Miracle-dz/UniFAR-FTUs huggingface dataset:
Ant colony system-based applications to electrical distribution system optimization

Electrical distribution networks are structurally weakly meshed, but are typically operated in radial configurations to simplify the network protection schemes. This implies the need to carry out suitable selection of the redundant network branches to open in normal conditions, as well as to define the control variables to set up in order to guarantee effective system operation and voltage quality. Furthermore, the structure of the distribution networks has to be upgraded to meet future needs, according to economic objective functions defined for distribution system planning. Finally, distribution systems need to face possible interruptions through the formulation of appropriate service restoration strategies, based on switching on and off a number of branches, reducing the effects of faults occurring in the networks by defining a time-dependent path to restore electricity supply to all consumers. All these aspects can be addressed by formulating and solving suitable optimization problems for distribution network operation and planning. Typically these problems are defined within discrete domains for the decision variables. However, for large real distribution systems the highly-dimensional and/or combinatorial nature of the optimization problems make it impracticable to resort to exhaustive search, since the number of combinations would be excessively high to be processed in reasonable computation times. Global optimizations can then be solved in terms of adopting suitable meta-heuristics. In this context, ant colony optimization (ACO) provides viable solutions to the various distribution system optimization problems. Key features of ACO, such as parallel search, shortest path finding, adaptability to changes in the search space, long-term memory and information sharing, have been fully exploited in their classical formulations, in advanced versions such as the hyper-cube ACO framework, as well as in hybrid formulations combining the ACO properties with those of other meta-heuristics. This chapter summarizes the formulation and use of ACO algorithms and variants to solve different types of electrical distribution system optimization problems, namely: • Distribution system optimal reconfiguration and voltage support. Various objective functions to minimize can be set up in the presence of a set of structural and operational constraints. The objectives include total distribution system losses, voltage deviations with respect to their reference values, maximum load on system components, load balancing, and capacitor placement. Performing network configuration changes and setting up the level of insertion of compensating devices or distributed generation and
Aggregating contour fragments for shape classification

In this paper, we address the problem of building a compact representation for shape. We first decompose shape into meaningful contour fragments, and each fragment is described by a certain descriptor, e.g., Shape Context. Then inspired by the coding scheme Vector of Locally Aggregated Descriptors widely used in image representation, we try to aggregate the contour fragments into a very compact vector of limited dimension to stand for a shape, and we name the new designed shape descriptor as Vector of Aggregated Contour Fragments (VACF). We apply VACF to shape classification task on the well-known MPEG-7 shape benchmark, and the experimental results show that the accuracy of our proposed method outperforms other state-of-the-art algorithms with much smaller memory usage.
RoboCupRescue 2006 - Robot League Team ALCOR (ITALY)

We present a stable and autonomous system based on a strong inter- action between visual cognitive inference and actions executed. Mapping, Vi- sion and Navigation are all collaborative agents that work together sharing data to accomplish to complex identification and mapping tasks. The visual process reacts to interesting features in the arena trying to approach the focused area in order to achieve more successful observations from the environment. This ac- tivity is performed coordinating the states that model the robot actions in the arena.
On the Performance of Multi-Channel Wireless Sensor Networks in Smart Grid Environments

Electric power grid contains three main subsystems, i.e., power generation, power transmission & distribution, and customer facilities. Recently, wireless sensor networks (WSNs) have been considered as a promising technology that can enhance all these three subsystems, making WSNs an important component of the smart grid. However, environmental noise and interference from nonlinear electric power equipments and fading in harsh smart grid environments, makes reliable communication a challenging task for single-channel WSNs for smart grid applications. To improve network capacity in smart grid environments, multi-channel WSNs might be the preferred solution while achieving simultaneous transmissions through multiple channels. In this paper, the performance of multi-channel WSNs is investigated for different spectrum environments of smart power grid, e.g., 500kV outdoor substation, main power control room and underground network transformer vaults. In addition, we also introduce potential applications of multi-channel WSNs along with the related technical challenges. Here, our goal is to envision potential advantages and applications of multi-channel WSNs for smart grid and motivate the research community to further explore this promising research area.
Efficient Pairwise Classification and Ranking

