Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical and Physical Modelling of Extreme Wave Impacts on a Fixed Truncated Circular Cylinder

With a history of international failures, the survival envelope for wave energy convertors (WECs) has become an important consideration in the design of such systems. Potential design solutions require a better understanding of the hydrodynamics and structural loading experienced during extreme events, like rogue wave impact. This paper concerns the numerical modelling and experimental validation of extreme rogue wave interactions with a fixed truncated circular cylinder. Typical extreme waves from the intermediate depth Wave Hub site were produced at 1:30 scale in the COAST Lab Ocean basin at Plymouth University from the 100 year wave statistics using the dispersive focussing method, NewWave. A fixed 0.4m diameter cylinder with a 0.4m draft was used to represent the geometry of a generic point-absorber type WEC. Physical conditions were duplicated in a numerical wave tank, solving the fully nonlinear Navier-Stokes equations, with a free surface, using the volume of fluid (VoF) method and open source CFD library OpenFOAM®. The comparison between the results shows that the CFD software is capable of simulating extreme wave interactions with a fixed cylinder and the associated hydrodynamic phenomena very well. Keywords— Wave Energy Converter (WEC), survivability, NewWave, OpenFOAM®, waves2Foam, VoF method
Notes on the use of variational autoencoders for speech and audio spectrogram modeling

Variational autoencoders (VAEs) are powerful (deep) generative artificial neural networks. They have been recently used in several papers for speech and audio processing, in particular for the modeling of speech/audio spectrograms. In these papers, very poor theoretical support is given to justify the chosen data representation and decoder likelihood function or the corresponding cost function used for training the VAE. Yet, a nice theoretical statistical framework exists and has been extensively presented and discussed in papers dealing with nonnegative matrix factorization (NMF) of audio spectrograms and its application to audio source separation. In the present paper, we show how this statistical framework applies to VAE-based speech/audio spectrogram modeling. This provides the latter insights on the choice and interpretability of data representation and model parameterization.
Shedding Light on Nucleic Acids and DNA under Construction

The first part of our research deals with the spatiotemporal regulation of biological processes. We use light as addressing mechanism and modify nucleic acids in a way to make them light-responsive. Light has the advantage that it can be easily generated and manipulated with well-established (laser and microscope) technologies and many of the currently investigated model organisms are light-accessible. On the other hand nucleic acids are the base of powerful techniques such as for example RNA interference for the regulation of genes and aptamers for the regulation of the function of proteins. In the second part of our research we are exploring new ways to assemble nanometer-scaled objects from DNA but instead of only relying on the Watson-Crick interaction we are using alternative – orthogonal – interaction strategies like for example ‘‘Dervan-type polyamides’’ which can sequence-selectively bind to double-stranded DNA. Two of these polyamides – combined via a linker – form a ‘‘DNA strut’’ which can sequence-selectively ‘‘glue’’ together two DNA double helices. 195 http://www.beilstein-institut.de/Bozen2008/Proceedings/Heckel/Heckel.pdf Systems Chemistry, May 26 – 30, 2008, Bozen, Italy
The Handbook of the Economics of Corporate Governance

Executive Compensation: A Survey of Theory and Evidence✶ Alex Edmans∗,†,‡,1, Xavier Gabaix§,¶,†,‡, Dirk Jenter‖,† ∗London Business School, London, United Kingdom †CEPR, London, United Kingdom ‡ECGI, Brussels, Belgium §Harvard University, Cambridge, MA, United States ¶NBER, Cambridge, MA, United States ‖London School of Economics, London, United Kingdom 1Corresponding author. E-mail: aedmans@london.edu
The relationship between the practice of physical activity and sport and the level of motor competence in primary school children

Gross motor skills are the precursors of complex activities used for performing typical daily activities and for playing games and sports. The development of this kind of skills happens between three and eight years and an adequate level of these skills is mandatory in order to develop psyco-motor, social and affective domains of learning. In this work, the gross motor development has been studied in 120 children aged 6 to 11 from three different schools in Sicily. Every child performed all the items of the TGMD test and was ranked according to their gross motor development quotient. Later, thanks to the parents’ help, every child had to complete the CLASS questionnaire which had investigated on their daily routine in terms of activities done. Regression analysis was performed to establish which determinants impact the most on the skills development. Results showed that males, the children of the lower classes and those who practice a sport are more likely to have a high motor level in comparison with the respective reference categories (i.e, females, highest school classes, and students with low level of motion, respectively). These evidences are useful to understand the role of the school in the development of FMS and to plan adequate strategies in order to overcome the up-to-date limits in the physical education teaching-learning process.
Normalization of Indonesian-English Code-Mixed Twitter Data

