Miracle-dz/UniFAR-FTUs huggingface dataset:
Intra-Household Allocations and Consumption Inequality under Cash Transfers and ViolenceI am grateful for constructive feedback and comments from Paul Glewwe, Joseph Ritter, Aaron Sojourner, Jason Kerwin, Joseph Mullins, Jeremy Lise, Julieth Santamaria, Salomon Garcia, Monika Fellmer and Natalia Ord

In this paper, I study how different types of households determine adult members’ allocations of time and consumption. Household types are characterized by the presence of partner violence and cash transfers. Using a collective intra-household decision making model, together with data from an experimental evaluation of a cash transfer program in Ecuador, I structurally estimate the parameters of the model. Then, I perform a poverty analysis at the individual level for the different types of households and find that women are substantially poorer than men, and that income distribution is more unequal for women than it is for men. I also find that the policy intervention generated welfare gains in terms of reducing overall and individual poverty. However, these welfare gains are heterogeneous among the different types of households. Particularly, I find that transfers are effective in reducing the gender poverty gap mainly in households where there is no violence. Finally, I estimate indifference scales for the different types of households to measure how much income an individual living alone needs to have in order to be as well off as when living as a couple. I find that men need a higher share of initial household resources compared to women, and that indifference scales for women decrease with violence and increase when the household is a beneficiary of the transfer. This work contributes to understand how intra-household allocation of resources takes place among different types of households, the important gender difference in poverty and inequality, and the effectiveness of poverty policies when there are factors that generate inequality in consumption. JEL Classification: D12, D13, D63, J12, J22.
ICSC 2006

Dynamic processes on the geographic scale and their monitoring are a growing topic in spatial sciences. Along with the technical advances, the need for a basic understanding of the conceptualization of dynamic processes in cognitive agents is required. The formal characterization of these conceptualizations is necessary to automate the identification and characterization of conceptual structures that discretize continuous dynamic processes into conceptual units. This research allows for transducing data, such as recorded by sensor networks, into conceptual knowledge. While research on the characterization of cognitive events has a long history within several sciences (Casati & Varzi 1996), we still lack a good understanding of the conceptualization of geographic events. Our research, therefore, aims at the core of conceptual structures of geographic events by employing a grouping paradigm to elicit conceptual knowledge. The research we report here does not aim to identify event boundaries, as, for example, reported in research on the perception on the structure of events (Zacks & Tversky 2001). In contrast, our research presupposes the existence of event classes as they can be identified by formal characterization of geometric transformations (and corresponding parameters), as well as the characterization of the behavior of regions, for example, the RCC calculi (e.g., Randell et al. 1992). For static relations, the RCC calculus has shown cognitive
X-Band Isoflux Concentric Circular Antenna Arrays for Image Data Download from LEO Satellites to Ground Stations

─ A circularly polarized isoflux beam synthesized by concentric circular arrays of printed microstrip patch antennas is proposed for X-band transmission of image data from land-imaging LEO satellites to the ground stations. This beam has a wide coverage angle of about 100° of uniform illumination. The paper introduces a novel design of a compact righthand circularly polarized microstrip patch as an element for the concentric circular arrays. The patch antenna has impedance matching bandwidth of about 600 MHz and axial ratio bandwidth of about 170 MHz at 8.1 GHz. A prototype of the proposed patch antenna is fabricated for experimental measurements. The electromagnetic simulation and experimental results show good agreement. A computationally efficient particle swarm optimization (PSO) procedure is developed and applied to find the distribution of the excitation magnitudes over the array elements so as to generate an isoflux beam in all the elevation planes with circular symmetry in the azimuth planes. The phase of excitation is the same for all the array elements. The method proposed to apply the PSO reduces the computational resources to about 6.5% of those required by the conventional method of application. Moreover, the proposed iterative PSO procedure is shown to be very fast convergent. Index Terms ─ Concentric circular arrays, isoflux beam, LEO satellite, particle swarm optimization, PSO.
OVERVIEW OF MILLIMETER WAVE COMMUNICATION

This paper presents the overview of the millimeter wave communication for 5G cellular systems. The millimeter wave that involves the frequencies from around 30 GHz to 300 GHz is one among the most valid competitors for 5G technology. The worldwide transfer speed insufficiency for wireless carriers leads to the development of millimeter wave communication. Millimeter wave frequencies supports larger transmission capacity assignments that provides the higher information rate. The current status of the worldwide regulations and standards for 60 GHz band are explained in this paper. To improve the signal strength, the directional antenna is used, which guarantees the signal delivery if there exists the line of sight path between the transmitter and receiver.
Distributed Learning in the Non-Convex World: From Batch to Streaming Data, and Beyond

