Miracle-dz/UniFAR-FTUs huggingface dataset:
Higher Education and the Business Cycle 1870-1990

The expansion of higher education is analyzed between 1870 and 1990 in five countries (Germany, Italy, France, United States, and Japan). Spectral analysis is applied for the first time to analyze the long-term causal relationship between the economic and the educational system. Two hypotheses are tested to explain the expansion: that of human capital and that of individual status competition. The analysis concentrates on the interaction between the educational and the economic systems, particularly during times of economic crisis. Spectral analysis is used to explore the cyclical character of the expansion and the relationship between economic growth and the increasing enrollment rates in higher education. For European countries the data support the theory of status competition: universities expanded particularly rapidly during times of economic recession. In the United States and Japan higher education expanded more or less apace with the economic system.
FIRE AND FLAME DETECTION METHODS IN IMAGES AND VIDEOS

FIRE AND FLAME DETECTION METHODS IN IMAGES AND VIDEOS Yusuf Hakan Habiboğlu M.S. in Electrical and Electronics Engineering Supervisor: Prof. Dr. A. Enis Çetin August 2010 In this thesis, automatic fire detection methods are studied in color domain, spatial domain and temporal domain. We first investigated fire and flame colors of pixels. Chromatic Model, Fisher’s linear discriminant, Gaussian mixture color model and artificial neural networks are implemented and tested for flame color modeling. For images a system that extracts patches and classifies them using textural features is proposed. Performance of this system is given according to different thresholds and different features. A real-time detection system that uses information in color, spatial and temporal domains is proposed for videos. This system, which is develop by modifying previously implemented systems, divides video into spatiotemporal blocks and uses features extracted from these blocks to detect fire.
Levels of Cocaine- and Amphetamine-Regulated Transcript in Vagal Afferents in the Mouse Are Unaltered in Response to Metabolic Challenges

Visual Overview Cocaine- and amphetamine-regulated transcript (CART) is one of the most abundant neuropeptides in vagal afferents, including those involved in regulating feeding. Recent observations indicate that metabolic challenges dramatically alter the neuropeptidergic profile of CART-producing vagal afferents. Here, using confocal microscopy, we reassessed the distribution and regulation of CART(55–102) immunoreactivity in vagal afferents of the male mouse in response to metabolic challenges, including fasting and high-fat-diet feeding. Importantly, the perikarya and axons of vagal C-fibers were labeled using mice expressing channelrodhopsin-2 (ChR2-YFP) in Nav1.8-Cre–expressing neurons. In these mice, approximately 82% of the nodose ganglion neurons were labeled with ChR2-YFP. Furthermore, ChR2-YFP–labeled axons could easily be identified in the dorsovagal complex. CART(55–102) immunoreactivity was observed in 55% of the ChR2-YFP–labeled neurons in the nodose ganglion and 22% of the ChR2-YFP–labeled varicosities within the area postrema of fed, fasted, and obese mice. The distribution of positive profiles was also identical across the full range of CART staining in fed, fasted, and obese mice. In contrast to previous studies, fasting did not induce melanin-concentrating hormone (MCH) immunoreactivity in vagal afferents. Moreover, prepro-MCH mRNA was undetectable in the nodose ganglion of fasted mice. In summary, this study showed that the perikarya and central terminals of vagal afferents are invariably enriched in CART and devoid of MCH.
The Potential to Narrow Uncertainty in Regional Climate Predictions

Abstract Faced by the realities of a changing climate, decision makers in a wide variety of organizations are increasingly seeking quantitative predictions of regional and local climate. An important issue for these decision makers, and for organizations that fund climate research, is what is the potential for climate science to deliver improvements—especially reductions in uncertainty—in such predictions? Uncertainty in climate predictions arises from three distinct sources: internal variability, model uncertainty, and scenario uncertainty. Using data from a suite of climate models, we separate and quantify these sources. For predictions of changes in surface air temperature on decadal timescales and regional spatial scales, we show that uncertainty for the next few decades is dominated by sources (model uncertainty and internal variability) that are potentially reducible through progress in climate science. Furthermore, we find that model uncertainty is of greater importance than internal variability. O.
A Survey of ERP System Implementation in Taiwan

This paper presents the results of a recently completed survey of Taiwanese firms that focuses on their implementation and use of packaged ERP systems. Objectives of the survey includes: to investigate the extent to which packaged ERP systems were applied in manufacturing and service firms, the implementation experience, and the major benefits obtained from the implementation. Findings of the survey indicate that the firms implemented with all planned modules tend to have more successful implementation, and use of ERP systems from evolution of the legacy systems, in-house redevelopment and outsourcings are critical to organizational performance and survival. Furthermore, our research results findings suggest that both researchers and companies should adopt broader definitions and multiple performance measures indexes of success and pay particular attention to the early identification and correction of problems.
Rape Myth Beliefs and Bystander Attitudes Among Incoming College Students

