Miracle-dz/UniFAR-FTUs huggingface dataset:
POLÍTICA CAMBIAL COM DÍVIDA INDEXADA EM MOEDA ESTRANGEIRA NO BRASIL, 1995-2004

This paper discusses exchange rate policy in presence of currency-linked domestic debt. It argues that the reasons of that debt are not to be found in the goals of optimal debt composition, but rather among the exchange rate policy objectives. It presents a historical perspective, starting with the introduction of the ORTN with dollar-indexation clause by the end of the 60’s. It is presented a brief survey of the literature on the short-term determinants of the exchange rate, starting with Meese and Rogoff (1983), which had introduced the idea that a random walk is a better forecast to exchange rate than any other model based on macroeconomic fundamentals. It is also presented a review of the literature on foreign exchange intervention, which offers plenty of works devoted on developed countries and scarce contributions on emerging countries. The offering of currency-linked domestic bonds is treated as foreign exchange intervention, in search for its effects on the exchange rate level and volatility. The econometric exercise built a new database of the stock of currency-linked domestic on a daily basis, through aggregation of primary auctions of bonds and swaps over the period between 1995 and 2004. The exercise estimated the impact of such debt over foreign currency prices in two periods, namely the administered regime (1995-1999) and the floating period (1999-2004). It was used a Vector Error Correction Model (VECM). Estimated elasticities showed a possible simultaneity bias, which was detected through a Granger causality test. The latter provided evidence of a feedback-type bi-directional causality. Estimations are subject to heteroskedasticity, which was corrected through GARCH modeling. This test showed that the elasticity of the exchange rate with respect to intervention with currency-linked domestic debt was -0.17 in the administered regime and 2.15 in the floating regime.
On Adaptive Learning Rate That Guarantees Convergence in Feedforward Networks

This paper investigates new learning algorithms (LF I and LF II) based on Lyapunov function for the training of feedforward neural networks. It is observed that such algorithms have interesting parallel with the popular backpropagation (BP) algorithm where the fixed learning rate is replaced by an adaptive learning rate computed using convergence theorem based on Lyapunov stability theory. LF II, a modified version of LF I, has been introduced with an aim to avoid local minima. This modification also helps in improving the convergence speed in some cases. Conditions for achieving global minimum for these kind of algorithms have been studied in detail. The performances of the proposed algorithms are compared with BP algorithm and extended Kalman filtering (EKF) on three bench-mark function approximation problems: XOR, 3-bit parity, and 8-3 encoder. The comparisons are made in terms of number of learning iterations and computational time required for convergence. It is found that the proposed algorithms (LF I and II) are much faster in convergence than other two algorithms to attain same accuracy. Finally, the comparison is made on a complex two-dimensional (2-D) Gabor function and effect of adaptive learning rate for faster convergence is verified. In a nutshell, the investigations made in this paper help us better understand the learning procedure of feedforward neural networks in terms of adaptive learning rate, convergence speed, and local minima
Awaiting the second big data revolution BRIE Working Paper 2014-03 ∗

“Big data” has been heralded as the agent of a third industrial revolution–one with raw materials measured in bits, rather than tons of steel or barrels of oil. Yet the industrial revolution transformed not just how firms made things, but the fundamental approach to value creation in industrial economies. To date, big data has not achieved this distinction. Instead, today’s successful big data business models largely use data to scale old modes of value creation, rather than invent new ones altogether. Moreover, today’s big data cannot deliver the promised revolution. In this way, today’s big data landscape resembles the early phases of the first industrial revolution, rather than the culmination of the second a century later. Realizing the second big data revolution will require fundamentally different kinds of data, different innovations, and different business models than those seen to date. That fact has profound consequences for the kinds of investments and innovations firms must seek, and the economic, political, and social consequences that those innovations portend. ∗Copyright 2014 Mark Huberty †Prepared for the 2013 BRIE-ETLA Conference, September 6-7, Claremont California. This paper has benefited from extended discussions with Cathrn Carson, Drew Conway, Chris Diehl, Stu Feldman, David Gutelius, Jonathan Murray, Joseph Reisinger, Sean Taylor, Georg Zachmann, and John Zysman. All errors commmitted, and opinions expressed, remain solely my own.
Desorption of 1,3,5-Trichlorobenzene from Multi-Walled Carbon Nanotubes: Impact of Solution Chemistry and Surface Chemistry

