Miracle-dz/UniFAR-FTUs huggingface dataset:
ASYMMETRIC GLOBALIZATION AND ACCESSION TO GATT/WTO

In this paper, using LSDV approach to testing the effects of accession to GATT/WTO in a panel of developed and developing countries, we find that accession, on average has positive effects on the GDP and export of developed countries and has negative impact on GDP of many developing countries with the negative effects on export for a number of them. Especially in Africa this negative effects is remarkable and this results, while shed some light on the conflicts in the literature on links between trade policy and growth, point to the challenges confront many countries in the process of globalization. It is shown that for 31 out of 46 developing countries in the sample the effects of membership on GDP are negative while only for 15 out of 46 the impact on export is negative.
Color Relationship Transfer for Digital Painting

Color Relationship Transfer for Digital Painting Gregory Eric Philbrick Department of Computer Science, BYU Master of Science A digital painter uses reference photography to add realism to a scene. This involves making n colors in a painting relate to each other more like n corresponding colors in a photograph, in terms of value and temperature. Doing this manually requires either experience or tedious experimentation. Color relationship transfer performs the task automatically, recoloring n regions of a painting so they relate in value and temperature more like n corresponding regions of a photograph. Relationship transfer also has applications in computational photography. In fact, it introduces a new paradigm for image editing in general, based on treating an image’s fundamental relationships.
Atomic Scale Structural Identification and Evolution of Co-WC Ternary SWCNT Catalytic Nanoparticles : HR-STEM imaging on SiO 2

Chirality control has been the greatest challenge in chemical vapor deposition (CVD) synthesis of single-walled carbon nanotubes (SWCNT). This challenge is largely caused by the difficulties in characterizing the nano-sized catalyst particles and thereby insufficient understanding of their behaviors. Recently W-based catalyst has provided a promising route to synthesize SWCNTs with specific chirality, but the mechanism of the growth selectivity is still vaguely understood and under intensive discussion. Here we propose a strategy to identify the atomic structure, as well as the structure evolution of Co-W-C ternary catalyst. The key is to use a thin SiO2 film as the catalyst support and observation window. As the catalyst can be uniformly prepared on this high-temperature stable film and directly used for SWCNT synthesis, this method possesses superior advantages over conventional methods: being able to obtain original, statistical, and dynamic understanding on the catalyst. With this technique, we illustrate that CoW-C ternary catalyst form junctions of metallic W and η carbide Co6W6C after reduction. But as the CVD starts, the catalyst undergoes a W loss, which finally results in a precipitation of pure hexagonal close packed (hcp) phase metallic Co. This precipitated Co phase is fully coherent to the parent crystal, having a strict crystal orientation to Co6W6C. As a method, the current TEM imaging directly on SiO2 serves as a powerful and versatile tool to investigate nanocrystals and high temperature reactions; for synthesis of SWCNTs, this work successfully visualizes the structure and evolution of catalyst, and illuminates the possible nucleation sites of chirality specific growth.
Approximating Missing Sensor Readings Using An Artificial Neural Network

