learnt by our proposed method outperform several previously proposed competitive word embedding learning methods on word similarity benchmark datasets.
Automatic Generation of Dynamic Skin Deformation for Animated Characters

Since non-automatic rigging requires heavy human involvements, and various automatic rigging algorithms are less efficient in terms of computational efficiency, especially for current curve-based skin deformation methods, identifying the iso-parametric curves and creating the animation skeleton requires tedious and time-consuming manual work. Although several automatic rigging methods have been developed, but they do not aim at curve-based models. To tackle this issue, this paper proposes a new rigging algorithm for automatic generation of dynamic skin deformation to quickly identify iso-parametric curves and create an animation skeleton in a few milliseconds, which can be seamlessly used in curve-based skin deformation methods to make the rigging process fast enough for highly efficient computer animation applications.
A Framework for the Needs of Different Types of Users in Multilingual Semantic Enrichment

The FREME framework bridges Language Technologies (LT) and Linked Data (LD). It establishes workﬂows between LT and LD in a well deﬁned, coherent way. FREME addresses common challenges that both researchers and industry face when integrating LT and LD: interoperability, ”silo” solutions and the lack of adequate tooling. Usability, reusability and interoperability are often attributes of frameworks and toolkits for LT and LD. In this paper, we take a novel approach: We deﬁne user types and user levels and describe how they inﬂuence design decisions in a LT and LD processing framework. In this way, we combine research outcomes from various communities: language technology, linked data and software interface engineering. This paper explains the different user types and how FREME addresses the speciﬁc needs of each user type. Core attributes of FREME are usability, reusability and interoperability.
Towards an artificial immune system for scheduling jobs and preventive maintenance operations in flowshop problems

This paper investigates permutation flowshop problem with preventive maintenance (PM). The objective functions are to minimize the total completion time for the production part and the total earliness/tardiness for PM part. The resolution consists of two steps: the one consists on scheduling production jobs using an artificial immune algorithm (AIA); the second one consists on deploying PM operations, taking the production schedule as a mandatory constraint of resources unavailability in the resolution of the problem. Furthermore, we use the principles of vaccination and receptor editing in order to strengthen search ability. The efficiency of the proposed AIAs with respect to minimization of makespan for the production part and performance loss after PM insertion, is compared to some referred in the related scheduling literature metaheuristics. Simulation results on both standard PFSP problems and non- standard integrated PFSP with PM problems show the superiority of our proposed algorithms.
Large-scale 3D Point Cloud Classification Based On Feature Description Matrix By CNN

Large-scale 3D Point cloud classification is a basic topic for various applications. Traditional geometries features are usually independent of each other and difficult to adapt to a fixed classification model. With the rise of the neural network, deep learning is considered in 3D point cloud application. 3D points are difficult to feed the neural network directly based on deep learning, as they cannot be arranged in a fixed order as image pixels. In this paper, we combine traditional feature-based methods with the Convolutional neural network(CNN) to finish the classification task. The core idea is to construct a feasible structure called Feature Description Matrix(FDM) which encapsulates the local feature of the point to feed CNN for training and testing. By extracting geometry features and designed Feature Description Vectors(FDV) for FDM, a simple mechanism for point cloud classification is given, and experiments validate the effectiveness of our method, with higher classification accuracy compared to state-of-art works.
Security risk analysis for smart grid automation

The reliability theory used in the design of complex systems including electric grids assumes random component failures and is thus unsuited to analyzing security risks due to attackers that intentionally damage several components of the system. In this paper, a security risk analysis methodology is proposed consisting of vulnerability analysis and impact analysis. Vulnerability analysis is a method developed by security engineers to identify the attacks that are relevant for the system under study, and in this paper, the analysis is applied on the communications network topology of the electric grid automation system. Impact analysis is then performed through co-simulation of automation and the electric grid to assess the potential damage from the attacks. This paper makes an extensive review of vulnerability and impact analysis methods and relevant system modeling techniques from the fields of security and industrial automation engineering, with a focus on smart grid automation, and then applies and combines approaches to obtain a security risk analysis methodology. The methodology is demonstrated with a case study of fault location, isolation and supply restoration smart grid automation.
A unified framework for testing in the linear regression model under unknown order of fractional integration

We consider hypothesis testing in a general linear time series regression framework when the possibly fractional order of integration of the error term is unknown. We show that the approach suggested by Vogelsang (1998a) for the case of integer integration does not apply to the case of fractional integration. We propose a Lagrange Multiplier-type test whose limiting distribution is independent of the order of integration of the errors. Different testing scenarios for the case of deterministic and stochastic regressors are considered. Simulations demonstrate that the proposed test works well for a variety of different cases, thereby emphasizing its generality.
Indra: A Word Embedding and Semantic Relatedness Server

