Miracle-dz/UniFAR-FTUs huggingface dataset:
On Preposition Copying and Preposition Pruning in wh-clauses in English

This paper reports on preposition copying and preposition pruning in what Milroy and Milroy (1993) term ‘Real English’, using data from unscripted live radio and TV broadcasts. We begin by discussing the nature of the two phenomena and showing that they cannot be given a straightforward characterisation in syntactic terms. We go on to propose a processing account, under which preposition copying and pruning errors have a common source, and may either involve the sloppy carrying forward of clause-initial material during the formation of filler-gap dependencies in language production, or result from the competition of two alternative structural representations during sentence planning.
Do new sources of traffic data make the application of Chaos Theory to traffic management a realistic possibility?

ABSTRACT Current signal systems for managing road traffic in many urban areas around the world lack a coordinated approach to detecting the spatial and temporal evolution of congestion across control regions within city networks. This severely inhibits these systems’ ability to detect reliably, on a strategic level, the onset of congestion and implement effective preventative action. As traffic is a time-dependent and non-linear system, Chaos Theory is a prime candidate for application to Urban Traffic Control (UTC) to improve congestion and pollution management. Previous applications have been restricted to relatively uncomplicated motorway and inter-urban networks, arguably where the associated problems of congestion and vehicle emissions are less severe, due to a general unavailability of high-resolution temporal and spatial data that preserve the variability in short-term traffic patterns required for Chaos Theory to work to its full potential. This paper argues that this restriction can now be overcome due to the emergence of new sources of high-resolution data and large data storage capabilities. Consequently, this opens up the real possibility for a new generation of UTC systems that are better able to detect the dynamic states of traffic and therefore more effectively prevent the onset of traffic congestion in urban areas worldwide.
A 72 Mb/s PRML disk-drive channel chip with an analog sampled-data signal processor

A high performance, 72 Mb/s PRML read/write channel chip has read path adaptive equalization, Viterbi metric calculation, and timing recovery functions accomplished with an analog sampled-data processor (SDP). The 1 /spl mu/m BiCMOS chip nominally operates at 100 Mb/s (112 MS/s) over a 0/spl deg/C to 70/spl deg/C temperature and /spl plusmn/10% supply voltage range. It nominally dissipates less than 800 mW from 5 V at 100 Mb/s. <<ETX>>
A Short Review of Deep Learning Neural Networks in Protein Structure Prediction Problems

Determining the structure of a protein given its sequence is a challenging problem. Deep learning is a rapidly evolving field which excels at problems where there are complex relationships between input features and desired outputs. Deep Neural Networks have become popular for solving problems in protein science. Various deep neural network architectures have been proposed including deep feed-forward neural networks, recurrent neural networks and more recently neural Turing machines and memory networks. This article provides a short review of deep learning applied to protein prediction problems.
Synthesis, Properties and Crystal Structure of Zr6Zn23Si with “Filled” Th6Mn23 Type Structure.

Abstract The new compound Zr 6 Zn 23 Si has been prepared by a solid state reaction of a zirconium-zinc alloy and silicon. It is weakly diamagnetic and this is interpreted as Pauli paramagnetism, which is overcompensated by the core diamagnetism. Its crystal structure has been determined from single-crystal diffractometer data: Fm 3 m , a = 1238.5(1) pm, Z = 4, R = 0.022 for 215 structure factors and 17 variable parameters. The Zr and Zn atoms occupy the positions of the Th and Mn atoms in Th 6 Mn 23 , while the Si atoms are situated at octahedral voids of that structure formed by the Zr atoms. Thus the atomic positions of Zr 6 Zn 23 Si correspond to those of Sc 11 Ir 4 (i.e. Sc 6 Sc 16 Ir 7 Ir). Various void positions of these structures correspond to hydrogen sites in previously reported rare earth (R) transition metal (T) hydrides R 6 T 23 H 75 − x .
Metropolis-Hastings sampling in a FilterBoost music classifier

Rejection sampling is a useful technique for performing supervised learning on training sets too large to be learned in their entirety. FilterBoost is a recent extension to AdaBoost which uses rejection sampling in an online learning framework and has been shown to work for automatic tagging of music. In this paper we improve on FilterBoost by adding Metropolis-Hastings sampling, thus allowing the algorithm to focus on hard-to-classify examples. We describe how our knowledge of artist-level similarity can be used eﬀectively in a Metropolis-Hastings framework and demonstrate a signiﬁcant increase in classiﬁcation accuracy over standard FilterBoost.
Magnetically Separable CuFe2O4 Nanoparticles in PEG: A Recyclable Catalytic System for the Amination of Aryl Iodides

