Miracle-dz/UniFAR-FTUs huggingface dataset:
Uma abordagem híbrida relacional para a desambiguação lexical de sentido na tradução automática

Crosslingual communication has become a very imperative task in the current scenario with the increasing amount of information dissemination in several languages. In this context, machine translation systems, which can facilitate such communication by providing automatic translations, are of great importance. Although research in Machine Translation dates back to the 1950‟s, the area still has many problems. One of the main problems is that of lexical ambiguity, that is, the need for lexical choice when translating a source language word that has several translation options in the target language. This problem is even more complex when only sense variations are found in the translation options, a problem named “sense ambiguity”. Several approaches have been proposed for word sense disambiguation, but they are in general monolingual (for English) and application-independent. Moreover, they have limitations regarding the types of knowledge sources that can be exploited. Particularly, there is no significant research aiming to word sense disambiguation involving Portuguese. The goal of this PhD work is the proposal and development of a novel approach for word sense disambiguation which is specifically designed for machine translation, follows a hybrid methodology (knowledge and corpus-based), and employs a relational formalism to represent various kinds of knowledge sources and disambiguation examples, by using Inductive Logic Programming. Several experiments have shown that the proposed approach overcomes alternative approaches in multilingual disambiguation and achieves higher or comparable results to the state of the art in monolingual disambiguation. Additionally, the approach has shown to effectively assist lexical choice in a statistical machine translation system.
Effects of Reading Proficiency and of Base and Whole-Word Frequency on Reading Noun- and Verb-Derived Words: An Eye-Tracking Study in Italian Primary School Children

The aim of this study is to assess the role of readers’ proficiency and of the base-word distributional properties on eye-movement behavior. Sixty-two typically developing children, attending 3rd, 4th, and 5th grade, were asked to read derived words in a sentence context. Target words were nouns derived from noun bases (e.g., umorista, ‘humorist’), which in Italian are shared by few derived words, and nouns derived from verb bases (e.g., punizione, ‘punishment’), which are shared by about 50 different inflected forms and several derived words. Data shows that base and word frequency affected first-fixation duration for nouns derived from noun bases, but in an opposite way: base frequency had a facilitative effect on first fixation, whereas word frequency exerted an inhibitory effect. These results were interpreted as a competition between early accessed base words (e.g., camino, chimney) and target words (e.g., caminetto, fireplace). For nouns derived from verb bases, an inhibitory base frequency effect but no word frequency effect was observed. These results suggest that syntactic context, calling for a noun in the target position, lead to an inhibitory effect when a verb base was detected, and made it difficult for readers to access the corresponding base+suffix combination (whole word) in the very early processing phases. Gaze duration was mainly affected by word frequency and length: for nouns derived from noun bases, this interaction was modulated by proficiency, as length effect was stronger for less proficient readers, while they were processing low-frequency words. For nouns derived from verb bases, though, all children, irrespective of their reading ability, showed sensitivity to the interaction within frequency of base+suffix combination (word frequency) and target length. Results of this study are consistent with those of other Italian studies that contrasted noun and verb processing, and confirm that distributional properties of morphemic constituents have a significant impact on the strategies used for processing morphologically complex words.
CM-CFAR Parameter Learning Based Square-Law Detector for Foreign Object Debris Radar

One of the main challenges in foreign object debris detection radar systems is the statistical characterization of the received signals. In this paper, Non-Central Chi-square Distribution with Unequal Variances is proposed to characterize the received background signal. A practical search algorithm for the determination of the detection threshold under this distribution is also suggested. Through both Monte Carlo simulations and real data measurements, the comparison of the proposed detection procedure with widely used statistical models is made in terms of fulfillment of the given false alarm probability.
Compression through decomposition into browse and residual images

Economical archival and retrieval of image data is becoming increasingly important considering the unprecedented data volumes expected from the Earth Observing System (EOS) instruments. For cost effective browsing the image data (possibly from remote site), and retrieving the original image data from the data archive, we suggest an integrated image browse and data archive system employing incremental transmission. We produce our browse image data with the JPEG/DCT lossy compression approach. Image residual data is then obtained by taking the pixel by pixel differences between the original data and the browse image data. We then code the residual data with a form of variable length coding called diagonal coding. In our experiments, the JPEG/DCT is used at different quality factors (Q) to generate the browse and residual data. The algorithm has been tested on band 4 of two Thematic mapper (TM) data sets. The best overall compression ratios (of about 1.7) were obtained when a quality factor of Q=50 was used to produce browse data at a compression ratio of 10 to 11. At this quality factor the browse image data has virtually no visible distortions for the images tested.
Visfatin is regulated by interleukin-6 and affected by the PPAR-γ pathway in BeWo cells

