Miracle-dz/UniFAR-FTUs huggingface dataset:
Remote sensing image registration via active contourmodel

Image registration is the process by which we determine a transformation that provides the most accurate match between two images. The search for the matching transformation can be automated with the use of a suitable metric, but it can be very time-consuming and tedious. In this paper, we introduce a registration algorithm that combines active contour segmentation together with mutual information. Our approach starts with a segmentation procedure. It is formed by a novel geometric active contour, which incorporates edge knowledge, namely Edgeflow, into active contour model. Two edgemap images filled with closed contours are obtained. After ruling out mismatched curves, we use mutual information (MI) as a similarity measure to register two edgemap images. Experimental results are provided to illustrate the performance of the proposed registration algorithm using both synthetic and multisensor images. Quantitative error analysis is also provided and several images are shown for subjective evaluation. 2008 Elsevier GmbH. All rights reserved.
Automatic Construction of High Quality Roadmaps for Path Planning

Path planning plays an important role in many virtual worlds, like computer games. Currently the motion of entities is often planned using a combination of scripting, grid-search methods, local reactive methods, flocking and crowd behavior. In this paper we describe a new approach, based on a technique from robotics, that computes a roadmap of smooth, collision-free, high-quality paths. This roadmap can be used to obtain instantly good paths for entities. We also describe applications of the technique for planning the motion of groups of entities and for creating smooth camera movements through an environment.
A Comprehensive Overview on Data Mining : Approaches and Applications

Data Mining represents the process of decocting a previously unknown information from data. Advancement has lead to a sudden upsurge in large number of algorithms that effectively tackle the regular and computational task of data mining. Data mining can also be considered as retrieving knowledge from large amount of data. Data mining offers great promise in helping organizations to uncover patterns hidden in their data that can be used to analyse the relationship and behaviour of customers and products with future trends.The key idea of this article is to provide an overview of data mining algorithm.The developed software will provide useful and profitable results. The software helps to discover concealed facts and interesting knowledge, to provide help in decision making process. KeywordsData mining, clustering, classification, Kmeans, Decision Tree.
Prognostic Significance of MicroRNA-375 Downregulation in Solid Tumors: A Meta-Analysis

Objective. Recently, many studies have shown that microRNAs (miRNA) exhibit altered expression in various cancers and may play an important role as prognostic biomarker of cancers. We performed a meta-analysis to evaluate the impact of miR-375 expression in solid tumors on patients' overall survival (OS). Methods. Studies were identified by searching PubMed, Embace, and Cochrane Library (last search update was in May 2014) and were assessed by further quality evaluation. The pooled hazard ratios (HRs) with 95% confidence intervals (CIs) for total and stratified analyses were calculated to investigate the association between miR-375 expression and cancer patients OS. Results. Our analysis results indicated that downregulation of miR-375 predicted poor OS (HR = 1.91, 95% CI 1.48–2.45, P < 0.001). Subgroup analyses showed that lower expression of miR-375 was significantly related with poor OS in patients with esophageal carcinoma (HR = 2.24, 95% CI 1.69–2.96, P < 0.001) and non-small-cell lung cancer (NSCLC) (HR = 1.71, 95% CI 1.31–2.24, P < 0.001). Conclusions. The findings from this meta-analysis suggest that miR-375 expression is associated with OS of patients with malignant tumors and could be a useful clinical prognostic biomarker.
Synthesis and high temperature thermoelectric properties of Yb0.25Co4Sb12-(Ag2Te)x(Sb2Te3)1−x nanocomposites

