Miracle-dz/UniFAR-FTUs huggingface dataset:
Income Composition Endogenous Fertility and Schooling Investments in Children

This paper studies how the composition of income between mothers and fathers affects fertility and schooling investments in children, using data from the 1976 and 1996 PNAD, a Brazilian household survey. Income composition affects the time cost of fertility because mothers and fathers allocate different amounts of time to child-rearing. These effects are in turn transmitted to investments in children through a tradeoff between quantity and quality of children. The main contribution of this paper is twofold. First, it derives new implications about the relationship between household income composition and schooling investments in children. Second, this paper devises and implements an empirical approach to assess these implications, using two cross-sections of fertility and schooling data from Brazil. The main empirical findings of the paper can be summarized as follows. First, the empirical analysis shows that a larger negative effect of the mother's labor income on fertility in 1996 is associated with a larger positive effect on the adult child's schooling, reflecting the interaction between quantity and quality of children. Second, the larger negative effect of the mother's labor income on fertility in 1996 is associated with a reduction in the effect of other determinants of number of children. This suggests that an increase in the relative importance of time costs of fertility may be an important determinant of variations in fertility over time in Brazil and other developing countries.
A Regional Payroll Forecasting Model That Uses Bayesian Shrinkage Techniques for Data Pooling

This paper introduces a Bayesian approach to pooling data from across the SMSA models into a linked, regional forecasting model. We argue that this approach overcomes the problems mentioned above that are traditionally encountered in producing linked models. The focus of our investigation is whether this approach has an advantage over unlinked models in providing improved forecasts.
The impact of longstanding messages in micro-blogging classification

Social networks are making part of the daily routine of millions of users. Twitter is among Facebook and Instagram one of the most used, and can be seen as a relevant source of information as users share not only daily status, but rapidly propagate news and events that occur worldwide. Considering the dynamic nature of social networks, and their potential in information spread, it is imperative to find learning strategies able to learn in these environments and cope with their dynamic nature. Time plays an important role by easily out-dating information, being crucial to understand how informative can past events be to current learning models and for how long it is relevant to store previously seen information, to avoid the computation burden associated with the amount of data produced. In this paper we study the impact of longstanding messages in micro-blogging classification by using different training time-window sizes in the learning process. Since there are few studies dealing with drift in Twitter and thus little is known about the types of drift that may occur, we simulate different types of drift in an artificial dataset to evaluate and validate our strategy. Results shed light on the relevance of previously seen examples according to different types of drift.
Low noise parametric quartz crystal oscillator

It is well known for a long time that oscillation in a circuit can be sustained by using a parametric action instead of a conventional amplifier. In that case, the resonant circuit loss is compensated by a periodic change of an oscillation loop parameter at an appropriate frequency (pump action). The modulated parameter should be, for instance, the series capacitance of a RLC resonant circuit by using a varactor diode. In fact, this component basically operates as a noiseless reverse-biased junction expected to be a good candidate for a low noise oscillator circuit design. Parametric amplifiers widely used in the microwave domain are much less used in the RF oscillator circuits, probably because their development leads to a more involved circuitry and the lack of suitable simulation tools. The paper recalls the principle of the parametric oscillator an example of which using a quartz resonator (PXO) will be presented. Various methods used to analyse the behaviour of the PXO are described, and comparison between theory, simulation, and experimental results are discussed. Encouraging power spectral density of phase noise fluctuations obtained so far are demonstrated
Indoor Scene Knowledge Acquisition using a Natural Language Interface

This paper proposes an interface that uses automatically-generated Natural Language (NL) descriptions to describe indoor scenes based on photos taken of that scene from smartphones or other portable camera-equipped mobile devices. The goal is to develop a non-visual interface based on spatio-linguistic descriptions which could assist blind people in knowing the contents of an in- door scene (e.g., room structure, furniture, landmarks, etc.) and supporting effi- cient navigation of this space based on these descriptions. In this paper, we concentrate on understanding the most salient content of a stereotypic indoor scene that is described by an observer, categorizing the description strategies employed in this process, and evaluating the best presentation of directional in- formation using NL descriptions in order to support the most accurate spatial behaviors and mental representations of these scenes by means of human be- havioral experiments. This knowledge will then be used to develop a domain specific indoor scene ontology, which in turn will be used to generate auto- mated NL descriptions of indoor scenes based on their photographs, which will finally be integrated into a real-time non-visual scene description system.
A Purchasing Sequences Data Mining Method for Customer Segmentation

