Miracle-dz/UniFAR-FTUs huggingface dataset:
Does Public Investment Enhance Labor Productivity Growth in Argentina? A Cointegration Analysis

This paper addresses the important question of whether public investment spending on economic infrastructure enhances economic growth and labor productivity in Argentina. Following the lead of the endogenous growth literature, it presents a simple modified production function that explicitly includes the positive or negative externality effects generated by public investment. Using cointegration analysis, the paper estimates a dynamic labor productivity function for the 1960-2005 period that incorporates the impact of public and private investment spending and the labor force (rather than the rate of population growth). The results suggest that (lagged) increases in public investment spending on economic infrastructure--as opposed to overall public investment spending--have a positive and significant effect on the rate of labor productivity growth. In addition, the model is estimated for a shorter period (1970-2005) to capture the impact of foreign direct investment. The estimates suggest that foreign direct investment spending has a lagged positive and significant impact on labor productivity growth, while increases in the labor force have a negative effect. Thus, the findings call into question the politically expedient policy in many Latin American countries, including Argentina during the 1990s, of disproportionately reducing public capital expenditures to meet reductions in the fiscal deficit as a proportion of GDP.
Self-tuning M-estimators

M-estimators are the de-facto standard method of robust estimation in robotics. They are easily incorporated into iterative non-linear least-squares estimation and provide seamless and effective handling of outliers in data. However, every M-estimator's robust loss function has one or more tuning parameters that control the influence of different data. The choice of M-estimator and the manual tuning of these parameters is always a source of uncertainty when applying the technique to new data or a new problem. In this paper we develop the concept of self-tuning M-estimators. We first make the connection between many common M-estimators and elliptical probability distributions. This connection shows that the choice of M-estimator is an assumption that the residuals belong to a well-defined elliptical distribution. We exploit this implication in two ways. First, we develop an algorithm for tuning the M-estimator parameters during iterative optimization. Second, we show how to choose the correct M-estimator for your data by examining the likelihood of the data given the model. We fully derive these algorithms and show their behavior on a representative example of visual simultaneous localization and mapping.
Chain based Conical Clustering (CCC) framework for data gathering in WSNs

Non uniform energy consumption of the nodes is a major area of concern in cluster based data gathering architectures proposed for wireless sensor networks in the literature and is an active area of research over recent past years. The solutions suggested in the literature, for uniform distribution of energy consumption, require rotation of clusterhead role among sensor nodes but every such rotation needs reclustering of the nodes in the network. The overhead associated with such reclustering are not actual part of data gathering activities and consume energy unnecessarily only for creating and maintaining the clusters. To avoid such unnecessary wastage of energy, due to forced reclustering associated with the clusterhead role rotation, a new hybrid clustering framework, chain based conical clustering (CCC) scheme, is proposed in this paper. CCC keeps the cluster stable and rotates the clusterhead role among the cluster members using layered chains of these cluster members. It is proved, theoretically, that CCC is expected to outperform the scheme used as a benchmark, in the clustered data gathering performance comparison, in wireless sensor network literature.
A Review on Semi Supervised Text Classification

With the ever-increasing quantity of text data from a variety of online sources, it is a significant task to categorize or classify these text documents into categories that are manageable and easy to understand. In our old world, learning has been studied either in the unsupervised paradigm which include clustering where all the data is unlabeled, or in the supervised paradigm which include classification where all the data is labeled. A supervised classification of text demands labeled instances which are often arduous, formidable, expensive, or time consuming to obtain. During the intervening time, unlabeled data may be relatively easy to collect, but there are a couple of ways to use them and this method often clusters blindly. Semi-supervised learning figure out this problem by using labeled data together with large amount of unlabeled data to build better classifiers. We also make contribution towards this goal along several dimensions. This paper presents a survey on semi supervised methods of text classification using several Methods.
Improving false alarm rate in intrusion detection systems using Hadoop

Intrusion Detection Systems are a vital part of an organization's security. This paper gives an account of the existing algorithms for Intrusion Detection using Machine Learning, along with certain new ideas for improving the same. The paper mainly talks about employing the Decision Tree mechanism for Intrusion Detection and improve it with the distributed file system, Hadoop. Initially a method that uses a dirty-flags to check the consistency of the Decision Tree, which changes with every wrong classification of the system is employed. The wrong classification is identified by a certain user who informs the system about the same and helps it learn. In the further sections, a new method which does not use a dirty-flag, but rather modifies the Key-Value pair in the results of the reduce() function is tested as an improvement to the previous method. The two methods are compared, with the help of the Hadoop Simulation Tool - YARN. The main aim of the paper is to propose the use of the Distributed File System for Machine Learning along with some improvements to the current Hadoop File System, so that it reduces the total Time Taken, when Machine Learning algorithms are employed along with it.
A survey on merging DNA fragments using soft computing algorithm