Professional mariners rely on accurate knowledge of ocean conditions to perform their jobs and prevent accidents. Events occurring in coastal waters can potentially impact not only the local economies of coastal communities but have consequences to the national economy as well. Ocean currents are monitored with coastal radar stations and buoys that have wireless communication links with each other. These may occasionally malfunction during harsh weather conditions however, or even due to normally-occurring atmospheric conditions. As continuous monitoring of ocean conditions allows naval workers to plan efficiently and reduce opportunities for any negative impact, a suggested approach to maintaining continuous ocean current readings under uncertain conditions is the artificial neural network computational model. Neural network models allow for missing data to be approximated by internalizing past patterns that have been observed in the data record. The model is inspired by biological networks of neurons and consists of interconnected computational units, forwarding intermediate results from one unit to the next. Weights associated with the connections between these computational units can dynamically adapt as new patterns and data are observed by the system. Internally, the model initializes all weights randomly between the neurons. The model is fitted, or trained, by introducing a set of data with known output to the model and each weight adjusts accordingly in small increments. After evaluating training data, the model is validated by executing over a separate set of data with known outputs to which the model has not been exposed. The average error of these results is compared to a desired level of tolerance. Alternation between the training and validation processes continues until the model’s error reaches an acceptable range. One difficulty that this model has to overcome in the proposed application is due to the region from which the data was collected and the tendencies of the current in that area. Naturally, currents in the Gulf of Maine display sinusoidal behavior; however, in some sub-regions, currents can rapidly change and present extremely high velocities. To overcome this difficulty, a model initially trained on overall data was subsequently retrained on smaller sub-regions, allowing for improved results in those areas. Approximations resulting from the neural model appear to reliably outperform common predictive techniques such as auto-regressive functions, areal averaging and the persistence model. As most linear extrapolation models perform poorly for this application due to the sinusoidal patterns exhibited in tidal signals as well as the difficulties presented by wireless communication links, the Neural Network appears to be a promising approach for modeling and approximating missing readings for these natural phenomena.
Exploration of Road Traffic Tweets for Congestion Monitoring

Online social network services such as Twitter and Facebook have gained popularity in recent years with continuous increase of users. This is especially true for Twitter, a popular micro-blogging service that enables users to send tweets which contain valuable data in real-time. Real-time tweets information can be used in many areas and one of the least explored areas is crowdsourcing of road traffic conditions. We have found that not many people tweet about traffic conditions; however, there are formal sources that keep their accounts updated with the latest traffic info. In this paper, we present an analysis of tweets that are related to the traffic conditions in Malaysia. Detailed analysis was conducted to understand the structures and the nature of the traffic tweets. Based on our analysis, we found that the real-time nature of the tweets is useful in reporting road traffic conditions and such information will be useful to the road-users
YAPAY SİNİR AĞLARINDA NEGAT İVE CORRELAT İON LEARN İNG MET ODUNU KULLANARAK OPT İK KARAKTER TANIMA

Bu çalışmada, yapay sinir a ğları kullanılarak Optik Karakter Tanıma (OCR) gerçekle ştirilmi ştir. Yapay sinir ağları eğitiminde negative correlation learning(NCL) metodu kullanılmıştır. Negative correlation learning; Yapay sinir ağlarını bireysel olarak e ğitmek yerine, onların grup halinde eğitilmesi işlemini içeren bir eğitim tekniğidir. Farklı bireysel sinir ağlarının birlikte tek bir ağ şeklinde eğitilmesi işlemi, eğitim sırasında farklı parçaların aynı anda e ğitilmesinden dolayı hem ö ğrenmenin daha iyi olmasını, hem de aynı anda tüm ağ eğitildi ği için eğitim süresinin kısalmasını sa ğlar. Bu çalışmada cep telefonu ile çekilen kelime resmi görüntü i şleme teknikleri ile işlenerek karakterlerine ayrılmı ş ve özellikleri belirlenmiştir. Harf resimleri bir matrise dönü ştürülerek yapay sinir ağı eğitim seti kullanılarak sırası ile tanınır. Harfler tanındıktan sonra sonra negative correlation learning(NCL) metodu ile e ğitilmiş YSA Ağ topluluğu ile sınıflandırılarak yazıya dönü ştürülmektedir. Sistem ba ş rı ile çalışmaktadır.
Limitation of the BLR testing in estimating nonlinearity ( Extended Abstract )

In this paper we concentrate on a limitation of the BLR (Blum-Luby-Rubinfeld) linearity test on a Boolean function f , which checks the weight of the function F (x, y) = f(x)+f(y)+f(x+y) for many random inputs x, y, which should be 0 for a linear function. We point out that this test, which considers the weight of F and G(x, y) = g(x) + g(y) + g(x+ y), does not provide proper information on the nonlinearity ordering of two functions f, g. The problem remains even if we extend the algorithm to analyze the nonlinearities of F,G, too. In this direction, we provide examples of functions on any number of input variables, such that the BLR test (and certain extensions of it) fails to estimate the nonlinearity hierarchy property. We also consider a quantum algorithm to demonstrate the problem in maintaining such hierarchy.
The Processing and Mechanical Performance of Cellulose Nanofiber-based Composites