Twitter is an excellent source of data for NLP researches as it offers tremendous amount of textual data. However, processing tweet to extract meaningful information is very challenging, at least for two reasons: (i) using nonstandard words as well as informal writing manner, and (ii) code-mixing issues, which is combining multiple languages in single tweet conversation. Most of the previous works have addressed both issues in isolated different task. In this study, we work on normalization task in code-mixed Twitter data, more specifically in Indonesian-English language. We propose a pipeline that consists of four modules, i.e tokenization, language identification, lexical normalization, and translation. Another contribution is to provide a gold standard of Indonesian-English code-mixed data for each module.
Bag-of-Words Transfer: Non-Contextual Techniques for Multi-Task Learning

Many architectures for multi-task learning (MTL) have been proposed to take advantage of transfer among tasks, often involving complex models and training procedures. In this paper, we ask if the sentence-level representations learned in previous approaches provide significant benefit beyond that provided by simply improving word-based representations. To investigate this question, we consider three techniques that ignore sequence information: a syntactically-oblivious pooling encoder, pre-trained non-contextual word embeddings, and unigram generative regularization. Compared to a state-of-the-art MTL approach to textual inference, the simple techniques we use yield similar performance on a universe of task combinations while reducing training time and model size.
Similarity notions in bipolar abstract argumentation

The notion of similarity has been studied in many areas of Computer Science; in a general sense, this concept is deﬁned to provide a measure of the semantic equivalence between two pieces of knowledge, expressing how “close” their meaning can be regarded. In this work, we study similarity as a tool useful to improve the representation of arguments, the interpretation of the relations between arguments, and the semantic evaluation associated with the arguments in the argumen-tative process. In this direction, we present a novel mechanism to determine the similarity between two arguments based on descriptors representing particular aspects associated with these arguments. This mechanism involves a comparison process in-ﬂuenced by the context in which the process develops, where this context provides the relevant aspects that need to be analyzed in the application domain. Then, we use this similarity measure as a quantity to compute the result of attacks and supports in the argumentation process. These valuations, applied to a Bipolar Argumentation Frameworks, allowed us to reﬁne the argu-ment relations, providing the tools to establish a family of new argumentation semantics that considers the similarity between arguments as a crucial part for the argumentation process.
WAT2019: English-Hindi Translation on Hindi Visual Genome Dataset

A multimodal translation is a task of translating a source language to a target language with the help of a parallel text corpus paired with images that represent the contextual details of the text. In this paper, we carried out an extensive comparison to evaluate the benefits of using a multimodal approach on translating text in English to a low resource language, Hindi as a part of WAT2019 shared task. We carried out the translation of English to Hindi in three separate tasks with both the evaluation and challenge dataset. First, by using only the parallel text corpora, then through an image caption generation approach and, finally with the multimodal approach. Our experiment shows a significant improvement in the result with the multimodal approach than the other approach.
Energy Saving of Data Transmission for Wireless Sensing Device by Selective Compressed Sensing

Compressed sensing is a sophisticated technique that performs high-efficiency compression/ reconstruction using sparsity of data. In the wireless sensing device, most of energy consumption is consumed by wireless transmission, and energy saving can be achieved by reducing the amount of transmission data using compressed sensing. Wireless sensing is used for failure analysis of buildings and machines, many of which use only a specific frequency band, while compressed sensing compresses all properties including the entire frequency. In this paper, we introduce efficient compressed sensing “selective compressed sensing” which selectively compresses the target band. The reconstruction process is conventional l1 norm optimization. We use following 3 techniques. (1) Compression process uses the Walsh-Hadamard transform matrix corresponding to the target band. (2) Walsh-Hadamard transform requires only addition, subtraction, and shift operation, while Fast Fourier Transform requires floating-point operation which is a burden to resourceconstrained microprocessors. (3) We succeeded in improving fast Walsh-Hadamard transform which can improve the frequency resolution by integration algorithm using successively received data and reducing the calculation time. Applying this to a diagnose device for motors by their vibrations reduces its energy consumption by half.
EFFECTS OF CURING PRESSURE ON TENSILE AND FATIGUE STRENGTH OF GLASS FIBER REINFORCED EPOXY COMPOSITES EMPLOYED

In this study, effects of the tensile and fatigue characteristics on glass fiber composites produced under various pressures have been investigated. For this intention, 8 laminate glass fiber composite plates are manufactured under 3 different curing pressures (15 inhg, 20 inhg , 25 inhg ) by manual (layup) spreading method. Tensile properties of laminated composites have been detected at 2mm / min jaw speed according to the ASTMD 3039-08 and fatigue experiments have been realized by the load control at f = 5 Hz in the frequency of sinusoidal and the tensile fatigue is determined with 8801 Universal Tensile Utensil, the capacity of 50 kN Maximum tensile resistance at 325 Pa under 20 inhg curing pressure and fatigue behavior under 20 inhg have been obtained better from the manufactured sample. Keywords— Polymer matrix composites, fatigue, glass fiber, effect of curing pressure.
A Foreigner Who Doesn’t Steal My Job: The Role of Unemployment Risk and Values in Attitudes towards Equal Opportunities

