Miracle-dz/UniFAR-FTUs huggingface dataset:
Subskills associated with spelling ability in children with and without autism spectrum disorders

Background and aims Effective literacy instruction demands a clear understanding of the subskills that underpin children’s reading and writing abilities. Some previous research on reading has questioned whether the same subskills support literacy acquisition for typically developing children and children with autism spectrum disorders. This study examined the subskills associated with spelling ability in a group of 20 children with ASD aged 5–12 years (ASD group). A group of 20 typically developing children matched for age and word spelling accuracy (TD group) provided comparative data. Methods Participants completed standardised assessments of vocabulary, phonological awareness, letter knowledge and word spelling. Errors produced in response to the word spelling assessment were analysed for evidence of phonological awareness. In addition, all spelling attempts were analysed for evidence of phonological, orthographic, and morphological awareness, ‘linguistic awareness’, using the Computerised Spelling Sensitivity System. Results Correlation and regression analyses showed statistically significant relationships between phonological awareness and word spelling accuracy for children in the ASD and TD groups. Spelling errors produced by both groups contained evidence of phonological awareness. Analysis of all spelling attempts showed that the overall level of linguistic awareness encoded by children in the ASD and TD groups was not significantly different. Conclusions These findings provide evidence that phonological awareness and other subskills support spelling in children with autism spectrum disorders as they do in typically developing children. Implications The similar spelling profiles exhibited by children with autism spectrum disorders and their typically developing peers suggest that these populations may benefit from literacy instruction that targets the same underpinning subskills.
Unsupervised Pixel Classification in Satellite Imagery Using Multiobjective Fuzzy Clustering Combined With SVM Classifier

The problem of unsupervised classification of a satellite image in a number of homogeneous regions can be viewed as the task of clustering the pixels in the intensity space. This paper proposes a novel approach that combines a recently proposed multiobjective fuzzy clustering scheme with support vector machine (SVM) classifier to yield improved solutions. The multiobjective technique is first used to produce a set of nondominated solutions. The nondominated set is then used to find some high-confidence points using a fuzzy voting technique. The SVM classifier is thereafter trained by these high-confidence points. Finally, the remaining points are classified using the trained classifier. Results demonstrating the effectiveness of the proposed technique are provided for numeric remote sensing data described in terms of feature vectors. Moreover, two remotely sensed images of Bombay and Calcutta cities have been classified using the proposed technique to establish its utility.
TURBULENT VORTEX SHEDDING FROM TRIANGLE CYLINDER USING THE TURBULENT BODY FORCE POTENTIAL MODEL

Numerical simulation of the turbulent flow around a triangular cylinder at a Reynolds number of 45,000 is presented in this paper. Both steady and unsteady vortexshedding results are presented. A body force potential model is used to model the turbulent motion. This approach is able to model non-equilibrium turbulence accurately at a cost and complexity comparable to k-e models. The numerical method used in this calculation is an unstructured staggered mesh scheme. The property that this method conserves kinetic energy both locally within cells and globally makes it a good choice for performing turbulence modeling. For the unsteady solution, the Strouhal number and time-averaged velocity profile agree well with experiments. However, the steady solution that was obtained by using a symmetric boundary condition at the centerline leads to poor predictions of the time-averaged mean velocity profile.
Experimentally Validated Dynamic Fault Model for PMSM with Stator Winding Inter-Turn Fault

In this paper a simple dynamic model for a PMSM with inter-turn winding fault is derived. FEM is used for parameter determination of the machine with inter-turn fault. This fault model is used to study the behavior of the machine with inter-turn fault. The experimental test is conducted to validate the fault model for different levels of fault severity. The comparison of the results obtained by the model simulation and experimental tests allowed verifying the precision of the proposed dynamic model. This fault model is used to study of the PMSM under various fault conditions and severity and seems to be well adapted for PM motors health monitoring and inter-turn fault diagnosis.
Coupling EA and high-level metrics for the automatic generation of test blocks for peripheral cores

Test of peripheral modules has not been deeply investigated by the research community. When embedded in a system on chip, however, peripherals pose accessibility problems that may make traditional test approaches ineffective. In this paper an evolutionary methodology, based upon coverage metrics at high-level, is described to automatically generate test sets for peripheral modules in a SoC. A general-purpose evolutionary tool, able to cultivate composite individuals, has been developed and isused for the test set generation. This tool is described and its basic concepts explained. The method compares favorably with results obtained by hand.
Comparing fault-proneness estimation models