Nanocomposites based on cellulose nanofibers have been studied for a considerable time since its first introduction, however real applications seem to have hardly developed to these days. The high-strength of cellulose nanofibers suggests the potential to reinforce plastics to produce composites for semi-structural or even structural applications. This paper discusses some of the attempts to produce such high-strength nanocomposites and the main challenges that have to be overcome to bring them into commercial products.
STRAIN ENGINEERING OF THE ELECTRONIC STRUCTURE OF 2 D MATERIALS

The research on graphene has attracted much attention since its first successful preparation in 2004. It possesses many unique properties, such as an extreme stiffness and strength, high electron mobility, ballistic transport even at room temperature, superior thermal conductivity and many others. The affection for graphene was followed swiftly by a keen interest in other two dimensional materials like transition metal dichalcogenides. As has been predicted and in part proven experimentally, the electronic properties of these materials can be modified by various means. The most common ones include covalent or non-covalent chemistry, electrochemical, gate or atomic doping, or quantum confinement. None of these methods has proven universal enough in terms of the devices’ characteristics or scalability. However, another approach is known mechanical strain/stress, but experiments in that direction are scarce, in spite of their high promises. The primary challenge consists in the understanding of the mechanical properties of 2D materials and in the ability to quantify the lattice deformation. Several techniques can be then used to apply strain to the specimens and thus to induce changes in their electronic structure. We will review their basic concepts and some of the examples so far documented experimentally and/or theoretically.
An e-learning framework For wound image understanding

This paper presents a Java based e-learning framework for analyzing, processing and understanding wound images, to be used in teaching, learning and research activities. We intend to promote e-learning technologies in medical, pharmaceutical and health care domains. Our approach to e-learning is so called blended learning, which combines traditional face-to-face and Web-based on-line learning, with focus on principles of active learning. Relying on a colour imaging system, we use statistical methods in assessment of potential methodologies for noninvasive wound evaluation. The colour image processing methods have many advantages over traditional human methods in assessment of wounds. Computer based methods are objective, repeatable and with a large potential of processing. We build in Java models for various categories of wounds, due to aetiologies such as pressure, burn, chilblain, vascular insufficiencies, diabetic foot ulcer, venous leg ulcer and other chronic disease states. Based on colour and texture analysis, we identify the main barriers to wound healing, such as tissue non-viable, infection, inflammation, moisture imbalance, or edge non-advancing. This framework provides the infrastructure for preparing e-learning scenarios based on practice and real world experiences. We make experiments for wound healing simulation based on various treatments and compare the results with experimental observations. Our experiments are supported by XML based databases containing knowledge extracted from previous wound healing experiences and from medical experts knowledge. Also, we rely on new paradigms of the Artificial Intelligence (Bayesian Inference, Case Based Reasoning and Intelligent Agents) for creating e-learning scenarios to be used in a context of active learning, for wound image understanding. To implement the e-learning tools, we use Java technologies for dynamic processes and XML technologies for dynamic content (data and documents).
XTE J1701−462 AND ITS IMPLICATIONS FOR THE NATURE OF SUBCLASSES IN LOW-MAGNETIC-FIELD NEUTRON STAR LOW-MASS X-RAY BINARIES

