Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelagem e posicionamento de para-raios em redes subterrâneas de parques eólicos conectadas a redes aéreas utilizando o ATP

Modeling and Positioning of Surge Arresters at Underground Networks of Wind Farms connected to Overhead Networks Using the ATP The main objective of this work is to analyze the protection against the occurrence of overvoltages in a wind system connected to the overhead network. Several discussions that deserve merit arise at this scenario. The first point is the literature on the characteristics and methods of application of the surge arrester. Once done, the useful features and an implementation method for this protection can be defined. The second point is regards to the modeling of the arrester. Since this is a problem of electromagnetic transient, the simulation will be realized in a software developed at time domain, the chosen interface is the ATPDraw, from ATP. The model of the arrester must include dynamic behavior for rapid surge. This definition already takes into account that the greatest possible overvoltage that occurs on a system arises from a direct lightning stroke. The third point is concerned to the modeling of the remaining system equipments which must conform to the simulation of lightning. To get the correct model for arresters, firstly it must be defined what type of non-linear branch to be used within existing ATP models. Then, the correct values that define the operation of the arrester has to be found. This is accomplished through the use of a model varying with frequency. The main models are compared to decide the most suitable for the operation. After the model was defined, a base case is simulated to apply the appropriate arrester model for fast surges. Protection is correctly allocated to ensure an adequate overvoltage for the system equipment, cables, transformers and generators. Finally, the key points of the work are described and highlighted the contributions and the interesting aspects for future works.
Foundations of 3D Digital Libraries: Current Approaches and Urgent Research Challenges

D documents are an indispensable data type in many im- portant application domains such as Computer Aided De- sign, Simulation and Visualization, and Cultural Heritage, to name a few. The 3D document type can represent arbi- trarily complex information by composing geometrical, topo- logical, structural, or material properties, among others. It often is integrated with meta data and annotation by the various application systems that produce, process, or con- sume 3D documents. We argue that due to the inherent complexity of the 3D data type in conjunction with and imminent pervasive usage and explosion of available content, there is pressing need to address key problems of the 3D data type. These problems need to be tackled before the 3D data type can be fully supported by Digital Library technology in the sense of a generalized document, unlocking its full potential. If the problems are addressed appropriately, the expected benefits are manifold and may lead to radically improved production, processing, and consumption of 3D content.
Action Recognition Using Local Spatio- Temporal Oriented Energy Features and Additive Kernel SVMs

Spatio-temporal oriented energy features have been proved to be an efficient feature for action recognition. It has satisfied performance on most of public databases. However, the oriented energy features were used as holistic action features for template matching in many literatures. In the paper, we proposed an action representation based on local spatio-temporal oriented energy features, and multiple feature channels are built to convert the features to descriptors. Moreover, inspired by additive kernel Support Vector Machine can offer significant improvements in accuracy on a wide variety of tasks while having the same run-time. We proposed action classifiers based on additive kernels and tested our system on KTH human action dataset for its performance evaluation. The experimental result shows our system outperforms most of recent action classification systems. 
Google Scholar's ranking algorithm: The impact of citation counts (An empirical study)

Google Scholar is one of the major academic search engines but its ranking algorithm for academic articles is unknown. In a recent study we partly reverse-engineered the algorithm. This paper presents the results of our second study. While the previous study provided a broad overview, the current study focused on analyzing the correlation of an article's citation count and its ranking in Google Scholar. For this study, citation counts and rankings of 1,364,757 articles were analyzed. Some results of our first study were confirmed: Citation counts is the highest weighed factor in Google Scholar's ranking algorithm. Highly cited articles are found significantly more often in higher positions than articles that are cited less often. Therefore, Google Scholar seems to be more suitable for searching standard literature than for gems or articles by authors advancing a view different from the mainstream. However, interesting exceptions for some search queries occurred. In some cases no correlation existed; in others bizarre patterns were recognizable, suggesting that citation counts sometimes have no impact at all on articles' rankings.
Regulation of Blood Glucose by Hypothalamic Pyruvate Metabolism

