Miracle-dz/UniFAR-FTUs huggingface dataset:
Bisphosphonate Treatment of Benign Multifocal and Unifocal Osteolytic Tumours of Bone

Growth of benign tumours and tumour-like lesions of bone results in osteolysis which may cause pathological fracture. Bisphosphonates are anti-osteolytic agents which have proved effective in the treatment of number of osteolytic conditions. In this study we report the results of treatment with the aminobisphosphonate, pamidronate, of three benign osteolytic tumours of bone, two cases of fibrous dysplasia and one of Langerhans cell histiocytosis. In all three cases there was clinical and radiological improvement following treatment. Radiologically, bone lesions did not exhibit progressive enlargement. Two cases of fibrous dysplasia also showed features suggestive of increased bone formation. These findings indicate that bisphosphonates are likely to be useful in controlling the osteolysis of benign tumours/tumour-like lesions of bone, particularly in those cases where surgical intervention is not possible or multifocal lesions are present.
A REFINEMENT OF DISCRETE PARTICLE SWARM OPTIMIZATION FOR LARGE-SCALE TRUSS STRUCTURES

This paper proposes a refined version of particle swarm optimization technique for the optim um design of steel structures. Swarm is composed of a number of particles and each particle in the swarm represents a candidate solution of the optimum design problem. Design constraints in accordance with ASD-AISC (Allowable Stress Design Code of American Institute of Steel Institution) are imposed by the particle swarm optimization based optimum design algorithm developed. A constraint handling method called the ‘penalty function method’ is introduced to maintain acceptable solutions. The refined version of the particle swarm optimization algorithm proposed in this paper is easy to implement and the results and convergence performance are better than the simple particle swarm optimization algorithm and some other meta-heuristic optimization techniques. The effect of different inertia weight parameters in finding the optimum design is also tested in two numerical examples. Keyword: Meta-heuristic optim ization; discrete particle swarm; trusses; discrete particle swarm; steel structures
Examining college students' uptake of Facebook through the lens of domestication theory

Given the prevalence of social network sites (SNS) uptake among college students, and Facebook use in particular, we seek to understand the variety of ways in which Facebook is embedded in the routine, everyday lives of undergraduates. In addition to Facebook use, we aim to shed light on non-use; why do some American undergraduates choose to use Facebook sparingly, or not at all? Our findings indicate that while the majority of students were frequent Facebook users, they sought control over their Facebook use by articulating a moral space of norms governing interactions with parents, friends, and strangers; through selective and temporary de-domestication of the technology; and through non-use.
Scene Text Recognition with Bilateral Regression

This paper focuses on improving the recognition of text in images of natural scenes, such as storefront signs or street signs. This is a dicult problem due to lighting conditions, variation in font shape and color, and complex backgrounds. We present a word recognition system that addresses these diculties using an innovative technique to extract and recognize foreground text in an image. First, we develop a new method, called bilateral regression, for extracting and modeling one coherent (although not necessarily contiguous) region from an image. The method models smooth color changes across an image region without being corrupted by neighboring image regions. Second, rather than making a hard decision early in the pipeline about which region is foreground, we generate a set of possible foreground hypotheses, and choose among these using feedback from a recognition system. We show increased recognition performance using our segmentation method compared to the current state of the art. Overall, using our system we also show a substantial increase in word accuracy on the word spotting task over the current state of the art on the ICDAR 2003 word recognition data set.
Fact Mutability in Counterfactual Reasoning

Recent work on the interpretation of counterfactual conditionals has given much attention to the role of causal independencies. One influential idea from the theory of Causal Bayesian Networks is that counterfactual assumptions are made by intervention on variables, leaving all of their causal non-descendants unaffected. But intervention is not applicable across the board. For instance, backtracking counterfactuals, which involve reasoning from effects to causes, cannot proceed by intervention in the strict sense, for otherwise they would be equivalent to their consequents. We discuss these and similar cases, focusing on two factors which play a role in determining whether and which causal parents of the manipulated variable are affected: Speakers’ need for an explanation of the hypothesized state of affairs, and differences in the ‘resilience’ of beliefs that are independent of degrees of certainty. We describe the relevant theoretical notions in some detail and provide experimental evidence that these factors do indeed affect speakers’ interpretation of counterfactuals.
A New Method for Mining Regression Classes in Large Data Sets