We report on an analysis of Rossi X-Ray Timing Explorer data of the transient neutron star low-mass X-ray binary (NS-LMXB) XTE J1701–462, obtained during its 2006–2007 outburst. The X-ray properties of the source changed between those of various types of NS-LMXB subclasses. At high luminosities, the source switched between two types of Z source behavior and at low luminosities we observed a transition from Z source to atoll source behavior. These transitions between subclasses primarily manifest themselves as changes in the shapes of the tracks in X-ray color–color (CD) and hardness-intensity diagrams (HID), but they are accompanied by changes in the kHz quasi-periodic oscillations, broadband variability, burst behavior, and/or X-ray spectra. We find that for most of the outburst the low-energy X-ray flux is a good parameter to track the gradual evolution of the tracks in CD and HID, allowing us to resolve the evolution of the source in greater detail than before and relate the observed properties to other NS-LMXBs. We further find that during the transition from Z to atoll, characteristic behavior known as the atoll upper banana can equivalently be described as the final stage of a weakening Z source flaring branch, thereby blurring the line between the two subclasses. Our findings strongly suggest that the wide variety in behavior observed in NS-LXMBs with different luminosities can be linked through changes in a single variable parameter, namely the mass accretion rate, without the need for additional differences in the neutron star parameters or viewing angle. We briefly discuss the implications of our findings for the spectral changes observed in NS-LMXBs and suggest that, contrary to what is often assumed, the position along the color–color tracks of Z sources is not determined by the instantaneous mass accretion rate.
Masquerade Detection Based on One Class SVM

Masqueraders invade into users’system and impersonate the real users to do whatever they want. Unfortunately, firewalls or misuse-based intrusion detection systems are generally ineffective in detecting masquerades. In this paper an abnormal detection method based on one class SVM are presented to detect masquerade activities using UNIX command sets. Firstly the performance of binary SVM classifier are studied to illustrated why one class SVM are adopted, then to improve the performance of one class SVM different feature selection methods are studied, experimental results show that for abnormal detection using UNIX command simplifying raw data and decreasing the dimensions of feature space is an effective approach to improve the performance of SVM classifiers for masquerade detection.
Tactile interaction in on-line communities C

To study the dynamics of on-line tactile interactions, we developed an original experimental device. It was conceived starting from sensory substitution systems that we develop for blind people. It allows an analysis of reciprocal perception (caresses) and of mutual recognition of the people in interaction. These observations show that it is possible to recognize the image which we present to others (our face) through the way in which they perceive us. Author
Egalitarianism and altruism in health: to what extent are they related?

The theoretical constructs of egalitarianism and altruism are different from each other, yet there may be associations between the two at the empirical level. This paper explores the empirical relationship between egalitarianism and altruism, in the context of health. A representative sample of the Spanish population was interviewed in 2004 (n=801). We specify a model that explains the propensity of an individual to be egalitarian in terms of altruism and other background characteristics. In this paper, individuals who prefer a hypothetical policy that reduces inequality in health outcomes over another that does not are regarded ‘egalitarian’. 'Altruism' in the health context is captured by whether or not the same respondents are (or have been) regular blood donors, provided they are medically able to donate. Probit models are specified to estimate the relationship between egalitarianism and altruism, thus defined. Overall, 75% of respondents are found to be egalitarians, whilst 34% are found to be altruists. We find that, once controlled for background characteristics, there is a statistically significant empirical relationship between egalitarianism and altruism in the health context. In particular, altruist individuals have an 11% higher probability to be egalitarians than those who are not.
What Are “X-shaped” Radio Sources Telling Us? II. Properties of a Sample of 87