The brain keenly depends on glucose for energy, and mammalians have redundant systems to control glucose production. An increase in circulating glucose inhibits glucose production in the liver, but this negative feedback is impaired in type 2 diabetes. Here we report that a primary increase in hypothalamic glucose levels lowers blood glucose through inhibition of glucose production in rats. The effect of glucose requires its conversion to lactate followed by stimulation of pyruvate metabolism, which leads to activation of adenosine triphosphate (ATP)–sensitive potassium channels. Thus, interventions designed to enhance the hypothalamic sensing of glucose may improve glucose homeostasis in diabetes.
MODELS FOR DISCRETE OBSERVATION DISTRIBUTIONS IN SPEECH RECOGNITION

Speech recognition has been traditionally associated to continuous random variables. The most successful models have been the HMM with mixture of Gaussians in the states to generate/capture observations. In this work we show how the graphical models can be used to extract the joint information of more than two features, which is not modeled with full covariance matrices. This is possible if we previously quantize the speech features to a small number of levels and work with discrete random variables. It is shown a method to estimate a constrained number of parents subset of the directed acyclic graph based model framework. Some experimental results are obtained with this method compared to baseline systems of full and diagonal covariance matrices. Additionally, it is shown that it is possible to improve the information of the discrete random variable with qualitative features, such us voicing class or pitch information.
Reasoning about Depth and Motion from an Observer's Viewpoint

ABSTRACT The goal of this paper is to present a logic-based formalism for representing knowledge about objects in space and their movements, and show how this knowledge could be built up from the viewpoint of an observer immersed in a dynamic world. In this paper space is represented using functions that extract attributes of depth, size and distance from snapshots of the world. These attributes compose a novel spatial reasoning system named Depth Profile Calculus (DPC). Transitions between qualitative relations involving these attributes are represented by an extension of this calculus called Dynamic Depth Profile Calculus (DDPC). We argue that knowledge about objects in the world could be built up via a process of abduction on DDPC relations.
A Temperature-Accelerated Method to Evaluate Data Retention of Resistive Switching Nonvolatile Memory

A switching model of conductivity modulation by a charge trapping process is proposed to describe the resistive switching in nonvolatile metal-insulator-metal (MIM) memory. Based on a quantitative detrapping analysis, retention is explained by the thermal release time of trapped charges, which is determined by trap depth and temperature. A characteristic temperature is defined at which a significant loss of retention would occur. A temperature-accelerated test is devised to measure the characteristic temperature and to give an early input on the worst-case retention for a given technology. The viability of this method is demonstrated using MIM memory.
Loss of imprinting of insulin-like growth factor 2 is associated with increased risk of primary lung cancer in the central China region.

BACKGROUND To determine the imprinting status of the IGF2 in Chinese patients with primary lung cancer and to analyze the clinical significance of the loss of imprinting (LOI) of IGF2. MATERIALS AND METHODS PCR- RFLP and RT-PCR-RFLP were carried out to select heterozygous cases for the ApaI polymorphism within exon 9 of the IGF2 gene and further analyze IGF2 LOI in 64 lung cancer patients, respectively. RESULTS Of 64 lung cancer patients, 31 were heterozygous for IGF2. The positive rates of IGF2 LOI of lung cancer foci, matched paracancer tissues, and normal lung tissues were 77.4% (24/31), 61.3% (19/31), and 29.0% (9/31), respectively. The LOI differences for IGF2 among the three groups were statistically significant (χ2=15.267, p=0.000), and the LOI frequency of IGF2 in normal lung tissue was significantly lower than that in lung cancer foci and paracancer tissues (χ2=14.577, p=0.000; χ2=6.513, p=0.011). No statistical difference was observed between the lung tumor group and the matched paracancer group (χ2=1.897, p=0.168). The prevalence of advanced clinical stages (χ2=2.379; p=0.017) and lymph node metastasis (χ2=5.552; p=0.018) was significantly higher for LOI- positive paracancer tissues than for LOI-negative paracancer tissues. CONCLUSIONS IGF2 LOI is highly frequent in Chinese primary lung cancer patients, especially those with increased risk of lymph node metastasis and advanced clinical stages. IGF2 LOI may be an early epigenetic event in human lung carcinogenesis.
On GeoSensor Network optimization using a model-based approach

