Miracle-dz/UniFAR-FTUs huggingface dataset:
Two Decades of Vector Autoregression (VAR) Modeling

A vector autoregression (VAR) is defined as a vector of endogenous variables regressed against its own lags. VARs therefore are considered part of a general class of simultaneous equations models. By construction, VAR analysis allows us to examine over time the dynamic impacts of innovations to variables on others. The following is a survey of the literature on vector autoregressions (VARs) in the last twenty years since it was first used for policy analysis by Christopher Sims [1980]. Initially, imposing a recursive structure on VAR disturbances had led to criticism that VAR modeling is atheoretical. In tha last decade, however, a number of authors have attempted to remedy the problem by introducing new structural identification techniques. This has enhanced the ability of VARs to model dynamic economic relationships. VAR studies have been used primarily to identify the impacts of aggregate demand and supply shocks on aggregate output, as well as to identify the channels and impacts of monetary policy. The frontiers of current VAR research focus on open economy extensions, as well as on improving lag selection and estimation. JEL classification: C32, C51
Integrated monetary and exchange rate frameworks: are there empirical differences?

The aim of the paper is to empirically estimate whether the different monetary and exchange rate frameworks observed in the accession countries of Central and Eastern Europe and the Baltic States do yield different outcomes in terms of level and variance of a set of nominal and real variables. The author follows and extends the methodology developed by Kuttner and Posen (2001), who perform a combined analysis of the individual effects of exchange rate regimes, central bank independence and announced targets in nominal variables for a large set of developed and developing countries. They also estimate that a set-up combining a free float, an independent currency board and inflation targeting yields an outcome that mimics the price stabilisation advantages of a hard peg without its drawbacks in terms of extreme volatility. This sample of countries, not covered by the Kuttner and Posen study, supports their conclusions for both nominal and real variables, testing for both the individual and combined effects of the frameworks and indicating that a flexible exchange rate regime, coupled with CBI and DIT, would be Pareto-improving when compared to harder regimes.
Testing the Opportunistic Approach to Monetary Policy

The opportunistic approach to monetary policy is an influential but untested model of optimal monetary policy. We provide the first tests of the model, using US data from 1983Q1 to 2004Q1. Our results support the opportunistic approach. We find that policy-makers respond to the gap between inflation and an intermediate target that reflects the recent history of inflation. We find that there is no response of interest rates to inflation when inflation is within 1 per cent of the intermediate target but a strong response when inflation is further from the intermediate target.
TGVizTab: An Ontology Visualisation Extension for Protégé

Ontologies are gaining a lot of interest and many are being developed to provide a variety of knowledge services. There is an increasing need for tools to graphically and in-teractively visualise such modelling structures to enhance their clarification, verification and analysis. Protege 2000 is one of the most popular ontology modelling tools currently available. This paper introduces TGVizTab; a new Protege plugin based on TouchGraph technology to graphically visualise Protege’s ontologies.
Asymptotic performance analysis of direction-finding algorithms based on fourth-order cumulants

In the narrow-band array processing context, the use of higher order statistics has been often advocated because consistent and asymptotically unbiased parameter estimates can be obtained without it being necessary to know, to model, or to estimate the spatial covariance of the noise as long as it is normally distributed. However, experimentation shows that this 'noise insensitivity' is traded for increased variability of the parameter estimates. The main purpose of this contribution is to derive and work out closed-form expressions of the asymptotic covariance of MUSIC-like direction-of-arrival estimates based on two fourth-order cumulant matrices: the diagonal slice and the contracted quadricovariance. This is compared with the standard covariance-based MUSIC estimate establishing on a rational basis the domain of applicability of higher order statistics for DOA estimation. In particular, the actual impact of the noise variance and of the dynamic range of the sources is investigated. This asymptotic performance analysis is achieved within a general framework, which we believe to be of general interest. >
The Next Generation: Immigrant Youth in a Comparative Perspective

Acknowledgments 1 Dimensions of Second-Generation Incorporation 2 Legalization and Naturalization Trajectories among Mexican Immigrants and Their Implications for the Second Generation 3 Early Childhood Education Programs 4 The Mexican American Second Generation in Census 2000: Education and Earnings 5 Downward Assimilation and Mexican Americans: An Examination of Intergenerational Advance and Stagnation in Educational Attainment 6 School Qualifications of Children of Immigrant Descent in Switzerland 7 Ethnic Community, Urban Economy, and Second-Generation Attainment 8 The Second Generation in the German Labor Market 9 Capitals, Ethnic Identity, and Educational Qualifications 10 National and Urban Contexts for the Integration of the Second Generation in the United States and Canada 11 "I Will Never Deliver Chinese Food" 12 Black Identities and the Second Generation: Afro-Caribbeans in Britain and the United States 13 How Do Educational Systems Integrate? Integration of Second-Generation Turks in Germany, France, the Netherlands, and Austria 14 The Employment of Second Generations in France References About the Contributors Index
A study on the effect of different image centres on stereo triangulation accuracy

