Miracle-dz/UniFAR-FTUs huggingface dataset:
Blue–green–red LEDs based on InGaN quantum dots grown by plasma‐assisted molecular beam epitaxy

Self‐assembled InGaN quantum dots were grown in the Stranski–Krastanov mode by plasma‐assisted molecular beam epitaxy. The average dot height, diameter and density are 3 nm, 30 nm and 7 × 1010 cm–2, respectively. The dot density was found to decrease as the growth temperature increases. The cathodoluminescence emission peak of the InGaN/GaN multiple layer quantum dots (MQDs) was found to red shift 330 meV with respect to the emission peak of the uncapped single layer of InGaN QDs due to Quantum Confined Stark effect. Blue LEDs based on InGaN/GaN multiple quantum wells (MQWs) as well as green and red LEDs based on InGaN MQDs emitting at 440, 560 and 640 nm, respectively, were grown and fabricated. The electroluminescence peak positions of both the green and red InGaN MQD LEDs are shown to be more blue‐shifted with increasing injection current than that of the blue InGaN/GaN MQW LEDs. (© 2007 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
The Simulation Research of Non-parametric Regression for Short-Term Traffic Flow Forecasting

Short-term traffic flow forecasting play an important pole in urban traffic control and induction system. Non-Parametric Regression (NPR) is one perfect method for short-term traffic flow forecasting based on pattern recognition. At present time, the application research of NPR for short-term traffic flow forecasting is confined in small-scale fields and has less study of forecasting mechanism. This paper is trying to use the simulation measure to research the applicability of NPR in short-term traffic flow forecasting and study the forecasting principles by adjusting different system parameters. A typical road network is constructed as the study object in this paper. The forecasting problems of NPR are studied based on it. The data pre-processing of principal component analysis and cluster analysis are used for better forecasting results. Other network structures have only different simulation parameters with the presented network.
Magnet Retained Cheek Plumper in Complete Denture Esthetics: A Case Report

This clinical report describes a technique to improve support for sunken cheeks using magnet retained detachable acrylic cheek plumpers. The new generation of magnets with improved technology provides sufficient denture retention for clinical application. However, further follow-up may be necessary to ascertain the long-term usefulness of the magnet-retained prosthesis, because of corrosion and further loss of magnetism.
Overview of models and methods for measuring economic agent’s expectations

Modern economic theory recognizes that the main difference between economics and natural sciences lies in the forward-looking decisions made by economic agents. Expectations play a key role in every segment of macroeconomics. In consumption theory the paradigm life-cycle and permanent income approaches stress the role of expected future incomes. In investment decisions present-value calculations are conditional on expected future prices and sales. Asset prices (equity prices, interest rates, and exchange rates) also clearly depend on expected future prices. Today we can distinct two major expectation paradigms and concepts, where the second became the mainstream approach for further development of expectation examination:
Automatic Programming : Towards Three ApproachesPierre

The prospects of inductive logic programming (ILP) with respect to automatic programming (program synthesis) are discussed. We argue that logic program synthesis from incomplete information is but a niche of ILP, and study consequences of this statement. Then, three approaches are described: schema-driven synthesis of logic programs from incomplete speciications, the role of transformation techniques in ILP, and interactive assumption-based inductive learning.
Towards a complete SCM ontology: the case of ontologising RosettaNet

This paper presents a methodology to derive a Supply Chain Management Ontology based on the RosettaNet specification framework. A prototype to mechanically derive a core ontology spanning all new Partner Interface Processes in the RosettaNet framework is developed and its algorithms to reconcile the ontology structure and to generate a proper subsumption hierarchy are presented. We further present how we designed and referenced outer layer ontologies we analysed to be required to resolve the remaining disparities in the core ontology. The resulting ontology framework enables to more easily deal with different message structures in dynamic Business-to-Business collaborations and thus ensures a better interoperability for the partners involved.
Human motion retargeting with trajectory constraints

