Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of inverter driven permanent magnet synchronous motors

The author presents a classification scheme for the inverter-driven PM (permanent-magnet) motor drive, by which the drive can be characterized according to five basic criteria. These criteria include motor design, power-circuit configuration, commutation control signals, current regulation, and motor control methods. This classification scheme provides a useful conceptual framework for comparing different PM motor drives. Several examples of PM motor drives are taken from the literature to illustrate the classification scheme. <<ETX>>
Direction of arrival estimation based on weighted minimum mean square error

It is a challenging issue to design a robust broad-band direction-of-arrival (DOA) estimator at the cost of low computational complexity. Many algorithms accelerate localization while maintaining robustness by optimizing the grid search. Few algorithms pay attention to the cost function of DOA estimators. This paper presents a far-filed DOA estimator based upon a novel cost function for planar arrays. It has two distinctions from conventional ones. On one hand, an explicit solution of DOA is represented as a linear function of all time-difference-of-arrivals (TDOAs), which leads to a considerable reduction of computational complexity. On the other hand, the reliability of each TDOA is considered, which relieves the outlier influence. The proposed algorithm was evaluated in a simulated environment. Its robustness is close to SRP-PHAT, but significantly outperforms GCC-PHAT. It runs about 5 times faster than GCC-PHAT, and 30 times faster than SRP-PHAT on general desktop computers for a 9-element array.
A geometric model of defensive peripersonal space

Potentially harmful stimuli occurring within the defensive peripersonal space (DPPS), a protective area surrounding the body, elicit stronger defensive reactions. The spatial features of the DPPS are poorly defined and limited to descriptive estimates of its extent along a single dimension. Here we postulated a family of geometric models of the DPPS, to address two important questions with respect to its spatial features: What is its fine-grained topography? How does the nervous system represent the body area to be defended? As a measure of the DPPS, we used the strength of the defensive blink reflex elicited by electrical stimulation of the hand (hand-blink reflex, HBR), which is reliably modulated by the position of the stimulated hand in egocentric coordinates. We tested the goodness of fit of the postulated models to HBR data from six experiments in which we systematically explored the HBR modulation by hand position in both head-centered and body-centered coordinates. The best-fitting model indicated that 1) the nervous system's representation of the body area defended by the HBR can be approximated by a half-ellipsoid centered on the face and 2) the DPPS extending from this area has the shape of a bubble elongated along the vertical axis. Finally, the empirical observation that the HBR is modulated by hand position in head-centered coordinates indicates that the DPPS is anchored to the face. The modeling approach described in this article can be generalized to describe the spatial modulation of any defensive response.
Agent-Based Control Framework for Distributed Energy Resources Microgrids

Distributed energy resources (DERs) provide many benefits for the electricity users and utilities. However, the electricity distribution system traditionally was not designed to accommodate active power generation and storage at the distribution level. The microgrid provides an effective approach to integrating many small-scale distributed energy resources into the bulk electric grid. This paper presents an agent-based control framework for distributed energy resources microgrids. The features of agent technology are first discussed. An agent-based control framework for DER microgrids is then presented. To demonstrate the effectiveness of the proposed agent- based control framework, simulation studies have been performed on a dc distributed energy system that can be used in a microgrid as a modular power generation unit. Simulation results clearly indicate that the agent-based control framework is effective to coordinate the various distributed energy resources and manage the power and voltage profiles.
Scalability-oriented multicast traffic characterization

Multicast on-chip communications are expected to become an important concern as the number of cores grows and we reach the manycore era. The increasing importance such traffic flows directly contrasts with the diminishing multicast performance of current Network-on-Chip (NoC) designs, and has lead to a surge of research works that seek to improve on-chip multicast support. Within this context, one-to-many traffic models may become useful for the early-stage design and evaluation of these proposals. However, existing models do not distinguish between unicast and multicast flows and often do not consider different multiprocessor sizes. To bridge this gap, a multicast scalability analysis is presented, aiming to provide tools for the modeling of multicast communications for NoC design and evaluation purposes.
Methods, caveats and the future of large-scale microelectrode recordings in the non-human primate