Pairwise classification is a class binarization procedure that converts a multi-class problem into a series of two-class problems, one problem for each pair of classes. While it can be shown that for training, this procedure is more efficient than the more commonly used one-against-all approach, it still has to evaluate a quadratic number of classifiers when computing the predicted class for a given example. In this paper, we propose a method that allows a faster computation of the predicted class when weighted or unweighted voting are used for combining the predictions of the individual classifiers. While its worst-case complexity is still quadratic in the number of classes, we show that even in the case of completely random base classifiers, our method still outperforms the conventional pairwise classifier. For the more practical case of well-trained base classifiers, its asymptotic computational complexity seems to be almost linear. We also propose a method for approximating the full class ranking, based on the Swiss System, a common scheme for conducting multi-round chess tournaments. Our results indicate that this adaptive scheme offers a better trade-off between approximation quality and number of performed comparisons than alternative, fixed schemes for ordering the evaluation of the pairwise classifiers.
Distributional properties of language and sub-syllabic processing units

During the past few years, several studies have drawn attention to the role of distributional characteristics in the segmentation of speech into word units. In the present chapter, we examine the role of distributional constraints in the acquisition of sub-syllabic language processing units. In particular, we suggest that specific processing units such as the rime might emerge from the distributional properties of sequences of vowels and consonants. We then summarize recent work showing that speakers are indeed sensitive to statistical distributions between vocalic and consonantal segments within the syllable. Finally, a simulation study using the Parser model (Perruchet & Vinter, 1998) indicates how probabilistic constraints can influence the acquisition of representational units. We conclude that what can be viewed as processing units probably represents only the tip of the iceberg of broader knowledge about statistical properties of language and that the acquisition of such representational units is, at least partially, shaped by general learning processes. 3 In most contemporary models, language recognition and use are supposed to hinge on a set of intermediate representational processing units of various sizes. For example, it has been claimed that syllables constitute processing units in auditory word recognition, at least for syllable-timed languages (e. have all been proposed as underlying reading processes. Two key characteristics of these proposals are that the processing units envisaged often correspond to units of linguistic description, and that their acquisition is generally not considered. In this chapter, we focus on the role of distributional properties of languages as one critical determinant in the specification of language processing units. We propose that what can be viewed as processing units probably represents only the tip of the iceberg of broader knowledge about statistical properties of language and that the emergence of such representations is, at least partially, shaped by general learning processes. During the last decade, the role of distributional properties of language has mainly addressed the problem of word segmentation in the speech stream. Several studies suggest that young children are able to segment the continuous speech flow into individual words by using prosodic and segmental cues. Learning of the statistical regularities of the language is also suggested by the observation that 9-month-old children listen longer to words that include frequent phonetic sequences than to have showed that adults exposed to an artificial language composed of continuous tri-syllabic sequences without signification were able to learn the language by using the …
Farmer Education and Agricultural Efficiency: Evidence from Lao PDR

We focus our study on the agrarian transition in Lao People's Democratic Republic (Lao PDR), a country overwhelmingly dependent on agriculture. Close to 80% of a population lives in rural areas and most are near-subsistence farmers engaged in rice-based agriculture. The large role agriculture plays can hardly be overlooked. Given the paucity of evidence, this paper provides a research on the determinants of agricultural efficiency and rural development by analyzing the marked differences between the upland and the lowland farming of the country. Our original contribution is to explore the association between farmer education and the adoption of modern agricultural practices in Lao PDR. We find that the returns to farmer education are strongly positive, particular for a completion of primary cycle. Moreover, a better educated farmer is more likely to use new technology, and to have market access and off-farm activities. Despite the large public investment in irrigation, its role is very limited. The research suggests that the support policy on farmer education will be decisive not only for the success of the agrarian transition process but also the long term development of the country.
Money in General Equilibrium Models

This essay describes and critically evaluates the important features of some approaches to the problem of incorporating money in general equilibrium models and ensuring a positive demand for money. The types of approaches considered are introducing money into the utility function, and modelling the transactions demand for money through the use of cash-in-advance constraints, real resource costs, search theory and imperfect information. The approaches based on search theory and imperfect information are found to be the most satisfactory from a general equilibrium point of view, however they are less straightforward to implement analytically and perhaps can only serve as motivation for the other approaches.
SURE: Shizuoka University REpository