Over the last, years, software quality has become one of the most important requirements in the development of systems. Fault-proneness estimation could play a key role in quality control of software products. In this area, much effort has been spent in defining metrics and identifying models for system assessment. Using this metrics to assess which parts of the system are more fault-proneness is of primary importance. This paper reports a research study begun with the analysis of more than 100 metrics and aimed at producing suitable models for fault-proneness estimation and prediction of software modules/files. The objective has been to find a compromise between the fault-proneness estimation rate and the size of the estimation model in terms of number of metrics used in the model itself. To this end, two different methodologies have been used, compared, and some synergies exploited. The methodologies were the logistic regression and the discriminant analyses. The corresponding models produced for fault-proneness estimation and prediction have been based on metrics addressing different aspects of computer programming. The comparison has produced satisfactory results in terms of fault-proneness prediction. The produced models have been cross validated by using data sets derived from source codes provided by two application scenarios.
A Neural network based approach for modeling of severity of defects in function based software systems

There is lot of work done in prediction of the fault proneness of the software systems. But, it is the severity of the faults that is more important than number of faults existing in the developed system as the major faults matters most for a developer and those major faults needs immediate attention. As, Neural networks, which have been already applied in software engineering applications to build reliability growth models predict the gross change or reusability metrics. Neural networks are non-linear sophisticated modeling techniques that are able to model complex functions. Neural network techniques are used when exact nature of input and outputs is not known. A key feature is that they learn the relationship between input and output through training. In this paper, five Neural Network Based techniques are explored and comparative analysis is performed for the modeling of severity of faults present in function based software systems. The NASA's public domain defect dataset is used for the modeling. The comparison of different algorithms is made on the basis of Mean Absolute Error, Root Mean Square Error and Accuracy Values. It is concluded that out of the five neural network based techniques Resilient Backpropagation algorithm based Neural Network is the best for modeling of the software components into different level of severity of the faults. Hence, the proposed algorithm can be used to identify modules that have major faults and require immediate attention.
130-320-GHz CMOS Harmonic Down-Converters Around and Above the Cutoff Frequency

We present an analytical model, design, and measurement results on fundamental, harmonic, and subharmonic down conversion mixing approaches in 65-nm CMOS around and above the transistor cutoff frequency fmax, targeting submillimeter-wave operation. Analytical expressions for the mixing approaches are derived and compared with the simulation and measurement results. To investigate how to improve the performance of a passive mixer around and beyond fmax, the relation between local oscillator (LO) harmonics (including both the harmonic amplitude and phase), mixer gate bias, and conversion gain is studied. To demonstrate mixing approaches, we present integrated 160- and 280-GHz down-converters with integrated LO manufactured in 65-nm CMOS process with fmax= 210 GHz. The LO in both down-converters is based on exploiting higher harmonics of a differential Colpitts topology voltage-controlled oscillator (VCO). Passive conversion losses of 22 and 25.5 dB are obtained over the 150-180-GHz and the 230-290-GHz frequency range, respectively. Two additional versions of the down converter at 170 and 280 GHz, respectively, without integrated VCO are also presented to examine the mixer performance in the 110-325-GHz range.
Adaptive synchronization of chaotic systems via T-S fuzzy model

This paper is concerned with the adaptive synchronization problem of chaotic systems with time delay. The Takagi and Sugeno (T-S) fuzzy model is utilized to represent the chaotic systems. Based on parallel distributed compensation (PDC) scheme and Lyapunov stability theorem, an adaptive controller is proposed to synchronize the master-slave chaotic systems. Numerical simulations are given to demonstrate the effectiveness of the proposed method.
Natural Language Techniques for Model-driven Semantic Constraint Extraction

This report describes a model-driven approach to natural language understanding (NLU) in which the “meaning” of natural language queries is extracted based on a domain model composed of a set of concepts and relations specified in the system’s domain. The extracted meaning is represented as a set of semantic constraints that describe concept instances and their relations. This representation can easily be translated into a form usable for the system, e.g. an SQL-query or a dialogue move. The method has been tested on a dialogue system for browsing and searching in a database of recorded multimodal meetings, and experimental results as well as a discussion on the robustification of the approach are provided.
2 0 N ov 2 00 7 Volumes of Space as Subsystems

As a novel approach with possible relevance to semiclassica l gravity, we propose to define regions of space as quantum subsystems. After recalling how to divid e a generic quantum system into “parts”, we apply this idea to a free scalar field in Minkowski pace and we compare two different localization schemes. The first scheme is the standard one, i nduced by the local relativistic fields; the alternative scheme that we consider is the one induced by the Newton-Wigner operators. If degrees of freedom are divided according to the latter, th e Hamiltonian of the field exhibits a certain amount of non-locality. Moreover, when a region of space is cut off from the rest according to the Newton-Wigner scheme, the geometric entro py is finite and exhibits a sensible thermodynamic behaviour.
Population and Phase Coherence during the Growth of an Elongated Bose-Einstein Condensate