Visfatin, an adipocytokine and cytosolic enzyme with nicotinamide phosphoribosyltransferase (Nampt) activity, is involved in the pathogenesis of numerous metabolic disorders. In addition, the nuclear receptor peroxisome proliferator-activated receptor-γ (PPAR-γ) serves important roles in anti-inflammatory reactions and regulates glucose and lipid metabolism. The aim of the present study was to investigate the effect of interleukin-6 (IL-6) on the expression and secretion of visfatin in BeWo cells, and to determine whether the PPAR-γ pathway is involved in the regulation of visfatin by IL-6. Therefore, BeWo cells were stimulated with serial concentrations of IL-6 or pioglitazone, and the expression levels of visfatin and PPAR-γ were determined by reverse transcription-quantitative polymerase chain reaction and western blotting. The results of the present study demonstrated that IL-6 downregulated the mRNA levels of visfatin and PPAR-γ, which were strongly associated. Activation of PPAR-γ by pioglitazone resulted in significantly increased expression of visfatin, which abrogated the inhibitory effect of IL-6 on visfatin in BeWo cells. Furthermore, treatment using pioglitazone alone increased the expression and secretion of the visfatin protein, compared with the control or IL-6 alone group. In summary, the findings of the present study suggested that IL-6 inhibited the expression of visfatin and PPAR-γ at the transcriptional level; in addition, activation of PPAR-γ upregulated visfatin at the mRNA and protein expression levels. Therefore, the PPAR-γ signaling pathway may be involved in the regulation of visfatin by IL-6 in BeWo cells. These results may provide novel insight into the roles of visfatin in trophoblastic cells. Furthermore, thiazolidinedione pioglitazone, by upregulating visfatin expression, may promote the energy metabolism of trophoblastic cells, maintain the function of the placenta and improve the outcome of pregnancy.
PROBLEMS AND POTENTIAL OF AUTOCALIBRATING A HYDROLOGIC MODEL

An investigation was conducted to evaluate strengths and limitations of manual calibration and the existing autocalibration tool in the watershed-scale model referred to as the Soil and Water Assessment Tool (SWAT). Performance of the model was tested on the Little River Experimental Watershed in Georgia and the Little Washita River Experimental Watershed in Oklahoma, both USDA-ARS watersheds. A long record of multi-gauge streamflow data on each of the watersheds was used for model calibration and validation. Model performance of the streamflow response in SWAT was assessed using a six-parameter manual calibration based on daily mass balance and visual inspection of hydrographs and duration of daily flow curves, a six-parameter autocalibration method based on the daily sum of squares of the residuals after ranking objective function (referred to as SSQRauto6), a six-parameter method based on the daily sum of squares of residuals (SSQauto6), and an eleven-parameter method based on the daily sum of square of residuals (SSQauto11). Results show that for both watersheds, manual calibration generally outperformed the autocalibration methods based on percent bias (PBIAS) and simulation of the range in magnitude of daily flows. For the calibration period on Little River subwatershed F, PBIAS was 0.0%, -24.0%, -21.5%, and +29.0% for the manual, SSQRauto6, SSQauto6, and SSQauto11 methods, respectively. Based on the coefficient of efficiency (NSE), the SSQauto6 and SSQauto11 methods gave substantially better results than manual calibration on the Little River watershed. On the Little Washita watershed, however, the manual approach generally outperformed the automated methods, based on the NSE error statistic. Results of this study suggest that the autocalibration option in SWAT provides a powerful, labor-saving tool that can be used to substantially reduce the frustration and uncertainty that often characterize manual calibrations. If used in combination with a manual approach, the autocalibration tool shows promising results in providing initial estimates for model parameters. To maintain mass balance and adequately represent the range in magnitude of output variables, manual adjustments may be necessary following autocalibration. Caution must also be exercised in utilizing the autocalibration tool so that the selection of initial lower and upper ranges in the parameters results in calibrated values that are representative of watershed conditions.
Stabilizer Design for Heterogeneous Types of Distributed Generators in Microgrids Operating in a Unified Control Mode

This paper proposes a stabilizer to suppress the large transients subsequent to disconnection/reconnection of microgrids (MGs) that are controlled in a unified mode. In the unified control mode, the control system requires to operate in grid-connected mode, islanded mode, and mode transition transients with a unique control configuration. The MG is assumed to contain heterogeneous types of distributed generators (DGs) that include electronically interfaced DGs (EI-DGs) and synchronous generators (SGs). The simultaneous operation of the EI-DGs and SGs due to their different voltage and frequency control strategies makes the design of the stabilizer more challenging. A general Lyapunov function is defined considering the response delay of the primary resources. The deteriorating effects of the primary resource response delays on the mode transition transients are evaluated analytically. Furthermore, the asymptotic stability in the frequency and voltage loops are guaranteed by the proposed stabilizer that solely utilizes local information. The proposed stabilizer is designed and implemented for both EI-DGs and SGs. Finally, the effectiveness of the proposed control strategy is demonstrated using time-domain simulations on a multiresource MG. The performance of the proposed method is also compared with that of an existing method.
A haptic system for a multifingered hand

