Miracle-dz/UniFAR-FTUs huggingface dataset:
Semiparametric estimation of weighted average derivatives

This paper studies the estimation of the density-weighted average derivative of a general regression function. Let y denote a dependent variable. x a vector of explanatory variables, g(x)=E(yvx) the true regression function and h(x) the marginal density of x. The density-weighted average derivative of the regression function is =Efh(x)(ag/8x)l. We present a computationally simple. N consistent, asymptotically normal estimator a of . , that relies on no functional form assumptions on g(x), h(x) or the joint distribution of (y.x). An estimator of the asymptotic variance of AN is N proposed. N provides a practical solution to the problem of estimating up to scale when g(x) can be written as g(x)=F(x'g). as with many models of limited dependent variables. aN also provides a solution to the problem of nonparametricaly testing linear first derivative constraints on g(x). The estimator AN is a sample anaiogue of the product-moment representation of the average derivative. that employs kernel estimates of the densitv of x. Extensions of classical U-statistic theorems are used to establish asymptotic normality of 6N. and a jackknifing procedure is used to remove asymptotic bias. The correctly-scaled weighted average Efh(x)ag/axj/EIh(x) is shown o be estimated by certain linear instrumental variable coefficients of y regressed on x. Issues in the estimation of more general weighted average derivatives are addressed. The relationship of the results to classical central limit theorems, as well as results on slow convergence rates of pointwise nonparametric estimators is discussed. SEMIPARAMETRIC ESTIMATION OF WEIGHTED AVERAGE DERIVATIVES bv J. L. Powell. J. H. Stock and T. M. Stoker
Oil, Disinflation, and Export Competitiveness: A Model of the "Dutch Disease"

This paper examines three possible sources of "de-industrialization" in an open economy : monetary disinflation, an increase in the international price of oil, and a domestic oil discovery. the analysis is conducted using a model which incorporates different speeds of adjustment in goods and asset markets ; domestic goods prices respond only sluggishly to excess demand while the exchange rate (and hence the price of imported goods) adjusts quickly. Monetary disinflation leads to reduce real balances, higher interest rates, and a lower nominal exchange rate. In the short-run this causes a real appreciation and a decline in domestic manufacturing output. Perhaps surprisingly, an increase in world oil prices can create similar effects even for a country which is a net exporter of oil. Although the direct effect of an oil price increase for such a country is an increase in the demand for the domestic manufacturing good, that effect may be swamped by real appreciation created by the increased demand for the home currency. This corresponds rather closely to the recent experiences of several oil and gas exporting countries, and is commonly referred to as the "Dutch-Disease". In our analysis, however, this is only a transitional phenomeon. Domestic oil discoveries, though necessarily finite in nature, generate permanent income effects in demand which last beyond the productive life of the new oil reserve. Initially, current income is above permanent income, leading to an improvement in the trade account ; this is eventually reversed when permanent income exceeds current income. A wide variety of output response patterns are possible.
A data hiding algorithm based on histogram re-quantization

Steganography, a technique of data hiding, is becoming more and more significant with expansion of the Internet communication. As a result, various steganographic algorithms have been proposed in recent years, for example, Ni et al. have carried out a lossless data-hiding algorithm based on the histogram modification. In order to reinforce more security and more data embedding capacity, this paper extends the Ni's algorithm with a random permutation and a histogram re-quantization. To begin with, applying a random permutation, the security is not easy to be broken by a brute-force attack. In addition, the embedding capacity is able to be strengthened approximately 3 times by adopting a histogram re-quantization. As to our approach, it is visually indistinguishable between a cover image and a stegoimage with a large embedding capacity.
E 3 PR : Efficient Privacy Preserving Prediction-based Routing in Mobile Delay Tolerant Networks

Message routing is one of the major challenges in Mobile Delay Tolerant Networks (MDTNs) due to frequent and long-term network partitions. A large number of routing protocols for MDTNs belong to the category of predictionbased routing protocols, which utilize the encounter probability of nodes to guide message forwarding. However, these prediction-based routing protocols compromise the privacy of the nodes by revealing their mobility patterns. In this paper, we propose a privacy preserving prediction-based routing protocol that forwards messages by comparing aggregated information about communities instead of individual nodes. Specifically, it compares the probability that at least one node in a community will encounter the destination node. We present theoretical security analyses as well as practical performance evaluations. Our simulations on a well established community-based mobility model demonstrate that our routing protocol has comparable performance to existing prediction-based protocols. Additionally, the community information is computed efficiently and independently of the routing protocol.
The Sisters in Science Program: Teaching the Art of Inquiry.