Nanocomposites are becoming a new paradigm in thermoelectric study: by incorporating nanophase(s) into a bulk matrix, a nanocomposite often exhibits unusual thermoelectric properties beyond its constituent phases. To date most nanophases are binary, while reports on ternary nanoinclusions are scarce. In this work, we conducted an exploratory study of introducing ternary (Ag2Te)x(Sb2Te3)1−x inclusions in the host matrix of Yb0.25Co4Sb12. Yb0.25Co4Sb12-4wt% (Ag2Te)x(Sb2Te3)1−x nanocomposites were prepared by a melting-milling-hot-pressing process. Microstructural analysis showed that poly-dispersed nanosized Ag-Sb-Te inclusions are distributed on the grain boundaries of Yb0.25Co4Sb12 coarse grains. Compared to the pristine nanoinclusion-free sample, the electrical conductivity, Seebeck coefficient, and thermal conductivity were optimized simultaneously upon nanocompositing, while the carrier mobility was largely remained. A maximum ZT of 1.3 was obtained in Yb0.25Co4Sb12-4wt% (Ag2Te)0.42(Sb2Te3)0.58 at 773 K, a ~ 40% increase compared to the pristine sample. The electron and phonon mean-free-path were estimated to help quantify the observed changes in the carrier mobility and lattice thermal conductivity.
A Tool for Staging Mixed-initiative Dialogs

We discuss and demonstrate a tool for prototyping dialog-based systems that, given a high-level specification of a human-computer dialog, stages the dialog for interactive use. The tool enables a dialog designer to evaluate a variety of dialogs without having to program each individual dialog, and serves as a proofof-concept for our approach to mixed-initiative dialog modeling and implementation from a programming language-based perspective.
A Self-Evolving Interval Type-2 Fuzzy Neural Network for Nonlinear Systems Identification

Abstract This paper proposes a Self-Evolving Interval Type-2 Fuzzy Neural Network (SEIT2FNN) for nonlinear systems identification. The SEIT2FNN has both on-line structure and parameter learning abilities. The antecedent parts in each fuzzy rule of the SEIT2FNN are interval type-2 fuzzy sets and the fuzzy rules are of the Takagi-Sugeno-Kang (TSK) type. An on-line clustering method is proposed to generate fuzzy rules which flexibly partition the input space. For parameter learning, the consequent part parameters are tuned by a rule-ordered Kalman filter algorithm for high accuracy learning performance. The antecedent part parameters are learned by gradient descent algorithms. Comparisons with identification using other type-1 and type-2 fuzzy systems verify the performance of the SEIT2FNN.
Multimodal person recognition for human-vehicle interaction

The authors combine two different biometric modalities for next-generation vehicles that use biometric person recognition. Next-generation vehicles will undoubtedly feature biometric person recognition as part of an effort to improve the driving experience. Today's technology prevents such systems from operating satisfactorily under adverse conditions. A proposed framework for achieving person recognition successfully combines different biometric modalities, borne out in two case studies.
Convectively Coupled Kelvin Waves in an Idealized Moist General Circulation Model

Abstract The dynamics of convectively coupled Kelvin waves and their dependence on convection scheme parameters are studied within a simplified moist general circulation model. The model consists of the primitive equations on the sphere over zonally symmetric aquaplanet, slab mixed layer ocean boundary conditions, and idealized physical parameterizations including gray radiative transfer and a simplified Betts–Miller convection scheme. This framework allows the authors to study the dependence of Kelvin waves on quantities such as the gross moist stability in a clean manner. A control simulation with the model produces convectively coupled Kelvin waves that are remarkably persistent and dominate the variability within the Tropics. These waves propagate with an equivalent depth of ≈40 m. Linear regression analysis with respect to a Kelvin-filtered time series shows that the waves are driven by evaporation–wind feedback and have structures broadly consistent with theoretical predictions for Kelvin waves. Nex.
A Novel Acyl-CoA Beta-Transaminase Characterized from a Metagenome

Background Bacteria are key components in all ecosystems. However, our knowledge of bacterial metabolism is based solely on the study of cultivated organisms which represent just a tiny fraction of microbial diversity. To access new enzymatic reactions and new or alternative pathways, we investigated bacterial metabolism through analyses of uncultivated bacterial consortia. Methodology/Principal Findings We applied the gene context approach to assembled sequences of the metagenome of the anaerobic digester of a municipal wastewater treatment plant, and identified a new gene which may participate in an alternative pathway of lysine fermentation. Conclusions We characterized a novel, unique aminotransferase that acts exclusively on Coenzyme A (CoA) esters, and proposed a variant route for lysine fermentation. Results suggest that most of the lysine fermenting organisms use this new pathway in the digester. Its presence in organisms representative of two distinct bacterial divisions indicate that it may also be present in other organisms.
Parameter estimation using a frequency domain least square approach for spaceborne SAR data