This paper evaluates the effect of mixing the distortion centre, principal point and arithmetic image centre on the distortion correction, focal length determination and resulting real-world stereo vision triangulation. A robotic arm is used to generate a ground truth set of known positions resulting in 2078 measurements per cameras. It is seen that compared to the naive use of the arithmetic image centre improvements of 10% to 27% in triangulation accuracy can be made by determining an optimal principal point. An optimal distortion centre has a smaller but still beneficial effect.
The Placebo Effect in Digital Games: Phantom Perception of Adaptive Artificial Intelligence

Play-testing of digital games is a crucial part of any game development process, used to gather feedback about the game and correct any existing and potential flaws with the design. However, due to the nature of human subject testing, the feedback being collected in such experiments is prone to biases. Players' expectations play a great role in dictating their gaming experience, which means the information players receive before trying a new game, as well as the knowledge they already possess, may affect their perception and experience of the game. Two studies were conducted in order to evaluate how priming players to expect a game technology can positively influence their experience. The results supported the hypothesis that even basic instructions can change players' perception of the game, and lead to a higher level of perceived immersion when knowing that the game contains an improved feature, the adaptive artificial intelligence (AI), while it is not present in the game.
Building Community in Schools: Narratives of Possibilities and Limitations in Critical Friends Groups

This study examined one particular type of Professional Learning Community (PLC) called a Critical Friends Group (CFG). School improvement experts strongly encourage educational leaders to cultivate Professional Learning Communities in schools; however, the problem arises in the assumption many school leaders hold that adults know how to learn together and how to effectively collaborate. Training for Critical Friends Groups is specifically designed to address this assumption as well as reveal to participants what the characteristics of a Professional Learning Community are and moreover what it requires to cultivate a true learning community. Therefore, the researcher examined how participants experienced a professional development program called a Critical Friends Group Coaches’ Institute. The purpose of the study was to understand the types of circumstances that lead to transformational learning for CFG participants and how participation in a CFG contributed to professionally significant, pivotal moments. The methodology for this study was qualitative inquiry using a case study approach. The researcher utilized a variety of qualitative data collection strategies including participant observations, participant questionnaires, participant interviews, and document analysis during a five day professional development training program called a Critical Friends Group Coaches’ Institute. The results of this study indicate that participation in a Critical Friends Group Coaches’ Institute provides educators with professionally significant transformational moments that work together to teach educators the skills and dispositions needed to be effective members of a PLC and contributing members to a collaborative culture. Moreover, the results of the study indicate that participation in a Critical Friends Group Coaches’ Institute reveals to participants what the characteristics of a Professional Learning Community are and what changes in their own educational practice and in their own school community need to take place in order to effectively build community in schools. INDEX WORDS: Communication, Collaboration, Professional learning community, Critical friends group BUILDING COMMUNITY IN SCHOOLS: NARRATIVES OF POSSIBILITES AND LIMITATIONS IN CRITICAL FRIENDS GROUPS
2012 6th International Conference on Pervasive Computing Technologies for Healthcare (pervasivehealth) and Workshops Ingredient Matching to Determine the Nutritional Properties of Internet-sourced Recipes