The captured motion data can be utilized to produce kinds of animation for different characters. We propose a method to represent simulated motion, especially exaggerative motion by filtering the joint trajectories. The motion style is changed by shrinking or squashing the corresponding 3D trajectories. The similar but exaggerative motion with articulated constraints is generated by the inverse kinematic method. The motion of different characters can be combined to generate interactive human animation. Our method can also be utilized for motion segmentation.
A theoretical comparison between integrated and realized volatility

In this paper we provide both qualitative and quantitative measures of the precision of measuring integrated volatility by realized volatility for a fixed frequency of observation. We start by characterizing for a general diffusion the difference between realized and integrated volatility for a given frequency of observation. Then we compute the mean and variance of this noise and the correlation between the noise and the integrated volatility in the Eigenfunction Stochastic Volatility model of Meddahi (2001a). This model has as special cases log-normal, affine and GARCH diffusion models. Using previous empirical results, we show that the noise is substantial compared with the unconditional mean and variance of integrated volatility, even if one employs five-minute returns. We also propose a simple approach to capture the information about integrated volatility contained in the returns through the leverage effect. We show that in practice, the leverage effect does not matter. Copyright © 2002 John Wiley & Sons, Ltd.
Resilience as a Political Ideal

Abstract “Resilience” is booming. No longer a mere metaphor or abstract reference to dispositional properties, the resilience of communities or social-ecological systems is increasingly grounded in specific first-order properties. Consequently, resilience now constitutes a contentful and achievable partial conception of a good society. Yet political philosophers have taken little notice. The current article first discerns within recent social-scientific literature a set of attainable and measurable first-order properties that constitute “community resilience” or “ecological resilience. ” Then, specifying “resilience” as the resilience of high-HDI democratic societies to environmental disasters, infrastructure failures, and economic collapse, the current article argues from within a liberal framework that both “classical” (Lockean) and “egalitarian” (Rawlsian) liberals must treat resilience so understood as a necessary condition of legitimacy and justice; and this conclusion forces an overhaul of these liberal theories. Taking community resilience seriously requires both methodological and substantive revisions of the liberal enterprise.
Improvement of Fetal Electrocardiogram Extraction by Application of Fuzzy Adaptive Resonance Theory to Adaptive Neural Fuzzy System

non-invasive fetal electrocardiogram (FECG) signal extraction from signals recorded at abdominal area of mother is a challenging problem for the biomedical and signal processing communities. In this paper, we improve the FECG extraction approaches which consist to find the relation-ships between the cardiac potentials generated at the heart level of mother and the potentials recorded on the abdominal area. We used an efficient signal processing method combining a hybrid learning algorithm based on the fuzzy adaptive resonance theory and the hybrid soft computing technique called Adaptive Neuro Fuzzy Inference System (ANFIS) trained with modified Particle Swarm Optimization (PSO) endowed with an initialization strategy to adjust the antecedent parameters of fuzzy rules. We implemented our algorithm and other algorithm on simulated signals, and we found that the proposed ANFIS with hybrid learning algorithm achieves superior performance in learning accuracy and allowed yielding best processing results to extract the FECG signal.
On the power and limitations of strictness analysis

Strictness analysis is an important technique for optimization of lazy functional languages. It is well known that all strictness analysis methods are <italic>incomplete</italic>, i.e., fail to report some strictness properties. In this paper, we provide a precise and formal characterization of the loss of information that leads to this incompletenss. Specifically, we establish the following characterization theorem for Mycroft's strictness analysis method and a generalization of this method, called <italic>ee-analysis</italic>, that reasons about exhaustive evaluation in nonflat domains: <italic>Mycroft's method will deduce a strictness property for program P iff the property is independent of any constant appearing in any evaluation of P.</italic> To prove this, we specify a small set of equations, called <italic>E-axioms</italic>, that capture the information loss in Mycroft's method and develop a new proof technique called <italic>E-rewriting</italic>. <italic>E</italic>-rewriting extends the standard notion of rewriting to permit the use of reductions using <italic>E</italic>-axioms interspersed with standard reduction steps. <italic>E</italic>-axioms are a syntactic characterization of information loss and <italic>E</italic>-rewriting provides and <italic>algorithm-independent</italic> proof technique for characterizing the power of analysis methods. It can be used to answer questions on completeness and incompleteness of Mycroft's method on certain natural classes of programs. Finally, the techniques developed in this paper provide a general principle for establishing similar results for other analysis methods such as those based on abstract interpretation. As a demonstration of the generality of our technique, we give a characterization theorem for another variation of Mycroft's method called <italic>dd</italic>-analysis.
The Main and Interaction Effects of Day-care Experiences and Maternal Parenting Behavior on Preschoolers' Problem Behaviors