Extracting patterns and models of interest from large databases is attracting much attention in a variety of disciplines. Knowledge discovery in databases (KDD) and data mining (DM) are areas of common interest to researchers in machine learning, pattern recognition, statistics, artificial intelligence, and high performance computing. An effective and robust method, the regression class mixture decomposition (RCMD) method, is proposed for the mining of regression classes in large data sets, especially those contaminated by noise. A concept, called "regression class" which is defined as a subset of the data set that is subject to a regression model, is proposed as a basic building block on which the mining process is based. A large data set is treated as a mixture population in which there are many such regression classes and others not accounted for by the regression models. Iterative and genetic-based algorithms for the optimization of the objective function in the RCMD method are also constructed. It is demonstrated that the RCMD method can resist a very large proportion of noisy data, identify each regression class, assign an inlier set of data points supporting each identified regression class, and determine the a priori unknown number of statistically valid models in the data set. Although the models are extracted sequentially, the final result is almost independent of the extraction order due to a dynamic classification strategy employed in the handling of overlapping regression classes. The effectiveness and robustness of the RCMD method are substantiated by a set of simulation experiments and a real-life application showing the way it can be used to fit mixed data to linear regression classes and nonlinear structures in various situations.
2th Gi/itg Conference on Measuring, Modelling and Evaluation of Computer and Communication Systems 3rd Polish-german Teletraffic Symposium Impact of Access Bandwidth on Aggregated Traffic Behavior and Queueing Performance

Recent traffic measurement of IP backbone networks discovered that aggregated IP traffic can be either uncorrelated or strongly correlated at small time scales, although at large time scales it exhibits long range dependence (LRD). Based on the infinite source Poisson traffic model, we show in this paper that the lack of correlation is an intrinsic small time scale property of LRD traffic due to the multiplexing of a large number of independent traffic flows. Particularly, the access bandwidth of users is a critical factor determining the boundary between the two ranges of time scales in which the uncorrelation and correlation property dominate the traffic behaviour respectively. A higher access bandwidth makes this boundary located at a smaller time scale. We refer to this time scale as boundary time scale and argue that the existence of a very small boundary time scale can explain the reported strong correlation at small time scales. Furthermore, the traffic behavior at different time scales leads to different queueing behaviors with respect to different queueing lengths. It is shown a large boundary time scale results in a large degree of buffer efficiency and thus brings substantial performance improvement in spite of the existence of LRD at large time scales.
2 00 0 Speckle Imaging : a boon for astronomical observations

The speckle imaging is a photographic technique that resolves objects viewed through severely distorted media. The results are insensitive to the errors caused by apparent size of the isoplanatic patch and the telescope aberrations. In this article, a short descriptions of the atmospheric turbulence and its effect on the flat wavefront from a stellar source is presented; the shortcomings of the conventional long-exposure images in the presence of Earth's atmosphere are discussed. The advantages of the speckle interferometric technique over conventional imaging are enumerated. The technical details of the method, basic Fourier optics, data analysis procedures are also described.
Learning sentiment classification model from labeled features

We propose a novel framework where an initial classifier is learned by incorporating prior information extracted from an existing sentiment lexicon. Preferences on expectations of sentiment labels of those lexicon words are expressed using generalized expectation criteria. Documents classified with high confidence are then used as pseudo-labeled examples for automatical domain-specific feature acquisition. The word-class distributions of such self-learned features are estimated from the pseudo-labeled examples and are used to train another classifier by constraining the model's predictions on unlabeled instances. Experiments on both the movie review data and the multi-domain sentiment dataset show that our approach attains comparable or better performance than exiting weakly-supervised sentiment classification methods despite using no labeled documents.
Integration of Markup Languages, Document Transformations and Software Components in the Development of Applications: the DTC Approach

This paper describes the DTC approach to the development of applications based on markup languages. DTC consistently combines componentware and markup technologies in a unified solution. Building an application according DTC supposes, on one hand, the provision of the set of documents describing the application at a purely declarative level (i.e. content, processes and interaction) and, on the other hand, the derivation of the application’s computational machinery by assembling (reusable) software components. Each software component considered in DTC gives operational support and operational meaning to a type of documents. If necessary, documents describing the application can be integrated into the projected application’s component structure using transformations. Transformations make possible to uncouple specific domain documents from reusable components. This explicit separation between the marked information, that describes the domain’s application, and its computational support improves the maintainability of the applications promoting reuse at both information and software levels (documents and components).
Synthesis and localization of two sulphated glycoproteins associated with basement membranes and the extracellular matrix

