Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel fused 1 × N polymer coupler for video surveillance applications

This paper demonstrate novel coupler 1 × N coupler based on polymer optical fiber (POF) was fabricated by handwork fusion method using yellow flame burner and metal tube. A new additional process - twisting-effect removal - is demonstrated to enhance fusion quality. For the purpose of performance characterization, LED fiber source with 650 wavelengths was injected into input fiber while optical power in output fibers was measured by optical power meter. The final results showed that the power-splitting performance of the coupler is satisfying since the insertion loss and excess loss is extremely low (&#60; 7 dB and 0.53 dB, respectively). For video surveillance application, the coupler has potential for high quality video transmission in tree-topology communication system.
Temporal pattern recognition in noisy non-stationary time series based on quantization into symbolic streams. Lessons learned from financial volatility trading.

In this paper we investigate the potential of the analysis of noisy non-stationary time series by quantizing it into streams of discrete symbols and applying finite-memory symbolic predictors. The main argument is that careful quantization can reduce the noise in the time series to make model estimation more amenable given limited numbers of samples that can be drawn due to the non-stationarity in the time series. As a main application area we study the use of such an analysis in a realistic setting involving financial forecasting and trading. In particular, using historical data, we simulate the trading of straddles on the financial indexes DAX and FTSE 100 on a daily basis, based on predictions of the daily volatility differences in the underlying indexes. We propose a parametric, data-driven quantization scheme which transforms temporal patterns in the series of daily volatility changes into grammatical and statistical patterns in the corresponding symbolic streams. As symbolic predictors operating on the quantized streams we use the classical fixed-order Markov models, variable memory length Markov models and a novel variation of fractal-based predictors introduced in its original form in (Tino, 2000b). The fractal-based predictors are designed to efficiently use deep memory. We compare the symbolic models with continuous techniques such as time-delay neural networks with continuous and categorical outputs, and GARCH models. Our experiments strongly suggest that the robust information reduction achieved by quantizing the real-valued time series is highly beneficial. To deal with non-stationarity in financial daily time series, we propose two techniques that combine ``sophisticated" models fitted on the training data with a fixed set of simple-minded symbolic predictors not using older (and potentially misleading) data in the training set. Experimental results show that by quantizing the volatility differences and then using symbolic predictive models, market makers can generate a statistically significant excess profit. However, with respect to our prediction and trading techniques, the option market on the DAX does seem to be efficient for traders and non-members of the stock exchange. There is a potential for traders to make an excess profit on the FTSE 100. We also mention some interesting observations regarding the memory structure in the studied series of daily volatility differences. (author's abstract)
Real Time Stereo Vision with a modified Census transform in FPGA

In this work, it is presented functional hardware architecture for stereo processing with a modified census transform. Architecture is segmented in image rectification to avoid lens distortion, stereo processing with a modified Census transform and finally post processing using a propagation algorithm to correct false disparity values. Proposed architecture uses minimal hardware and memory requirement in a way that Spartan Low Cost FPGA is used for implementation.
Glucagon-Like Peptide 1/Glucagon Receptor Dual Agonism Reverses Obesity in Mice

OBJECTIVE Oxyntomodulin (OXM) is a glucagon-like peptide 1 (GLP-1) receptor (GLP1R)/glucagon receptor (GCGR) dual agonist peptide that reduces body weight in obese subjects through increased energy expenditure and decreased energy intake. The metabolic effects of OXM have been attributed primarily to GLP1R agonism. We examined whether a long acting GLP1R/GCGR dual agonist peptide exerts metabolic effects in diet-induced obese mice that are distinct from those obtained with a GLP1R-selective agonist. RESEARCH DESIGN AND METHODS We developed a protease-resistant dual GLP1R/GCGR agonist, DualAG, and a corresponding GLP1R-selective agonist, GLPAG, matched for GLP1R agonist potency and pharmacokinetics. The metabolic effects of these two peptides with respect to weight loss, caloric reduction, glucose control, and lipid lowering, were compared upon chronic dosing in diet-induced obese (DIO) mice. Acute studies in DIO mice revealed metabolic pathways that were modulated independent of weight loss. Studies in Glp1r−/− and Gcgr−/− mice enabled delineation of the contribution of GLP1R versus GCGR activation to the pharmacology of DualAG. RESULTS Peptide DualAG exhibits superior weight loss, lipid-lowering activity, and antihyperglycemic efficacy comparable to GLPAG. Improvements in plasma metabolic parameters including insulin, leptin, and adiponectin were more pronounced upon chronic treatment with DualAG than with GLPAG. Dual receptor agonism also increased fatty acid oxidation and reduced hepatic steatosis in DIO mice. The antiobesity effects of DualAG require activation of both GLP1R and GCGR. CONCLUSIONS Sustained GLP1R/GCGR dual agonism reverses obesity in DIO mice and is a novel therapeutic approach to the treatment of obesity.
Dissociable Neural Responses in Human Reward Systems

