Miracle-dz/UniFAR-FTUs huggingface dataset:
McNamara cohesion of text and writing The epistemic stance between the author and reader : A driving force in the

This article explores the role of text cohesion in the comprehension and production of text. While most discourse models have considered the roles of the text features and the reader, the crucial role of writers’ epistemic stance has not been widely considered. The thesis explored here is that levels of cohesion emerge in text based on the epistemic stance of the author relative to the reader. Evidence is provided indicating that text genres (i.e. science, narrative) show compensatory relationships between different features related to text difficulty. For example, while science texts have more challenging words than do narratives, they tend to have higher cohesion and simpler syntax. These text profiles indicate that skilled writers have an awareness of readers’ needs. By contrast, less skilled writers seem to have less sensitivity to the interplay between textual dimensions and less audience awareness. For example, evidence is reviewed showing that more proficient essays are characterized by lower cohesion than less proficient essays: less skilled writers tend to use more cohesive cues (when they are likely unnecessary) than do more skilled writers. To the extent that an author understands the readers’ needs, the author has a more successful epistemic stance toward the reader. This stance is partially evidenced by the crucial role of cohesion in text comprehension and writing.
Improving Teaching Quality in Secondary Schools through Professional Development : Evidence from Two RCT ’ s of The My Teaching Partner Program

In the context of education reform, there is an extreme lack of rigorously evaluated teacherdevelopment programs that can produce reliable gains in student achievement. This paper discusses findings from two randomized control trials of the My Teaching Partner-Secondary (MTP-S) program, which is a web-mediated approach focused on improving the quality of teacher-student interactions in the classroom. MTP-S is based upon a validated observational approach to assessing the quality of classroom interactions, the Classroom Assessment Scoring System-Secondary (CLASS-S). The most unique feature of the intervention is that it employs a year-long personalized, video-based, iterative coaching process that allows the teacher and a consultant to focus intensively on review and analysis of observed teacher-student interactions. Both study 1 and study 2 are randomized controlled trials of the My Teaching Partner–Secondary professional development program. In Study 1 (see Allen, Pianta, Gregory, Mikami, & Lun, 2011), there were 78 secondary school teachers and 2237 students across two years. Teachers participated in the intervention in year 1 only. The program produced significant gains in both student engagement and positive peer interactions in year 1. More interestingly, the program produced substantial gains in measured student achievement in year 2, which were mediated by observationally-coded changes to teacher behavior and student engagement (CLASS-S) in the classroom. In Study 2, there were 97 secondary school teachers and 1360 students. For this study, only one year of data is available. The program produced significant gains in CLASS-S dimensions, in some cases with larger effect sizes than in the first study.
Benthic community structure characterization of the bed-sediment layer composition in the Musura Bay and Sakhalin area

The aim of this study was to assess the existing benthic organisms of the Musura Bay and Sakhalin area, in order to evaluate the habitat status within these areas. Musura Bay and Sakhalin Area belong to the avandelta, part of the Danube Delta Biosphere Reserve (DDBR). The ecological evolution of the Musura Bay and Sakhalin Area was significantly distressed by the hydro morphological changes developed in the catchment and by the local environmental circumstances. The two investigated areas are exposed to both continental and marine impacts, been marked by rapid changes in local environmental conditions (i.e., salinity levels, water transparency, temperature, pH, dissolved oxygen concentration, wind speed etc.). At present, salinity has become an extensive threat to the structure and ecological functioning of coastal wetlands. For instance, salinity long-term monitoring showed a variation from 12 ‰ (in 1942) to 0.2 ‰ (at present) for Musura Bay, whereas for Sakhalin area the salinity is 6.9 ‰ (at present). In order to evaluate the interactions of benthic organisms and their natural environment, several analyses were performed (i.e., water quality status and the nature of the bed sediments). Resultsrevealed that the surface water quality is generally included in good-moderate category, even if some inadvertences were encountered as a result of local environmental conditions. Based on the obtained results, the nature of the bed sediments from Musura Bay and Sakhalin area, can be included in the terrigenous (siliciclastic) rich-sediment type with values higher than 50% of the total weight of dry residue. The faunistic research highlighted the presence of 17 taxa belonging to 11 major invertebrate taxonomic groups for the Musura Bay and the presence of 46 taxa belonging to 20 groups in the Sakhalin Area. The average general density of the benthic populations in the Musura Bay was 8 times smaller than in the Sakhalin Area. This study indicated that most of the identified species seem to be extremely tolerant to temperature and salinity variations.
Joint Grapheme and Phoneme Embeddings for Contextual End-to-End ASR