Sisters in Science, like many other programs aimed at fostering girls' interest and achievement in science, is designed to encourage more girls and women to prepare for the careers in math, science, and technology that will dominate the 21st century. One of the foci of Sisters in Science was on the role of teachers as decision-makers in promoting science literacy for all students. The overall goal was to familiarize teachers with reform initiatives in science education and to chart their progress as change agents of gender equity in the classroom. This paper reports results of year one and two on teachers' reflections and dialogue concerning their conception changes of teaching science and mathematics as they confront the issue of equitable practice. (Contains 59 references.) (Author/ASK) Reproductions supplied by EDRS are the best that can be made from the original document.
Towards a Model-Driven Approach for Mapping Requirements on AADL Architectures

Software architectural design is a key activity in the software development process. It is especially critical for embedded systems subject to specific non-functional requirements such as performance or reliability. However, the activity of designing an architecture fulfilling a set of requirements still remains largely informal, based onexperience with the support of some informal patterns and styles.The purpose of this paper is to explore how to build more systematically architectures for embedded systems using precise models both for the requirements and the architecture. A mapping is proposed between those models together with guidelines on how to inject functional and non-functional requirements at various stages. The process is illustrated and discussed using a cruise control system.
Research Progress of the Scale Invariant Feature Transform (SIFT) Descriptors

The SIFT (Scale Invariant Feature Transform) is a computer vision algorithm that is used to detect and describe the local image features. The SIFT features are robust to changes in illumination, noise, and minor changes in viewpoint. The SIFT features have been used object recognition, image retrieval and matching, and so on. The research of SIFT descriptors and improved SIFT descriptors is important. In this paper, we conducted in-depth research on research results of SIFT in recent years, summed up the thinking and properties of SIFT algorithm. Focused on analyzing SIFT algorithm's development and improvement strategy, and give SIFT’s future development and improvement trends.
Segmenting extended structures in radio astronomical images by filtering bright compact sources and using wavelets decomposition

The automatic segmentation of extended real structures (such as SNRs, HII regions, bow shocks, etc) in large surveys is a difficult task due to their morphological complexity and their wide variety in scale and surface brightness. With the aim of dealing with these issues we propose in this paper an automatic segmentation method based on applying wavelet decomposition in the residual thresholded images. The use of this strategy, instead of a wavelet decomposition on the original image, allows to avoid the artifacts produced by strong sources. Experimental results with radio images demonstrate that the proposed method successfully segments extended structures at different scales and therefore is suitable for further morphological analysis and object recognition processes.
Comparative methods for studying primate adaptation and allometry

A well-known comparative biologist was once asked by a field biologist whether the latter’s detailed and painstaking field study of orangutan behavior, carried out over many years, qualified as an example of the comparative method. “No,” replied the comparative biologist, “that’s an anecdote. ” The reply is somewhat harsh, as useful comparisons can be conducted both within and across species. The reply does emphasize, however, that analysis of patterns across species is fundamental to the study of adaptive evolution, particularly when variation needed to test hypotheses is present only at this interspecific level.1‐5 Examples in primatology include the occurrence of female sexual swellings in species with habitually multimale, rather than single-male, breeding systems,6,7 the relationship between polygynandrous mating and relatively large testes size,3,8 and the association between brain size and social group size.9 Thus, in many cases, interspecific variation is required to test adaptive hypotheses.10
Bricolage research in history education as a scholarly mixed-methods design

Research using a mixed-methods design is increasingly becoming the norm, crossing the myriad of educational fields of research, including history education. While commonly interpreted as a combining of qualitative and quantitative methods, mixed methods in history education can also extend to a bricolage approach, whereby the epistemological aspect of research is explicitly used to frame a study incorporating a combination of interdisciplinary methodologies and theoretical underpinnings. It extends beyond the often asserted binary of qualitative and quantitative research. In considering directions of qualitative research in the broad discipline area of education, the work of researchers such as Kincheloe (2005) and Denzin and Lincoln (2005) is used throughout this paper within a qualitative research context based on the work of Kincheloe and Tobin (2006). Adopting their approach of investigating the complexity of the lived world means placing research within a number of contexts. Research can be framed – from conceptualization to data gathering to analysis – in a range of contexts, appropriately matched between stage of research and underpinning theories. This paper reports on how bricolage can be used to frame research in history education.
An Assessment of the Air Force Weather Agency's Readiness for Knowledge Management Initiatives

Abstract : The successful implementation of knowledge management (KM) initiatives requires a level of commitment and preparedness by the organization that is commensurate with the expected results. An organization must be willing to allocate the necessary resources to ensure its readiness to undertake new KM projects. The purpose of this study is to review the Air Force Weather Agency's organizational culture and measure its propensity for accepting KM initiatives. The study uses theory-based constructs to evaluate and assess the organization's people, culture, climate, processes, and attitudes as they relate to KM. The research was conducted using semi-structured interviews to elicit respondents' views about KM practices. The study points toward a strong corollary relationship between the organizational environment and its readiness to embrace KM principles.
Creative industry clusters in Shanghai: a success story?