Reward is one of the most important influences shaping behavior. Single-unit recording and lesion studies in experimental animals have implicated a number of regions in response to reinforcing stimuli, in particular regions of the extended limbic system and the ventral striatum. In this experiment, functional neuroimaging was used to assess neural response within human reward systems under different psychological contexts. Nine healthy volunteers were scanned using functional magnetic resonance imaging during the performance of a gambling task with financial rewards and penalties. We demonstrated neural sensitivity of midbrain and ventral striatal regions to financial rewards and hippocampal sensitivity to financial penalties. Furthermore, we show that neural responses in globus pallidus, thalamus, and subgenual cingulate were specific to high reward levels occurring in the context of increasing reward. Responses to both reward level in the context of increasing reward and penalty level in the context of increasing penalty were seen in caudate, insula, and ventral prefrontal cortex. These results demonstrate dissociable neural responses to rewards and penalties that are dependent on the psychological context in which they are experienced.
Perspectives of Young People Enrolled in Apprenticeship Courses in Portugal About Learning in Work Contexts

This paper is grounded in a research project about apprenticeship courses in Portugal. The analysis conducted aims to grasp whether on-the-job learning might constitute an advantage to apprenticeship courses that could contribute to improving the social image of initial vocational education and training (IVET) in Portugal. We want to understand the matter from the standpoints of young apprentices, focusing on their experiences with on-the-job training. The results were analysed within the expansive−restrictive continuum of Fuller and Unwin (2003), adapted to the Portuguese context. The project uses a mixed methods research design. A survey was conducted and semi-structured interviews were held with young apprentices. The survey was carried out online across the nation and resulted in 620 valid and relevant responses. The interviewed apprentices (54) were enrolled at nine training centres near Porto, Northern Portugal. Appraisals in the survey of the work-context training component of the apprenticeship courses were mainly positive. Nonetheless, data from the interviews revealed both expansive and restrictive features of workplaces that influenced learning outcomes. While apprenticeship courses have existed in Portugal for a long time (34 years), the cultural change to employers' views regarding work-based training can be considered to be still in progress. The results of the study support both the perspective that there is still a lot to put in place in order to change the social image of apprenticeship courses and IVET in Portugal through work-context training and also the idea that several ongoing changes are promising.
An Unsupervised Approach for Feature Based Sentiment Analysis of Product Reviews

Opinion mining or sentiment analysis is the task of mining polarity of opinions which comprises of the area such as natural language processing, data mining. As Eshops grow at rapidly increasing rates, customers on web write product reviews. These product reviews are useful for a customer in his decision making process on whether to purchase the product. Thousands or Hundreds of reviews may appear for a popular product. It is difficult for a customer to read and analyze large number of reviews and form an opinion on product reviews. In order to solve this problem we use an automated approach to mine polarity of reviews. Feature based sentiment analysis focus on different aspects or features of a product. Features of camera are battery life, zoom etc. Earlier works are based on document level and sentence level opinion mining which give a polarity of the review of the product as a whole rather than considering its features. This paper focus on an unsupervised model for analysis of product reviews at feature level and combined use of co-reference resolution, dependency parsing, linguistic rules, adverb adjective combinations, adverb verb combinations and SentiWordNet together for sentiment analysis.
Coordinated multi-agent exploration

Many successful robotic systems use maps of the environment to perform their tasks. In this paper, we propose a cooperative exploration strategy for multi-agent robots. This proposal is a parallelization of the basic SRT method, the following functionalities were added to it: cooperation to increase the eﬃciency, coordination to avoid conﬂicts and communication to cooperate and to coordinate. The goal in robot exploration must be to minimize the overall exploration time, and multiple robots produce more accurate maps by merging overlapping information that helps to stabilize the sensor uncertainty and to reach the goal. We present simulation results to show the performance of the proposed technique.
Toward Integrating Cognitive Linguistics and Cognitive Language Processing

We present a system to comprehend natural language that combines cognitive linguistics with known properties of human language processing. It is built on Embodied Construction Grammar (ECG) and the Soar cognitive architecture. Its core is a novel grounded semantic parser. Experiments show the system produces actionable meanings and fulfills ten cognitive criteria we set out.
Clustering of Web Users using ART1 NN based Clustering Approach with a Complete Preprocessing Methodology