Abstract Objective: The bystander approach to rape prevention is gaining popularity on college campuses, although research is limited. This study explored bystander attitudes and their relationship with rape myths in a sample of college students. Participants: Surveys from 2,338 incoming undergraduate students at a large, northeastern university were analyzed. Methods: Participants completed revised versions of the Illinois Rape Myth Acceptance Scale and the Bystander Attitude Scale. Results: A higher acceptance of rape myths was reported by males, those pledging a fraternity/sorority, athletes, those without previous rape education, and those who did not know someone sexually assaulted. A greater willingness to intervene as a bystander was reported by females, those who had previous rape education, and those who knew someone sexually assaulted. Acceptance of rape myths was negatively related to willingness to intervene. Conclusions: Bystander intervention programs should include content on rape myths as well as focus on the role of gender.
Exploit imbalanced cell writes to mitigate write disturbance in dense Phase Change Memory

Recent studies have shown that Phase Change Memory faces significant write disturbance (WD) when scaling in deep submicron regime, i.e., resetting a cell may disturb the values of its adjacent cells if these cells are in amorphous state. A preventive approach to mitigate WD errors is to allocate sufficient inter-cell thermal band. However, this approach greatly reduces chip capacity due to low cell density. A cost effective approach VnC (verify-and-correct), relies on Verification after each write and Correction if errors do happen. Simple VnC improves chip capacity but introduces large performance degradation. In this paper, we propose to exploit the cell level write imbalance to mitigate WD errors. A memory line is often split into cell groups so that the cells within one group are written synchronously. We observe that only few cell groups are critical, which prolong write latency and degrade system performance. By rescuing the disturbance errors from critical groups with unused Error Correction Pointer(ECP) entries, we can greatly minimize the VnC impact on performance. The experimental results show that our proposed scheme effectively eliminates VnC overhead with minimal hardware adjustments.
Floating Chip Mounting System Driven by Repulsive Force of Permanent Magnets for Multiple On-Site SPR Immunoassay Measurements

We have developed a measurement chip installation/removal mechanism for a surface plasmon resonance (SPR) immunoassay analysis instrument designed for frequent testing, which requires a rapid and easy technique for changing chips. The key components of the mechanism are refractive index matching gel coated on the rear of the SPR chip and a float that presses the chip down. The refractive index matching gel made it possible to optically couple the chip and the prism of the SPR instrument easily via elastic deformation with no air bubbles. The float has an autonomous attitude control function that keeps the chip parallel in relation to the SPR instrument by employing the repulsive force of permanent magnets between the float and a float guide located in the SPR instrument. This function is realized by balancing the upward elastic force of the gel and the downward force of the float, which experiences a leveling force from the float guide. This system makes it possible to start an SPR measurement immediately after chip installation and to remove the chip immediately after the measurement with a simple and easy method that does not require any fine adjustment. Our sensor chip, which we installed using this mounting system, successfully performed an immunoassay measurement on a model antigen (spiked human-IgG) in a model real sample (non-homogenized milk) that included many kinds of interfering foreign substances without any sample pre-treatment. The ease of the chip installation/removal operation and simple measurement procedure are suitable for frequent on-site agricultural, environmental and medical testing.
JointZone: An Adaptive Web-based Learning Application

This report presents the work involved in developing an adaptive website, JointZone that personalises student learning on the web. The work combined user modeling, domain modeling and adaptive hypermedia techniques to deliver a personalised web-based learning environment. The idea of keyword indexing and the site layout structure was used to model the domain giving a conceptual and structural representation of the content. The student model involves the novel idea of using effective reading speed to better gauge if a student has read a page. The project applied the combination of adaptive link hiding and link annotation on a fully functional website to present an adaptive web-based learning environment.
Cross-Domain Mining of Argumentative Text through Distant Supervision

Argumentation mining is considered as a key technology for future search engines and automated decision making. In such applications, argumentative text segments have to be mined from large and diverse document collections. However, most existing argumentation mining approaches tackle the classiﬁcation of argumentativeness only for a few manually annotated documents from narrow domains and registers. This limits their practical applicability. We hence propose a distant supervision approach that acquires argumen-tative text segments automatically from online debate portals. Experiments across domains and registers show that training on such a corpus improves the effectiveness and robustness of mining argumentative text. We freely provide the underlying corpus for research.
Merging Galaxies in GOODS-S: First Extragalactic Results from Keck Laser Adaptive Optics