Abstract A recyclable catalytic system comprising magnetically separable CuFe2O4 nanoparticles in a poly(ethylene glycol) medium has been established as an inexpensive, nontoxic, environmentally benign system for the amination of aryl iodides with aqueous ammonia. A wide variety of aryl iodides underwent amination to afford the corresponding aryl amines in moderate to good yields. The catalytic system was recycled five times with consistent activity.
Website Structure Improvement through Web Mining

facilitating effective user navigation through designing well-structured web site becomes a big challenge. This is because gap between users expectations and web developers understanding of how the website sh6uld be structured. The numbers of methods have been proposed to reorganize website to improve user navigation. As they completely reorganize website, new structure becomes unpredictable. We propose a mathematical programming method to reorganize Web structure with minimum changes in order to achieve better navigation efficiency. Heavily disoriented user should get more benefit from less disoriented user.
Stereo-enhanced displacement estimation by genetic block matching

In this paper we introduce a block based vector field estimation technique which is based upon a genetic algorithm and which exploits the dual sensor nature of a stereoscopic signal in order to accelerate its convergence. This vector field estimation technique has been designed to produce smooth vector fields at a small block size without sacrificing accuracy. Conversely (false) accuracy does not impinge upon the smoothness of the vector field.
Modeling Period Effects in Multi-Population Mortality Models: Applications to Solvency II

Recently Cairns et al. introduced a general framework for modeling the dynamics of mortality rates of two related populations simultaneously. Their method ensures that the resulting forecasts do not diverge over the long run by modeling the difference in the stochastic factors between the two populations with a mean-reverting autoregressive process. In this article, we investigate how the modeling of the stochastic factors may be improved by using a vector error correction model. This extension is highly intuitive, allowing us to visualize the cross-correlations and the long-term equilibrium relation between the two populations. Another key benefit is that this extension does not require the user to assume which one of the two populations is dominant. This benefit is important because, as we demonstrate, it is not always easy to identify the dominant population, even if one population is much larger than the other. We illustrate our proposed extension with data from a pair of populations and apply it to the calculation of Solvency II risk capital.
Exploiting Network Technologies to Support Blended Learning and Collaborative Problem Solving in E-learning System

Educational benefits of online collaborative group work and blended learning have been confirmed in numerous studies for e-learning. Many of its advantages have been well cited including the development of skills of critical thinking, improved problem solving, knowledge discovery and construction of knowledge. However, a major challenge facing e-learning providers in the area of online collaboration and blended learning is the provisions of meaningful social experience and the emphasize of “live” mentor; need of virtual tutor that readily help learners in their difficulty; and sharing of learning materials that automatically accessed, modified and published among the groups. While this environment has been extensively studied and increasingly becoming an instructional approach, to the best of our knowledge, it has not yet been applied and exploited readily available network technologies to support online collaboration and blended learning. In this paper, we present an initial study of the use of social network media such as FB, YM, WLM, Skype to support social aspect of learning while TeamViewer and NetMeeting act as “live tutor” and virtual support mechanism as well as the use of DropBox for sharing learning materials to support online collaboration and blended learning.
Identity Verification of Individuals Based on Retinal Features Using Gabor Filters and SVM

Authentication reliability of individuals is a demanding service and growing in many areas, not only in the military barracks or police services but also in applications of community and civilian, such as financial transactions. In this paper, we propose a human verification method depends on extraction a set of retinal features points. Each set of feature points is representing landmarks in the tree of retinal vessel. Extraction and matching of the pattern based on Gabor filters and SVM are described. The validity of the proposed method is verified with experimental results obtained on three different commonly available databases, namely STARE, DRIVE and VARIA. We note that the proposed retinal verification method gives 92.6%, 100% and 98.2% recognition rates for the previous databases, respectively. Furthermore, for the authentication task, the proposed method gives a moderate accuracy of retinal vessel images from these databases.
Research on Optimal Strategy of HWSN Coverage Based on Force-Directed Differential Evolution Algorithm

A dynamic sensor deployment strategy for heterogeneous mobile wireless sensor networks is proposed, so-called virtual force-directed differential evolution algorithm (VFDE). To ensure efficient coverage of networks, VFDE combines the virtual force (VF) with differential evolution algorithm (DE), where the position vector of each population is updated according to not only the historical local and global optimal solutions but also the virtual forces of sensor nodes. The key motivation of this strategy is to use the virtual force to direct the updating of DE for improving the convergence speed, and DE is used to enhance the global searching ability. Simulation results show that VFDE has better performance on regional convergence and global searching than VF algorithm and DE algorithm, and it can implement dynamic heterogeneous mobile sensor deployment efficiently and rapidly.
Validation of UML Class Model through Finite-State Machine

