Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonlinear sliding mode block control of fractional-order systems

This paper presents some novel types of fast convergence robust controllers for Caputo derivative based fractional-order nonlinear systems with model uncertainties and external disturbances. First, a new fractional-order model is derived from the original system based on the block transformation strategy. In the second step, two different nonlinear sliding manifolds are proposed to reach a short time convergence. Subsequently, appropriate nonlinear sliding mode control laws are developed to assure the robustness and fast converging behaviors. The stability of both controllers is achieved by the fractional-order stability theorems. Finally, comprehensive numerical simulations are carried out to indicate the effectiveness of the suggested robust fractional-order controllers.
Vector Quantization-Block Constrained Trellis Coded Quantization of Speech Line Spectral Frequencies

In this paper, a vector quantization-block constrained trellis coded quantization (VQ-BCTCQ) is presented to quantize line spectrum frequency (LSF) parameters of the wideband speech codec. Both the predictive structure and safety-net concept are combined into VQ-BCTCQ to develop the predictive VQ-BCTCQ. The performance of this quantization is compared with that of the linear predictive coding (LPC) vector quantizer used in the AMR-WB codec, and reductions in spectral distortion (SD) and encoding complexity are demonstrated.
On graduation from scal procyclicality

In the past, industrial countries have tended to pursue countercyclical or, at worst, acyclical scal policy. In sharp contrast, emerging and developing countries have followed procyclical scal policy, thus exacerbating the underlying business cycle. We show that, over the last decade, about a third of the developing world has been able to escape the procyclicality trap and actually become countercyclical. We then focus on the role played by the quality of institutions, which appears to be a key determinant of a countrys ability to graduate. We show that, even after controlling for the endogeneity of institutions and other determinants of scal procyclicality, there is a causal link running from stronger institutions to less procyclical or more countercyclical scal policy. JEL Classi cation: E62, E32, E02, F41. Keywords: business cycle, institutional quality, cyclicality, graduation, emerging markets. We would like to thank seminar participants at the IMF, World Bank, Central Bank of Chile, Central Bank of Hungary, and, especially, an editor and two anonymous referees for helpful comments and suggestions. Vuletin acknowledges the Division of Social Sciences at Colby College for nancial support. yCorresponding author. Address: 5235 Mayower Hill, Waterville, Maine 04901-8852; Email: gvuletin@colby.edu; Phone: 207-872-6430; Fax: 207-859-5248.
Classification of Hydrocephalus using TAN

Hydrocephalus is considered to be one of the diseases that may cause damage in children brain especially infants. MRI (Magnetic resonance Imaging) is one source of hydrocephalus detection tools, but using MRI in children brain diseases classification is considered to be difficult process according to the variance and complexity of brain diseases. This paper presents a solution of detecting one of the children brain diseases which is hydrocephalus. The proposed system consists of four stages, namely, MRI Preprocessing stage, Segmentation stage, Feature extraction, and Classification stage. In the first stage, the main task is to eliminate the medical resonance images (MRI) noise found in images due to light reflections or operator performance which may cause inaccuracies in the classification process. The second stage, which is the stage where ROI is extracted (tumor region). In the third stage, the features related with MRI images using Haar wavelet transform (HWT) will be obtained. The features of magnetic resonance images (MRI) have been decreased using (HWT) to essential features only. And finally the fourth stages, where new classifier will be presented and finally the result will compare the proposed classifier with six other classifiers have been used. Image classification is an important task in the medical field and computer vision. Image classification refers to the process of labeling images into one of a number of predefined categories. This survey will use the Tree augmented Naive Bayes classification technique to detect and classify one of the children brain diseases, and classify the hydrocephalus type depending on MRI. And it's expected to achieve a high accuracy in hydrocephalus detection to help the radiologist in the disease detection process.
Stock Trend Prediction Using Regression Analysis - A Data Mining Approach