In an earlier paper, we presented Jansky Very Large Array multi-frequency, multi-array continuum imaging of a unique sample of low-axial ratio radio galaxies. In this paper, the second in the series, we examine the images to learn the phenomenology of how the off-axis emission relates to the main radio source. Inversion-symmetric offset emission appears to be bimodal and to originate from one of two strategic locations: outer ends of radio lobes (outer-deviation) or from inner ends (inner-deviation). The latter sources are almost always associated with edge-brightened sources. With S- and Z-shaped sources being a subset of outer-deviation sources, this class lends itself naturally to explanations involving black hole axis precession. Our data allow us to present a plausible model for the more enigmatic inner-deviation sources with impressive wings; as for outer-deviation sources these too require black hole axis shifts, although they also require plasma backflows into relic channels. Evolution in morphology over time relates the variety in structures in inner-deviation sources including XRGs. With features such as non-collinearities, central inner-S “spine,” corresponding lobe emission peaks, double and protruding hotspots not uncommon, black hole axis precession, drifts, or flips could be active in a significant fraction of radio sources with prominent off-axis emission. At least 4% of radio galaxies appear to undergo black hole axis rotation. Quasars offer a key signature for recognizing rotating axes. With a rich haul of sources that have likely undergone axis rotation, our work shows the usefulness of low-axial ratio sources in pursuing searches for binary supermassive black holes.
Antibody responses by calves after vaccination with commercial and enhancedpotency inactivated pneumo-3 vaccines

A highly potent pneumo-3 vaccine of combined inactivated Bovine viral diarrhea virus types 1 (BVDVI), bovine herpesvirus type 1 (BHV-I) and parainfluenza 3 virus (PI3V) could play a valuable role in respiratory disease control program in calves. We determined the influence of virus concentration by polyethylene glycol (PEG) precipitation on the immune response of calves to the inactivated pneumo-3 vaccine. Both the concentrated and commercial pneumo-3 vaccine was administered twice to six month old seronegative calves through intramuscular (IM) route. Different doses, 2ml, 2.5ml and 3ml of the concentrated vaccine were tested compared with 5ml for the commercial pneumo-3 vaccine. The induced BVDV-I, BHV-I, PI3Vantibodies were followed up in the sera of vaccinated calves up to 24 week post vaccination (wpv) using serum neutralization test (SNT) and indirect enzyme linked immune sorbent assay (ELISA). Virus concentration had a significant effect on serum antibody levels by the 2nd wpv till 24th wpv as well as the economy of the vaccine, where the same immunological effect for the commercial vaccine produced by half dose (2.5ml) of our concentrated pneumo-3 vaccine.
The Analysis of Marketing Milk Factories and Effects on Customers Behaviours Mechanism

Consumption of its way on the incidence of many physical illnesses opens. However, according to statistics available per capita consumption of this nutrient among Indian is well below the world average that this rate of prevalence of diseases such as osteoporosis, calcium deficiency and many other diseases among Indians enhances the frequency of losses and losses for the country. Though very heavy losses and calculate an integral charges physical and mental growth can be accounted for essentially not. Due to this short introduction to the need for extensive research in order to check the effects of various factors on consumer behaviour mechanism dairy products is inevitable. In line with this study it was felt necessary to do. This research attempts to answer this question is the impact of marketing milk factors (income, familiarity with various types of dairy products, easy access to milk, packaging and the importance of awareness of the benefits of milk) on the mechanisms and behavioural mechanisms people use this product what. This research on the topic was above 250 citizens and consumers of milk in the city of Mysore in India (for example) that were selected by stratified random, was examined. The results showed that the above factors and determine the main factors in the marketing and consumption of dairy products.
Single Frequency Precise Point Positioning Using GPS and Galileo Observables

SUMMARY This paper develops a precise point positioning (PPP) model for combined GPS/Galileo single-frequency observations. Although improves the solution availability and accuracy, combining GPS and Galileo observables introduces additional biases that must be modelled. These include the GPS-to-Galileo time offset and the inter-system bias. Additionally, to take full advantage of the Galileo E1 signal, it is essential that its stochastic characteristics are rigorously modelled. In this paper, various sets of GPS and Galileo measurements collected at two stations with short separation were used to investigate the stochastic characteristics of Galileo E1 signal. As a by-product, the stochastic characteristics of the legacy GPS P1 code was obtained and then used to verify the developed stochastic model of the Galileo signal. It is shown that sub-decimeter level accuracy is possible through our single-frequency GPS/Galileo PPP model. As well, the addition of Galileo improves the PPP solution convergence by about 30% in comparison with GPS-only solution.