We study the growth of an elongated phase-fluctuating condensate from a nonequilibrium thermal cloud in the axially hydrodynamic regime, obtained by shock cooling. Quantitative measurements using momentum Bragg spectroscopy reveal the evolution of the phase coherence as the condensate grows to equilibrium. We find no delay between the population growth and the development of the phase coherence. We also compare the growth of the condensate with numerical simulations based on kinetic theory, revealing quantitative agreement except for an unexplained time delay.
Some simple nonlinear PDE's without solutions

where Ω is a smooth bounded domain in R , N ≥ 3. If a(x) ∈ L(Ω) and p > N/2, then for any f ∈ L(Ω) with ‖f‖p small, problem (0.1) has a unique small solution u in W (Ω). This is an easy consequence of the Inverse Function Theorem applied to F (u) = −∆u − a(x)u which maps X = W (Ω) ∩W 1,p 0 (Ω) into Y = L(Ω) (recall that W (Ω) ⊂ L∞(Ω) by the Sobolev imbedding theorem), since its differential at 0, DF (0) = −∆ is bijective. As a special case suppose a(x) = |x|−α in a domain Ω containing 0, with 0 < α < 2. Then for any small constant c the problem
A Feasibility Trial of Power Up: Smartphone App to Support Patient Activation and Shared Decision Making for Mental Health in Young People

Background Digital tools have the potential to support patient activation and shared decision making in the face of increasing levels of mental health problems in young people. There is a need for feasibility trials of digital interventions to determine the usage and acceptability of interventions. In addition, there is a need to determine the ability to recruit and retain research participants to plan rigorous effectiveness trials and, therefore, develop evidence-based recommendations for practice. Objective This study aimed to determine the feasibility of undertaking a cluster randomized controlled trial to test the effectiveness of a smartphone app, Power Up, co-designed with young people to support patient activation and shared decision making for mental health. Methods Overall, 270 young people were screened for participation and 52.5% (142/270) were recruited and completed baseline measures across 8 specialist child mental health services (n=62, mean age 14.66 (SD 1.99) year; 52% [32/62] female) and 2 mainstream secondary schools (n=80; mean age 16.88 [SD 0.68] years; 46% [37/80] female). Young people received Power Up in addition to management as usual or received management as usual only. Posttrial interviews were conducted with 11 young people from the intervention arms (specialist services n=6; schools n=5). Results Usage data showed that there were an estimated 50 (out of 64) users of Power Up in the intervention arms. Findings from the interviews indicated that young people found Power Up to be acceptable. Young people reported (1) their motivation for use of Power Up, (2) the impact of use, and (3) barriers to use. Out of the 142 recruited participants, 45.0% (64/142) completed follow-up measures, and the approaches to increase retention agreed by the steering group are discussed. Conclusions The findings of this study indicate that the app is acceptable, and it is feasible to examine the effectiveness of Power Up in a prospective cluster randomized controlled trial. Trial Registration ClinicalTrials.gov NCT02552797; https://clinicaltrials.gov/ct2/show/NCT02552797 (Archived by WebCite at http://www.webcitation.org/6td6MINP0)
Effect of intrahippocampal dexamethasone on the levels of amino acid transmitters and neuronal excitability

Direct effect of type-II corticosteroid receptor agonist dexamethasone on extracellular amino acid levels and neuronal excitability in the hippocampus was studied by simultaneous application of in vivo microdialysis and recording hippocampal evoked responses in freely moving male rats. Microdialysis probes and hippocampal recording electrodes were implanted to the CA1-CA3 regions of dorsal hippocampus. Local dexamethasone infusion via microdialysis resulted in a transient increase in glutamate level at 30 min, while glutamine decreased by 30-40% throughout the 180-min sampling period. Taurine increased by 50% and remained elevated up to 180 min. No significant changes were detected in extracellular concentration of asparagine, arginine, glycine, threonine, alanine and serine. In contrast, dexamethasone infusion to the striatum had no effect on the extracellular levels of amino acid transmitters. Effect of dexamethasone injected via microdialysis on the neural activity elicited by perforant path stimulation was a decrease in population spikes after 60 min starting dexamethasone infusion. Steroid effect on neural excitability was reversible. Our data indicate that local infusion of type-II receptor agonist dexamethasone has a complex effect in the hippocampus, starts with a change in extracellular glutamate and glutamine concentration and followed by a reduced synaptic excitability.
The properties and deposition process of GaN films grown by reactive sputtering at low temperatures