Organizations have been collecting data for decades, building massive data warehouses in which to store the data. Even though this data is available, very few of these organizations have been able to realize the actual value stored in it. The question these organizations are asking is how to extract meaningful data and uncover patterns and relationship from their databases. This paper presents a study of regression analysis for use in stock price prediction. Data were obtained from the daily official list of the prices of all shares traded on the stock exchange published by the Nigerian Stock Exchange using banking sector of Nigerian economy with three banks namely:- First Bank of Nigeria Plc, Zenith Bank Plc, and Skye Bank Plc to build a database. A data mining software tool was used to uncover patterns and relationships and also to extract values of variables from the database to predict the future values of other variables through the use of time series data that employed moving average method. The tools were found capable technique to describe the trends of stock market prices and predict the future stock market prices of three banks sampled.
Local grammars and their representation by finite automata

In the study of collocations and of frozen sentences (idioms, cliches, collocations, many metaphors and figurative meanings, etc.) one often encounters sets of similar forms that cannot be related by formal rules of either type: phrase structure or transformational. We present examples of such situations and we show how the formalism of finite automata can be used to represent them in a natural way.
Working paper MACROECONOMIC POLICY AND POTENTIAL GROWTH

We make the case for investigating the gap between the potential and the actual level of production, and review contributions that point to the reduced power of standard policy instruments in presence of a prolonged gap. We also highlight di¢ culties in measuring where an economy stands relative to its potential. We review links between human capital accumulation and technology, and sketch a basic Schumpeterian model that puts at the center stage of the growth process investments in innovation and the foundation of new rms, arguably two key sources of growth that could revitalize the faltering European Economies. The gap between the short and long run behavior is illustrated through quantitative experiments.
Compatibility of basic social perceptions determines perceived attractiveness

The human body's shape and motion afford social judgments. The body's shape, specifically the waist-to-hip ratio, has been related to perceived attractiveness. Early reports interpreted this effect to be evidence for adaptation, a theory known generally as the waist-to-hip ratio hypothesis. Many of the predictions derived from this perspective have been empirically disconfirmed, leaving the issue of natural selection unresolved. Knowing the cognitive mechanisms undergirding the relationship between judgments of attractiveness and body cues is essential to understanding its evolution. Here we show that perceived attractiveness covaries with body shape and motion because they cospecify social percepts that are either compatible or incompatible. The body's shape and motion provoke basic social perceptions, biological sex and gender (i.e., masculinity/femininity), respectively. The compatibility of these basic percepts predicts perceived attractiveness. We report evidence for the importance of cue compatibility in five studies that used diverse stimuli (animations, static line-drawings, and dynamic line-drawings). Our results demonstrate how a proximal cognitive mechanism, itself likely the product of selection pressures, helps to reconcile previous contradictory findings.
An Improved Camshift Algorithm Based on Dynamic Background

In this paper, an improved Camshift algorithm based on dynamic background is presented, detecting motion objects with Twice-Frame-Difference Method (TFDM) and tracking objects with Multi-Features-Fusion Method (MFFM). By the comparing experiments, the presented improved algorithm can acquire the better detecting effects and improve the robustness and accuracy of tracking.
2MASS NIR photometry for 693 candidate globular clusters in M 31 and the Revised Bologna Catalogue