The Center for Adaptive Optics Treasury Survey aims to combine deep Hubble Space Telescope (HST) images in the optical with deep Keck adaptive optics (AO) data in the near-infrared (NIR) to study distant galaxies, active galactic nuclei, and supernovae. We recently achieved an important new milestone by securing the first Keck laser guide star AO image of faint galaxies. Six galaxies with redshifts ranging from 0.3 to 1.0 were targeted in one pointing in the GOODS-S field. Two are Chandra Deep Field-South sources, XID-56 and XID-536, with complex morphologies suggestive of recent merger activity. Substructures seen in the NIR AO image (FWHM ~0.″1), including multiple tight knots in XID-56 and a double nucleus in XID-536, are confirmed in the optical HST images. These structures are unresolved in the best seeing-limited (~0.″5 FWHM) NIR images. Stellar population synthesis models of the substructures indicate that XID-56 is a gas-rich merger with a recent burst of star formation and significant amounts of dust. XID-536 appears to be a merger of two evolved stellar populations.
Planning for Semantic Web Services

Using Semantic Web ontologies to describe Web Services has proven to be useful for various different tasks including service discovery and composition. AI planning techniques have been employed to automate the composition of Web Services described this way. Planners use the description of the preconditions and effects of a service to do various sorts of reasoning about how to combine services into a plan. OWL-S 1.1 will support the description of the preconditions and effects of services using OWL statements similar to atoms in Semantic Web Rule Language (SWRL). Thus, planners are required to understand the semantics of OWL in order to evaluate such preconditions. However, planners typically support only fairly limited reasoning capabilities which cannot handle the expressivity of Semantic Web ontologies. In particular, planners typically make the closed world assumption, whereas OWL has open world semantics. In this paper, we demonstrate how an OWL reasoner can be integrated with an AI planner to overcome these problems. We identify the challenges of writing the service descriptions and reasoning about them when OWL is used to describe preconditions and effects. We also investigate the efficiency of such an integrated system and show how OWL reasoning can be optimized for this system. Finally, we present the performance results of our prototype implementation.
Confounding in case control studies

Sir, I read with interest the article on computerized tests to evaluate psychomotor performance in children with specific learning disabilities (SpLD) by Taur and colleagues1. The study was conducted with the aim to compare the performance on a battery of six psychomotor tests of children with SpLD and those without any learning disabilities (controls) using computerized tests. The authors deserve credit for their effort. For the purpose of this study, as stated by the authors, 25 children with SpLD and 25 controls (matched for age, socio-economic status and medium of instruction) were given three training sessions over one week. After that children were asked to perform on the six computerized psychomotor tests. Results were compared between the two groups. I have a concern with the way this study was conducted and results interpreted thereof. The authors in the results stated that the children with SpLD fared significantly worse on finger tapping test, choice reaction test, digit picture substitution test and card sorting test compared to the controls. For arriving at this conclusion a baseline comparison on computer literacy of the two groups should have been assessed. This baseline comparison should have been a part of matching as difference in the use of computers or their awareness may act as a confounder in this study. A confounding variable gives rise to situations in which the effects of two processes are not separated, or the contribution of causal factors cannot be separated, or the measure of the effect of exposure or risk is distorted because of its association with other factors influencing the outcome of the study. Therefore, matching for computer knowledge at the initial stage would have prevented it or alternatively an adjustment for difference in knowledge about computers should have been conducted. This is important in view of ever increasing use of technology by children across all sections of society.
A cross-domain adaptation method for sentiment classification using probabilistic latent analysis

Sentiment classification is becoming attractive in recent years because of its potential commercial applications. It exploits supervised learning methods to learn the classifiers from the annotated training documents. The challenge in sentiment classification lies in that the sentiment domains are diverse, heterogeneous and fast-growing. The classifiers trained on one domain (source domain) could not classify a document from another domain (target domain). The domain adaptation technique is to address the problem by making use of labeled samples in the source domain, and unlabeled samples in the target domain. This paper presents a new solution, a cross-domain topic indexing (CDTI) method, with which a common semantic space is found from the prior between-domain term correspondences and the term co-occurrences in the cross-domain documents. These observations are characterized with the mixture model in CDTI, with each component being a possible topic shared by the source and target domains. Such common topics are found to index the cross-domain content. We evaluate the algorithms on a multi-domain sentiment classification task, which shows that CDTI outperforms the state-of-the-art domain adaptation method, i.e. spectral feature alignment (SFA), and the traditional latent semantic indexing method.
R-Mo: A new mobile robotic platform to reduce variations in height and pitch angle on rugged terrain