본 연구의 목적은 보육경험과 어머니의 양육행동이 유아의 문제행동에 미치는 주효과와 상호작용 효 과를 살펴보는 것이다. 만 3-5세 유아 248명(남아: 112명, 여아: 136명)과 그들의 어머니가 본 연구에 참여하였으며, 어머니들은 보육경험(보육 시작 시기, 1일 평균 보육시간, 주당 평균 보육일, 보육기관 변경횟수), 양육행동, 유아의 문제행동에 관한 문항들이 포함된 질문지에 응답하였다. 본 연구의 주 요결과는 다음과 같다. 첫째, 보육경험 중 보육기관 변경횟수가 잦을수록 유아는 공격성을 더 보였 다. 둘째, 어머니의 양육행동 중 논리적 설명, 강압, 방임이 유아의 문제행동에 영향을 미쳤다. 마지막 으로 보육경험과 어머니의 양육행동은 상호작용하여 유아의 문제행동에 영향을 미쳤다. 이러한 결과 는 보육경험이 모든 유아에게 동일한 영향을 미치는 것이 아니며, 어머니의 양육행동에 따라 보육경 험의 영향에 차이가 있을 수 있음을 제안한다.
Image forgery detection using Markov features in undecimated wavelet transform

Image forgery has become common due to the availability of high-quality image editing softwares. For detecting image forgery there is a need to have important features of the image. For obtaining the image features we need a suitable transform. One of the important and commonly used transform is discrete wavelet transform that can provide spatial and frequency related information of a signal. However, it provides ambiguous information due to its shift variant property. This ambiguity can be overcome using the UWT due to its shift invariance property. In forgery, some operations are applied to an image at different locations. For instance same type of details at different locations, the UWT provides the output of same nature, whereas the DWT doesn't. Due to this property, the features extracted in undecimated wavelet transform (UWT) domain provide better results in many applications like denoising, change detection, etc. In this paper, image features are extracted using the Markov model after transforming it into UWT domain. To evaluate the performance CASIA v1.0, Columbia Color and DSO-1 databases are used. The support vector machine with the linear kernel applied to separate the forged and pristine images. We experimentally obtain better results using the UWT transform as compared to the DWT transform on all these three databases.
Mutual information-based image registration for remote sensing data

Registration is a fundamental operation in image processing to align images taken at different times, from different sensors or from different viewing angles. Automatic image registration procedures are gaining importance to efficiently register the large volumes of remote sensing data available these days. In this Letter, we investigate an automated mutual information-based registration technique for remote sensing data. The performance of a number of interpolation algorithms to compute mutual information for registration of multi-sensor and multi-resolution Landsat TM, Radarsat SAR and IRS PAN images is evaluated.
Optimized coordinated precoding in multicell MIMO wireless systems

This paper is concerned with downlink transmissions in coordinated multiple-input multi-output (MIMO) multicell wireless networks. The design of the transmit precoding matrices in this paper aims at the maximin optimization of user rates subject to realistic power constraints at the transmitters. This problem is a nonconvex objective function subject to convex constraints with respect to high-dimensional matrix variables. Up to date, a closed-form solution has not been properly proposed. Nevertheless, we show the objective functions can be analytically represented by d.c. (difference of two convex) functions. A simple but very efficient d.c. iterative algorithm (DCI) with guaranteed convergence is shown to locate the approximately optimal matrix solutions with the help of only a few converging semi-definite programs (SDP).
Joint Power Allocation and Interference Suppression Techniques for Cooperative CDMA Systems