The strong affinity of carbon nanotubes (CNTs) to environmental contaminants has raised serious concern that CNTs may function as a carrier of environmental pollutants and lead to contamination in places where the environmental pollutants are not expected. However, this concern will not be realized until the contaminants are desorbed from CNTs. It is well recognized that the desorption of environmental pollutants from pre-laden CNTs varies with the environmental conditions, such as the solution pH and ionic strength. However, comprehensive investigation on the influence of solution chemistry on the desorption process has not been carried out, even though numerous investigations have been conducted to investigate the impact of solution chemistry on the adsorption of environmental pollutants on CNTs. The main objective of this study was to determine the influence of solution chemistry (e.g., pH, ionic strength) and surface functionalization on the desorption of preloaded 1,3,5-trichlorobenzene (1,3,5-TCB) from multi-walled carbon nanotubes (MWNTs). The results suggested that higher pH, ionic strength and natural organic matter in solution generally led to higher desorption of 1,3,5-TCB from MWNTs. However, the extent of change varied at different values of the tested parameters (e.g., pH < 7 vs. pH > 7). In addition, the impact of these parameters varied with MWNTs possessing different surface functional groups, suggesting that surface functionalization could considerably alter the environmental behaviors and impact of MWNTs.
Technology definition and users' participation: A case study on actor roles in systems development

The paper presents a reflection on the different actors roles in constructing a new technological system. The importance and the effects of active or passive user participation are discussed within two theoretical frameworks: Social Construction of Technology and Actor Network Theory. This paper describes an ethnographical observation on the development of a Customer Relationship Management system. During the development process, managers in the client organization seemed to only consider their own strategies, disregarding users’ expectations. Which difficulties can arise when developing an information system in this way? The study presented in this paper tries to set the ground for answering this question, following the steps of the development of the information system and analysing the moments in which the different actors’ strategies and ideas create unplanned situations.
The facial expression recognition method of random forest based on improved PCA extracting feature

In this paper, we propose a new framework for facial expression classification. This framework utilizes random forest as the classifier based on the features extracted from improved principal component analysis (PCA). Traditional PCA has two drawbacks: it is difficult to estimate the covariance matrix, and it is computational prohibitive to get the eigenvectors. In order to solve the two problems, we adopt an improved PCA to extract the features and uses the random forest algorithm as the classifier. The experiments are conducted based on the JAFFE facial expressions library, and are compared with the support vector machine (SVM) classifier. Theoretical analysis and experimental results show that the facial expression recognition system of random forest based on improved PCA extracting feature, whether in terms of facial expression recognition rate or running speed, has shown significant improvement.
MCM Based Fault Tolerant Filter Affirmed with Advanced ECC Method

Many applications relay on the different filters for the processing the signal at the different levels their entire operation is depends on the performance of the filter. After increase of the advance VLSI system design the area is important aspect. we cannot tolerate the huge area considerations hence causes the huge occurrence of the faults and failures which cannot be detected at designing and also in testing but that introduces at the real time operation conditions. We are turning on to the fault determination and diagnosis system, but in previous man We are turning on to the fault determination and diagnosis system, but in previous man systems like triple modular redundancy but they results in increases in area and power. We are providing the novel method for the fault detection and correction mechanism for the similar response filters by this method provides the less consideration and performance improve also can be attained above can be designed using the VERILOG HDL, simulated in model-sim and synthesized with XILINX
Neocortical neuron morphology in Afrotheria: comparing the rock hyrax with the African elephant

The mammalian neocortex contains a great variety of neuronal types. In particular, recent studies have shown substantial morphological diversity among spiny projecting neurons in species that diverged close to the base of the mammalian radiation (e.g., monotremes, afrotherians, and xenarthrans). Here, we used a Golgi technique to examine different neuronal morphologies in an afrotherian species, the rock hyrax (Procavia capensis), and provide a comparison with the related African elephant (Loxodonta africana). Results showed that spiny neurons in the rock hyrax neocortex exhibit less morphological variation than in elephants, displaying a higher frequency of relatively “typical” pyramidal neurons. A quantitative comparison of rock hyrax pyramidal neuron morphology between frontal and visual areas, moreover, revealed greater spine density of neurons in frontal cortex, but no differences in other morphological aspects. Regional variations in pyramidal structure have also been observed in the African elephant, as well as a number of primate species.
Gαo Represses Insulin Secretion by Reducing Vesicular Docking in Pancreatic β-Cells

OBJECTIVE Pertussis toxin uncoupling–based studies have shown that Gαi and Gαo can inhibit insulin secretion in pancreatic β-cells. Yet it is unclear whether Gαi and Gαo operate through identical mechanisms and how these G-protein–mediated signals inhibit insulin secretion in vivo. Our objective is to examine whether/how Gαo regulates islet development and insulin secretion in β-cells. RESEARCH DESIGN AND METHODS Immunoassays were used to analyze the Gαo expression in mouse pancreatic cells. Gαo was specifically inactivated in pancreatic progenitor cells by pancreatic cell–specific gene deletion. Hormone expression and insulin secretion in response to different stimuli were assayed in vivo and in vitro. Electron microscope and total internal reflection fluorescence–based assays were used to evaluate how Gαo regulates insulin vesicle docking and secretion in response to glucose stimulation. RESULTS Islet cells differentiate properly in Gαo−/− mutant mice. Gαo inactivation significantly enhances insulin secretion both in vivo and in isolation. Gαo nullizygous β-cells contain an increased number of insulin granules docked on the cell plasma membrane, although the total number of vesicles per β-cell remains unchanged. CONCLUSIONS Gαo is not required for endocrine islet cell differentiation, but it regulates the number of insulin vesicles docked on the β-cell membrane.
An Algorithm for Automated View Reduction in Weighted Clustering of Multiview Data