The presented model-based approach comprises of a phenomenon model and a model of the mobile geosensor network (GSN). The phenomenon model estimates the characteristics of the observed phenomenon by means of a geostatistical approach. Based on this phenomenon model it is determined where additional information is needed. Also the limitations and constraints of the GSN are taken into account during the optimization process. Therefore, we use Hägerstrand’s time geography, which allows modeling the potential activity area of the geosensor nodes. The potential activity area of a geosensor node is the area in which it can operate. Existing optimization methods are either based on geometric considerations like coverage and exposure based approaches or communication and energy based approaches. The presented approach combines the existing technologies of kriging and time geography in order to come up with a holistic optimization method. A table top experiment proved the efficiency of the model-based approach.
Analysis and Design of Class- ${\rm E}_{\rm M}$ Power Amplifier

This paper presents analytical expressions for the class-EM power amplifier, taking into account the fundamental-frequency and harmonic components of output currents in the main and auxiliary circuits. By using the proposed expressions, design values for satisfying not only the class-EM ZVS/ZDVS/ZCS/ZDCS conditions of the main circuit, but also the ZVS condition of the auxiliary circuit can be achieved without applying any tuning process. The analytical predictions agreed with experimental and PSpice-simulation results quantitatively, which showed the validity of analytical expressions given in this paper.
A sequential Monte Carlo approach to Thompson sampling for Bayesian optimization

Bayesian optimization through Gaussian process regression is an effective method of optimizing an unknown function for which every measurement is expensive. It approximates the objective function and then recommends a new measurement point to try out. This recommendation is usually selected by optimizing a given acquisition function. After a sufficient number of measurements, a recommendation about the maximum is made. However, a key realization is that the maximum of a Gaussian process is not a deterministic point, but a random variable with a distribution of its own. This distribution cannot be calculated analytically. Our main contribution is a sequential Monte Carlo approach towards approximating this maximum distribution. Subsequently, by taking samples from this distribution, we enable Thompson sampling to be applied to (armed-bandit) optimization problems with a continuous input space. All this is done without requiring the optimization of a nonlinear acquisition function. Experiments have shown that the resulting optimization method has a competitive performance at keeping the cumulative regret limited.
Phagocytosis of Opsonized Yeast Induces Tumor Necrosis Factor‐α mRNA Accumulation and Protein Release by Human Polymorphonuclear Leukocytes

In this report we show that phagocytosis of yeast particles opsonized with IgG (Y‐IgG) by human polymorphonuclear cells (PMN) results in the selective induction of tumor necrosis factor (TNF‐α) messenger RNA (mRNA) and release of its mature protein. Lipopolysaccharide (LPS) was also found able to induce TNF‐α secretion by PMN, but was a less potent stimulus compared with Y‐lgG. There was no evidence of interleukin‐6 (IL‐6) gene expression in PMN after phagocytosis of Y‐lgG or in response to LPS, whereas IL‐6 mRNA expression and secretion were induced by either stimulus in monocytes. These findings demonstrate that a physiological function such as phagocytosis modulates the gene expression for a cytokine in PMN and shed new light on the understanding of the pathogenesis of the inflammatory process.
TokyoTech+Canon at TRECVID 2011