: One type of belief that has gained popularity in educational psychology research over the past four decades and that is informative when examining teachers ʼ attitudes and capabilities, is that of self-efficacy. Research has established that teachers ʼ self-efficacy beliefs have a considerable impact on a wide variety of features that are important to both teaching and learning. The current study, in order to complete the essential groundwork for a new and methodologically sound quantitative teacher self-efficacy scale, qualitatively examined what potentially supports and challenges Japanese university English language teachers ʼ self-efficacy beliefs. Four themes found in the data (Autonomy, Colleagues, Money, and Students) spoke to qualities that could potentially support teachers ʼ self-efficacy. Three themes found in the data (Administration, Students, and Limited-term Contracts) spoke to qualities that could potentially weaken teachers ʼ self-efficacy.
The Bone Marrow Microenvironment as Niche Retreats for Hematopoietic and Leukemic Stem Cells

Leukemia poses a serious challenge to current therapeutic strategies. This has been attributed to leukemia stem cells (LSCs), which occupy endosteal and sinusoidal niches in the bone marrow similar to those of hematopoietic stem cells (HSCs). The signals from these niches provide a viable setting for the maintenance, survival, and fate specifications of these stem cells. Advancements in genetic engineering and microscopy have enabled us to critically deconstruct and analyze the anatomic and functional characteristics of these niches to reveal a wealth of new knowledge in HSC biology, which is quite ahead of LSC biology. In this paper, we examine the present understanding of the regulatory mechanisms governing HSC niches, with the goals of providing a framework for understanding the mechanisms of LSC regulation and suggesting future strategies for their elimination.
Investigating Price Discovery Using a VAR-GARCH ( 1 , 1 ) Model of Order Flow and Stock Returns ”

The VAR-GARCH(1,1) price discovery model developed and tested with ASX data represents an extension of both the Chordia, Roll and Subrahmanyam (2005) and Hasbrouck (1991) models. The VAR-GARCH(1,1) price discovery model functions in accordance with the explanation for price discovery described by Chordia, Roll and Subrahmanyam (2005). This model allows the causal relationships between order flow and stock returns to be assessed, and for important aspects of the price discovery process to be measured. Both these analyses and the model itself represent contributions to the literature. This study also introduces an active trader order flow variable for fiveminute intervals using broker identification data from the ASX, which enables the brokers who executed each trade to be identified, including whether they were retail or institutional brokers. The findings highlight that active trader order flow, including institutional order flow, has a positive causal relationship with stock returns. In addition, they show that active retail order flow has a negative causal relationship with stock returns. Discussion Paper: 2016-001
Perceptual Techniques for Scientific Visualization

This talk describes our investigation of methods for choosing color, texture, orientation, shape, and other features to visualize certain types of large, multidimensional datasets. These datasets are becoming more and more common; examples include scientific simulation results, geographic information systems, satellite images, and biomedical scans. The overwhelming amount of information contained in these datasets makes them difficult to analyze using traditional mathematical or statistical techniques. It also makes them difficult to visualize in an efficient or useful manner.
Separation of concerns in compiler development using aspect-orientation

A major difficulty in compiler development regards the proper modularization of concerns among the various compiler phases. The traditional object-oriented development paradigm has difficulty in providing an optimal solution towards modularizing the analysis phases of compiler development, because implementation of each phase often crosscuts the class hierarchy defined by language syntax constructs. Object-oriented design patterns, such as the Visitor pattern, also cannot solve the crosscutting problem adequately because an object is not a natural representation of a collection of operations. This paper demonstrates the benefits of applying aspect-oriented programming languages (e.g., AspectJ) and principles to compiler design and implementation. The experience result shows that the various language constructs in AspectJ (e.g., inter-type declaration, pointcut-advice model, static aspect members and aspect inheritance) fit well with the various computation needs of compiler development, which results in a compiler implementation with improved modularity and better separation of concerns. The ideas utilized in this paper can also be generalized to other software systems with a tree-like structure.
Subband coding of images using vector quantization

A novel two-dimensional subband coding technique is presented that can be applied to images as well as speech. A frequency-band decomposition of the image is carried out by means of 2D separable quadrature mirror filters, which split the image spectrum into 16 equal-rate subbands. These 16 parallel subband signals are regarded as a 16-dimensional vector source and coded as such using vector quantization. In the asymptotic case of high bit rates, a theoretical analysis yields that a lower bound to the gain is attainable by choosing this approach over scalar quantization of each subband with an optimal bit allocation. It is shown that vector quantization in this scheme has several advantages over coding the subbands separately. Experimental results are given, and it is shown the scheme has a performance that is comparable to that of more complex coding techniques. >
Acoustic calibration of the exterior effects room at the NASA Langley Research Center.