DNA material is present in all living things and it contains all relative information. DNA material is very long in nature and contains more than 10,000 base pairs. These base pairs contains mainly four element i.e. adenine (A), guanine (G), cytosine(C), thymine (T). To study DNA directly is very difficult task. DNA fragmentation is the separation or breaking of DNA strands into pieces. DNA fragmentation helps in analyzing or processing on DNA fragments. After DNA fragmentation, it requires reconstruction to form original DNA to identify the living thing. Research has shown that the problems like Computational errors, Unknown origin of DNA, Repetition of similar sequence occur while reconstructing the fragments into the original one. To solve or minimize these problems many researchers have proposed different methods. In this paper, several methods are discussed as well as reviewed. Also a new method is proposed. The Proposed algorithm is based on Particle Swarm Optimization (PSO).
Constrained modifications of non-manifold B-reps

Non-manifold boundary representations (b-reps) are increasingly used in Geosciences for a variety of applications (3D geographical information systems, basin modeling, geophysical processing, etc.). Meanwhile, the uncertainties associated with subsurface data make it desirable to modify such models efficiently.We present a method to deform locally a surface in a triangulated b-rep while maintaining a constant number of spatial regions in the model. This method does not require completely rebuilding the model, and thus allows efficient and robust updates of the model definition.The method requires that the reshaped surface does not intersect the boundaries of its adjoining regions, which can be checked using existing collision detection algorithms. Also, the non-manifold contacts must be updated after the modification, and the triangles must be altered, to maintain sealed regions.For this, we propose to parameterize locally the surfaces that the modified surface moves along. This parametric space is used to 1) constrain the displacement of the deformed surface border and, 2) re-triangulate in the plane the neighboring surfaces around the modified contacts. The method, tested in the context of an interactive graphical manipulator, is very efficient and independent from the deformation mechanism.
Effect of albumin dialysis on intracranial pressure increase in pigs with acute liver failure: A randomized study*

Background:Increased intracranial pressure (ICP) worsens the outcome of acute liver failure (ALF). This study investigates the underlying pathophysiological mechanisms and evaluates the therapeutic effect of albumin dialysis in ALF with use of the Molecular Adsorbents Recirculating System without hemofiltration/dialysis (modified, M-MARS). Methods:Pigs were randomized into three groups: sham, ALF, and ALF + M-MARS. ALF was induced by hepatic devascularization (time = 0). M-MARS began at time = 2 and ended with the experiment at time = 6. ICP, arterial ammonia, brain water, cerebral blood flow (CBF), and plasma inflammatory markers were measured. Results:ICP and arterial ammonia increased significantly over 6 hrs in the ALF group, in comparison with the sham group. M-MARS attenuated (did not normalize) the increased ICP in the ALF group, whereas arterial ammonia was unaltered by M-MARS. Brain water in the frontal cortex (grey matter) and in the subcortical white matter at 6 hrs was significantly higher in the ALF group than in the sham group. M-MARS prevented a rise in water content, but only in white matter. CBF and inflammatory mediators remained unchanged in all groups. Conclusion:The initial development of cerebral edema and increased ICP occurs independently of CBF changes in this noninflammatory model of ALF. Factor(s) other than or in addition to hyperammonemia are important, however, and may be more amenable to alteration by albumin dialysis.
An Examination of the Applicability of Social Cognitive Career Theory for African American College Students

The purpose of the present study is to extend the literature on social cognitive career theory (SCCT) by examining (a) the applicability of SCCT for African Americans with constructs defined in terms of Holland’s realistic, investigative, artistic, social, enterprising, and conventional (RIASEC) themes and (b) the role of specific learning experiences (performance accomplishments, vicarious learning, and verbal persuasion) in the formation of corresponding self-efficacy and outcome expectations. Structural equation modeling (SEM) results based on a sample of 208 African American college students revealed support for hypothesized relations of self-efficacy with outcome expectations, self-efficacy and outcome expectations with interests and choice goals, and interests with choice goals for all six RIASEC themes. Results revealed partial support for the hypothesized relations of learning experiences with self-efficacy and outcome expectations. Finally, results indicated limited support for the hypothesis that verbal persuasion would be a stronger positive predictor of self-efficacy and outcome expectations than would performance accomplishments and vicarious learning. These findings offer preliminary support for the applicability of SCCT in explaining African American college students’ RIASEC-based interest development and career choice goals. Implications of the findings for career counseling interventions and future research are discussed.
Real-time hand tracking using integrated optical flow and CAMshift algorithm