A frequency domain least square approach is proposed to estimate Doppler parameters of Synthetic Aperture Radars (SAR). The estimator includes Doppler centroid and Doppler frequency rate. Power Spectrum Density (PSD) of radar echoes is derived from spaceborne SAR data. Doppler centroid and Doppler frequency are obtained by minimizing the Euclidian distance of PSD between analytic form and data. Instead of extensive search, an iterative algorithm based on steep descent method is developed. Simulation and experimental results demonstrate the accurate performance.
Tilburg University Characterization of the Walrasian equilibria of the assignment model

We study the assignment model where a collection of indivisible goods are sold to a set of buyers who want to buy at most one good. We characterize the extreme and interior points of the set of Walrasian equilibrium price vectors for this model. Our characterizations are in terms of demand sets of buyers. Using these characterizations, we also give a unique characterization of the minimum and the maximum Walrasian equilibrium price vectors. Also, necessary and sufficient conditions are given under which the interior of the set of Walrasian equilibrium price vectors is non-empty. Several of the results are derived by interpreting Walrasian equilibrium price vectors as potential functions of an appropriate directed graph. © 2009 Elsevier B.V. All rights reserved.
Individual vs. Parental Consent in Marriage: Implications for Intra-Household Resource Allocation and Growth

Marrying individuals' consent has been requirement for marriage in Europe since the Middle Ages - in most of the rest of the world parental consent reigned until at least until the 1950s. This paper investigates the role of consent in marriage for intra-household allocation of resources and growth. We argue that a shift from parental to individual consent moves resources in the same direction, favouring young men and young women over old men. If young adults have greater incentives to invest in child human capital than the old (who will be around fewer periods), this may impact on growth. We formulate a simple endogenous growth model capturing these aspects.
Prioritizing Replenishments of the Forward Reserve Area Harwin de Vries

Having sufficient inventories in the piece-picking area of a warehouse is an essential condition for warehouse operations. This requires a timely replenishment of the products from a reserve area in case they could run out of stock. In this paper we develop analytical models to arrive at priority rules for these replenishments in case replenishments and order picking are done simultaneously because of time pressure. This problem was observed in a warehouse of a large cosmetics firm. The priority rules are compared by means of simulation and regression. Finally we present the results of applying one of these rules in practice. ∗Econometric Institute, Erasmus University Rotterdam, P.O. Box 1738, NL-3000 DR, Rotterdam, The Netherlands. E-mail: harwindv@hotmail.com. †Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, C\José Gutierrez Abascal 2, 28006 Madrid Spain. E-mail: ruth.carrasco@upm.es. ‡Econometric Institute, Erasmus University Rotterdam, P.O. Box 1738, NL-3000 DR, Rotterdam, The Netherlands. E-mail:{farenhorst,rdekker}@ese.eur. nl.
Formal Guarantees for Localized Bug Fixes

Bug traces produced in simulation serve as the basis for patching the RTL code in order to fix a bug. It is important to prove that the patch covers all instances of the bug scenario; otherwise, the bug may return with a different valuation of the variables involved in the bug scenario. For large circuits, formal methods do not scale well enough to comprehensively eliminate the bug, and achieving adequate coverage in simulation and regression testing becomes expensive. This paper proposes formal methods for analyzing the control trace leading to the observed manifestation of the bug and verifying the robustness of the bug fix with respect to that control trace. We propose a classification of the bug fix based on the guarantee that our analysis can provide about the quality of the bug fix. Our method also prescribes the types of tests that are recommended to validate the bug fix on other types of scenarios. Since our methods are more scalable by orders of magnitude than model checking the entire design, we believe that the proposed formal methods hold immense promise in analyzing bug fixes in practice.
Selected Code-Related and Language-Related Factors on Reading Comprehension for Deaf and Hard of Hearing Students