Clustering multiview data is one of the major research topics in the area of data mining. Multiview data can be defined as instances that can be viewed differently from different viewpoints. Usually while clustering data the differences among views are ignored. In this paper, a new algorithm for clustering multiview data is proposed. Here, both view and variable weights are computed simultaneously. The view weight is used to determine the closeness or density of view. Those views which have a weight less than a predefined value are considered insignificant and are eliminated. Variable weight is used to identify the significance of each variable. In order to determine the cluster of objects both these weights are used in the distance function. In the proposed method, enhancement to the usual iterative k-means is done so that it automatically computes both view and variable weights.
The cost of selective recompilation and environment processing

When a single software module in a large system is modified, a potentially large number of other modules may have to be recompiled. By reducing both the number of compilations and the amount of input processed by each compilation run, the turnaround time after changes can be reduced significantly. Potential time savings are measured in a medium-sized, industrial software project over a three-year period. The results indicate that a large number of compilations caused by traditional compilation unit dependencies may be redundant. On the available data, a mechanism that compares compiler output saves about 25 percent, smart recompilation saves 50 percent, and smartest recompilation may save up to 80 percent of compilation work. Furthermore, all compilation methods other than smartest recompilation process large amounts of unused environment data. In the project analyzed, the average environment symbols are actually used. Reading only the actually used symbols would reduce total compiler input by about 50 percent. Combining smart recompilation with a reduction in environment processing might double to triple perceived compilation speed and double linker speed, without sacrificing static type safety.
Identification of the Imprinted KLF14 Transcription Factor Undergoing Human-Specific Accelerated Evolution

Imprinted genes are expressed in a parent-of-origin manner and are located in clusters throughout the genome. Aberrations in the expression of imprinted genes on human Chromosome 7 have been suggested to play a role in the etiologies of Russell-Silver Syndrome and autism. We describe the imprinting of KLF14, an intronless member of the Krüppel-like family of transcription factors located at Chromosome 7q32. We show that it has monoallelic maternal expression in all embryonic and extra-embryonic tissues studied, in both human and mouse. We examine epigenetic modifications in the KLF14 CpG island in both species and find this region to be hypomethylated. In addition, we perform chromatin immunoprecipitation and find that the murine Klf14 CpG island lacks allele-specific histone modifications. Despite the absence of these defining features, our analysis of Klf14 in offspring from DNA methyltransferase 3a conditional knockout mice reveals that the gene's expression is dependent upon a maternally methylated region. Due to the intronless nature of Klf14 and its homology to Klf16, we suggest that the gene is an ancient retrotransposed copy of Klf16. By sequence analysis of numerous species, we place the timing of this event after the divergence of Marsupialia, yet prior to the divergence of the Xenarthra superclade. We identify a large number of sequence variants in KLF14 and, using several measures of diversity, we determine that there is greater variability in the human lineage with a significantly increased number of nonsynonymous changes, suggesting human-specific accelerated evolution. Thus, KLF14 may be the first example of an imprinted transcript undergoing accelerated evolution in the human lineage.
Explauto: an open-source Python library to study autonomous exploration in developmental robotics

The Explauto library provides a common platform to the Developmental Robotics community allowing the integration and comparison of various exploration strategies driving various sensorimotor learning algorithms in various simulated or robotics systems. Many other exploration strategies could be easily integrated into the library, as for example Direction Sampling [7], compression progress [4], empowerment [8] and thus be compared in a proper way on various sensorimotor systems and using various sensorimotor internal models.
Fiscal Policy Formulation and Implementation in Oil-Producing Countries

Countries with large oil resources can benefit substantially from them. However, despite their huge natural resources, many oil producers have had disappointing growth, widespread poverty, and continuing vulnerability to oil and other external shocks. Fiscal policy can play a central role in determining the extent to which a country benefits from its oil wealth. This book brings together studies that provide analysis and findings on fiscal policy issues in oil-producing countries from a diverse international perspective. A key focus for the authors has been on how to manage oil resources in a way that contributes to a stable macroeconomic environment, sustainable growth, and poverty reduction.
Problems with automatic restructurers