Hand gesture interfaces are more convenient, natural, intuitive, and user-friendly form of input for HumanComputer Interaction(HCI). Hand detection and tracking are the most vital stages for any kind of hand gesture based interface and the accuracy of the final gesture recognition algorithm depends substantially on the proper and correct segmentation of hand from incoming video frames in real time. This paper proposes a novel hand tracking algorithm, that combines Continuous Adaptive Mean Shift Algorithm(CAMshift), Shi-Tomasi points, and Lukas Kanade Optical flow to track hand with high accuracy in real time using only a single camera in non-limiting and unrestrained environment. Results obtained reflect that the algorithm can precisely track the hand of an operator in an input video sequence obtained from a web-cam at 30fps.
Bayesian Inference for Continuous-Time ARMA Models Driven by Jump Diffusions

This paper investigates the problem of Bayesian parameter estimation for continuous-time autoregressive moving average (CARMA) models driven by jump diffusions. Inference is performed through the evaluation of the likelihood function conditional on jump times, and the realized jump sizes are marginalized assuming they are normally distributed. A Markov chain Monte Carlo (MCMC) algorithm is then developed to explore the parameter space based on the conditional likelihood and an assumed prior structure. Finally, simulations are provided to demonstrate the potential of our methods.
ELECTRON MICROSCOPIC AND HISTOCHEMICAL OBSERVATIONS OF MUSCLE DEGENERATION AFTER TOURNIQUET

As an experimental model for the different forms of muscle degeneration, injury caused by 2 hours' ischemia has been studied from 20 minutes to 16 hours after release of the tourniquet. Discoid degeneration developed in stretched fibers by dissolution of the I bands (Z substances and actin). The discs represented the Q bands (A-H-A). In fibers which passively maintained contraction lengths during degeneration, the Z substances were dissolved, but the continuity of the fibrils was preserved, since the filaments are continuous over all sarcomeres under these conditions. Mitochondria and the tubules of the endoplasmic reticulum swelled, ruptured, and disintegrated. Granular degeneration developed in fibers where mitochondria were abundant. Unstretched degenerating fibers with few mitochondria gave a homogeneous or hyaline appearance. The different forms of degeneration therefore were dependent on the status of stretch and the fiber type. The extent of degeneration was not a function of time after ischemia, there being both nearly normal and severely damaged fibers at 20 minutes and 16 hours after the release of tourniquets. When degeneration occurred, however, the basic alterations were the same in all fibers; there was mitochondrial and reticular swelling, dissolution of the Z substances, and finally disintegration of the contractile material. Some damage developed in the sarcolemmas and capillaries. The mitochondrial disintegration was not linked with inactivation of the succinic dehydrogenase system.
Fine Tuning and Efficient T Cell Activation with Stimulatory aCD3 Nanoarrays

Anti-CD3 (aCD3) nanoarrays fabricated by self-assembled nanopatterning combined with site-directed protein immobilization techniques represent a novel T cell stimulatory platform that allows tight control over ligand orientation and surface density. Here, we show that activation of primary human CD4+ T cells, defined by CD69 upregulation, IL-2 production and cell proliferation, correlates with aCD3 density on nanoarrays. Immobilization of aCD3 through nanopatterning had two effects: cell activation was significantly higher on these surfaces than on aCD3-coated plastics and allowed unprecedented fine-tuning of T cell response.
Face recognition using eyes, nostrils and mouth features

This paper describes a face recognition algorithm that extracts the eyes, nostrils and mouth features from cumulative distribution function (CDF) by applying Otsu thresholding. The algorithm, which is inspired by the probability of white pixels of binary facial image, has been tested using the BioID frontal face large database in different illuminations, expressions and lighting conditions. Illumination and lighting variations are addressed using a selective equalization technique. The experimental results have confirmed an average recognition rate of 93.55%.
Knowledge Sources for Textual CBR Applications

Textual CBR applications address issues that have traditionally been dealt with in the Information Retrieval community, namely the handling of textual documents. As CBR is a knowledge-based technique, the question arises where items of knowledge may come from and how they might contribute to the implementation of a Textual CBR system. In this paper, we will show how various pieces of knowledge available in a specific domain can be utilized for acquiring the knowledge required for a CBR system.
Tau Oligomers: The Toxic Player at Synapses in Alzheimer’s Disease

Alzheimer’s disease (AD) is a progressive disorder in which the most noticeable symptoms are cognitive impairment and memory loss. However, the precise mechanism by which those symptoms develop remains unknown. Of note, neuronal loss occurs at sites where synaptic dysfunction is observed earlier, suggesting that altered synaptic connections precede neuronal loss. The abnormal accumulation of amyloid-β (Aβ) and tau protein is the main histopathological feature of the disease. Several lines of evidence suggest that the small oligomeric forms of Aβ and tau may act synergistically to promote synaptic dysfunction in AD. Remarkably, tau pathology correlates better with the progression of the disease than Aβ. Recently, a growing number of studies have begun to suggest that missorting of tau protein from the axon to the dendrites is required to mediate the detrimental effects of Aβ. In this review we discuss the novel findings regarding the potential mechanisms by which tau oligomers contribute to synaptic dysfunction in AD.
Accuracy of muscle moment arms estimated from MRI-based musculoskeletal models of the lower extremity.