This paper presents joint power allocation and interference mitigation techniques for the downlink of CDMA systems which employ multiple relays and the amplify and forward cooperation strategy. We propose a joint constrained optimization framework that considers the allocation of power levels across the relays subject to individual power constraints and the design of linear receivers for interference suppression. We derive constrained minimum mean-squared error (MMSE) expressions for the parameter vectors that determine the optimal power levels across the relays and the linear receivers. In order to solve the proposed optimization problem efficiently, we develop a joint adaptive power allocation and interference suppression stochastic gradient (SG) algorithm. The proposed SG algorithm mitigates the interference by adjusting the power levels across the relays and estimating the parameters of the linear receiver. An SG channel estimation algorithm is also derived to determine the coefficients of the channels across the base station, the relays and the destination terminal. The results of simulations show that the proposed methods obtain significant gains in performance and capacity over non-cooperative systems and cooperative schemes with equal power allocation.
LONG STRANGE SEGMENTS OF A STOCHASTIC PROCESS AND LONGRANGE

We study long strange intervals in a linear stationary stochastic process with regularly varying tails. It turns out that the length of the longest strange interval grows, as a function of the sample size, at diierent rates in diierent parts of the parameter space. We argue that this phenomenon may be viewed in a fruitful way as a phase transition between short and long range dependence. We prove a limit theorem that may form a basis for statistical detection of long range dependence.
Control of Hand Prostheses Using Peripheral Information

Several efforts have been carried out to enhance dexterous hand prosthesis control by impaired individuals. Choosing which voluntary signal to use for control purposes is a critical element to achieve this goal. This review presents and discusses the recent results achieved by using electromyographic signals, recorded either with surface (sEMG) or intramuscular (iEMG) electrodes, and electroneurographic (ENG) signals. The potential benefits and shortcomings of the different approaches are described with a particular attention to the definition of all the steps required to achieve an effective hand prosthesis control in the different cases. Finally, a possible roadmap in the field is also presented.
Complete genome of Pieris rapae, a resilient alien, a cabbage pest, and a source of anti-cancer proteins

The Small Cabbage White ( Pieris rapae) is originally a Eurasian butterfly. Being accidentally introduced into North America, Australia, and New Zealand a century or more ago, it spread throughout the continents and rapidly established as one of the most abundant butterfly species. Although it is a serious pest of cabbage and other mustard family plants with its caterpillars reducing crops to stems, it is also a source of pierisin, a protein unique to the Whites that shows cytotoxicity to cancer cells. To better understand the unusual biology of this omnipresent agriculturally and medically important butterfly, we sequenced and annotated the complete genome from USA specimens. At 246 Mbp, it is among the smallest Lepidoptera genomes reported to date. While 1.5% positions in the genome are heterozygous, they are distributed highly non-randomly along the scaffolds, and nearly 20% of longer than 1000 base-pair segments are SNP-free (median length: 38000 bp). Computational simulations of population evolutionary history suggest that American populations started from a very small number of introduced individuals, possibly a single fertilized female, which is in agreement with historical literature. Comparison to other Lepidoptera genomes reveals several unique families of proteins that may contribute to the unusual resilience of Pieris. The nitrile-specifier proteins divert the plant defense chemicals to non-toxic products. The apoptosis-inducing pierisins could offer a defense mechanism against parasitic wasps. While only two pierisins from Pieris rapae were characterized before, the genome sequence revealed eight, offering additional candidates as anti-cancer drugs. The reference genome we obtained lays the foundation for future studies of the Cabbage White and other Pieridae species.
Logging versus fire: how does disturbance type influence the abundance of Pinus strobus regeneration?