Polycrystalline gallium nitride films, 100nmto1μm thick, were deposited under a range of conditions. Substrate electrode temperatures during sputtering were varied from room temperature to 450°C, the pressure from 0.15to6. 0Pa, the nitrogen fraction of the deposition atmosphere from 10% to 100% and the target bias from −400to−1800V. The deposition rates as functions of these conditions are in the range 0.5–25nm∕min. The growth rate is considered to be controlled respectively by the thermally activated desorption from the substrate, changes in the mean free path and concentration of gas particles, differences between the sputter yields of Ga and GaN in Ar and N2, and changes in the ion current and sputter yields. The films are generally columnar, with the grain size increasing with film thickness. The most crystalline films were grown at mid range temperatures, low N2 concentrations, and low target biases, and the most disordered were grown at low pressures. The latter two cases suggest that decreasing the .
Multi-instance learning for skin biopsy image features recognition

In this paper, a multi-instance learning framework is introduced to solve the problem of skin biopsy image features recognition. Previously reported methods for skin surface images were mostly based on color features extraction. They are incapable to be directly applied to skin biopsy image features recognition because biopsy images are often dyed and have obvious inner structures with different textures. Therefore, we regard skin biopsy images as multi-instance samples, whose instances are regions or structures captured by applying Normalized Cut. Texture feature extraction methods are used to express each region as a vectorial expression. Then two multi-instance learning algorithms reported successful in various image retrieval tasks were applied. Nine features were manually selected as target features to evaluate the proposed method on a skin disease diagnosis datasets of 6579 biopsy images from 2010 to 2011. The result showed that the proposed method is effective and medically acceptable.
An evaluation of local features on satellite images

Satellite image matching is an important task when integrating information from different sensors, viewpoints or taken at different times. This paper presents an evaluation of local features on satellite images. Different blob, edge and corner based detectors have been tested against many variables and parameters. During the extraction process, the detected keypoints are sub-sampled in order to solve the high hardware requirements and the processing time problems with high resolution satellite data. The matching process showed variant performance for the detectors where SURF, FAST, GFTT and Harris detectors have recorded the best matching performance on optical images in terms of higher number of inliers, less running time and robustness to noise. On the other hand, SIFT detector has recorded better performance on radar images. The Experiments were done on different optical and radar images from Rapid-Eye, Pléiades, and TerraSAR-X satellite data for the area of Berlin Brandenburg airport.
A Price Theory of Multi-Sided Platforms

I develop a general theory of monopoly pricing of networks. Platforms use insulating tariffs to avoid coordination failure, implementing any desired allocation. Profit-maximization distorts in the spirit of Spence (1975) by internalizing only network externalities to marginal users. Thus the empirical and prescriptive content of the popular Rochet and Tirole (2006) model of two-sided markets turns on the nature of user heterogeneity. I propose a more plausible, yet equally tractable, model of heterogeneity in which users differ in their income or scale. My approach provides a general measure of market power and helps predict the effects of price regulation and mergers. JEL:
Noise Robust Adaptive Blind Channel Identification Using Spectral Constraints

A class of adaptive blind channel identification algorithms were proposed recently and were demonstrated to be able to successfully identify various types of channels when the observed signals are free from significant levels of measurement noise. In this paper, we provide a study of the effects of noise on these algorithms and show that they misconverge even at moderate values of SNR. We introduce a spectral constraint into the adaptation rule and show that the robustness to noise can be considerably improved. Simulation results are presented for the new algorithm, which demonstrate a significant performance improvement in terms of normalized projection misalignment
Music as a trigger of religious experience: What role does culture play?

Music and religion are linked in many ways. For example, music can trigger religious experiences, which has been a topic since the beginnings of the study of the psychology of religion. Whether this musical effect is culture-dependent, a pure neuropsychological phenomenon, or a combination of both remains empirically unanswered. This cross-cultural experiment among n = 84 Turks andn = 63 Germans shows that religious music can trigger religious experience but this is, at least partially, a culture-dependent experience. Moreover, certain kinds of religious music can fail to trigger a religious experience independent of culture, which can also underpin a neuropsychological effect of musical features on religious experience. Religious experience during music is strongly predicted by positive emotions that are felt during the musical experience. Future studies should be more interdisciplinary, focusing on the effect of certain musical features on the religious experience of individuals from different cultural backgrounds.
A FORMAL ONTOLOGICAL APPROACH TO IMPERFECTION IN GEOGRAPHIC INFORMATION Full paper

Traditional computational models of geographic phen omena offer no room for imperfection. Underlying t his tradition is the simplifying assumption that realit y s certain, crisp, unambiguous, independent of co ntext, and capable of quantitative representation. The paper will report on work, undertaken as part of a joint project between the universities of Keele and Leeds, which explicitly recognises that most geographic informat ion is intrinsically imperfect. Based on an ontology of i mperfection it is argued that the interpretation of imperfect geographic information is facilitated by reference to formal models of imperfection, in particular mul ti-valued logics. The development of Java software able to a ssist with a geodemographic retail site assessment application is used to illustrate the utility of a formal ontol ogical approach.