OBJECTIVE Biomechanical models that compute the lengths and moment arms of soft tissues are broadly applicable to the treatment of movement abnormalities and the planning of orthopaedic surgical procedures. The goals of this study were to: (i) develop methods to construct subject-specific biomechanical models from magnetic resonance (MR) images, (ii) create models of three lower-extremity cadaveric specimens, and (iii) quantify the accuracy of muscle-tendon lengths and moment arms estimated using these models. MATERIALS AND METHODS Models describing the paths of the medial hamstrings and psoas muscles for a wide range of body positions were developed from MR images in one joint configuration by defining kinematic models of the hip and knee, and by specifying "wrapping surfaces" that simulate interactions between the muscles and underlying structures. Our methods for constructing these models were evaluated by comparing hip and knee flexion moment arms estimated from models of three specimens to the moment arms determined experimentally on the same specimens. Because a muscle's moment arm determines its change in length with joint rotation, these comparisons also tested the accuracy with which the models could estimate muscle-tendon lengths over a range of hip and knee motions. RESULTS Errors in the moment arms calculated with the models, averaged over functional ranges of hip and knee flexion, were less than 4 mm (within 10% of experimental values). CONCLUSION The combination of MR imaging and graphics-based musculoskeletal modeling provides an accurate and efficient means of estimating muscle-tendon lengths and moment arms in vivo.
Auction Markets for Evaluations

When the value of a product or service is uncertain, outcomes can be inefficient. A market for evaluations can theoretically increase efficiency by voluntarily eliciting an evaluation that would otherwise not be provided. This paper uses a controlled laboratory experiment to test the performance of four market mechanisms to provide product evaluations. The mechanisms considered are derived from the oft studied uniform price sealed bid, discriminatory price sealed bid, English clock auction, and Dutch clock auction. Our results indicate for this nonrivalrous product that (i) each of these institutions improves social welfare and (ii) the performances of the four mechanisms are equivalent. This second point is particularly noteworthy given that differing behavior is routinely observed in traditional private value auctions.
A Real Business Cycles Model with Changing Sentiments by Kirill

In this paper the modification of the real business cycles model in which risk aversion parameter of agents’ utility function follows bivariate markov chain is developed and estimated using simulated VAR. The model’s ability to replicate properties of US quarterly data is compared with that of the standard real business cycles model. The main finding is that the model with markov switching performs at least well as the standard model. The model with markov switching also matches some features of the data which the standard RBC model is unable to match.
Removing Brakes on Adult Brain Plasticity: From Molecular to Behavioral Interventions

Adult brain plasticity, although possible, remains more restricted in scope than during development. Here, we address conditions under which circuit rewiring may be facilitated in the mature brain. At a cellular and molecular level, adult plasticity is actively limited. Some of these “brakes” are structural, such as perineuronal nets or myelin, which inhibit neurite outgrowth. Others are functional, acting directly upon excitatory-inhibitory balance within local circuits. Plasticity in adulthood can be induced either by lifting these brakes through invasive interventions or by exploiting endogenous permissive factors, such as neuromodulators. Using the amblyopic visual system as a model, we discuss genetic, pharmacological, and environmental removal of brakes to enable recovery of vision in adult rodents. Although these mechanisms remain largely uncharted in the human, we consider how they may provide a biological foundation for the remarkable increase in plasticity after action video game play by amblyopic subjects.
Advanced methodology useful for exploring distortions in confocal microscopy medical images: Application to neurodegenerative pathology

Alzheimer's disease is a neurodegenerative disease that primarily affects the nervous system and is characterized by a progressive loss of nerve cells. In order to understand and explore such pathology, confocal microscope images could be widely used because of their interesting high clarity in term of cell identification. In fact, confocal microscopy is a potential tool that enables real-time imaging of living tissue at high resolution and high contrast. However, in practical microscopy, aberration-free confocal imaging is rarely achieved. So, aberration corrected images of biological specimens show considerable improvement. Geometric aberrations are considered as the main sources of image degradation. The purpose of this project was to develop an advanced method that is able to estimate the distortion parameters and to test these parameters to correct image deformations. Such method was based on the adaptation of the Zhang's algorithm implemented in the OpenCV library in order to calibration of confocal microscopy images and results of simulation were interesting and promoting and could involve other research areas such as stitching confocal microscopic images and working in high resolution.