Selected Code-Related and Language-Related Factors on Reading Comprehension for Deaf and Hard of Hearing Students Jodi L. Falk This study examined the interrelationships among English language structures (phonological knowledge, morphological knowledge, silent word reading fluency) and reading comprehension in a group of 45 deaf and hard of hearing students in grades 3 to 8, taking into account their demographic characteristics. Simple Pearson correlations, multiple regression analyses, and an independent samples t test were used in this study. Results indicated that morphological knowledge was the significant variable positively associated with reading comprehension over and beyond the other predictors (phonological knowledge and silent word reading fluency) and the demographic covariates (home language, age, and gender).
Signal Processing and Classification Approaches for Brain-Computer Interface

Research on brain-computer interface (BCI) systems began in the 1970s at the University of California Los Angeles (UCLA) (Vidal, 1973; 1977). The author gave in his papers the expression "Brain Computer Interface" which is the term currently used in literature. A BCI system is a direct communication pathway between a brain and an external artificial device. BCI systems were aimed at assisting, augmenting or repairing human cognitive or sensory-motor functions. The BCI systems (BCIs) allow control of an artificial device based on the features extracted from voluntary electric, magnetic, or other physical manifestations of brain activity collected from epior subdurally from the cortex or from the scalp or in invasive electrophysiological manner, i.e. brain signals recorded intracortically with single electrode or multi-electrode arrays (Dornhege et al., 2007). There is a variety of non-invasive techniques for measuring brain activity. These non-invasive techniques include, the electroencephalography (EEG), magnetoencephalography (MEG), positron emission tomography (PET), functional magnetic resonance imaging (fMRI), and optical imaging. However, for technical, time resolution, realtime, and price constraints, only EEG monitoring and related techniques are employed in the BCI community. For more details refer to (Wolpaw et al., 2002; Mason et al., 2007; Dobkin, 2007). The neuronal electrical activity contain a broad band frequency, so the monitored brain signals are filtered and denoised to extract the relevant information (see section 3) and finally this information is decoded (see section 6) and commuted into device commands by synchronous control or more efficiently by self-paced or asynchronous control in order to detect whether a user is intending something or not (see chapter 7 in (Dornhege et al., 2007) for details), Fig. 1. For some specific BCI tasks, raw brain signal serves as stimulus as well as a control interface feedback. The direct BCIs can be seen as a new means of communication that may be used to allow tetraplegic or individuals with severe motor or neuromuscular diseases (e.g. Amyotrophic lateral sclerosis (ALS), brainstem stroke, brain or spinal cord injury, cerebral palsy, muscular
The Achievable Distortion Region of Sending a Bivariate Gaussian Source on the Gaussian Broadcast Channel

We provide a complete characterization of the achievable distortion region for the problem of sending a bivariate Gaussian source over bandwidth-matched Gaussian broadcast channels, where each receiver is interested in only one component of the source. This setting naturally generalizes the simple single Gaussian source bandwidth-matched broadcast problem for which the uncoded scheme is known to be optimal. We show that a hybrid scheme can achieve the optimum for the bivariate case, but neither an uncoded scheme alone nor a separation-based scheme alone is sufficient. We further show that in this joint source channel coding setting, the Gaussian scenario is the worst scenario among the sources and channel noises with the same covariances.
Inconsistency Identification and Resolution in Goal-Driven Requirements Analysis.