Purchasing behavior serves a base for online customer segmentation. Online purchasing behavior is characterized by purchasing sequences. This paper reviews the existing three major techniques of sequence data analysis, and discusses their limitations in online purchasing sequences analysis for customer segmentation. The study proposes a new data mining method for online customer segmentation, and applies this method for an online nutrition product store. The data mining results indicate that the proposed data mining method is novel and effective for online customer segmentation
Rescue of Infectious Particles from Preassembled Alphavirus Nucleocapsid Cores

ABSTRACT Alphaviruses are small, spherical, enveloped, positive-sense, single-stranded, RNA viruses responsible for considerable human and animal disease. Using microinjection of preassembled cores as a tool, a system has been established to study the assembly and budding process of Sindbis virus, the type member of the alphaviruses. We demonstrate the release of infectious virus-like particles from cells expressing Sindbis virus envelope glycoproteins following microinjection of Sindbis virus nucleocapsids purified from the cytoplasm of infected cells. Furthermore, it is shown that nucleocapsids assembled in vitro mimic those isolated in the cytoplasm of infected cells with respect to their ability to be incorporated into enveloped virions following microinjection. This system allows for the study of the alphavirus budding process independent of an authentic infection and provides a platform to study viral and host requirements for budding.
An open process for software development in classified environments as prerequisite for military app stores

Development of military software is mainly done by large companies specialized on the military domain. For security reasons high barriers for market entry exist, which excludes small companies and independent developers. This leads to monopolization inducing long development cycles as well as labored and cost-intensive development processes. The idea of a military app store as a distribution platform for military software could be one way to overcome these issues. Enabling independent developers and small companies to develop software for such an app store fosters agile and fast software development targeted to the users demands. However for security reasons this is not possible today. This paper presents a concept for a development process that enables independent developers to write software for the military domain. Our concept lowers the barrier for market entry significantly while keeping the high security standards. Opening the development process in such a way is an important prerequisite for the realization of military app stores.
Design and Analysis of Adaptive Neuro Fuzzy Logic Controlled Wireless Intelligent Telemetry System

Telemetry systems is an innovative design of electronics and computer system which can be used to take measurements from a remote site by using any type of link like Radio link, optical fiber, co-axial cables etc. Telemetry systems can take any of the measurement like pressure, temperature, density, humidity etc. from a target location and sends the taken measurements via wired or wireless links that may be sent over few meters or may be sent over wide range. There are great and flawless changes in various telemetry systems from some past years that brought a nice popularity to use these systems rather than keeping a human eye on measurements. Telemetry systems have made a user free from taking measurements at target site, noting down the readings, turning the machines on or off. In this paper adaptive neuro fuzzy Logic is combined with wireless telemetry systems to control water flow in water/any fluid Tank used in industries. This paper presents the design of a wireless telemetry system for controlling the flow of water in water tanks used in industrial process using adaptive neuro fuzzy logic called adaptive neuro fuzzy inference system. To incorporate artificial intelligence basically fuzzy logic and artificial networks are used. In this research these two models are initially studied. The advantages and disadvantages of both are discussed. Then these two models are compared by taking different parameters. By combining positive measures of fuzzy logic and artificial neural network, adaptive neuro fuzzy logic is considered as better method to incorporate artificial intelligence in the present research work. The resultant system is an assemblage of computer and communication equipments designed to work together for the purpose of measuring and controlling the flow of water in water tanks(tanks may contain any liquid) placed at far side . Besides control, The systems performs monitoring, data logging, alarming and diagnostic functions so that large, complicated process systems can be operated in a safe manner and maintained by a relatively small staff or just by a computer. Keywordsfuzzy logic; artificial neural networks; adaptive neuro fuzzy inference system; telemetry system; adaptive neuro fuzzy logic.
The revised IPSS is a powerful tool to evaluate the outcome of MDS patients treated with azacitidine: the GFM experience.

To the editor: The International Prognostic Scoring System (IPSS), which is based on cytogenetics, BM blast percentage, and number of cytopenias, has played a major role in prognosis assessment in myelodysplastic syndrome (MDS) since its publication in 1997. The recently published revised IPSS (
Adaptive Tracking Control for a Quad-Rotor

This paper illustrates the application of an adaptive flight control architecture to a scale quad-rotor. For autonomous VTOL (Vertical Takeoff and Landing) flight, it is common to separate the control problem into an inner fast loop that controls attitude and an outer slow loop that controls the trajectory of the VTOL. In this paper we augment a conventional PD controller conceived mainly for hovering, with an adaptive element using a real-time tuning single hidden layer neural network in a inner-outer loop combined architecture to account for model inversion error cancellation, issued in the feedback linearization process. The results shown in simulations reveal the superior performance of the augmented controller in tracking maneuvers.
Modularity and economic organization: concepts, theory, observations, and predictions