This paper presents a new mobile robotic platform (R-Mo) which can reduce unexpected variations in height as well as pitch angle of its main body while traversing rough terrains. As a measure for the smooth movement of mobile platform, the variations in height and pitch angle are chosen in this study. Then, the kinematic analysis on the Rocker-Bogie mechanism is carried out to investigate its variations in height and pitch angle on rough terrains. Based on this result, a new mobile platform is systematically designed by combining the Rocker-Bogie with the inverse four bar linkage. The extensive experiments are carried out by using the Rocker-Bogie mechanism and the proposed R-Mo against rough terrain, which validate that in comparison with the Rocker-Bogie mechanism, the average and maximum variations in height of proposed R-Mo are reduced by 12.72% and 5.96%, respectively, and the average and maximum variations in pitch angle of proposed R-Mo are considerably reduced by 65.87 % and 60.53 %, respectively.
A Medical Multilingual Information Retrieval

The Web is full of documents and resources. Users employ different strategies to find information they need: by browsing, using search engines, by following existing categories in a Web catalog. For technical sublanguages such as the medical one, document indexing based on lexical entities at a subword level has proved useful. However, it still remains challenging to identify and to delimit the meaningful lexical entities, as well as to group them in synonymy classes. We present a lexicographic and semantic foundation underlying the multilingual MORPHOSAURUS lexicon. Resumo. A Web é repleta de documentos e outros recursos. Usuários utilizam diferentes estratégias para encontrar as informações que eles necessitam: navegando entre sites, usando máquinas de busca ou usando catalógos de domı́nio. Para linguagens técnicas como a linguagem médica, a indexação de documentos usando entidades lexicais em nı́vel estruturante menor que a palavra tem se mostrado útil. Porém, ainda há desafios com relação à indentificação e delimitação de entidades lexicais apropriadas, assim como ao agrupamento em conjuntos de sinônimos. Apresentamos os fundamentos lexicográficos e semânticos do léxico multilı́ngue MORPHOSAURUS.
A particle swarm optimization-based approach for hyperspectral band selection

In this paper, a feature selection algorithm based on particle swarm optimization for processing remotely acquired hyperspectral data is presented. Since particle swarm optimization was originally developed to search only continuous spaces, it could not deal with the problem of spectral band selection directly. We propose a method utilizing two swarms of particles in order to optimize simultaneously a desired performance criterion and the number of selected features. The candidate feature sets were evaluated on a regression problem using artificial neural networks to construct nonlinear models of chemical concentration of glucose in soybean crops. Experimental results attesting the viability of the method utilizing real- world hyperspectral data are presented. The particle swarm optimization-based approach presented superior performance in comparison with a conventional feature extraction method.
Using symmetric causal independence models to predict gene expression from sequence data

We present an approach for inferring transcriptional regulatory modules from genome sequence and gene expression data. Our method, which is based on symmetric causal independence models, is both able to model the logic behind transcriptional regulation and to incorporate uncertainty about the functionality of putative transcription factor binding sites. Applying our approach to the deadliest species of human malaria parasite, Plasmodium falciparum, we obtain several striking results that deserve further (biological) investigation.
NEW DEMANDS FOR APPLICATION OF NUMERICAL SIMULATION TO IMPROVE RESERVOIR STUDIES IN CHINA

After years of production, most oilfields with nonmarine deposits in China have been at their mature stage with high water cut and high recovery. The remaining oil is, on one hand, highly scattered in the reservoir, but on the other hand, relatively concentrated in some locations. The identification of the exact distribution of these locations with relatively abundant remaining oil is of great importance for improving oil recovery, but is very difficult. The oilfield development, which has been complicated by all the above factors, calls for more powerful numerical reservoir simulation techniques. The large-scale sophisticated numerical simulation technique with high efficiency, high precision, and high computing speed will be the key to the study on the remaining oil distribution for oilfields at their mature stage with high water cut. As for various types of complicated reservoirs, it is essential to develop different fluid flowing models and corresponding numerical simulation techniques.
Distributed Storage and Analysis of Microarray Data in the Terabyte Range: An Alternative to Bioconductor

Novel high-throughput technologies such as DNA microarray analyses are allowing biologists to generate sets of data in the terabyte realm. Many of these data will be deposited in the public domain, necessitating a common standard. Currently available database systems are not appropriate for these intentions. In this paper, I will introduce ROOT (http://root.cern.ch), an objectoriented framework that has been developed at CERN for distributed data warehousing and data mining of particle data in the petabyte range. Data are stored as sets of objects in machine-independent files, and specialized methods are used to get direct access to separate attributes of selected data objects. ROOT has been designed in such a way that it can query its databases in parallel on SMP/MPP machines, on clusters of PC’s, or using common GRID services. In order to demonstrate the applicability of ROOT to microarray data, I will present a functional prototype system, called XPS - eXpression Profiling System, which can be considered to be an alternative to the Bioconductor project. The current implementation handles the storage of Aymetrix GeneChip schemes and data, and the pre-processing, normalization and filtering of GeneChip data. Based on this system, I will propose a novel standard for the distributed storage of microarray data. Finally, I will emphasize the similarities between R and ROOT, and show how R could be easily extended to access ROOT from within R.