— In this paper, a complete preprocessing methodology for discovering patterns in web usage mining process to improve the quality of data by reducing the quantity of data has been proposed. A dynamic ART1 neural network clustering algorithm to group users according to their Web access patterns with its neat architecture is also proposed. Several experiments are conducted and the results show the proposed methodology reduces the size of Web log files down to 73-82% of the initial size and the proposed ART1 algorithm is dynamic and learns relatively stable quality clusters. We compare the quality of clustering of our ART1 based clustering technique with that of traditional K-Means and SOM clustering algorithms. The results show the average inter-cluster distance of ART1 is high compared to K-Means and SOM when there are fewer clusters. As the number of clusters increases, average inter-cluster distance of ART1 is low compared to K-Means and SOM which indicates the high quality of clusters formed by our approach.
TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic

Abstract. We present a detailed description of TOPAZ4, the latest version of TOPAZ – a coupled ocean-sea ice data assimilation system for the North Atlantic Ocean and Arctic. It is the only operational, large-scale ocean data assimilation system that uses the ensemble Kalman filter. This means that TOPAZ features a time-evolving, state-dependent estimate of the state error covariance. Based on results from the pilot MyOcean reanalysis for 2003–2008, we demonstrate that TOPAZ4 produces a realistic estimate of the ocean circulation in the North Atlantic and the sea-ice variability in the Arctic. We find that the ensemble spread for temperature and sea-level remains fairly constant throughout the reanalysis demonstrating that the data assimilation system is robust to ensemble collapse. Moreover, the ensemble spread for ice concentration is well correlated with the actual errors. This indicates that the ensemble statistics provide reliable state-dependent error estimates – a feature that is unique to ensemble-based data assimilation systems. We demonstrate that the quality of the reanalysis changes when different sea surface temperature products are assimilated, or when in-situ profiles below the ice in the Arctic Ocean are assimilated. We find that data assimilation improves the match to independent observations compared to a free model. Improvements are particularly noticeable for ice thickness, salinity in the Arctic, and temperature in the Fram Strait, but not for transport estimates or underwater temperature. At the same time, the pilot reanalysis has revealed several flaws in the system that have degraded its performance. Finally, we show that a simple bias estimation scheme can effectively detect the seasonal or constant bias in temperature and sea-level.
A clinical comparison of nano-crystalline hydroxyapatite (Ostim) and autogenous bone graft in the treatment of periodontal intrabony defects.

OBJECTIVES The aim of the present study was to evaluate the efficacy of autogenous bone graft (ABG) and nano-crystalline hydroxyapatite (Ostim) in the management of human intrabony periodontal defects. STUDY DESIGN Twenty-four similar two and three wall intrabony periodontal defects with ≥5 mm probing depths and ≥3 mm depths of intrabony component in 12 healthy patients were selected. One defect in each subject was treated with ABG (ABG group) and the contralateral one with ABG and Ostim (Ostim group). Plaque index (GI), gingival index (PI), clinical probing depth (CPD), clinical attachment level (CAL), gingival margin level, bone fill, crestal resorption and defect resolution were measured at baseline and during reentry surgery after 6 months. Wilcoxon signed-rank test and Mann-Whitney U test were used to analyze the data. RESULTS Groups showed statistically significant improvements in soft and hard tissue parameters after 6 months except in gingival margin level and crestal level. However, the between-group differences after 6 months were not statistically significant with regard to soft and hard tissue measurements. CONCLUSION Within the limits of this study, both treatments resulted in marked clinical improvement, and Ostim treatment seemed to be effective in the treatment of two & three-wall intrabony defects as well as autogenous bone graft.
An exploratory study of leadership differences in the Central Bank of Ireland through grade levels

This thesis aims to explore in what ways leadership in the Central Bank of Ireland, hereafter referred to as ‘the Bank’ or ‘CBI,’ differs dependant on grade levels. To be precise, the main goals are to identify differences in leadership styles and skills in the Bank at team lead and manager grades. In examining skills, the researcher focused on change, teamwork and collaboration, conflict management, communication, emotional intelligence, motivation and teaching. The researcher followed a qualitative, inductive approach with an exploratory focus. Semi structured interviews were conducted with ten interviewees from the CBI – five team leads and five managers. The Bank’s Behavioural Competency Framework (Central Bank of Ireland, 2013) acted as a main reference point for this thesis, describing the expected behaviours of team leads and managers within the Bank. The overall results demonstrated similarities and dissimilarities in leadership style and skills across both grade levels, which both supported and contradicted previous findings. Precisely, managers focus more on flexibility and collaboration in terms of style compared to team leads who are more affiliative and focused on the development of followers. In terms of similarities, both grades demonstrated authority and an outcome focus. In relation to overall skill differences, managers focus more on vision, strategy, tailoring, encouragement and rationale, while team leads place more emphasis on emotional intelligence and again, staff development. With regard to overall similarities in skills, team leads and managers emphasised integrity, trust, intellect, understanding and acknowledgement. In addition, both grades consider communication as an important skill for leaders. However, the results showed managers apply this skill more than team leads. The findings from this thesis provide more understanding on the ways in which leadership differs generally at grade levels but particularly within the CBI. There are also implications for the Bank’s response to the 2017 GPTW Trust Index Survey results (Central Bank of Ireland, 2018a), the design of career development programmes and succession planning within the CBI. The views expressed in this thesis are those of the author and the unofficial responses from interviewees. Neither of which represent the official views of the CBI.
Effects of Metalinguistic Awareness on Reading Comprehension and the Mediator Role of Reading Fluency from Grades 2 to 4