To utilise the vast recipe databases on the In­ ternet in intelligent nutritional assistance or recommender systems, it is important to have accurate nutritional data for recipes. Unfortunately, most online recipes have no such data available or have data of suspect quality. In this pa­ per we present a system that automatically calculates the nutritional value of recipes sourced from the Internet. This is a challenging problem for several reasons, including lack of formulaic structure in ingredient descriptions, ingredi­ ent synonymy, brand names, and unspecific quantities be­ ing assigned. We present a system that exploits linguistic properties of ingredient descriptions and nutritional knowl­ edge modelled as rules to estimate the nutritional content of recipes. We evaluate the system on a large Internet sourced recipe database (23.5k recipes) and examine performance in terms of ability to recognise ingredients and error in nutri­ tional values against values established by human experts. Our results show that our system can match all of the in­ gredients for 91 % of recipes in the collection and generate nutritional values within a 10% error bound from human assessors for calorie, protein and carbohydrate values. We show that the error is less than that between multiple hu­ man assessors and also less than the error reported for dif­ ferent standard measures of estimating nutritional intake. 1. INTRODUCTION AND MOTIVATION Poor dietary habits are a major cause of global health problems in the modern world. The World Health Organ­ isation (WHO) predicts that the number of obese adults worldwide will reach 2.3 billion by 2015 [1]. In England nearly 1 in 4 adults and over 1 in 10 children aged 2-10 are obese [2], over 5% of the population are registered diabetic [3] and around 1 in 3 adults are hypertensive [4]. Similar statistics can be found for most developed countries and are representative of poor overall health, strongly related to dietary problems. There is a large body of evidence that these problems can be prevented and sometimes even reversed through good nutrition [5], [6]. Two core problems are that people are generally very poor at judging the healthiness of their own diet [7], [8] and even if they do recognise a problem, they lack the requisite knowledge of nutritional principles in order to implement positive dietary changes [9]. There is evidence that as knowledge of nutrition increases eating habits tend to improve [10]. Much of the information …
A Hybrid HMM/BN Acoustic Model Utilizing Pentaphone-Context Dependency

The most widely used acoustic unit in current automatic speech recognition systems is the triphone, which includes the immediate preceding and following phonetic contexts. Although triphones have proved to be an efficient choice, it is believed that they are insufficient in capturing all of the coarticulation effects. A wider phonetic context seems to be more appropriate, but often suffers from the data sparsity problem and memory constraints. Therefore, an efficient modeling of wider contexts needs to be addressed to achieve a realistic application for an automatic speech recognition system. This paper presents a new method of modeling pentaphone-context units using the hybrid HMM/BN acoustic modeling framework. Rather than modeling pentaphones explicitly, in this approach the probabilistic dependencies between the triphone context unit and the second preceding/following contexts are incorporated into the triphone state output distributions by means of the BN. The advantages of this approach are that we are able to extend the modeled phonetic context within the triphone framework, and we can use a standard decoding system by assuming the next preceding/following context variables hidden during the recognition. To handle the increased parameter number, tying using knowledge-based phoneme classes and a data-driven clustering method is applied. The evaluation experiments indicate that the proposed model outperforms the standard HMM based triphone model, achieving a 9--10% relative word error rate (WER) reduction.
Construction of a multi-level Hierarchical Quasi-Cyclic matrix with layered permutation for partially-parallel LDPC decoders

Implementation of partially-parallel (Low-Density Parity-Check) LDPC decoders using unstructured random matrices is very complex and requires huge hardware resources. To alleviate the complexity and minimize resource requirements, structured LDPC matrices are used. This paper presents a novel technique for constructing a multi-level Hierarchical Quasi-Cyclic (HQC) structured matrix for LDPC decoders. A unique multi-level structure of the proposed matrix provides flexibility in generating different code lengths and code rates for various applications such as WiMAX, WLAN and DVB-S2. In addition, different combinations of permuted sub-matrices are inserted in layers, to provide virtual randomness in the LDPC matrix. Simulations results show that the HQC matrices generated using the proposed technique have a marginal loss of less than 0.1 dB at a bit error rate (BER) performance of 10−5, compared to unstructured random matrices. The proposed matrix therefore provides BER performance close to random matrices while significantly reducing hardware resource requirements.
Increased levels of NMDA receptor NR2A subunits at pre‐ and postsynaptic sites of the hippocampal CA1: An early response to conditional double knockout of presenilin 1 and 2

Greater than 90% of familial Alzheimer's disease (AD) is linked to mutations of presenilin (PS), and the loss of PS function altogether within mouse brains by conditional double knockout of the PS 1 and 2 genes (PS‐cDKO) leads to age‐dependent emergence of AD phenotypes, including neurodegeneration and reduced synaptic plasticity in the hippocampal CA1. The goal of our study was to identify the ultrastructural and molecular changes at synapses in the hippocampal CA1 of this PS‐cDKO mouse model of AD. We examined the asymmetric (excitatory) synapses formed on apical dendrites of CA1 pyramidal neurons at 2 months postnatal, an age when AD‐like symptoms emerge but brain morphology, as assessed by light microscopy, is still normal. Our quantitative electron microscopic analyses confirm that PS‐cDKO hippocampi at 2 months postnatal do not yet exhibit synapse losses or spine size alterations. However, immunocytochemistry reveals that the same region exhibits a 28% increase in the proportion of spines labeled for the NR2A subunits of NMDA receptors (NMDAR), with a 31% increase specifically at postsynaptic densities and a concomitant reduction of these subunits at nonsynaptic sites within spine heads. In contrast, no change in levels or the distribution pattern of NR2B subunit levels were detected within spine heads. Presynaptically, NR2A levels are elevated at axo‐spinous junctions and these may contribute to the timing‐dependent, long‐term depression. These observations point to an early‐onset trapping of NMDAR at synapses that are subtle but may underlie the reduced synaptic plasticity at 2 months of age and excitotoxicity at later stages. J. Comp. Neurol. 517:512–523, 2009. © 2009 Wiley‐Liss, Inc.
Does Market Irrationality in the Media Affect Stock Returns?