In recent years word embedding/distributional semantic models evolved to become a fundamental component in many natural language processing (NLP) architectures due to their ability of capturing and quantifying semantic associations at scale. Word embedding models can be used to satisfy recurrent tasks in NLP such as lexical and semantic generalisation in machine learning tasks, ﬁnding similar or related words and computing semantic relatedness of terms. However, building and consuming speciﬁc word embedding models require the setting of a large set of conﬁgurations, such as corpus-dependant parameters, distance measures as well as compositional models. Despite their increasing relevance as a component in NLP architectures, existing frameworks provide limited options in their ability to systematically build, parametrise, compare and evaluate different models. To answer this demand, this paper describes I NDRA , a multi-lingual word embedding/distributional semantics framework which supports the creation, use and evaluation of word embedding models. In addition to the tool, I NDRA also shares more than 65 pre-computed models in 14 languages.
Trade Liberalization and the coffee sub-sectors: some implications for the food Sub-sector

In this study an attempt to examine trade liberalization and its implication for the coffee sub-sector is made. The analysis is based on ‘the before after approach’. Data in pre and post reform period are examined. The study shows that the liberalization carried is strongly associated with an increase in the level of export. This result is chiefly attributed to the exchange rate policy pursued. The micro evidences examined shows hat the supply price elasticity for coffee is either small or statistically insignificant. This renders the implication that price liberalization by itself may not bring major change in the coffee sub-sector. The study also underscored the problem of ensuring food security relying on unreliable world primary commodity market.
Uroprotective mechanism of quercetin against cyclophosphamide‐induced urotoxicity: Effect on oxidative stress and inflammatory markers

The urotoxicity is a common complication associated with patients receiving cyclophosphamide (CYP). This study was designed to investigate the uroprotective mechanism of quercetin (Quer) flavonoid against CYP induced urotoxicity via determination of oxidative stress markers as well as inflammatory mediators in bladder tissue. Forty male Wistar rats were divided into four groups; Normal group: received saline for 10 days. Quer control group: received quercetin 50 mg/kg/day for 10 days. CYP group: received saline for 10 days and injected with a single dose of 150 mg/kg CYP intraperitoneal (i.p) at day 8. The Quer + CYP group: received Quer 50 mg/kg/day for 10 days plus CYP 150 mg/kg i.p. injection at day 8. The CYP injection produced a significant elevation in bladder contents of malondialdehyde (MDA), and nitric oxide (NO), and bladder protein levels and expressions of tumor necrosis factor‐alpha (TNF‐α), and interleukin‐6 (IL‐6) in addition to the upregulation of cyclooxygenase‐2 (COX‐2) bladder gene expression. Also, CYP injection showed a marked reduction in bladder levels of catalase, superoxide dismutase (SOD), and IL‐10 when compared with normal group. Moreover, histopathological examination of the bladder showed degenerative alterations, severe edema, and inflammation following CYP injection. Quer attenuated the biochemical markers and histopathological changes induced by CYP. The uroprotective effect of Quer was exerted by restoring the balance between oxidative/antioxidative status and pro‐/anti‐inflammatory cytokines via its antioxidant and anti‐inflammatory activities.
Adaptation and validation of the Gambling Motives Questionnaire-Financial (GMQ-F) in a sample of French-speaking gamblers

Abstract Previous research has identified specific gambling motives and linked them with both healthy and disordered gambling. The Gambling Motives Questionnaire (GMQ) is currently the most widely used measure for these motives. The present study aimed to offer a French validation of the latest version of this scale, the GMQ-Financial (GMQ-F), which measures four distinct motives (enhancement, social, coping, financial). The French GMQ-F was completed by 278 gamblers from the community and 22 treatment-seeking pathological gamblers, along with scales assessing gambling cognitions, impulsivity, disordered gambling symptoms and psychopathological symptoms. Confirmatory factor analysis supported the expected four-factor model. The GMQ-F subscales have good internal reliability. Validity of the GMQ-F is supported by specific correlations with the other constructs measured. Pathological gamblers differed from gamblers from the community on all but one (social) of the GMQ-F subscales. The French GMQ-F presents good psychometric properties and constitutes a reliable instrument for measuring gambling motives in research and clinical practice.
Edinburgh Research Explorer Management accounting goes digital

This essay places current fascinations with the digital revolution into the historical and cultural contexts that have intertwined with the evolution of management accounting as a practice involved in the production of knowledge for decision-making. In outlining similarities and differences in the production of management accounting information from aural to digital cultures, it argues that while the effects of the digital revolution on management accounting and decision-making are still unclear, these effects surely (and hopefully) will not deliver the dream of perfect information and rational decision-making as one may be lead to believe by the growth of data-driven organizations and societies. Becoming aware of this impossibility is the first step for bringing wisdom back into decision-making processes and making management accounting gaining central stage again in the organizational arena. Acknowledgements : I wish to thank the founding editors of Management Accounting Research , Bob Scapens and Michael Bromwich, for their kind invitation to join a panel to reflect on the future of management accounting at the 25 th Anniversary Conference of the Journal, celebrated at the London School of Economics and Political Science on 17 April 2015. I also thank Wim Van der Stede, who chaired the panel, for inviting me to develop my thoughts further for this article, and even more so for his patience in waiting for this manuscript to be delivered. I also wish to thank Bob Scapens, again, and especially, as some of the thinking he has stimulated in all of these years of fruitful collaboration is contained in these pages. The usual disclaimers apply.