This paper addresses modularity as a basis for organizing economic activity. We first define the key concepts of architecture and of modularity as a special form of architecture. We then suggest how modular systems of all types may exhibit several properties of fundamental importance to the organization of economic activities, including greater adaptability and evolvability than systems that lack modular properties. We draw extensively on our original 1996 paper on modularity and subsequent research to suggest broad theoretical implications of modularity for (i) firms' product strategies and the nature of product market competition, (ii) the organization designs firms may adopt and the industry structures that can result when significant numbers of firms adopt modular product architectures, and (iii) learning processes and knowledge structures at the firm and industry levels in modular product markets. We also discuss an evolutionary perspective on modularity as an emergent phenomenon in firms and industries. We explain how modularity as a relatively new field of strategy and economic research may provide a new theoretical perspective on economic organizing that has significant potential for achieving important integrations of microeconomic and macroeconomic theory. We suggest some areas for further research that may be especially fruitful in this regard.
Sparse Manifold Learning and Its Applications in Image Classification

Graph-based dimensionality reduction algorithms are important and have been commonly applied in image classification and computer vision applications. To date many approaches have been proposed, e.g. Laplacian Eigenmaps (LE), Locally Linear Embedding (LLE), Locality Preserving Projections (LPP) and ISOMAP and so on. However, all these methods need to set the k nearest neighbor parameter to address the problem. In this paper, we proposed Sparse Patch Alignment Framework to settle it. Patch Alignment Framework which unified manifold learning algorithms through two stages: local patch optimization and whole alignment. We use Sparse Coding to construct the local patch instead of using KNN, thus, the k nearest neighbor parameter is set adaptively. A lot of experiments are done to show the performance of our method. The experiment results illustrate that our method is stable and robust.
Lighting Deep G-Bu↵ers: Single-Pass, Layered Depth Images with Minimum Separation Applied to Indirect Illumination

Weintroduceanewmethodforcomputing two-levelLayeredDepth Images (LDIs) [Shade et al. 1998] that is designed for modern GPUs. The method is order-independent, can guarantee a minimum separation distance between the layers, operates within small, bounded memory, and requires no explicit sorting. Critically, it also operates in a single pass over scene geometry. This is important because the cost of streaming geometry through a modern game engine pipeline can be high due to work expansion (from patches to triangles to pixels), matrix-skinning for animation, and the relative scarcity of main memory bandwidth compared to caches and registers. We apply the new LDI method to create Deep Geometry Buffers for deferred shading and show that two layers with a minimum separation distance make a variety of screen-space illumination effects surprisingly robust. We specifically demonstrate improved robustness for Scalable Ambient Obsurance [McGuire et al. 2012b], an extended n-bounce screen-space radiosity [Soler et al. 2009], and screen-space reflection ray tracing. All of these produce results that are necessarily view-dependent, but in a manner that is plausible based on visible geometry and more temporally coherent than results without layers.
Effects of Elevation of Brain Magnesium on Fear Conditioning, Fear Extinction, and Synaptic Plasticity in the Infralimbic Prefrontal Cortex and Lateral Amygdala

Anxiety disorders, such as phobias and posttraumatic stress disorder, are among the most common mental disorders. Cognitive therapy helps in treating these disorders; however, many cases relapse or resist the therapy, which justifies the search for cognitive enhancers that might augment the efficacy of cognitive therapy. Studies suggest that enhancement of plasticity in certain brain regions such as the prefrontal cortex (PFC) and/or hippocampus might enhance the efficacy of cognitive therapy. We found that elevation of brain magnesium, by a novel magnesium compound [magnesium-l-threonate (MgT)], enhances synaptic plasticity in the hippocampus and learning and memory in rats. Here, we show that MgT treatment enhances retention of the extinction of fear memory, without enhancing, impairing, or erasing the original fear memory. We then explored the molecular basis of the effects of MgT treatment on fear memory and extinction. In intact animals, elevation of brain magnesium increased NMDA receptors (NMDARs) signaling, BDNF expression, density of presynaptic puncta, and synaptic plasticity in the PFC but, interestingly, not in the basolateral amygdala. In vitro, elevation of extracellular magnesium concentration increased synaptic NMDAR current and plasticity in the infralimbic PFC, but not in the lateral amygdala, suggesting a difference in their sensitivity to elevation of brain magnesium. The current study suggests that elevation of brain magnesium might be a novel approach for enhancing synaptic plasticity in a regional-specific manner leading to enhancing the efficacy of extinction without enhancing or impairing fear memory formation.
Appropriate approaches to summarizing GxE studies: Reply to Laoutidis and Luckhous

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Hepatitis B virus kinetics under antiviral therapy sheds light on differences in hepatitis B e antigen positive and negative infections.