Two sulphated glycoproteins (sgps) of apparent molecular weight (Mr) 180,000 and 150,000, are synthesized by murine PYS and PF HR9 parietal endoderm and Swiss 3T3 cells. The Mr 150,000 sgp has a similar chemical structure to the sulphated glycoprotein, C, synthesized and laid down in Reichert's membrane by mouse embryo parietal endoderm cells (Hogan, B. L.M., A. Taylor, and A.R. Cooper, 1982, Dev. Biol., 90:210-214). Both the Mr 180,000 and 150,000 sgps are deposited in the detergent- insoluble matrix of cultured cells, but they do not apparently undergo any disulphide-dependent intermolecular interactions and are not precursors or products of each other. They contain asparagine-linked oligosaccharides, but these are not the exclusive sites of sulphate labeling. Antiserum raised against the Mr 150,000 sgp C of Reichert's membranes has been used in an immunohistochemical analysis of rat skin. In early foetal and adult skin the antigen is present only in basement membranes, but transiently before and after birth it is also found throughout the upper part of the dermis. This suggests that 150,000 sgp C is at times synthesized by nonepithelial cells and contributes to the extracellular matrix of mesenchymal tissues.
Associations between inverted repeats and the structural evolution of bacterial genomes.

The stability of the structure of bacterial genomes is challenged by recombination events. Since major rearrangements (i.e., inversions) are thought to frequently operate by homologous recombination between inverted repeats, we analyzed the presence and distribution of such repeats in bacterial genomes and their relation to the conservation of chromosomal structure. First, we show that there is a strong under-representation of inverted repeats, relative to direct repeats, in most chromosomes, especially among the ones regarded as most stable. Second, we show that the avoidance of repeats is frequently associated with the stability of the genomes. Closely related genomes reported to differ in terms of stability are also found to differ in the number of inverted repeats. Third, when using replication strand bias as a proxy for genome stability, we find a significant negative correlation between this strand bias and the abundance of inverted repeats. Fourth, when measuring the recombining potential of inverted repeats and their eventual impact on different features of the chromosomal structure, we observe a tendency of repeats to be located in the chromosome in such a way that rearrangements produce a smaller strand switch and smaller asymmetries than expected by chance. Finally, we discuss the limitations of our analysis and the influence of factors such as the nature of repeats, e.g., transposases, or the differences in the recombination machinery among bacteria. These results shed light on the challenges imposed on the genome structure by the presence of inverted repeats.
Arabic/english word translation disambiguation approach based on naive bayesian classifier

We present a word sense disambiguation approach with application in machine translation from Arabic to English. The approach consists of two main steps: First, a natural language processing method that deals with the rich morphology of Arabic language and second, the translation including word sense disambiguation. The main innovative features of this approach are the adaptation of the Naive Bayesian approach with new features to consider the Arabic language properties and the exploitation of a large parallel corpus to find the correct sense based on its cohesion with words in the training corpus. We expect that the resulting system will overcome the problem of the absence of the vowel signs, which is the main reason for the translation ambiguity between Arabic and other languages.
SPEAKER RECOGNITION USING AN INTELLIGENT APPROACH Prof . Y .

This paper describes the analysis of sound signals with the help of intelligent techniques, such as the neural networks and fuzzy systems for specific speaker recognition. In the first step, we use the neural networks for analyzing the sound signal of an unknown speaker, and then, a set of type-2 fuzzy rules are used for decision making. Here we use fuzzy logic due to the uncertainty of the decision process. And to optimize the architecture of the neural networks we make use of genetic algorithms. In this study, we illustrate our approach with a sample of sound signals from real speakers in our institution. KeywordsRecognition,Fuzzysystems,Cepstralcoefficients,Speaker,identification,Linearpredictiv e coding.
High-Speed Force Spectroscopy Unfolds Titin at the Velocity of Molecular Dynamics Simulations

Bridging the Titin Gap The muscle protein titin is a molecular spring that has been extensively studied by single-molecule unfolding experiments and by molecular simulation. However, experimental and simulated unfolding could not be compared directly because they differ by orders of magnitude in pulling velocity. Rico et al. (p 741) developed high-speed force spectroscopy to pull titin molecules at speeds that reach the lower limits of molecular dynamics simulations. Bridging the gap between simulation and experiment clarified the mechanism of conformational changes in titin. Experimental time scales previously accessible only to simulations provide insight into forced protein unfolding. The mechanical unfolding of the muscle protein titin by atomic force microscopy was a landmark in our understanding of single-biomolecule mechanics. Molecular dynamics simulations offered atomic-level descriptions of the forced unfolding. However, experiment and simulation could not be directly compared because they differed in pulling velocity by orders of magnitude. We have developed high-speed force spectroscopy to unfold titin at velocities reached by simulation (~4 millimeters per second). We found that a small β-strand pair of an immunoglobulin domain dynamically unfolds and refolds, buffering pulling forces up to ~100 piconewtons. The distance to the unfolding transition barrier is larger than previously estimated but is in better agreement with atomistic predictions. The ability to directly compare experiment and simulation is likely to be important in studies of biomechanical processes.
First Daughter Effects in Japan