Cognitive processes play out on massive brain-wide networks, which produce widely distributed patterns of activity. Capturing these activity patterns requires tools that are able to simultaneously measure activity from many distributed sites with high spatiotemporal resolution. Unfortunately, current techniques with adequate coverage do not provide the requisite spatiotemporal resolution. Large-scale microelectrode recording devices, with dozens to hundreds of microelectrodes capable of simultaneously recording from nearly as many cortical and subcortical areas, provide a potential way to minimize these tradeoffs. However, placing hundreds of microelectrodes into a behaving animal is a highly risky and technically challenging endeavor that has only been pursued by a few groups. Recording activity from multiple electrodes simultaneously also introduces several statistical and conceptual dilemmas, such as the multiple comparisons problem and the uncontrolled stimulus response problem. In this perspective article, we discuss some of the techniques that we, and others, have developed for collecting and analyzing large-scale data sets, and address the future of this emerging field.
Changing Semantic Role Representations with Holographic Memory

Semantic roles describe “who did what to whom” and as such are central to many subfields of AI and cognitive science. Each subfield or application tends to use its own “flavor” of roles. For analogy processing, logical deduction, and related tasks, roles are usually specific to each predicate: for loves there is a LOVER and a BELOVED, for eats an EATER and an EATEN, etc. Language modeling, on the other hand, requires more general roles like AGENT and PATIENT in order to relate form to meaning in a parsimonious way. Commitment to a particular type of role makes it difficult to model processes of change, for example the change from specific to general roles that seems to take place in language learning. The use of semantic features helps solve this problem, but still limits the nature and number of changes that can take place. This paper presents a new model of semantic role change that addresses this problem. The model uses an existing technique, Holographic Reduced Representation (HRR) for representing roles and their fillers. Starting with specific roles, the model learns to generalize roles through exposure to language data. The learning mechanism is simple and efficient, and is scaling properties are well-understood. The model is able to learn and exploit new representations without losing the information from existing ones. We present experimental data illustrating these principles, and conclude with by discussing some implications of the model for the issues of changing representations as a whole.
Slaves as capital investment in the Dutch Cape Colony, 1652-1795

The Cape Colony of the eighteenth century was one of the most prosperous regions in the world. This paper shows that Cape farmers prospered, on average, because of the economies of scale and scope achieved through slavery. Slaves allowed farmers to specialise in agricultural products that were in high demand from the passing ships – notably, wheat, wine and meat – and the by-products from these products, such as tallow, skins, soap and candles. In exchange, farmers could import cheap manufactured products from Europe and the East. Secondly, the paper investigates why the relative affluence of the early settlers did not evolve into a high growth trajectory. The use of slaves as a substitute for wage labour or other capital investments allowed farmers to prosper, but it also resulted in severe inequality. It was this high inequality that drove the growth-debilitating institutions posited by Engerman and Sokoloff (2000). The immigration of Europeans was discouraged after 1717, and again during the middle of the century, while education was limited to the wealthy. Factor endowments interacted with institutions to create a highly unequal early South African society, with long-term development consequences.
Who's afraid of naked singularities? Probing timelike singularities with finite energy waves

To probe naked spacetime singularities with waves rather than with particles we study the well-posedness of initial value problems for test scalar fields with finite energy so that the natural function space of initial data is the Sobolev space. In the case of static and conformally static spacetimes we examine the essential self-adjointness of the time translation operator in the wave equation defined in the Hilbert space. For some spacetimes the classical singularity becomes regular if probed with waves while stronger classical singularities remain singular. If the spacetime is regular when probed with waves we may say that the spacetime is `globally hyperbolic. '
Medical image enhancement by using cellular neural networks

The paper presents a medical image enhancement method taking the noise reduction and the contrast enhancement into consideration, as well as the possibility of implementation on an existing cellular neural network universal chip (CNN-UC), in a single step, by using only linear templates of 3times3 dimensions. Due to complete parallel processing, computing-time reduction is achieved
The Kalshnikov 691 Dependency Bank

The PARC 700 dependency bank has a number of features that would seem to make it less than optimally suited for its intended purpose, parser evaluation. However, it is difficult to know precisely what impact these problems have on the evaluation results, and as a first step towards making comparison possible, a subset of the same sentences is presented here, marked up using a different format that avoids them. In this new representation, the tokens contain exactly the same sequence of characters as the original text, word order is encoded explicitly, and there is no artificial distinction between full tokens and attribute tokens. There is also a clear division between word tokens and empty nodes, and the token attributes are stored together with the word, instead of being spread out individually in the file. A standard programming language syntax is used for the data, so there is little room for markup errors. Finally, the dependency links are closer to standard grammatical terms, which presumably makes it easier to understand what they mean and to convert any particular parser output format to the Kalashnikov 691 representation. The data is provided both in machine-readable format and as graphical dependency trees.
Challenges and Approaches for Developing Ultrafine Particle Emission Inventories for Motor Vehicle and Bus Fleets