The Unified Modeling Language (UML) is an independent to programming language which has a collection of modeling tools through which software engineers and researcher represent the complex research problems in the diagrammatic form. The various tools represent the static as well as the dynamic behavior of an object-oriented software system. The state chart diagram is a well known tool of UML which shows the dynamic behavior of states of an object-oriented system. The entire life of an object is represented by this tool. The state transformation of an object is depending on the three major components like transition function, action and possible inputs. The paths through which an object changes its state are determined by the state chart diagrams. These paths can be represented in the graphical form with the use of Finite State Machine (FSM). The graphical representation is very useful for determining the correctness of the diagram. In the present work, an approach to validate the UML class model through FSM is described with a creation of the transition table. For testing purpose, some test cases are generated to test the correctness of UML state chart diagram by taking a real case study of Life Insurance Corporation (LIC) of India. An approach to verify the correctness of UML diagram is presented.
MARGINAL ANALYSIS FOR CLUSTER-BASED CASE-CONTROL STUDIES

Cluster-based case-control design refers to a design where the sampling unit is a cluster and the sampling probability depends on the responses from individuals within the cluster. Data from a cluster-based case-control design arise in many prac tical applications. For example, in some epidemiologic genetic studies, due to the low prevalence of the disease of interest, families with more members having the disease are sampled with a higher probability. Current approaches for analyzing this type of data rely mainly on parametrically modeling the joint distribution of the responses within a cluster. In this paper, we develop a marginal approach to analyze data from cluster-based case control studies when the main interest is the mean structure of the association between exposures and outcomes and the correlation within cluster is considered nuisance. We specify a marginal regression model for an individual response given covariates and leave the correlation within the cluster unspecified. We establish the statistical properties for the proposed estimator and investigate its finite sample performance through simulation studies. We apply the proposed method to a data set from the Baltimore Eye Survey.
Channel Extension Uniting Information Theoretic Feature Extraction Based Multiclass Common Spatial Pattern Used in BCI

Common Spatial Pattern (CSP) has been widely used in decoding the spatial patterns of corresponding neuronal activities from electroencephalogram (EEG) signals in Brain-computer Interface (BCI), and has attained good performance in the discrimination of two-class motor imagery. However, for multiple classes of motor imagery, the efiect of discrimination is unsatisfactory. Information Theoretic Feature Extraction (ITFE) provides a method to choose Independent Components (ICs) that approximately maximizes mutual information of ICs and eliminates the need for heuristics in multiclass CSP. But the efiect of discrimination is also unsatisfactory in the classiflcation of multiclass EEG data with a small number of recording channels. To solve the problem, this paper proposes a method for extending channels, named Channel Extension (CE), through the time delay on the original signal and superimposition of the new signals on the spatial pattern, by which we extend channels without increasing electrodes. We pointed out the relationship between spatial patterns and the classiflcation accuracy, and gave a general rule to choose the spatial patterns. We also tested the best multiple of channel extension. The experimental results showed that the proposed method outperforms the original ITFE based CSP algorithm in terms of classiflcation accuracy.
Inverse kinematics: a review of existing techniques and introduction of a new fast iterative solver

Inverse Kinematics (IK) is defined as the problem of determining a set of appropriate joint configurations for which the end effectors move to desired positions as smoothly, rapidly, and as accurately as possible. During the last decades, several methods and techniques, sophisticated or heuristic, have been presented to produce fast and realistic solutions to the IK problem. However, most of the currently available methods suffer from high computational cost and production of unrealistic poses. This report reviews and compares the most popular IK methods regarding reliability, computational cost and conversion criteria, with a novel heuristic and iterative method, called Forward And Backward Reaching Inverse Kinematics (FABRIK). FABRIK avoids the use of rotational angles or matrices, and instead finds each joint position via locating a point on a line. Thus, it converges in fewer iterations, has low computational cost and produces realistic poses. Constraints can easily be incorporated within the FABRIK methodology and multiple chains with multiple end effectors are also easily supported.
Set-class surface analysis: a hierarchical multi-scale approach

This work presents a methodology for hierarchical multiscale set-class analysis of music pieces, based upon a systematic approach to the segmentation, description and representation stages. This is done by combining existing systematic approaches to segmentation and representation with a set-class description domain. The method benefits from the representation completeness, the compromise between generalisation and discrimination of the set-class spaces, and the access to hierarchical relations of analytical interest. A set of data structures are derived from the method, and exploited in varied music information retrieval applications.
Changes in the Volatile Composition of Virgin Olive Oil during Oxidation: Flavors and Off-Flavors