The aim of this section is to develop a high-performance semantic indexing system using Gaussian mixture model (GMM) supervectors and tree-structured GMMs [1, 2]. GMM spervectors corresponding to six types of audio and visual features are extracted from video shots by using tree-structured GMMs. The computational cost of maximum a posteriori (MAP) adaptation for estimating GMM parameters are reduced by tree-structured GMMs by keeping accuracy at high levels. Our best result was 17.3 % in terms of Mean InfAP, which was ranked 1st over all semantic indexing runs in the full task.
Joint Text Embedding for Personalized Content-based Recommendation

Learning a good representation of text is key to many recommendation applications. Examples include news recommendation where texts to be recommended are constantly published everyday. However, most existing recommendation techniques, such as matrix factorization based methods, mainly rely on interaction histories to learn representations of items. While latent factors of items can be learned effectively from user interaction data, in many cases, such data is not available, especially for newly emerged items. In this work, we aim to address the problem of personalized recommendation for completely new items with text information available. We cast the problem as a personalized text ranking problem and propose a general framework that combines text embedding with personalized recommendation. Users and textual content are embedded into latent feature space. The text embedding function can be learned end-to-end by predicting user interactions with items. To alleviate sparsity in interaction data, and leverage large amount of text data with little or no user interactions, we further propose a joint text embedding model that incorporates unsupervised text embedding with a combination module. Experimental results show that our model can significantly improve the effectiveness of recommendation systems on real-world datasets.
The VPN conjecture is true

We consider the following network design problem. We are given an undirected graph G=(V,E) with edges costs c(e) and a set of terminal nodes W. A hose demand matrix for W is any symmetric matrix [Dij] such that for each i, ∑ j ≠ i Dij ≤ 1. We must compute the minimum cost edge capacities that are able to support the oblivious routing of every hose matrix in the network. An oblivious routing template, in this context, is a simple path Pij for each pair i,j ∈ W. Given such a template, if we are to route a demand matrix D, then for each i,j we send Dij units of flow along each Pij. Fingerhut et al. and Gupta et al. obtained a 2-approximation for this problem, using a solution template in the form of a tree. It has been widely asked and subsequently conjectured [Italiano 2006] that this solution actually results in the optimal capacity for the single path VPN design problem; this has become known as the VPN conjecture. The conjecture has previously been proven for some restricted classes of graphs [Hurkens 2005, Grandoni 2007, Fiorini 2007]. Our main theorem establishes that this conjecture is true in general graphs. This also gives the first polynomial time algorithm for the single path VPN problem. We also show that the multipath version of the conjecture is false.
Generalized Agent-mediated Procurement Auctions

Procurement auctions (where the auctioneer needs a service and bidders offer it at their own conditions) are an appealing method for on-line service selection. They can improve service features and cost by exploiting the competition between different service providers. Software agents, acting on behalf of human users and organizations, are essential in making such auctions practical and usable. Since conveying user preferences to the agents in a faithful and complete way is virtually impossible, we advocate an approximate approach, where only partial preferences are formalized, and users pick their choice from a short list of options selected by the agents by means of those partial preferences. Another peculiarity of our scenarios is that there may be no contracts with null utility for a given bidder. These features affect the classical, desirable properties of standard auction mechanisms. We prove some impossibility results concerning truthfulness and (a qualitative analogue of) revenue. Then, we investigate a novel auction mechanism that is "almost" truthful in the sense that any strategic deviation from truthfulness has limited impact on the auctioneer's revenue.
The impacts of magnetized water treatment on different morphological and physiological factors of plant species in arid regions