We have identified in the 2MASS database 693 known and candidate globular clusters in M 31. The 2MASS J, H, K magnitudes of these objects have been transformed to the same homogeneous photometric system of existing near infrared photometry of M 31 globulars, finally yielding J, H, K integrated photometry for 279 confirmed M 31 clusters, 406 unconfirmed candidates and 8 objects with controversial classification. Of these objects 529 lacked any previous estimate of their near infrared magnitudes. The newly assembled near infrared dataset has been implemented into a revised version of the Bologna Catalogue of M 31 globulars, with updated optical (UBVRI) photometry taken, when possible, from the most recent sources of CCD photometry available in the literature and transformed to a common photometric system. The final Revised Bologna Catalogue (available in electronic form) is the most comprehensive list presently available of confirmed and candidate M 31 globular clusters, with a total of 1164 entries. In particular, it includes 337 confirmed GCs, 688 GC candidates, 10 objects with controversial classification, 70 confirmed galaxies, 55 confirmed stars, and 4 H  regions lying within ∼3 ◦ from the center of the M 31 galaxy. Using the newly assembled database we show that the V − K color provides a powerful tool to discriminate between M 31 clusters and background galaxies, and we identify a sample of 83 globular cluster candidates, which is not likely to be contaminated by misclassified galaxies.
A self-learning sensor fusion system for object classification

We propose a learning system for object classification which fuses information from a camera, a radar and a localization unit. The system is illustrated in application to categorization of objects on a highway. The system learns not only prior to its deployment in a supervised mode but also on-board a vehicle during its operation in a self-learning mode. The radar guides a selection of candidate images provided by the camera for subsequent analysis by our learning method. The Multilayer Inplace Learning Network (MILN) is used to distinguish between representations of different objects. Radar information gets coupled with navigational information for accurate localization of objects during self-learning. One of the MILN layers helps to resolve labeling conflicts when localization is not sufficient. A Multi-Resolution MILN which uses higher-resolution levels to reinforce training of lower-resolution levels is also proposed for improved performance when dealing with a wide range of distances to objects.
Development of 325 MHz Single Spoke Resonators

single spoke resonators, operating at 325 MHz, comprise the first stage of the linac cold section. In this paper we present the current status of the production and testing of the first two prototype cavities. This includes descriptions of the fabrication, frequency tuning, chemical polishing, high pressure rinse, and high-gradient cold tests.
Development of a HAM-style Cognitive Robot

A robotic-aid system could be more effective if the system were intelligent enough to understand human needs and adapt its behaviors accordingly. This paper presents our efforts to realize such a robot that we call a cognitive robot through a multi-agent-based cognitive robot architecture with three distinctive memory systems. Several applications illustrate how the system interacts with humans.
A Non Mouse-Adapted Dengue Virus Strain as a New Model of Severe Dengue Infection in AG129 Mice

The spread of dengue (DEN) worldwide combined with an increased severity of the DEN-associated clinical outcomes have made this mosquito-borne virus of great global public health importance. Progress in understanding DEN pathogenesis and in developing effective treatments has been hampered by the lack of a suitable small animal model. Most of the DEN clinical isolates and cell culture-passaged DEN virus strains reported so far require either host adaptation, inoculation with a high dose and/or intravenous administration to elicit a virulent phenotype in mice which results, at best, in a productive infection with no, few, or irrelevant disease manifestations, and with mice dying within few days at the peak of viremia. Here we describe a non-mouse-adapted DEN2 virus strain (D2Y98P) that is highly infectious in AG129 mice (lacking interferon-α/β and -γ receptors) upon intraperitoneal administration. Infection with a high dose of D2Y98P induced cytokine storm, massive organ damage, and severe vascular leakage, leading to haemorrhage and rapid death of the animals at the peak of viremia. In contrast, very interestingly and uniquely, infection with a low dose of D2Y98P led to asymptomatic viral dissemination and replication in relevant organs, followed by non-paralytic death of the animals few days after virus clearance, similar to the disease kinetic in humans. Spleen damage, liver dysfunction and increased vascular permeability, but no haemorrhage, were observed in moribund animals, suggesting intact vascular integrity, a cardinal feature in DEN shock syndrome. Infection with D2Y98P thus offers the opportunity to further decipher some of the aspects of dengue pathogenesis and provides a new platform for drug and vaccine testing.
Decomposition of High Speed Jet Noise: Source Characteristics and Propagation Effects