End-to-end approaches to automatic speech recognition, such as Listen-Attend-Spell (LAS), blend all components of a traditional speech recognizer into a uniﬁed model. Although this simpliﬁes training and decoding pipelines, a uniﬁed model is hard to adapt when mismatch exists between training and test data, especially if this information is dynamically changing. The Contextual LAS (CLAS) framework tries to solve this problem by encoding contextual entities into ﬁxed-dimensional embeddings and utilizing an attention mechanism to model the probabilities of seeing these entities. In this work, we improve the CLAS approach by proposing several new strategies to extract embeddings for the contextual entities. We compare these embedding extractors based on graphemic and phonetic input and/or output sequences and show that an encoder-decoder model trained jointly towards graphemes and phonemes out-performs other approaches. Leveraging phonetic information obtains better discrimination for similarly written graphemic sequences and also helps the model generalize better to graphemic sequences unseen in training. We show signiﬁcant improvements over the original CLAS approach and also demonstrate that the proposed method scales much better to a large number of contextual entities across multiple domains.
Competition and Inequality y

This paper builds a framework with strategic interactions between an endogenous number of producers that matches the distributions of income and wealth in the US. It explains recent trends in markups, factors share, and business dynamism through an increase in entry costs for new rms, which limits competition. Through those trends, it accounts for 25% to 50% of the increase in income inequality observed between 1989 and 2007 and for 30% of the increase in wealth inequality. It nds that just 3% of the population experiences a welfare gain during the transition from a high to a low competition environment. Keywords: inequality, entry, oligopoly, markups, incomplete markets. JEL classi cations: D4, E2, L1. Colciago (Correspondig Author): andreacolciago@gmail.com; Mechelli: rajssa.mechelli@gmail.com. yAn earlier version of the paper circulated with the title "Competition and Inequality: Aiyagari meets Bertrand and Cournot" (DEMS WP, February 2019). We thank Alice Albonico, Guido Ascari, Florin Bilbiie, Pierpaolo Benigno, Corina Boar, Paolo Bonomolo, Davide Debortoli, Federico Etro, Francesco Furlanetto, Gabriele Galati, Jordi Galì, Basile Grassi, Jakob de Haan, Bart Hobijn, Keith Kuster, Marco Ma¤ezzoli, Kostas Mavromatis, Virgiliu Midrigan, Lorenza Rossi, Emiliano Santoro, Petr Sedlàμcek, Patrizio Tirelli, Antonella Trigari, Razvan Vlahu, Sweder van Wijnbergen and Gabriele Zinna for helpful discussions and comments. We also thank conference and seminar participants at the University of Antwerp, De Nederlandsche Bank, the Universitatea Babes-Bolyai, the University of Milano Bicocca, the Catholic University of Milan, the University of Crete, the University of Lancaster and Oxford University.
An Efficient Data Mining Approach to Improve Students’ Employability Prediction

Students’ Employability Prediction is a major concern for the institutions offering higher education. A method for early prediction of employability of the students is always desirable to take timely action. In this paper, an efficient data mining approach is proposed to improve students’ employability prediction. For Students’ Employability Prediction, there are some steps which include data acquisition, data pre-processing, feature subset selection and decision and ranking. For feature subset selection, Chi-Square, Gini index, Information Gain and Correlation Coefficient methods are used and for best feature selection Crow Search based Feature Selection Algorithm is used. Hybridized Hidden Markov Model and Support Vector Machine (HMM-SVM) is used for the prediction of employability. The experimental results are carried out and compared with some existing methods which includes the classification based on Support Vector Machine (SVM), Hidden Markov Model (HMM), k-Nearest Neighbour (kNN) and Artificial Neural Network (ANN). When comparing with the existing methods, the experimental outcomes showed that 93.4% accuracy was obtained by using HMM-SVM classifier.
An Approach to Online Speaker Change Point Detection Using DNNs and WFSTs

In this paper, a new approach to speaker change point (SCP) detection is presented. This method is suitable for online applications (e.g., real-time broadcast monitoring). It is designed in a series of consecutive experiments, aiming at quality of detection as well as low latency. The resulting scheme utilizes a convolution neural network (CNN), whose output is smoothed by a decoder. The CNN is trained using data complemented by artificial examples to reduce different types of errors, and the decoder is based on a weighted finite state transducer (WFST) with the forced length of the transition model. Results obtained on data taken from the COST278 database show that our online approach yields results comparable with an offline multi-pass LIUM toolkit while operating online with a low latency.
AIAA-2001-1307 DYNAMIC MODELING AND TESTING OF MSRR-1 FOR USEIN MICROGRAVITY ENVIRONMENTS ANALYSIS

Delicate microgravity science is unlikely to succeed on the International Space Station if vibratory and transient disturbers corrupt the environment. An analytical approach to compute the on-orbit acceleration environment at science experiment locations within a standard payload rack resulting from these disturbers is presented. This approach has been grounded by correlation and comparison to test-verified transfer functions. The method combines the results of finite element and statistical energy analysis using tested damping and modal characteristics to provide a reasonable approximation of the total root-mean-square (RMS) acceleration spectra at the interface to microgravity science experiment hardware.
Hidden Wealth : Asset Holdings and Land Acquisition Strategies of Political Households Warn