Immigration has become systematically politicized and opposed by many individuals. We examine individual attitudes toward equal opportunities for foreigners and Swiss citizens, using cross‐ sectional data from the Swiss Household Panel. Individuals with low levels of education tend to oppose foreigners, while the opposition by individuals with high levels of education increases with the risk of unemployment. Values and beliefs explain the negative attitudes of individuals with low levels of education, but not the association with the risk of unemployment for individuals with high levels of education. Clearly, both values and economic factors are important for explaining attitudes toward foreigners.
Transcription factors involved in stem cell maintenance are downstream of Slug/Snail2 and repressed by TGF-β in bronchial epithelial progenitors from Chronic Obstructive Pulmonary Disease

Objectives: Patients with Chronic Obstructive Pulmonary Disease (COPD) have a bronchial epithelium with many anomalies and basal/progenitor cells showing a decrease of self-renewal and differentiation potential. The objective of this study was to identify deregulations in the genetic program of COPD bronchial progenitors that could account for their exhaustion. The transcription factor Slug/Snail2 is highly expressed in bronchial progenitors and we aimed at identifying genes downstream of Slug whose expression is deregulated in COPD progenitors. Results: We knocked down Slug in primary basal cells from COPD subjects and, since COPD subjects have higher levels of Transforming Growth Factor (TGF)-β Slug is regulated by TGF-β, we selected genes downstream of Slug involved in differentiation that respond to TGF-β. We identified transcription factors involved in stem cell maintenance downstream of Slug and repressed by TGF-β in COPD but not normal progenitors. We found that the effect of TGF-β on the expression of these genes is correlated to Slug knockdown effect. We also found a correlation between the mRNA levels of Slug and these genes only in presence of TGF-β. These results reveal that stem cell maintenance genes are deregulated in COPD bronchial progenitors, Slug and TGF-β being involved in that deregulation.
Satellite remote sensing of wildlife habitats using a multi-scale, object-based, decision-tree classifier (MOOSC)

This paper describes an object-based methodology devised to enable accurate and detailed habitat mapping using IKONOS VHR satellite images. The Multi-scale Object Oriented Segmentation with Decision Tree Classification (MOOSC) comprises a suite of image processing techniques applied interactively by the user in response to study area characteristics, and data dimensionality. Because of the inherent flexibility of the method, MOOSC can obtain high mapping accuracy even in the rugged terrain of the present study area combined with the low solar zenith angle of the wintertime images used. The accuracy of 90% obtained for MOOSC increases to 94% if habitat classes are based purely on physical structure rather than life form, ie. if shrub-sized tree seedlings planted on grassy slopes are classified as shrubby grassland, which they resemble physically and spectrally, although logically they belong to the forest class. Thus the MOOSC method is able to achieve similar accuracy to stereo air photo interpretation from large scale, high quality colour photos (which achieved 95% accuracy), but at only one third of the cost.
Precise Modeling with UML: Why OCL?

Nowadays UML is the most common graphical notation for objectoriented software development. But UML itself is not enough to model some software aspects precisely. Hence, IBM has proposed the Object Constraint Language (OCL) as the UML standard specification language. To be attractive, OCL is announced to be easy as well as avoid mathematical and logical descriptions. In this paper we argue that OCL is not special, being easily comparable to Object-Z, an object-oriented extension of the model-based language Z. In particular, aspects concerning formal semantics and refinement reveals that OCL is indeed an immature language. The comparison also yields a translation from OCL to Object-Z.
Statistical modeling of stochastic level shifts in time series

Methods are developed for estimating trends in time series subject to level shifts. The approach is based on specifying stochastic models for breaks as part of the model structure, using heavy-tailed densities to allow for a positive probability of such a large change at any given time. Examining changes in trend movements, estimated from the dynamics of the dataset, provides more information than a yes/no criterion for making decisions on level shift events. Continuous-valued innovations in the trend are assessed using a statistical model; with the arrival of a data point that constitutes a break, timely warning is given with a smooth shift in the assessment. The empirical illustrations show how more robust trend estimates are obtained in practice.
Genetic Diversity and Lineage Based on SSR Markers of Two Genomic Resources among Trifolium Collections Held within the Australian Pastures Geneban