This paper examines the development of creative industry clusters in Shanghai. It looks at the cautious adoption of the creative industries agenda by the Chinese government and how Shanghai was to adopt this more positively. The paper also looks at the complex provenance of the creative clusters concept and how Shanghai focused more on its urban regeneration effects rather than its role as ‘industry base’. We try to show how the creative industries agenda, viewing this sector as advanced business services, allowed creative clusters to be linked to a powerful real estate model. However, the paper suggests that this undermined much of the functioning of creative clusters and uncoupled them from most their original intent, retaining mostly just the aesthetic appeal to a ‘creative class’. The paper ends by an examination of how these clusters might be repurposed as part of the adoption of a more holistic urban cultural economy approach.
Evolving Sets of Symbolic Classifiers into a Single Symbolic Classifier Using Genetic Algorithms

For a given data set, different learning algorithms typically provide different classifiers. Although it is possible to simply select the most successful classifier, the less successful classifiers could have potentially valuable information that may be wasted. This work proposes GAESC, an algorithm for evolving a set of classifiers into a single symbolic classifier using genetic algorithms. Individuals are formed by rules collected from symbolic classifiers and rules from association classification rules. Experimental results in three data sets from UCI show that GAESC outperforms the single symbolic classifiers in terms of classification error rate.
An improved symbolic aggregate approximation distance measure based on its statistical features

The challenges in efficient data representation and similarity measures on massive amounts of time series have enormous impact on many applications. This paper addresses an improvement on Symbolic Aggregate approXimation (SAX), is one of the efficient representations for time series mining. Because SAX represents its symbols by the average (mean) value of a segment with the assumption of Gaussian distribution, it is insufficient to serve the entire deterministic information and causes sometimes incorrect results in time series classification. In this work, SAX representation and distance measure is improved with the addition of another moment of the prior distribution, standard deviation; SAX_SD is proposed. We provide comprehensive analysis for the proposed SAX_SD and confirm both the highest classification accuracy and the highest dimensionality reduction ratio on University of California, Riverside (UCR) datasets in comparison to state of the art methods such as SAX, Extended SAX (ESAX) and SAX Trend Distance (SAX_TD).
Improving Face Verification Using Symmetric Transformation Improving Face Verification Using Symmetric Transformation

One of the major problem in face verification is to deal with a few number of images per person to train the system. A solution to that problem is to generate virtual samples from an unique image by doing simple geometric transformations such as translation, scale, rotation and vertical mirroring. In this paper, we propose to use a symmetric transformation to generate a new virtual sample. This symmetric virtual sample is obtained by computing the average between the original image and the vertical mirrored image. The face verification system is based on LDA feature extraction, successfully used in previous studies, and MLP for classification. Experiments were carried out on a difficult multi-modal database, namely BANCA. Results on this database show that our face verification system performs better that the state-of-the-art and also that the addition of the symmetric virtual sample improves the performance.
Endogenous dopamine can modulate inhibition of substantia nigra pars reticulata neurons elicited by GABA iontophoresis or striatal stimulation

Previous reports from this laboratory have described an ability of iontophoretically applied dopamine to attenuate the inhibitory effects of iontophoresed GABA on neurons of the substantia nigra pars reticulata. This finding raised the question of whether endogenous dopamine, released from dendrites of neighboring pars compacta dopamine neurons, might act as a neuromodulator which diminishes the inhibition of pars reticulata neurons evoked by either GABA iontophoresis or electrical stimulation of the striatonigral GABAergic pathway. Extracellular, single-unit activity of pars reticulata neurons was recorded in male rats anesthetized with chloral hydrate. In one set of studies, d-amphetamine, a drug reported to release dopamine from nigral dendrites, was administered intravenously (1.6 mg/kg) during regular, intermittent iontophoretic pulses of GABA. As had been previously observed with iontophoresed dopamine, i.v. amphetamine significantly lessened the inhibition of reticulata neurons produced by GABA application. This change was reflected by a decrease in GABA's inhibitory potency by 22% relative to the control level of inhibition achieved prior to amphetamine administration. Amphetamine caused no decreases in GABA's effectiveness, however, in animals that had previously received treatments that depleted or destroyed nigral dopamine stores, i.e., in rats pretreated with reserpine and alpha- methyl-p-tyrosine, or in rats with 6-hydroxydopamine lesions of the nigrostriatal dopamine pathway. In a second set of experiments, amphetamine or dopamine was delivered iontophoretically while monitoring the GABA-mediated (bicuculline-reversible) inhibition of reticulata neurons that can be elicited by striatal stimulation. (ABSTRACT TRUNCATED AT 250 WORDS)
Uncertainty in Artificial Intelligence Proceedings 2000 Marginalization in Composed Probabilistic Models