Eastern white pine (Pinus strobus) has decreased in abundance over the past century throughout the Great Lakes Region of North America, but the relative constraints placed on recruitment under contrasting disturbance regimes are not well understood. The objectives of this study were to determine the extent to which white pine could invade areas recently disturbed by fire or logging (within 10–28 years), and assess the relative limitations placed on recruitment by seed supply, microsite habitat, and competition. We compared white pine regeneration on 61 sites disturbed by fire or logging that were adjacent to intact mature stands that provided a seed source. White pine seedling and sapling densities declined with increasing distance from a seed source, and the rate of decrease was determined by the interaction between seed supply and variation in number and quality of safe sites. For a given combination of seed source and site, white pine seedlings were three times more abundant on burned than logged sites. White pine seedlings grew into the sapling size class more often on burned than logged sites due to lower shrub cover on burned sites. At 25 years after disturbance, regeneration densities of white pine sufficient to achieve eventual future dominance occurred up to 80 m and 20 m from the edge of mature white pine stands after fire and logging, respectively. To attain a similar level of white pine stocking after disturbance, three to four times as many patches of mature white pine need to be left after logging than after fire.
Replacing Mechanical Turkers? Challenges in the Evaluation of Models with Semantic Properties

Some machine-learning algorithms offer more than just predictive power. For example, algorithms provide additional insight into the underlying data. Examples of these algorithms are topic modeling algorithms such as Latent Dirichlet Allocation (LDA) [Blei et al. 2003], whose topics are often inspected as part of the analysis that many researchers perform on their data. Recently, deep learning algorithms such as word embedding algorithms like Word2Vec [Mikolov et al. 2013] have produced models with semantic properties. These algorithms are immensely useful; they tell us something about the environment from which they generate their predictions. One pressing challenge is how to evaluate the quality of the semantic information produced by these algorithms. When we employ algorithms for their semantic properties, it is important that these properties can be understood by a human. Currently, there are no established approaches to carry out this evaluation automatically. This evaluation (if done at all) is usually carried out via user studies. While this type of evaluation is sound, it is expensive from the perspective of both time and cost. It takes a great deal of time to recruit crowdsourced workers to complete tasks on crowdsourcing sites. This adds a huge amount of time to the research process. Furthermore, crowdsourced workers do not work for free. Each individual task costs real currency that could be spent on other parts of the research endeavor. Both the time and financial cost associated with these crowdsourced experiments mean that these types of experiments are difficult to perform. They greatly reduce the probability that future researchers will be able to reproduce these experiments.
The lipid composition of autophagic vacuoles regulates expression of multilamellar bodies

Multilamellar bodies (MLBs) are responsible for surfactant secretion in type II alveolar cells but also accumulate in other cell types under pathological conditions, including cancer and lysosomal storage diseases such as Niemann-Pick C (NPC), a congenital disease where defective cholesterol transport leads to its accumulation in lysosomes. Mv1Lu type II alveolar cells transfected with Golgi β1,6 N-acetylglucosaminyltransferase V (Mgat5), enhancing the polylactosamine content of complex-type N-glycans, exhibit stable expression of MLBs whose formation requires lysosomal proteolysis within dense autophagic vacuoles. MLBs of Mgat5-transfected Mv1Lu cells are rich in phospholipids and have low levels of cholesterol. In Mv1Lu cells treated with the NPC-mimicking drug U18666A, cholesterol-rich MLBs accumulate independently of both Mgat5 expression and lysosomal proteolysis. Inhibition of autophagy by blocking the PI 3-kinase pathway with 3-methyladenine prevents MLB formation and results in the accumulation of non-lamellar, acidic lysosomal vacuoles. Treatment with 3-methyladenine inhibited the accumulation of monodansylcadaverine, a phospholipid-specific marker for autophagic vacuoles, but did not block endocytic access to the lysosomal vacuoles. Induction of autophagy via serum starvation resulted in an increased size of cholesterol-rich MLBs. Although expression of MLBs in the Mv1Lu cell line can be induced by modulating lysosomal cholesterol or protein glycosylation, an autophagic contribution of phospholipids is critical for the formation of concentric membrane lamellae within late lysosomal organelles.