BACKGROUND Hepatitis B e antigen (HBeAg)–negative chronic hepatitis B infection has a presentation and clinical course that is divergent from that of HBeAg‐positive infection. The former usually presents with lower viral levels but faster progression to liver disease. We sought to better understand the balance between replication and the immune response against hepatitis B virus (HBV). METHODS Viral kinetics in 50 HBeAg‐negative patients under various treatment protocols with interferon α and/or nucleoside or nucleotide analogs was analyzed. HBV DNA level was measured frequently and the data fitted to a viral dynamic model. A meta‐analysis of all published studies of viral kinetics in HBeAg‐positive and HBeAg‐negative infection was also conducted. RESULTS We found that the clearance of both HBV virions and infected cells was significantly faster in HBeAg‐negative infection than in HBeAg‐positive infection. In HBeAg‐negative infection, there was also a negative correlation between baseline HBV DNA levels and infected cell half‐life, suggesting that the higher the viral load the faster the turnover of infected cells. CONCLUSIONS These results reveal the dual role played by the immune response in maintaining lower viral levels and inducing faster turnover of infected cells, the latter of which may be responsible for the more aggressive nature of HBeAg‐negative infection.
A kernel-based approach to direct action perception

The direct perception of actions allows a robot to predict the afforded actions of observed objects. In this paper, we present a non-parametric approach to representing the affordance-bearing subparts of objects. This representation forms the basis of a kernel function for computing the similarity between different subparts. Using this kernel function, together with motor primitive actions, the robot can learn the required mappings to perform direct action perception. The proposed approach was successfully implemented on a real robot, which could then quickly learn to generalize grasping and pouring actions to novel objects.
Using a Driving Game to Increase the Realism of Laboratory Studies of Automobile Passenger Thermal Comfort

The thermal comfort of automobile occupants depends on many complex heat exchanges, and it follows that many comfort research projects are done in laboratories, where conditions can be more readily controlled and the human subjects closely monitored. The laboratory setting, however, may appear unrealistic to a subject because it does not move and it does not afford the experience of driving or being a passenger. This could affect the research results if the test subject’s level of exertion were inappropriate, or if the unrelieved focus on comfort questionnaires caused the subject to be unduly sensitive to his or her thermal comfort. In a recent laboratory study, a computer driving game was used as an inexpensive way to increase the realism for the subjects, and this paper examines the physiological effects of using the game. The metabolic rates of subjects playing or watching the game were found to be very close to their rates when driving or riding in a real car. The method used is new and may be of equal interest for vehicular research: the metabolic rates were estimated from heartbeat data, which can be conveniently obtained from portable and non-intrusive fitness monitors.
Combinational Logic Design for AES SubByte Transformation on Masked Data

Low power consumption, low gate count and high throughput used to be standard design criteria for cryptographic coprocessors designated for smart cards and related embedded devices. Not anymore. With the advent of side channel attacks, the first and foremost concern is device resistance to such attacks, at a price. General-purpose hardware countermeasures, such as metal shields, tamperdetectors, clock randomization, random current generators, seem not anymore a sufficient protection against sophisticated differential power analysis or fault attacks specifically tailored to break a particular device. In this paper we describe an attempt to embed data masking technique at a hardware design level for an AES coprocessor. We concentrate on inversion in GF since it is the only non-linear operation, and requires complex transformations on the masked data and masks.
Effect of the Basel Accord Capital Requirements on the Loan-Loss Provisioning Practices of Australian Banks

There are two distinct regimes for bank provisioning in Australia: a forward-looking model for regulatory purposes and an incurred loss model for financial reporting. This study examines the former using a unique but confidential database. We find evidence that: (i) banks increase provisions in anticipation of future lending growth, (ii) banks allocate part of surplus capital above regulatory requirements to pre-fund future credit losses through provisions, and (iii) banks allocate part of higher earnings for the same purpose. These results suggest that bank managers use their discretion in setting provisions to dampen the impact of fluctuations in credit market conditions on their lending activities. For internal ratings-based banks, results suggest that the revised Basel framework allowing these banks to estimate expected losses using their own credit risk models may come at a cost of reduced general provisions.