The aim of this study was to investigate the effects of magnetized water treatment (MWT) on some vegetation growth indices in arid areas of northeast Iran. For this purpose, the impacts of MWT were examined in terms of leaf area, special leaf area, and some of the physiographic indicators such as relative water capacity and foliar chlorophyll content in three species endemic to arid environments, namely Nitraria, Haloxylon, and Atriplex. In addition to the factors mentioned, acidity (pH) and electrical conductivity (EC) were also measured. This research was conducted in a completely randomized block design with three replications. Data analysis was carried out via the analysis of variance using MSTAT software. The results showed that species irrigated with magnetized water had better performance on measured growth indices. On average, the amount of increase for morphology and physiology parameters in the three species irrigated by MW (comparing with those irrigated by normal water) were: leaf development by 121.74 (19.59%), leaf area index (LAI) by 108.97 (17.5%) mm2, special leaf area (SAL) by 8.68%, relative water content (RWC) by 9.81%, and special product analysis division (SPAD) by 14.77%. The water magnetization process also reduced pH by 0.5 and EC by 0.3 μmhos. cm−1.
A Systematic Review of Randomized, Double-Blind, Placebo-Controlled Trials Examining the Clinical Efficacy of Vitamin D in Multiple Sclerosis

Background: An association between multiple sclerosis (MS) prevalence as well as MS mortality and vitamin D nutrition has led to the hypothesis that high levels of vitamin D could be beneficial for MS. The purpose of this systematic review is to establish whether there is evidence for or against vitamin D in the treatment of MS. Methods: Systematic literature searches were performed to locate randomized, placebo-controlled, double-blind trials measuring the clinical effect of vitamin D on MS in human participants. Data were extracted in a standardized manner, and methodological quality was assessed by the Jadad score. Results: Five trials were located that met the selection criteria. Of the 5 trials, 4 showed no effect of vitamin D on any outcome, and 1 showed a significant effect, namely by a reduction in the number of T1 enhancing lesions on brain magnetic resonance imaging. Three studies commented on adverse effects of vitamin D, with gastrointestinal adverse effects being the most frequently reported. The literature is limited by small study sizes (ranging from 23 to 68 patients), heterogeneity of dosing, form of vitamin D tested (vitamin D3 in 4 trials and vitamin D2 in 1) and clinical outcome measures. Therefore, a meta-analysis was not performed. Conclusions: The evidence for vitamin D as a treatment for MS is inconclusive. Larger studies are warranted to assess the effect of vitamin D on clinical outcomes in patients with MS. We further encourage researchers to also test the effect of vitamin D on the health-related quality of life experienced by patients and their families.
Query processing in the TAMBIS bioinformatics source integration system

Conducting bioinformatic analyses involves biologists in expressing requests over a range of highly heterogeneous information sources and software tools. Such activities are laborious, and require detailed knowledge of the data structures and call interfaces of the different sources. The TAMBIS (Transparent Access to Multiple Bioinformatics Information Sources) project seeks to make the diversity in data structures, call interfaces and locations of bioinformatics sources transparent to users. In TAMBIS, queries are expressed in terms of an ontology implemented using a description logic, and queries over the ontology are rewritten to a middleware level for execution over the diverse sources. The paper describes query processing in TAMBIS, focusing in particular on the way source-independent concepts in the ontology are related to source-dependent middleware calls, and describing how the planner identifies efficient ways of evaluating user queries.
A rapid and simple method for Bacillus subtilis transformation on solid media.

Cells of Bacillus subtilis strains 168 and W23 deprived of an amino acid or a base on a given solid medium were found to develop competence. We describe a rapid and simple method of genetic transformation of this organism consisting in spreading a sample containing 1 microgram DNA and 10(7) exponentially growing cells of an auxotrophic mutant onto plates devoid of the required amino acid or base. After overnight incubation, about 100-200 prototrophic transformants per plate were obtained, i.e. a frequency of about 10(-5), as compared to 10(-4) routinely obtained by the method of transformation in liquid medium with frozen competent cells. Plasmids and other chromosomal or plasmid-borne markers, which cannot be directly selected for, were transferred by congression. The dependence of the transformation efficiency on cell density, medium richness, incubation time and the nature of transforming DNA was investigated. We conclude that the development of competence accompanies amino acid or base starvation of cells under appropriate physiological conditions.