The value of automatic restructurers in the maintenance phase of the software life cycle will be discussed. Our discussions will centre on programs written in COBOL but the arguments are applicable to programs written in other languages. Some of the dangers of automatically restructuring a program are highlighted and the use of interactive restructurers to overcome these dangers are considered.
A fast and simple method of extracting the backbone of one-mode projections: An aid in the detection of communities

This paper introduces a computationally inexpensive method of extracting the backbone of one-mode networks projected from bipartite networks. We show that the edge weights in one-mode projections are distributed according to a Poisson binomial distribution. Finding the expected weight distribution of a one-mode network projected from a random bipartite network only requires knowledge of the bipartite degree distributions. Being able to extract the backbone of a projection proves to be highly beneficial to filter out redundant information in large complex networks and to narrow down the information in the one-mode projection to the most relevant. We further demonstrate that the backbone of a one-mode projection aids in the detection of communities.
CAPILLARY NETWORK ANOMALIES IN BRANCH RETINAL VEIN OCCLUSION ON OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY

Purpose: To analyze the foveal microvasculature features in eyes with branch retinal vein occlusion (BRVO) using optical coherence tomography angiography based on split spectrum amplitude decorrelation angiography technology. Methods: A total of 10 BRVO eyes (mean age 64.2 ± 8.02 range between 52 years and 76 years) were evaluated by optical coherence tomography angiography (XR-Avanti; Optovue). The macular angiography scan protocol covered a 3 mm × 3 mm area. The focus of angiography analysis were two retinal layers: superficial vascular network and deep vascular network. The following vascular morphological congestion parameters were assessed in the vein occlusion area in both the superficial and deep networks: foveal avascular zone enlargement, capillary non-perfusion occurrence, microvascular abnormalities appearance, and vascular congestion signs. Image analyses were performed by 2 masked observers and interobserver agreement of image analyses was 0.90 (&kgr; = 0.225, P < 0.01). Results: In both superficial and deep network of BRVO, a decrease in capillary density with foveal avascular zone enlargement, capillary non-perfusion occurrence, and microvascular abnormalities appearance was observed (P < 0.01). The deep network showed the main vascular congestion at the boundary between healthy and nonperfused retina. Conclusion: Optical coherence tomography angiography in BRVO allows to detect foveal avascular zone enlargement, capillary nonperfusion, microvascular abnormalities, and vascular congestion signs both in the superficial and deep capillary network in all eyes. Optical coherence tomography angiography technology is a potential clinical tool for BRVO diagnosis and follow-up, providing stratigraphic vascular details that have not been previously observed by standard fluorescein angiography. The normal retinal vascular nets and areas of nonperfusion and congestion can be identified at various retinal levels. Optical coherence tomography angiography provides noninvasive images of the retinal capillaries and vascular networks.
A robotic systems capable of some degree of self-sufficiency is the overall objective of an Autonomous Mobile Robot

, we present an algorithm for path planning to a target for mobile robot in unknown environment. The proposed algorithm allows a mobile robot to navigate through static obstacles, and finding the path in order to reach the target without collision. This algorithm provides the robot the possibility to move from the initial position to the final position (target). The proposed path finding strategy is designed in a grid-map form of an unknown environment with static unknown obstacles. The robot moves within the unknown environment by sensing and avoiding the obstacles coming across its way towards the target. When the mission is executed, it is necessary to plan an optimal or feasible path for itself avoiding obstructions in its way and minimizing a cost such as time, energy, and distance. In order to get an intelligent component, the use of Fuzzy Logic In order to get an intelligent component, the use of Fuzzy Logic (FL), and Expert Systems (ES) is necessary to bring the behavior of Intelligent Autonomous Vehicles (IAV). To present a real intelligent task and to deal with autonomy requirements such as power and thermal, (FL), and Expert Systems (ES) is necessary to bring the behavior of Intelligent Autonomous Vehicles (IAV). The aim work must make the robot able to achieve these tasks: to avoid obstacles, and to make ones way toward its target by ES_FL system capturing the behavior of a human expert. The integration of ES and FL has proven to be a way to develop useful realworld applications, and hybrid systems involving robust adaptive control. The proposed approach has the advantage of being generic and can be changed at the user demand. The results are satisfactory to see the great number of environments treated. The results are satisfactory and promising. Key-Words: Expert System, Fuzzy Logic FL), Intelligent Autonomous Vehicles (IAV), navigation, Path planning.
Geographic and Industrial Diversification of Developing Country Firms

This paper examines the impact of the industrial and geographical diversification activities of developing country firms on their performance, and draws attention to the unique attributes of these firms and of the circumstances under which their diversification activities take place. The empirical analysis is based on data from 345 developing country firms. The findings suggest significant and positive association between industrial and geographic diversification and performance, and considerable variation of these relationships across developing regions and diversification strategies. Copyright Blackwell Publishing Ltd 2004.