Ample evidence points that politicians exploit their political connections for private gains throughout the world. This paper investigates to what extent politicians are capable of enriching themselves and their family members through shrewd investment decisions. We do so with a unique longitudinal dataset on mandatory disclosures of asset holdings by political households in Thailand. Landholdings are a particularly attractive type of asset for political households as they allow politicians to exploit their office for private gain while simultaneously making it possible to hide these gains from detection by public authorities. Exploiting a high-resolution spatial dataset on all their land plots, we find that politicians hold a strikingly large fraction of their wealth in land and that these land holdings are highly concentrated around their place of birth and their constituency. The concentration of land ownership in a politician’s own local constituency increases even further once she has taken office. The land plots acquired by politicians after taking office and located in their local constituency outperform their other holdings, in term of changes in night-time light intensity, infrastructure investments, and closeness to commercial hubs. Meanwhile, politicians' asset disclosures indicate zero increases in wealth during their time in office. We discuss different explanations for these patterns and find that only private returns to public office are consistent with the data. In light of these findings, we suggest ways to improve the monitoring of politicians' assets. JEL Classification: D72, D73, G11
Robust Anomaly Detection in Cyber Physical System using Kullback-Leibler Divergence in Error Distributions

We investigate anomaly detection in Cyber-Physical System (CPS), where anomalies are attacks to CPS to disrupt the operations of critical infrastructures. We use the Secure Water Treatment (SWaT) systems dataset, where normal and attack states are simulated in the water tanks. Among different types of anomalies, we focus on detecting the contextual anomalies, which can be challenging to detect with the Out-Of-Limit threshold method. Recent research shows promising results in detecting anomalies from analyzing error distributions from the machine learning classifier. Similarly, we statistically analyze prediction error patterns from Recurrent Neural Network (RNN) and Mixture Density Network (MDN) classifiers to detect anomalies. First, we generate anomaly scores with Local Outlier Factor (LOF) and remove point anomalies. With the fixed window size, an empirical probability distribution is estimated, and we apply the sliding window to measure the difference of probability distributions between the other windows. To measure the difference efficiently between anomalies and normal data, we use Kullback-Leibler divergence. Our preliminary result shows that we can effectively detect contextual anomalies compared with Nearest Neighbor Distance (NND) approach.
PRODUCTION AND PERCEPTION OF ENGLISH LOW-MID VOWELS BY SPEAKERS OF COLOMBIAN SPANISH IN AN ENGLISH LANGUAGE IMMERSION ENVIRONMENT

The study examines the perception and production of the English vowels [æ] and [2] by native speakers of Colombian Spanish who are experienced L2 English speakers. Participants recorded three minimal pairs and performed a multiple-choice identification task. Acoustic data were compared to participants’ Spanish vowels and monolingual English speakers’ productions. Perceptual results demonstrated that although [æ] and [2] had fewer correct identifications than other English vowels, the difference was not statistically significant. When incorrectly identified, [æ] and [2] were typically confused with each other or another low vowel, [A]. In production, although participants realized [æ] and [2] as acoustically distinct, both were statistically different from the native targets. Participants’ English [æ] was not statistically different from their native Spanish [a] in either height or backness. The results suggest that English low-mid vowels present a moderate challenge to experienced Spanish learners immersed in an English language environment.
Maximum a posteriori Speech Enhancement Based on Double Spectrum

While the acoustic frequency domain has been widely used for speech enhancement, usage of the modulation domain is less common. In this paper, we investigate single-channel speech enhancement in the recently proposed Double Spectrum (DS) framework and provide insights on the statistical properties of speech and noise in the DS domain. Relying on our statistical analysis in the DS, we derive a maximum a posteriori estimator of speech in the DS domain. By means of experiments, we evaluate the speech enhancement performance of the proposed method and relevant benchmarks in the acoustic frequency and modulation domains and show that the proposed method achieves a good balance between noise attenuation and speech distortion for various SNRs and noise types.
How Distant is Lisbon from Maastricht ? The Short-run Link Between Structural Reforms and Budgetary Performance

This study analyzes the mutual short-run link between structural reforms and budgetary performance. The empirical basis is the reform experience of 20 OECD countries since 1975 with respect to reforms of product, financial and labour markets and the tax system. The testing identifies the typical budgetary features both on the eve of reform and over reform cycles. Furthermore, a dynamic reform model and a model relating reforms to economic sentiments are estimated. Overall, the results do not point towards a general short-run trade-off between Maastricht and Lisbon since the link between budgetary phenomena and structural reforms is rather weak. While Maastricht and Lisbon tend to be mutually reinforcing for the liberalization of financial and product markets, there can be short-term conflicts for tax and labour market reforms. For all covered policy fields, the results indicate that expectation effects may be a more important part of the link between reforms and the budget compared to other often debated dimensions such as compensation issues. JEL-Classification: E 63, H00
Edinburgh Research Explorer Geographic Network Visualization Techniques:A Work-In-Progress Taxonomy