A thermoxidation process has been applied to extra-virgin olive oil to develop new knowledge on the evolution of the volatile compounds responsible for virgin olive oil flavor during oxidative deterioration. The initial volatiles (a total of 60), many of them responsible for the pleasant sensory characteristics of the oil and produced mainly through biochemical pathways, disappeared in the first hours, and the formation of off-flavors, produced through oxidative pathways, gradually increased. The main volatile compounds possibly responsible for off-flavors (51) were identified, and their evolution during the oxidative process was studied. The fatty acids content was determined during the process. Unsaturated fatty acids were found to be the main precursors of the volatile compounds found in oxidized samples. The early measurement of nonanal (which was not detected at all, or only at trace levels, in extra-virgin olive oil samples) could be an appropriate method to detect the beginning of the oxidation. Th.
Distributed Predictive Control and Estimation for Systems with Information Structures Exemplified by Control of Smart Grid

Abstract In this paper, we investigate a distributed predictive control and estimation problem with information structured constraints. A system with information structures is defined as a system in which each subsystem collects spatio-temporally different information, and in general it is necessary to collect and process information in a distributed fashion. We propose a distributed predictive control and estimation laws with local information that each distributed subsystem obtains. The effectiveness of a proposed control and estimation law is demonstrated through a numerical simulation of smart grid.
Sequential adaptive designs in computer experiments for response surface model fit

Computer simulations have become increasingly popular as a method for studying physical processes that are difficult to study directly. These simulations are based on complex mathematical models that are believed to accurately describe the physical process. We consider the situation where these simulations take a long time to run (several hours or days) and hence can only be conducted a limited number of times. As a result, the inputs (design) at which to run the simulations must be chosen carefully. For the purpose of fitting a response surface to the output from these simulations, a variety of designs based on a fixed number of runs have been proposed. In this thesis, we consider sequential adaptive designs as an “efficient” alternative to fixed-point designs. We propose new adaptive design criteria based on a cross validation approach and on an expected improvement criterion, the latter inspired by a criterion originally proposed for global optimization. We compare these new designs with others in the literature in an empirical study and they shown to perform well. The issue of robustness for the proposed sequential adaptive designs is also addressed in this thesis. While we find that sequential adaptive designs are potentially more effective and efficient than fixed-point designs, issues such as numerical instability do arise. We address these concerns and also propose a diagnostic tool based on cross validation prediction error to improve the performance of sequential designs. We are also interested in the design of computer experiments where there are control variables and environmental (noise) variables. We extend the implementation of the proposed sequential designs to achieve a good fit of the unknown integrated response surface (i.e., the averaged response surface taken over the distributions of the environmental variables) using output from the simulations. The goal is to find an optimal choice of the control variables while taking into account the distributions of the noise variables.
SecP2PSIP: A Distributed Overlay Architecture for Secure P2PSIP

Although Peer-to-Peer (P2P) protocols are known to be highly insecure, they have proven significant improvements over traditional client-server models for ad-hoc deployments. P2PSIP is an architecture for deploying Session Initiation Protocol (SIP) services over a P2P network overlay, and thus leverages the limitations of client-server architectures for SIP. However, vanilla P2PSIP is highly insecure and is a well-known issue. Proposed secure versions of P2PSIP implements the security features mostly as a centralized operation or security through obfuscation, and imposes a complex deployment scenario, a single point of failure, limited usability, and a severe bottleneck. In this paper, we present SecP2PSIP ‐ a novel scheme for a distributed overlay architecture for secure P2PSIP. Our model allows an ad-hoc deployment with a scalable design for a completely distributed security infrastructure, which allows attack containment for at least k out of n nodes. The proposed overlay architecture is fully adaptable with standard off-the-shelf SIP clients. We present a detailed design, feature, architecture, and security analysis of SecP2PSIP to illustrate its applicability. We have also implemented a proof-of-concept prototype for the proposed SecP2PSIP framework. The paper presents simulation and experimental evaluation for small to medium sized networks to validate the feasibility of the proposed scheme.
A new electronically tunable first-order all-pass filter using only three NMOS transistors and a capacitor

In this paper, a new first-order voltage-mode (VM) all-pass filter that consists of only three NMOS transistors and a capacitor is proposed. The resonance frequency of the proposed circuit can be tuned electronically by a control current. It has high input impedance; thus, it can be easily cascaded with other VM circuits. Furthermore, it does not require any critical passive component matching conditions. Nevertheless, it does not have low output impedance. Although a floating capacitor is used in the proposed circuit, it can be easily realized with today’s technologies. Ideal and nonideality analyses are performed for the proposed circuit. The behavior of the filter is verified by a number of SPICE simulations and an experimental test. The total harmonic distortion variations of the proposed circuit are found low enough.