A series of haptic exploratory procedures (EPs) implemented for a multifingered, articulated, sensory robot hand is discussed. These EPs are designed to extract specific tactile and kinesthetic information from an object via their purposive invocation by an intelligent robotic system. Taken together, they form an active robotic touch perception system. This system utilizes a PUMA 560 robot arm, a JPL/Stanford robot hand with joint torque sensing in the fingers, a wrist force/torque sensor, and a 256-element spatially resolved fingertip tactile array. The EPs are described, and experimental results are given. <<ETX>>
Online inverse reinforcement learning for nonlinear systems

This paper focuses on the development of an online inverse reinforcement learning (IRL) technique for a class of nonlinear systems. The developed approach utilizes observed state and input trajectories, and determines the unknown reward function and the unknown value function online. A parameter estimation technique is utilized to facilitate estimation of the reward function in the presence of unknown dynamics. Theoretical guarantees for convergence of the reward function estimates are established, and simulation results are presented to demonstrate the performance of the developed technique.
Application of Relevance Vector Machines in Real Time Intrusion Detection

In the recent years, there has been a growing interest in the development of change detection techniques for the analysis of Intrusion Detection. This interest stems from the wide range of applications in which change detection methods can be used. Detecting the changes by observing data collected at different times is one of the most important applications of network security because they can provide analysis of short interval on global scale. Research in exploring change detection techniques for medium/high network data can be found for the new generation of very high resolution data. The advent of these technologies has greatly increased the ability to monitor and resolve the details of changes and makes it possible to analyze. At the same time, they present a new challenge over other technologies in that a relatively large amount of data must be analyzed and corrected for registration and classification errors to identify frequently changing trend. In this research paper an approach for Intrusion Detection System (IDS) which embeds a Change Detection Algorithm with Relevance Vector Machine (RVM) is proposed. IDS are considered as a complex task that handles a huge amount of network related data with different parameters. Current research work has proved that kernel learning based methods are very effective in addressing these problems. In contrast to Support Vector Machines (SVM), the RVM provides a probabilistic output while preserving the accuracy. The focus of this paper is to model RVM that can work with large network data set in a real environment and develop RVM classifier for IDS. The new model consists of Change Point (CP) and RVM which is competitive in processing time and improve the classification performance compared to other known classification model like SVM. The goal is to make the system simple but efficient in detecting network intrusion in an actual real time environment. Results show that the model learns more effectively, automatically adjust to the changes and adjust the threshold while minimizing the false alarm rate with timely detection.
Part-level Car Parsing and Reconstruction from Single Street View

Part information has been shown to be resistant to occlusions and viewpoint changes, which is beneficial for various vision-related tasks. However, we found very limited work in car pose estimation and reconstruction from street views leveraging the part information. There are two major contributions in this paper. Firstly, we make the first attempt to build a framework to simultaneously estimate shape, translation, orientation, and semantic parts of cars in 3D space from a single street view. As it is labor-intensive to annotate semantic parts on real street views, we propose a specific approach to implicitly transfer part features from synthesized images to real street views. For pose and shape estimation, we propose a novel network structure that utilizes both part features and 3D losses. Secondly, we are the first to construct a high-quality dataset that contains 348 different car models with physical dimensions and part-level annotations based on global and local deformations. Given these models, we further generate 60K synthesized images with randomization of orientation, illumination, occlusion, and texture. Our results demonstrate that our part segmentation performance is significantly improved after applying our implicit transfer approach. Our network for pose and shape estimation achieves the state-of-the-art performance on the ApolloCar3D dataset and outperforms 3D-RCNN and DeepMANTA by 12.57 and 8.91 percentage points in terms of mean A3DP-Abs.
Densely-Connected Multi-Magnification Hashing for Histopathological Image Retrieval

Content-based medical image retrieval is an important computer-aided diagnosis technique providing the clinicians with interpretative references based on visual similarity. In this paper, we focus on the tasks of histopathological image retrieval for breast cancer diagnosis. The densely-connected multi-magnification (DCMMH) framework is proposed to generate the discriminative binary codes by exploiting the histopathological images with multiple magnification factors. The low-magnification images are boosted by the accumulated similarity based on local patches that also regularize the feature learning of high-magnification images. In order to fully utilize the information across different magnification levels, a densely-connected architecture is finally deployed for high–low magnification pairs of datasets. Experiments on BreakHis dataset demonstrate that, DCMMH outperforms the previous hashing methods on histopathological image retrieval.
Dosimetric evaluation of an atlas‐based synthetic CT generation approach for MR‐only radiotherapy of pelvis anatomy