Current research programs directed at supersonic engine exhaust noise reduction are demonstrating benefits of 3-4 dBA using passive methods to increase jet mixing and break up shock cells in over-expanded flows. While progress is being made, high speed jet noise continues to be a research challenge for small business jets and tactical military aircraft. The current work benchmarks high speed jet noise using laboratory scale jets for the purpose of a) identifying source and propagation mechanisms, and b) providing validation data for simulation/modeling methods. Laboratory scale experiments are presented over a Mach number range of M = 0.68 to 1.5 with static temperature ratio ranging from Tr = 0.68 to 2. A unique near field rotating phased microphone array technique was used to identify the large-scale turbulence structure noise source and Mach waves in supersonic shock-free jets. A companion paper documents the near field pressure statistics and projection of the convected wave packet to the far field. Validation against the directly measured far field levels quantitatively establishes the large scale structure noise contributions. The combined studies underpin a long term effort to develop modeling methods and new concepts for jet noise suppression based on controlling the evolution of the large-scale turbulence structures.
Investigations on the Characteristics of Random Decision Tree Ensembles

An ensemble is viewed as a machine learning system that combines multiple models to work collectively in the hope of producing a better performance than that of individuals. However, an ensemble's accuracy cannot be easily determined as it involves several factors, e.g. individual model's accuracy, diversity between its member models, decision- making strategy and number of members and the relationships between them are unclear. This paper, taking random decision tree ensembles as testing platforms, investigates these relationships and the strategies for creating ensembles from randomly generated trees. Specifically, we devised three sets of procedures for conducting experiments using twelve data sets from the UCI repository to determine the importance of individual model accuracy and the diversity between decision tree models within an ensemble. The main findings of the investigations are presented and discussed in the paper.
Efficiency in intrahousehold resource allocation

This paper examines the allocation of productive resources within rural households of poor countries. Building upon the existing literature, it provides a consistent framework from which to study productive efficiency and intrahousehold equity. The topics discussed include returns to scale and household centralization; specialization and gender casting; separate spheres and commitment failure; labor market cartelization and discrimination; and the provision of home public goods in the presence of free riding. We show that intrahousehold productive inefficiency should not arise unless household members are prevented from entering into enforceable side contracts. Our analysis predicts that intrahousehold inefficiency increases with factors that exacerbate commitment failure such as short time horizon, low assets, unequal stakes in the household, and poor external enforcement. Patrimonial laws and customs regarding inheritance and divorce can be understood as efforts to mitigate commitment failure within the household.
The student experience of a blended learning accounting course : a case study in Hong Kong

The research is an inquiry into students’ learning experiences within a blended learning Accounting course in a sub-degree programme at a university in Hong Kong. In this course, the students were required to attend face-to-face classes and to participate in learning activities in the online platform. A case study research approach was adopted that involved 2 classes of 2 teachers and 80 students. Qualitative data were generated through classroom observations, online participation observations, student learning logs and reflections, student focus group interviews, student individual interviews, individual teacher interviews and an individual interview with the course leader. Thematic data analysis was used and a Community of Inquiry (CoI) model was used as a theoretical framework. The analysis showed that the students engaged in learning by integrating traditional and online learning activities and many of these were located within the social, cognitive and teaching presences within the CoI model. However, the students were found to be involved actively in non-prescribed activities that included the use of social network applications. The active learning exploration driven by students’ intrinsic motivation and the consequent collaborative learning among students using social media tools were not reflected in the CoI model. Hence, a new element of autonomy is proposed as an addition to the framework, to reveal the link of autonomous learning to the learning community. By extending the CoI framework, the contribution of this research is to provide a holistic model for the successful design and implementation of blended learning in higher education institutions.
Effect of Body Mass Index on Breast Cancer during Premenopausal and Postmenopausal Periods: A Meta-Analysis