This thesis addresses the need for inconsistency identification and resolution techniques to assist practitioners employing goal-based requirements analysis. Several approaches for inconsistency identification exist, however, the techniques introduced in this thesis are targeted specifically for analyzing goals in conjunction with the Goal-Based Requirements Analysis Method (GBRAM). Reducing inconsistency in a requirements specification greatly impacts its quality, as well as the overall success of a software development effort. Addressing and reducing the number of inconsistencies before development begins, minimizes the need to continually revisit what needs to be developed (the requirements), and consequently reduces development time and costs since inconsistencies are resolved early on (prior to system design and implementation). This thesis introduces a series of techniques and associated heuristics for identifying and resolving inconsistency in requirements specifications. These techniques and heuristics are demonstrated in the analysis of two case studies, the second of which is further described in Deriving Goals from a Use Case Based Requirements Specification for an Electronic Commerce System [ADS00]. The main contributions of this thesis include techniques that augment the GBRAM which provide specific guidance to the identification of inconsistency, and a structured goal syntax to aid analysts in forming goals for future analysis, and heuristics to aids in inconsistency identification and resolution
Optimizing DNA sequences using Tetra-nucleotide RankList

Recent advancement in the field of life science has caused the generation of massive amount of genomic data. Storing such huge amount of data require a lot of memory. However, by using efficient algorithm we can optimize the size of the dataset and save memory storage. In this paper we have proposed an algorithm which uses Tetra-nucleotide RankList for optimizing the storage requirement for storing DNA sequences in database which can be easily retrieved in a time efficient manner. Tetra-nucleotide RankList has been generated by testing several DNA sequences to confirm the uniformity of the RankList over all DNA sequences. The algorithm has been applied on the genome sequences of different bacteria and viruses and the space for storing those DNA sequences has been reduced up to 30%.
Emerging Genotype (GGIIb) of Norovirus in Drinking Water, Sweden

From May through June 2001, an outbreak of acute gastroenteritis that affected at least 200 persons occurred in a combined activity camp and conference center in Stockholm County. The source of illness was contaminated drinking water obtained from private wells. The outbreak appears to have started with sewage pipeline problems near the kitchen, which caused overflow of the sewage system and contaminated the environment. While no pathogenic bacteria were found in water or stools specimens, norovirus was detected in 8 of 11 stool specimens and 2 of 3 water samples by polymerase chain reaction. Nucleotide sequencing of amplicons from two patients and two water samples identified an emerging genotype designated GGIIb, which was circulating throughout several European countries during 2000 and 2001. This investigation documents the first waterborne outbreak of viral gastroenteritis in Sweden, where nucleotide sequencing showed a direct link between contaminated water and illness.
Pretend Play and Emergent Literacy for Aboriginal Children

In educational contexts during early childhood, play has a central role. Pretend play, a leading activity for preschool children, supports several aspects of their overall development, including skills in emergent literacy. Aboriginal children also engage in pretend play to strengthen their oral language and literacy skills in their Aboriginal mother tongue or second language (in French or in English). This article examines the relationship between pretend play and emergent literacy, particularly with respect to Aboriginal children, and provides some strategies for early childhood educators and caregivers to infuse children’s pretend play with emergent literacy activities and support.
A new detection scheme for multiple object tracking in fluorescence microscopy by joint probabilistic data association filtering

Tracking of multiple objects in biological image data is a challenging problem due largely to poor imaging conditions and complicated motion scenarios. Existing tracking algorithms for this purpose often do not provide sufficient robustness and/or are computationally expensive. In this paper we propose a new object detection scheme, based on importance sampling from image intensity distributions, and show how it can be easily incorporated into a probabilistic tracking framework based on Kalman or particle filtering. Experiments on synthetic as well as real fluorescence microscopy image data from different biological studies show that the resulting tracking algorithm yields smaller localization errors at much lower execution times compared to other available methods.
Three-dimensional imaging algorithm for tomography SAR based on multiple signal classification

The height imaging quality of tomography synthetic aperture radar obtained by the classic Fourier transform method is limited by the resolution and sidelobe problems. The spatial spectrum estimation methods can obtain lower sidelobe and narrower spectrum, and can obtain higher resolution in height direction with small number of baseline. In this paper, the multiple signal classification (MUSIC) algorithm is proposed for tomography synthetic aperture radar to obtain high quality image, and the estimated accuracy of the proposed method is investigated with different number of looks. The simulation results confirm the validity of the proposed method.