Motor vehicles in urban areas are the main source of ultrafine particles (diameters < 0.1 µm). Ultrafine particles are generally measured in terms of particle number because they have little mass and are prolific in terms of their numbers. These sized particles are of particular interest because of their ability to enter deep into the human respiratory system and contribute to negative health effects. Currently ultrafine particles are neither regularly monitored nor regulated by ambient air quality standards. Motor vehicle and bus fleet inventories, epidemiological studies and studies of the chemical composition of ultrafine particles are urgently needed to inform scientific debate and guide development of air quality standards and regulation to control this important pollution source. This article discusses some of the many challenges associated with modelling and quantifying ultrafine particle concentrations and emission rates for developing inventories and microscale modelling of motor vehicles and buses, including the challenge of understanding and quantifying secondary particle formation. Recommendations are made concerning the application of particle emission factors in developing ultrafine particle inventories for motor vehicle fleets. The article presents a precis of the first published inventory of ultrafine particles (particle number) developed for the urban South-East Queensland motor vehicle and bus fleet in Australia, and comments on the applicability of the comprehensive set of average particle emission factors used in this inventory, for developing ultrafine particle (particle number) and particle mass inventories in other developed countries.
Inhibition of Hedgehog Signaling Decreases Proliferation and Clonogenicity of Human Mesenchymal Stem Cells

Human mesenchymal stem cells (hMSC) have the ability to differentiate into osteoblasts, adipocytes and chondrocytes. We have previously shown that hMSC were endowed with a basal level of Hedgehog signaling that decreased after differentiation of these cells. Since hMSC differentiation is associated with growth-arrest we investigated the function of Hh signaling on cell proliferation. Here, we show that inhibition of Hh signaling, using the classical inhibitor cyclopamine, or a siRNA directed against Gli-2, leads to a decrease in hMSC proliferation. This phenomenon is not linked to apoptosis but to a block of the cells in the G0/G1 phases of the cell cycle. At the molecular level, it is associated with an increase in the active form of pRB, and a decrease in cyclin A expression and MAP kinase phosphorylation. Inhibition of Hh signaling is also associated with a decrease in the ability of the cells to form clones. By contrast, inhibition of Hh signaling during hMSC proliferation does not affect their ability to differentiate. This study demonstrates that hMSC are endowed with a basal Hedgehog signaling activity that is necessary for efficient proliferation and clonogenicity of hMSC. This observation unravels an unexpected new function for Hedgehog signaling in the regulation of human mesenchymal stem cells and highlights the critical function of this morphogen in hMSC biology.
Effect of Paced and Unpaced Practice on Skill Application and Retention: How Much Is Enough?

This study examined the relative benefits of mastery learning, overlearning, and fluency-building instructions for academic performance and long-term retention. College students enrolled in introductory quantitative methods classes (n = 168) were asked to practice every week with a computerized flash-card program until they attained various mastery criteria. The results confirmed that practicing until mastery improved individual exam scores, group success rates, and long-term retention. Moreover, overlearning provided additional benefits, especially in long-term retention. However, fluency-building instructions did not further increase academic achievement or long-term retention. Despite the alleged detrimental effects of drill and practice on motivation, a positive relationship was found between amount of practice and attitudes toward the course, the subject matter, and practice activities.
Fast Speaker Recognition using Efficient Feature Extraction Fast Speaker Recognition using Efficient Feature Extraction Fast Speaker Recognition using Efficient Feature Extraction Fast Speaker Recognition using Efficient Feature Extraction Technique

Digital processing of speech signal and speaker recognition algorithm is very important for fast and accurate automatic voice recognition technology. A direct analysis of the voice signal is complex due to too much information contained in the signal. Therefore the digital signal processes such as Feature Extraction and Feature Matching are introduced to represent the voice signal. The non-parametric method for modeling the human auditory perception system, Mel Frequency Cepstral Coefficients (MFCCs) is utilized as extraction technique. MFCC imitates the human hearing system; therefore it provides better recognition rates than Linear Predictive Coefficients (LPC). For the present work, work, the non linear sequence alignment known as Dynamic Time Warping (DTW) is used as features matching technique. Since voice signal tends to have different temporal rate, the alignment is important to produce better performance. This paper presents the viability of MFCC to extract features of speech signal and DTW to compare the corresponding test patterns.
Using Business Process Models to Retrieve Information from Governing Documents