Trifolium alexandrinum, an important forage legume, suffers from narrow genetic base. The present investigation was envisaged to reveal the interand intra-species genetic diversity and lineage among 64 accessions, representing a global collection, of T. alexandrinum; it’s two probable progenitor species (T. salmoneum and T. subterraneum) and the three genetically distant species (T. repens, T. vesiculosum, T. michelianum). A set of Simple Sequence Repeats (SSR) primer-pairs developed from T. alexandrinum have shown to amplify alleles across the species under study, suggesting utility of the newly developed resource for assessing molecular diversity among Trifolium species. These SSRs markers together with previously reported SSRs, derived from T. repens, enabled to reveal high intra-species polymorphism in T. alexandrinum and successfully discriminate different species investigated in this study. The diverse accessions determined herein provide a superior resource for further breeding of T. alexandrinum. High allelic similarity of T. alexandrinum with T. subterraneum and T. salmoneum indicated close relatedness among the species, suggesting polyphyletic evolution of T. alexandrinum.
Probabilistic Risk Assessment of Aging Layered Pressure Vessels

The National Aeronautics and Space Administration (NASA) operates approximately 300 aging layered pressure vessels that were designed and manufactured prior to ASME Boiler and Pressure Vessel (B&PV) code requirements. In order to make decisions regarding the continued fitness-for-service of these non-code carbon steel vessels, it is necessary to perform a relative risk of failure assessment for each vessel. However, risk assessment of these vessels is confounded by uncertainties and variabilities related to the use of proprietary materials in fabrication, missing construction records, geometric discontinuities, weld residual stresses, and complex service stress gradients in and around the welds. Therefore, a probabilistic framework that can capture these uncertainties and variabilities has been developed to assess the fracture risk of flaws in regions of interest, such as longitudinal and circumferential welds, using the NESSUS® probabilistic modeling software and NASGRO® fracture mechanics software. In this study, the probabilistic framework was used to predict variability in the stress intensity factor associated with different reference flaws located in the head-to-shell circumferential welds of a 4-layer and 14-layer pressure vessel. The probabilistic studies predict variability in flaw behavior and the important uncertain parameters for each reference flaw location.
MARSHALL PLAN REPORT

Profile Hidden Markov Models (HMMs) are often used in the field of bioinformatics. They allow for the comparison of different proteins in order to determine similar characteristics and functions. Proteins can be represented by a chain of characters and each of these characters symbolizes one of the twenty natural occurring Amino Acids (AAs). The purpose of Profile HMMs is that such a model is generated for a specific protein family and then any protein sequence can be aligned to this model in order to determine the degree of the similarity. The main focus of this report is on the implementation of a visual display of the 9-state Profile Hidden Markov Model (HMM) and on the rendering of transition and emission probabilities in user friendly ways. Furthermore, this visual display will be integrated in the HMModeler protein modelling software developed at Salzburg University of Applied Sciences and Salzburg University.
Job Satisfaction among a Multigenerational, Critical Care, Nursing Workforce

Despite the slight ease of the nursing shortage due to the current recession, the United States is expected to still have a shortage of approximately 260,000 registered nurses by the year 2025 (American Association of Colleges of Nursing, 2011). Healthcare is an ever changing profession where nurses, no matter their generation, must learn to adapt to their constantly changing environment and expectations. Job satisfaction is a strong and constant predictor of retention and can vary greatly across generations. The purpose of this study was to get insight into generational differences related to job satisfaction and to use these data to increase clarity concerning retention approaches specific to these generations. The organizing framework was based on Abraham Maslow’s Hierarchy of Needs Theory. The study participants were registered nurses who have had at least three months experience in the critical care setting at their current organization. Spearman’s Correlation was used to determine relationships between their generation of birth and certain factors of their job satisfaction along with their overall job satisfaction. The data provided support that there is a significant relationship between overall job satisfaction and the registered nurse’s birth year. These data also points out windows of opportunities for improvement for certain categories among the specific generations in question. Recommendations for further research include using a broader sample incorporating various regions of the United States and facilities of larger size. Determining how the different generations perceive their working environments is the first step in developing a plan and strategies to improve their overall satisfaction leading to improved retention.
The Money or the Morals?

We examine the effectiveness of economic and moral language used by employees when selling social issues to management. In contrast to prior work finding that employees believe it is best to use economic language to influence management to address social issues, we draw on the issue selling, persuasion, and behavioral ethics literatures to demonstrate that moral language is actually most influential—especially when the language is framed to align with the organization’s values and/or mission. The results from a combination of 3 field survey studies and 1 experimental vignette study provide support for this hypothesis. In addition, we find support for obligation (i.e., manager’s anticipated guilt), rather than inspiration (i.e., manager’s prosocial motivation), as a mediator of this interactive effect. We discuss implications for literatures on issue selling, persuasion, and behavioral ethics.