Abstract Some influential studies suggest that a firstborn daughter is associated with (1) lower marital quality and (2) greater participation in the labor market by mothers. They also point out that a strong preference for sons among fathers may explain these first daughter effects. This study replicates these findings in large cities in Japan, where parents generally exhibit no son preference. In order to derive causal effects, I exploit quasi-random assignment of the firstborn child’s sex. The main results from two surveys consistently find sizable effects from the firstborn child’s sex on later marital life: a firstborn daughter is associated with an increased incidence of domestic violence between her father and mother. In addition, a firstborn daughter is associated with lower marital and family happiness among mothers. Concerning the effects of the firstborn child’s sex on maternal labor supply, mothers with a firstborn daughter were consistently found to exhibit a greater labor supply. Finally, I find statistically significant first daughter effects for many outcome variables among mothers but not fathers. The overall findings from this paper suggest that first daughter effects in Japan appear to be similar to those reported in countries with a strong parental son preference.
Ensemble Learning in Recommender Systems: Combining Multiple User Interactions for Ranking Personalization

In this paper, we propose a technique that uses multimodal interactions of users to generate a more accurate list of recommendations optimized for the user . Our approach is a response to the actual scenario on the Web which allows users to interact with the content in different ways, and thus, more information about his preferences can be obtained to improve recommendation. The proposal consists of an ensemble learning technique that combines rankings generated by unimodal recommenders based on particular interaction types. By using a combination of different types of feedback from users, we are able to provide better recommendations, as shown by our experimental evaluation.
Assessment of a Modern Farsi Corpus

The development of Language Engineering (LE) and Information Retrieval (IR) applications requires availability of sizeable, reliable and representative corpora. This paper describes how we have constructed a well-structured 345 MB tagged corpus of news, and presents some beneficial statistics of this corpus based upon the characteristics of Farsi language. It also goes into particular detail on the fitness of the frequency and rank of Farsi words with Zipf-Mandelbrot’s law. We will then present our measurement of Entropy of Farsi for this corpus.
Inferring Risk-Averse Probability Distributions from Option Prices Using Implied Binomial Trees: Additional Theory and Extensions

Arnold, Crack and Schwartz (ACS) (2010) generalize the Rubinstein (1994) risk-neutral implied binomial tree (R-IBT) model by introducing a risk premium. Their new risk-averse implied binomial tree model (RA-IBT) has both probabilistic and pricing applications. They use the RA-IBT model to estimate the pricing kernel (i.e., marginal rate of substitution) and implied relative risk aversion for a representative agent. They also use the RA-IBT to explore the differences between risk-neutral and risk-averse moments of returns. They also discuss practical applications of the RA-IBT model to Value at Risk and stochastic volatility option pricing models. This paper presents additional theoretical details not contained in ACS. We present a deeper discussion on the assumptions required for the risk-averse trees, we discuss details for extending ACS through the use of general utility functions to generate discount rates in the RA-IBT, and we present further theoretical details on the propagation of risk-averse probabilities through an RA-IBT. We also present an alternate CAPM-driven derivation of the certainty equivalent risk-adjusted discounting formula that is derived using no-arbitrage principles in ACS and an alternate direct estimation routine for the RA-IBT that is similar to Rubinstein’s “one-two-three” technique.
Factors Affecting Part-of-Speech Tagging for Tagalog

This paper investigates factors contributing to the performance of the POS Tagger for Tagalog language. Tagalog, a morphologically rich language, exhibits complex morphological structure, makes use of morphological information in determining parts of speech of the word, aspect and voice. As word feature information plays important role in efficient tagging, tag set definition capturing word information also contributes to the success or failure of the tagger. A refinement of tag set is defined to possibly improve tagging performance.
Feature Level Fusion of Palm Veins and Signature Biometrics

Traditional biometric systems that based on single biometric usually suffer from problems likeimposters' attack or hacking, unacceptable erro r rate and low performance. So the need of using multimodal biometric system occurred . In this paper, a study o f multimodal palm veins and signature identification is presented.Features of both modalities are extracted by using morphological operations and Scale Invariant Features Transform (SIFT)algorithm and a comparison for both methods is developed.Feature level fusion for both modalitiesis achieved by using a simple sum rule. F used features vectors are subjected to discrete cosine t ransform (DCT) to reduce their dimensionalities. Linear Vect or Quantization (LVQ) classifier is used with changed parameters to classify the different people in the database. Preliminary results are encouraging a nd supporting using palm veins and signature as a robu st and reliable identification system.