This paper investigates whether news suggestive of irrationality within financial markets have an impact on stock returns. We construct a lexicon of words for 'market irrationality' and score daily news articles based on the number and proportion of words they contain from the lexicon. We find that reported market irrationality has a significant negative impact on subsequent stock market returns and exacerbates stock market volatility. Furthermore, stocks with large, negative irrationality risk betas outperform stocks with large, positive betas on average by 10.3% annually. Accounting for the Fama-French factors, this results in an alpha of 8.6% annually, which is significant at the 1% level. These results suggest that a large, positive IRR beta amplifies a stock's reaction to market irrationality while a large, negative IRR beta dampens it. The subsequent IRR betas' mean-reversion is consistent with this conjecture.
Opinion Extraction Using a Learning-Based Anaphora Resolution Technique

This paper addresses the task of extracting opinions from a given document collection. Assuming that an opinion can be represented as a tuple 〈Subject, Attribute, Value〉, we propose a computational method to extract such tuples from texts. In this method, the main task is decomposed into (a) the process of extracting Attribute-Value pairs from a given text and (b) the process of judging whether an extracted pair expresses an opinion of the author. We apply machine-learning techniques to both subtasks. We also report on the results of our experiments and discuss future directions.
Genetic erosion impedes adaptive responses to stressful environments

Biodiversity is increasingly subjected to human‐induced changes of the environment. To persist, populations continually have to adapt to these often stressful changes including pollution and climate change. Genetic erosion in small populations, owing to fragmentation of natural habitats, is expected to obstruct such adaptive responses: (i) genetic drift will cause a decrease in the level of adaptive genetic variation, thereby limiting evolutionary responses; (ii) inbreeding and the concomitant inbreeding depression will reduce individual fitness and, consequently, the tolerance of populations to environmental stress. Importantly, inbreeding generally increases the sensitivity of a population to stress, thereby increasing the amount of inbreeding depression. As adaptation to stress is most often accompanied by increased mortality (cost of selection), the increase in the ‘cost of inbreeding’ under stress is expected to severely hamper evolutionary adaptive processes. Inbreeding thus plays a pivotal role in this process and is expected to limit the probability of genetically eroded populations to successfully adapt to stressful environmental conditions. Consequently, the dynamics of small fragmented populations may differ considerably from large nonfragmented populations. The resilience of fragmented populations to changing and deteriorating environments is expected to be greatly decreased. Alleviating inbreeding depression, therefore, is crucial to ensure population persistence.
An Energy-Balanced Routing Protocol for MANETs

Energy balance problem in mobile Ad Hoc networks (MANETs) is studied and a new energy-balanced routing protocol (EBR) with route selection and route maintenance using remaining energy information of nodes is proposed. In the period of route discovery, nodes with more ample energy are selected. When the nodal energy is lower than some threshold, the node sends warning message to the uplink node. Then the uplink node updates its route or finds a new one to avoid route interruption due to energy exhausting and to decrease route repair. The EBR protocol is compared with the DSR protocol by simulation. The results indicate that the EBR protocol improves the energy utilization efficiency, prolongs lifetime of the network, decreases the number of packages lost during transmission, and increases the network throughput ratio in energy -limited MANETs.
Face Recognition Based on Wavelet Transform Weighted Modular PCA

A new algorithm named wavelet transform weighted modular PCA is proposed for face recognition. Firstly, the training images and the testing image are preprocessed with wavelet transform and the LL band and the LH/HL average band are divided into sub-images with the same size. Secondly, the prospective classify contribution of each sub-model of the two bands are computed. Thirdly, each sub-image of the two bands of the testing image is projected to its corresponding subspace and the confidence values with each image are obtained. Finally, the two confidence values with each image are added with a weight and the total confidence value is obtained to classify the testing image. Experimental results show that the recognition rate of the proposed algorithm is about 4%-6% superior to traditional methods.