Governing documents are long textual documents containing information about how different operations are to be carried out in a business. The information spans from overall policies and standards to detailed guidelines, and the amount of such documents in larger corporations tend to be substantial. In business process models the aim is to model the same domain from a process perspective. In this project work we investigate the potential of utilizing text mining technologies together with a standard information retrieval system to link these two sources of information in a dynamic way. To test out the concepts we employ business process models and governing documents from Statoil ASA, a Norwegian oil and gas company.
Finding Information in Multimedia Records of Meetings

This paper surveys the work carried out within two large consortia, AMI and IM2, on improving access to records of human meetings thanks to multimodal interfaces called meeting browsers. These tools help users navigate through multimedia records containing audio, video, documents and metadata, in order to obtain a general idea about what happened in a meeting or to find specific pieces of information. To explain the increasing importance of meeting browsers, the paper summarizes findings of user studies, discusses features of prototypes from AMI and IM2, and outlines a proposed evaluation protocol, providing reference scores for benchmarking. These achievements result from an iterative software process, alternating user studies, prototypes or products, and evaluation.
Performance evaluation of superscalar processor with multi-bank register file using SPEC2000

Recently, register files in highly parallel superscalar processors tend to have large chip area and many access ports. This trend causes problems with chip-size, access time and power consumption. As one of the approaches for solving these problems, researchers have proposed several methods using a multi-bank register file instead of multi-port register file. And we have proposed a method to achieve higher performance as compared with other methods. In this paper, we evaluate the effectiveness of our method by software simulation using SPECint2000. The results shows that a superscalar processor with our proposal method has only 1% performance degradation in a cycle-based comparison with a conventional multi-port register file under the condition that each register bank in multi-bank register file has two read ports and two write ports. Additionally, our method keeps only 3% performance degradation even if each bank register has only one port.
DO GEOGRAPHICAL VARIATIONS IN CLIMATE INFLUENCE LIFE-SATISFACTION?

Accounting for socioeconomic and demographic variables, as well as country-specific effects, households' marginal willingness to pay for climate is revealed using European data on life-satisfaction. Individuals located in areas with lower average levels of sunshine and higher average levels of relative humidity are less satisfied as are individuals in locations subject to significant seasonal variation in monthly mean temperatures and rain days. Ranking regions by climate households appear strongly to favor the Mediterranean climate over the climate of Northern Europe.
Cloning and Functional Expression of a DrosophilaMetabotropic Glutamate Receptor Expressed in the Embryonic CNS

The excitatory neurotransmitter glutamate plays important roles in the mammalian brain, ranging from synaptic plasticity to memory. To mediate these functions, glutamate activates two types of receptors: ligand-gated channels and metabotropic receptors coupled to G-proteins. Both families of glutamate receptors share no sequence homology and possess original structural features compared with other ligand-gated channels and G-protein-coupled receptors, respectively. Glutamate-gated receptor-channel subunits have already been characterized in invertebrates. Here we report the cloning and functional characterization of an invertebrate metabotropic glutamate receptor (DmGluRA) isolated from Drosophila melanogaster. This receptor displays 45 and 43% amino acid sequence identity with its mammalian homologs mGluR3 and mGluR2, respectively. Moreover, its pharmacology and transduction mechanisms are surprisingly similar to those of mGluR2 and mGluR3. DmGluRA is expressed in the CNS of the late embryo. These results indicate that the original structural features of both glutamate receptor types are conserved from insects to mammals and suggest that the functions of these receptors have been highly conserved during evolution.
Using Fuzzy Measure Entropy to improve the stability of traditional entropy measures

Traditional entropy measures, such as Approximate Entropy (ApEn) and Sample Entropy (SampEn), are widely used for analyzing heart rate variability (HRV) signals in clinical cardiovascular disease studies. Nevertheless, traditional entropy measures have a poor statistical stability due to the 0–1 judgment of Heaviside function. The objective of this study is to introduce a new entropy measure — Fuzzy Measure Entropy (FuzzyMEn) in order to improve the stability of traditional entropy measures through introducing the concept of fuzzy sets theory. By drawing on Chen et al's research in fuzzy entropy (FuzzyEn), FuzzyMEn uses the membership degree of fuzzy function instead of the 0–1 judgment of Heaviside function as used in the ApEn and SampEn. Simultaneity, FuzzyMEn utilizes the fuzzy local and fuzzy global measure entropy to reflect the whole complexity implied in physiological signals and improves the limitation of FuzzyEn, which only focus on the local complexity. Detailed contrastive analysis and discussion of ApEn, SampEn, FuzzyEn and FuzzyMEn were also given in this study.