The Exterior Effects Room (EER) at the NASA Langley Research Center is a 39-seat auditorium built for psychoacoustic studies of aircraft community noise. The original reproduction system employed monaural playback and hence lacked sound localization capability. In an effort to more closely recreate field test conditions, a significant upgrade was undertaken to allow simulation of a three-dimensional audio and visual environment. The 3D audio system consists of 27 mid and high frequency satellite speakers and 4 subwoofers, driven by a real-time audio server running an implementation of Vector Base Amplitude Panning. The audio server is part of a larger simulation system, which controls the audio and visual presentation of recorded and synthesized aircraft flyovers. The focus of this work is on the calibration of the 3D audio system, including gains used in the amplitude panning algorithm, speaker equalization, and absolute gain control. Because the speakers are installed in an irregularly shaped room, the speaker equalization includes time delay and gain compensation due to different mounting distances from the focal point, filtering for color compensation due to different installations (half space, corner, baffled/unbaffled), and cross-over filtering.
An epidemiological investigation of a Forkhead box protein E3 founder mutation underlying the high frequency of sclerocornea, aphakia, and microphthalmia in a Mexican village

Purpose To investigate the molecular epidemiological basis for the unusually high incidence of sclerocornea, aphakia, and microphthalmia in a village in the Tlaxcala province of central Mexico. Methods A population census was performed in a village to identify all sclerocornea, aphakia, and microphthalmia cases. Molecular analysis of the previously identified Forkhead box protein E3 (FOXE3) mutation, c.292T>C (p.Y98H), was performed with PCR amplification and direct DNA sequencing. In addition, DNA from 405 randomly selected unaffected villagers was analyzed to establish the carrier frequency of the causal mutation. To identify the number of generations since the mutation arose in the village, 17 polymorphic markers distributed in a region of 6 Mb around the mutated locus were genotyped in the affected individuals, followed by DMLE software analysis to calculate mutation age. Results A total of 22 patients with sclerocornea, aphakia, and microphthalmia were identified in the village, rendering a disease prevalence of 2.52 cases per 1,000 habitants (1 in 397). The FOXE3 homozygous mutation was identified in all 17 affected subjects who consented to molecular analysis. Haplotype analysis indicated that the mutation arose 5.0–6.5 generations ago (approximately 106–138 years). Among the 405 unaffected villagers who were genotyped, ten heterozygote carriers were identified, yielding a population carrier frequency of approximately 1 in 40 and a predicted incidence of affected of 1 in 6,400 based on random marriages between two carriers in the village. Conclusions This study demonstrates that a cluster of patients with sclerocornea, aphakia, and microphthalmia in a small Mexican village is due to a FOXE3 p.Y98H founder mutation that arose in the village just over a century ago at a time when a population migrated from a nearby village because of land disputes. The actual disease incidence is higher than the calculated predicted value and suggests non-random marriages (i.e., consanguinity) within the population. We can now offer the community more informed genetic counseling based on an accurate genetic test, thus increasing the likelihood of a better outcome for the families.
Hybrid evolutionary multiobjective fuzzy c-medoids clustering of categorical data

In this article, we have considered the problem of fuzzy clustering of categorical data. In this regard, the well-known fuzzy C-medoids algorithm for categorical data clustering is posed as a multiobjective optimization problem where the cluster medoids are encoded in the chromosomes of a multiobjective genetic algorithm. The chromosomes are of variable lengths to permit automatic evolution of the number of clusters. The chromosomes are updated through the medoid updating process of fuzzy C-medoids clustering. The fuzzy cluster variance and cluster separation are taken as the two objectives to be optimized simultaneously. The performance of the proposed algorithm has been compared with that of different well-known categorical data clustering algorithms and demonstrated for a variety of synthetic and real-life categorical data sets.
Optimal Solvers for PDE-Constrained Optimization

Optimization problems with constraints which require the solution of a partial differential equation arise widely in many areas of the sciences and engineering, particularly in problems of design. The solution of such PDE-constrained optimization problems is usually a major computational task. Here we consider simple problems of this type: distributed control problems in which the 2- and 3-dimensional Poisson problem is the PDE. The large-dimensional linear systems which result from discretization and which need to be solved are of saddle-point type. We introduce two optimal preconditioners for these systems, which lead to convergence of symmetric Krylov subspace iterative methods in a number of iterations which does not increase with the dimension of the discrete problem. These preconditioners are block structured and involve standard multigrid cycles. The optimality of the preconditioned iterative solver is proved theoretically and verified computationally in several test cases. The theoretical proof indicates that these approaches may have much broader applicability for other PDEs.