Purpose This study examined the contribution of metalinguistic awareness including morphological awareness, phonological awareness and orthographical awareness to reading comprehension, and the role of reading fluency as a mediator of the effects of metalinguistic awareness on reading comprehension from grades 2 to 4. Methods Four hundred and fifteen elementary students in China mainland were administered a test battery that included measures of morphological awareness, phonological awareness, orthographical awareness, reading fluency, reading comprehension and IQ. Hierarchical regression and structural equation models (SEM) were used to analyze the data. Results Morphological awareness uniquely explained 9%, 10% and 13% variance of reading comprehension respectively from grade 2 to grade 4, however, phonological awareness and orthographical awareness did not contribute to reading comprehension; Reading fluency partially mediated the effect of morphological awareness on reading comprehension in grades 2-4. Conclusions These findings indicated that reading fluency and morphological awareness should be facilitated in the Chinese instruction. Morphological awareness played an important role in Chinese reading and affected reading comprehension in grades 2 to 4; Reading fluency was a significant link between morphological awareness and reading comprehension in grades 2-4.
In-Depth Analysis of Anaphora Resolution Requirements

This paper aims to lay the foundations of an anaphora resolution framework able to process all types of hypertexts and treat all types of anaphors for the English language. To this end, we provide a linguistically unambiguous and extensive definition and categorization of the concept of anaphora. We introduce a new corpus, and use our proposed categorization to statistically analyze it. Finally, we describe a preliminary version of our framework and outline promising results of the first experimental evaluations.
Bayesian Evidence for a cosmological constant using new high-redshift supernova data

We carry out a Bayesian model selection analysis of different dark energy parametrizations using the recent luminosity distance data of high redshift supernovae from Riess et al. 2007 and from the new ESSENCE Supernova Survey. Including complementary cosmological datasets, we found substantial evidence (�ln(E) � 1) against a time-varying dark energy equation of state parameter, and against phantom dark energy models. We find a small preference for a standard cosmological constant over accelerating non-phantom models where w is constant, but allowed to vary in the range 1 to 0.33.
The Foraging Ecology of the Mountain Long-Eared Bat Plecotus macrobullaris Revealed with DNA Mini-Barcodes

Molecular analysis of diet overcomes the considerable limitations of traditional techniques for identifying prey remains in bat faeces. We collected faeces from individual Mountain Long-eared Bats Plecotus macrobullaris trapped using mist nets during the summers of 2009 and 2010 in the Pyrenees. We analysed their diet using DNA mini-barcodes to identify prey species. In addition, we inferred some basic features of the bat's foraging ecology that had not yet been addressed. P. macrobullaris fed almost exclusively on moths (97.8%). As prey we detected one dipteran genus (Tipulidae) and 29 moth taxa: 28 were identified at species level (23 Noctuidae, 1 Crambidae, 1 Geometridae, 1 Pyralidae, 1 Sphingidae, 1 Tortricidae), and one at genus level (Rhyacia sp., Noctuidae). Known ecological information about the prey species allowed us to determine that bats had foraged at elevations between 1,500 and 2,500 m amsl (above mean sea level), mostly in subalpine meadows, followed by other open habitats such as orophilous grasslands and alpine meadows. No forest prey species were identified in the diet. As 96.4% of identified prey species were tympanate moths and no evidence of gleaning behaviour was revealed, we suggest P. macrobullaris probably forages by aerial hawking using faint echolocation pulses to avoid detection by hearing moths. As we could identify 87.8% of the analysed sequences (64.1% of the MOTUs, Molecular Operational Taxonomic Units) at species level, we conclude that DNA mini-barcodes are a very useful tool to analyse the diet of moth-specialist bats.
Statistical Modeling of Diversity in Top-N Recommender Systems

This paper provides the first statistical analysis of recommendation diversity. We propose a model that allows diversity to be evaluated analytically using the concentration index, a statistical measure of diversity. While this model abstracts the recommendation process, it provides good overall agreement with real recommendation algorithms. Using the model we are able to analytically demonstrate the trade-off between diversity and overall system performance. Moreover, the model makes explicit the various choices that are available to the algorithm designer to improve recommendation diversity. Our exploration of these choices provides good insight into what can be achieved in practice by algorithms that attempt to provide greater recommendation diversity without significant degradation of system precision.