Composition of low-dimensional distribu­ tions, whose foundations were laid in the pa­ per published in the Proceedings of UAI'97 (Jirousek 1997), appeared to be an alterna­ tive apparatus to describe multidimensional probabilistic models. In contrast to Graphi­ cal Markov Models, which define multidimen­ sional distributions in a declarative way, this approach is rather procedural. Ordering of low-dimensional distributions into a proper sequence fully defines the respective compu­ tational procedure; therefore, a study of dif­ ferent types of generating sequences is one of the central problems in this field. Thus, it ap­ pears that an important role is played by spe­ cial sequences that are called perfect. Their main characterization theorems are presented in this paper. However, the main result of this paper is a solution to the problem of marginalization for general sequences. The main theorem describes a way to obtain a generating sequence that defines the model corresponding to the marginal of the distri­ bution defined by an arbitrary generating se­ quence. From this theorem the reader can see to what extent these computations are l o­ cal; i.e., the sequence consists of marginal dis­ tributions whose computation must be made by summing up over the values of the vari­ able eliminated (the paper deals with a finite model).
Fast free-form deformable registration via calculus of variations

In this paper, we present a fully automatic, fast and accurate deformable registration technique. This technique deals with free-form deformation. It minimizes an energy functional that combines both similarity and smoothness measures. By using calculus of variations, the minimization problem was represented as a set of nonlinear elliptic partial differential equations (PDEs). A Gauss–Seidel finite difference scheme is used to iteratively solve the PDE. The registration is refined by a multi-resolution approach. The whole process is fully automatic. It takes less than 3 min to register two three-dimensional (3D) image sets of size 256 × 256 × 61 using a single 933 MHz personal computer. Extensive experiments are presented. These experiments include simulations, phantom studies and clinical image studies. Experimental results show that our model and algorithm are suited for registration of temporal images of a deformable body. The registration of inspiration and expiration phases of the lung images shows that the method is able to deal with large deformations. When applied to the daily CT images of a prostate patient, the results show that registration based on iterative refinement of displacement field is appropriate to describe the local deformations in the prostate and the rectum. Similarity measures improved significantly after the registration. The target application of this paper is for radiotherapy treatment planning and evaluation that incorporates internal organ deformation throughout the course of radiation therapy. The registration method could also be equally applied in diagnostic radiology.
EASARD: Towards an efficient Energy-Aware Service and Route Discovery scheme in MANET

MANETs are highly dynamic and suffer from frequent and unpredictable changes in the network topology due to the fact that devices are mobile and operate with low battery power. Due to limited computing power, high mobility and the lack of a central coordinating entity, service discovery in MANETs has been an attractive area of research. However, proposed solutions are broadcast-based and result in overloading the constrained network. The issue addressed in this paper is therefore to provide a cross-layer Service Discovery in order to reduce energy consumption. In this work we propose an efficient Energy-Aware Service And Route Discovery scheme called EASARD. We extended the Zone Routing Protocol in order to encapsulate service information in its routing messages. We present a performance comparison of EASARD versus another integrated protocol in terms of energy consumption. We evaluate how the different approaches affect the energy usage of mobile devices. Extensive simulation demonstrate the superiority of our proposed approach over other cross-layer schemes.
The cognitive coprocessor architecture for interactive user interfaces

The graphics capabilities and speed of current hardware systems allow the exploration of 3D and animation in user interfaces, while improving the degree of interaction as well. In order to fully utilize these capabilities, new software architectures must support multiple, asynchronous, interacting agents (the <italic>Multiple Agent Problem</italic>), <italic>and</italic> support smooth interactive animation (the <italic>Animation Problem</italic>). The <italic>Cognitive Coprocessor</italic> is a new user interface architecture designed to solve these two problems, while supporting highly interactive user interfaces that have 2D and 3D animations. This architecture includes <italic>3D Rooms</italic>, a 3D analogy to the Rooms system with Rooms Buttons extended to <italic>Interactive Objects</italic> that deal with 3D, animation, and gestures. This research is being tested in the domain of <italic>Information Visualization</italic>, which uses 2D and 3D animated artifacts to represent the structure of information. A prototype, called the <italic>Information Visualizer</italic>, has been built.