Objective There is no universal consensus on the relationship between body mass index (BMI) and breast cancer. This meta-analysis was conducted to estimate the overall effect of overweight and obesity on breast cancer risk during pre- and post-menopausal period. Data Sources All major electronic databases were searched until April 2012 including Web of Knowledge, Medline, Scopus, and ScienceDirect. Furthermore, the reference lists and related scientific conference databases were searched. Review Methods All prospective cohort and case-control studies investigating the association between BMI and breast cancer were retrieved irrespective of publication date and language. Women were assessed irrespective of age, race and marital status. The exposure of interest was BMI. The primary outcome of interest was all kinds of breast cancers confirmed pathologically. Study quality was assessed using the checklist of STROBE. Study selection and data extraction were performed by two authors separately. The effect measure of choice was risk ratio (RRi) and rate ratio (RRa) for cohort studies and odds ratio (OR) in case-control studies. Results Of 9163 retrieved studies, 50 studies were included in meta-analysis including 15 cohort studies involving 2,104,203 subjects and 3,414,806 person-years and 35 case-control studies involving 71,216 subjects. There was an inverse but non-significant correlation between BMI and breast cancer risk during premenopausal period: OR = 0.93 (95% CI 0.86, 1.02); RRi = 0.97 (95% CI 0.82, 1.16); and RRa = 0.99 (95% CI 0.94, 1.05), but a direct and significant correlation during postmenopausal period: OR = 1.15 (95% CI 1.07, 1.24); RRi = 1.16 (95% CI 1.08, 1.25); and RRa = 0.98 (95% CI 0.88, 1.09). Conclusion The results of this meta-analysis showed that body mass index has no significant effect on the incidence of breast cancer during premenopausal period. On the other hand, overweight and obesity may have a minimal effect on breast cancer, although significant, but really small and not clinically so important.
Blockade of vascular endothelial growth factor receptor and epidermal growth factor receptor signaling for therapy of metastatic human pancreatic cancer.

We determined whether concurrent blockage of vascular endothelial growth factor (VEGF) receptor and epidermal growth factor (EGF) receptor signaling by two novel tyrosine kinase inhibitors, PTK 787 and PKI 166, respectively, can inhibit angiogenesis and, hence, the growth and metastasis of human pancreatic carcinoma in nude mice. Highly metastatic human pancreatic carcinoma L3.6pl cells were injected into the pancreas of nude mice. Seven days later, groups of mice began receiving oral doses of PTK 787 and PKI 166 three times weekly. Some groups of mice also received i.p. injections of gemcitabine twice a week. The mice were necropsied when the control mice became moribund. Treatment with PTK 787 and PKI 166, with gemcitabine alone, or with the combination of PTK 787, PKI 166, and gemcitabine produced 69, 50, and 97% reduction in the volume of pancreatic tumors, respectively. Administration of protein tyrosine kinase inhibitors and gemcitabine also significantly decreased the incidence of lymph node and liver metastasis. The therapeutic efficacy directly correlated with a decrease in circulating proangiogenic molecules (VEGF, interleukin-8), a decrease in microvessel density, a decrease in proliferating cell nuclear antigen staining, and an increase in apoptosis of tumor cells and endothelial cells. Therapies produced by combining gemcitabine with either PKI 166 or PTK 787 were similar to those produced by combining gemcitabine with both PKI 166 and PTK 787. These results suggest that blockade of either epidermal growth factor receptor or VEGF receptor signaling combined with chemotherapy provides an effective approach to the therapy of pancreatic cancer.
Synthesis of guitar by digital waveguides: Modeling the plectrum in the physical interaction of the player with the instrument

In this paper, we provide a model of the plectrum, or guitar pick, for use in physically inspired sound synthesis. The model draws from the mechanics of beams. The profile of the plectrum is computed in real time based on its interaction with the string, which depends on the movement impressed by the player and the equilibrium of dynamical forces. A condition for the release of the string is derived, which allows to drive the digital waveguide simulating the string to the proper state at release time. The acoustic results are excellent, as verified in the sound examples provided.