Abstract Purpose To investigate the potential of an atlas‐based approach in generation of synthetic CT for pelvis anatomy. Methods Twenty‐three matched pairs of computed tomography (CT) and magnetic resonance imaging (MRI) scans were selected from a pool of prostate cancer patients. All MR scans were preprocessed to reduce scanner‐ and patient‐induced intensity inhomogeneities and to standardize their intensity histograms. Ten (training dataset) of 23 pairs were then utilized to construct the coregistered CT‐MR atlas. The synthetic CT for a new patient is generated by appropriately weighting the deformed atlas of CT‐MR onto the new patient MRI. The training dataset was used as an atlas to generate the synthetic CT for the rest of the patients (test dataset). The mean absolute error (MAE) between the deformed planning CT and synthetic CT was computed over the entire CT image, bone, fat, and muscle tissues. The original treatment plans were also recomputed on the new synthetic CTs and dose–volume histogram metrics were compared. The results were compared with a commercially available synthetic CT Software (MRCAT) that is routinely used in our clinic. Results MAE errors (±SD) between the deformed planning CT and our proposed synthetic CTs in the test dataset were 47 ± 5, 116 ± 12, 36 ± 6, and 47 ± 5 HU for the entire image, bone, fat, and muscle tissues respectively. The MAEs were 65 ± 5, 172 ± 9, 43 ± 7, and 42 ± 4 HU for the corresponding tissues in MRCAT CT. The dosimetric comparison showed consistent results for all plans using our synthetic CT, deformed planning CT and MRCAT CT. Conclusion We investigated the potential of a multiatlas approach to generate synthetic CT images for the pelvis. Our results demonstrate excellent results in terms of HU value assignment compared to the original CT and dosimetric consistency.
The Luohu Model: A Template for Integrated Urban Healthcare Systems in China

Introduction: Emerging from the epidemiological transition and accelerated aging process, China’s fragmentated healthcare systems struggle to meet the demands of the population. On Sept 1st 2017, China’s National Health and Family Planning Commission encouraged all cities to learn from the Luohu model of integration adopted in Luohu as an approach to meeting these challenges. In this paper, we study the integration process, analyze the core mechanisms, and conduct preliminary evaluations of integrated policy development in the Luohu model. Policy development: The Luohu hospital group was established in Aug 2015, consists of five district hospitals, 23 community health stations and an institute of precision medicine. The group adopted a series of professional, organizational, system, functional and normative strategies for integrated care, which was provided for the residents of Luohu, especially for the elderly population and patients with chronic conditions. According to a preliminary evaluation of the past two years, the Luohu model showed improvement in the structure and process towards integrated care. New preventive programs conducted in the hospital group resulted in changes of disease incidence. Residents were more satisfied with the Luohu model. However, spending exceeded the global budget for health insurance because of short-term increases in the demand for health care. Lessons learned: First, engagement of multiple stakeholders is essential for the design and implementation of reform. Second, organizational integration is a prerequisite for integrated care in China. Third, effective care integration requires alignment with payment reforms. Fourth, normative integration could promote collaboration in an integrated healthcare system. Conclusion: Core strategies and mechanisms of the Luohu model will promote integrated care in urban China and other countries facing the same challenges. However, it is necessary to study the effects of the Luohu model over the long term and continue to strive for integrated care.
The morphology and metabolic potential of the Chloroflexi in full‐scale activated sludge wastewater treatment plants

&NA; Filamentous bacteria belonging to the phylum Chloroflexi have received considerable attention in wastewater treatment systems for their suggested role in the operational problem of impaired sludge settleability known as bulking. Their consistently high abundance in full‐scale systems, even in the absence of bulking, indicates that they make a substantial contribution to the nutrient transformations during wastewater treatment. In this study, extensive 16S rRNA amplicon surveys of Danish wastewater treatment plants (WWTPs) with nutrient removal were screened to identify numerically important Chloroflexi genera. Fluorescence in situ hybridization probes were designed for their in situ characterization. All abundant Chloroflexi phylotypes were putatively identified as facultative anaerobic chemoorganotrophs involved in sugar fermentation. They were all filamentous but differed in their morphology and spatial arrangement. ‘Candidatus Villigracilis’ was predominantly located within the activated sludge flocs, where they possibly have structural importance, and their abundance was relatively stable. Conversely, the abundance of ‘Candidatus Amarolinea’ was highly dynamic, relative to other genera, sometimes reaching abundances in excess of 30% of the biovolume, suggesting their likely role in bulking episodes. This study gives an important insight into the role of Chloroflexi in WWTPs, thus contributing to the broader goal of understanding the ecology of these biotechnologically important systems. &NA; Graphical Abstract Figure. The core members of the Chloroflexi community in full‐scale wastewater treatment plants were identified as novel genera and their ecophysiology characterised, suggesting roles in carbohydrate breakdown and sludge settleability problems.