This poster presents a survey of visualization techniques for geographic networks. Based on 60 techniques, we provide an initial taxonomy based on categorizing each technique across four facets: how the geographic aspect is represented, how the network aspect is represented, how these two visual representations are integrated, and whether the technique relies on user interaction. The current collection can be found online: https://geographic-networks.github. io .
Circuits and architectures for high frequency synthesizer in CMOS

This thesis proposes a novel architecture for high frequency synthesizer design focusing mainly on the 60 GHz frequency range. It consists of a PLL cascaded to an ILO. In order to generate narrow pulses and to relax the multiplication ration of the ILO, a DLL with a pulse generator is used. Passive delay line stacked on top of LC VCO is used for power efficiency and replica-biasing technique of frequency tracking is used for increasing the locking range of ILO. The synthesizer operates at 50 GHz with a phase noise of -98, -117 and -128 dBc/Hz at 1 MHz, 10 MHz and 40 MHz respectively. The total power consumed by the frequency synthesizer from 1.2 V supply is 57 mW. To have channel selection capability, fractional PLL may be used. A novel fractional PLL architecture is also proposed which de-couples the residual jitter from the PLL bandwidth.
iMAT: An Iterative Text Style Transfer Algorithm and its Application to Paraphrasing

Language style transfer is the task to change the style of text while preserving the content. It plays key roles in many scenarios in where rephrasing text is necessary. The key technical challenge to produce high-quality style transfer systems is to effectively learn from large-scale unsupervised data; however, existing models trained in an unsupervised setting demonstrate limitations in content preservation and grammatical accuracy. This project proposes a more effective approach by constructing a pseudo-parallel corpus from the non-parallel data, and iteratively refining it by a sequence-to-sequence model. Currently, we have compared our model with three state-of-the-art approaches on two benchmark datasets, where our method outperforms the strongest previous approach by success rates of +6.5% and +22%, respectively, according to human evaluation. This approach potentially contributes to applications such as paraphrasing for Question Answering (QA) and Information Extraction (IE) datasets.
Determination of Managers’ Attitudes Towards Eco-Labeling Applied in the Context of Sustainable Tourism and Evaluation of the Effects of Eco-Labeling on Accommodation Enterprises

The attitudes of departmental managers towards eco-labels are extremely important for the realization of sustainable tourism practices in accommodation enterprises. Research focused on determining the attitudes of hotel managers towards eco-labels is very limited. Therefore, the first aim of this study was to determine the attitude of departmental managers towards eco-labels applied in the context of sustainable tourism. The second purpose of this study was to determine whether the sustainable tourism and management activities of accommodation enterprises that did and did not have an eco-label were differentiated. To this end, this study was designed and conducted with a sample of 408 hotel managers in 83 different accommodation enterprise. Data were collected through questionnaires using convenience sampling, which is a non-probability sampling method. As a result of this research, it was determined that the attitudes of managers towards eco-labels differed according to their personal and professional characteristics. Another important finding of this study was that the sustainable management and operation activities of the hotels differed significantly according to whether or not they were eco-labeled certified. Overall, it is obvious that eco-labels have a significant impact on the implementation of environmentally friendly, responsible, and sustainable tourism practices in the accommodation sector.
Zero Resource Speech Synthesis Using Transcripts Derived from Perceptual Acoustic Units

Zerospeech synthesis is the task of building vocabulary independent speech synthesis systems, where transcriptions are not available for training data. It is, therefore, necessary to convert training data into a sequence of fundamental acoustic units that can be used for synthesis during the test. This paper attempts to discover, and model perceptual acoustic units consisting of steady-state, and transient regions in speech. The transients roughly correspond to CV, VC units, while the steady-state corresponds to sonorants and fricatives. The speech signal is first preprocessed by segmenting the same into CVC-like units using a short-term energy-like contour. These CVC segments are clustered using a connected components-based graph clustering technique. The clustered CVC segments are initialized such that the onset (CV) and decays (VC) correspond to transients, and the rhyme corresponds to steady-states. Following this initialization, the units are allowed to re-organise on the continuous speech into a final set of AUs in an HMM-GMM framework. AU sequences thus obtained are used to train synthesis models. The performance of the proposed approach is evaluated on the Zerospeech 2019 challenge database. Subjective and objective scores show that reasonably good quality synthesis with low bit rate encoding can be achieved using the proposed AUs.