Distributed learning has become a critical enabler of the massively connected world envisioned by many. This article discusses four key elements of scalable distributed processing and real-time intelligence --- problems, data, communication and computation. Our aim is to provide a fresh and unique perspective about how these elements should work together in an effective and coherent manner. In particular, we {provide a selective review} about the recent techniques developed for optimizing non-convex models (i.e., problem classes), processing batch and streaming data (i.e., data types), over the networks in a distributed manner (i.e., communication and computation paradigm). We describe the intuitions and connections behind a core set of popular distributed algorithms, emphasizing how to trade off between computation and communication costs. Practical issues and future research directions will also be discussed.
Prospective Evaluation of Biofield Energy Therapy for Burdensome Symptoms of Burn Injury: A Pilot Study

Background: Burn injuries are associated with a variety of symptoms including pain, pruritis, anxiety and fatigue. Current pharmacotherapy is able to alleviate one or more of these symptoms, but typically requires different drugs for each symptom. An ideal therapy would be one in which several of these symptoms could be assuaged or eliminated by a single intervention. Biofield Energy Therapy is a unique complementary therapy that was designed to help modulate this myriad of symptoms. Methods: A population of burn patients underwent Biofield Energy Therapy and were surveyed for levels of pain, pruritis, anxiety and fatigue prior to and after the therapy sessions. Additionally skin perfusion measurements were collected using a Field Laser Perfusion Imager before and after therapy. Results: Survey responses revealed multi-symptom improvement for most patients as well as general satisfaction with Biofield Energy Therapy and interest in further therapy sessions. Likewise, skin perfusion measurements increased following the period of therapy in a subset of patients. Conclusions: There is a need for new modalities targeted to treat the symptoms associated with burn injury. Biofield Energy Therapy may serve as an adjunct treatment for injured patients. This pilot study suggests that Biofield Energy Therapy should be evaluated further for use in burn care settings.
Memory deficits in Parkinson’s disease are associated with reduced beta power modulation

Abstract There is an increasing recognition of the significant non-motor symptoms that burden people with Parkinson’s disease. As such, there is a pressing need to better understand and investigate the mechanisms underpinning these non-motor deficits. The electrical activity within the brains of people with Parkinson’s disease is known to exhibit excessive power within the beta range (12–30 Hz), compared with healthy controls. The weight of evidence suggests that this abnormally high level of beta power is the cause of bradykinesia and rigidity in Parkinson’s disease. However, less is known about how the abnormal beta rhythms seen in Parkinson’s disease impact on non-motor symptoms. In healthy adults, beta power decreases are necessary for successful episodic memory formation, with greater power decreases during the encoding phase predicting which words will subsequently be remembered. Given the raised levels of beta activity in people with Parkinson’s disease, we hypothesized that the necessary decrease in power during memory encoding would be diminished and that this would interfere with episodic memory formation. Accordingly, we conducted a cross-sectional, laboratory-based experimental study to investigate whether there was a direct relationship between decreased beta modulation and memory formation in Parkinson’s disease. Electroencephalography recordings were made during an established memory-encoding paradigm to examine brain activity in a cohort of adults with Parkinson’s disease (N = 28, 20 males) and age-matched controls (N = 31, 18 males). The participants with Parkinson’s disease were aged 65 ± 6 years, with an average disease duration of 6 ± 4 years, and tested on their normal medications to avoid the confound of exacerbated motor symptoms. Parkinson’s disease participants showed impaired memory strength (P = 0.023) and reduced beta power decreases (P = 0.014) relative to controls. Longer disease duration was correlated with a larger reduction in beta modulation during encoding, and a concomitant reduction in memory performance. The inability to sufficiently decrease beta activity during semantic processing makes it a likely candidate to be the central neural mechanism underlying this type of memory deficit in Parkinson’s disease. These novel results extend the notion that pathological beta activity is causally implicated in the motor and (lesser appreciated) non-motor deficits inherent to Parkinson’s disease. These findings provide important empirical evidence that should be considered in the development of intelligent next-generation therapies.
An Illumination Modeling System for Human Factors Analyses

Seeing is critical to human performance. Lighting is critical for seeing. Therefore, lighting is critical to human performance. This is common sense, and here on earth, it is easily taken for granted. However, on orbit, because the sun will rise or set every 45 minutes on average, humans working in space must cope with extremely dynamic lighting conditions. Contrast conditions of harsh shadowing and glare is also severe. The prediction of lighting conditions for critical operations is essential. Crew training can factor lighting into the lesson plans when necessary. Mission planners can determine whether low-light video cameras are required or whether additional luminaires need to be flown. The optimization of the quantity and quality of light is needed because of the effects on crew safety, on electrical power and on equipment maintainability. To address all of these issues, an illumination modeling system has been developed by the Graphics Research and Analyses Facility (GRAF) and Lighting Environment Test Facility (LETF) in the Space Human Factors Laboratory at NASA Johnson Space Center. The system uses physically based ray tracing software (Radiance) developed at Lawrence Berkeley Laboratories, a human factors oriented geometric modeling system (PLAID) and an extensive database of humans and environments. Material reflectivity properties of major surfaces and critical surfaces are measured using a gonio-reflectometer. Luminaires (lights) are measured for beam spread distribulion, color and intensity. Video camera performances are measured for color and light sensitivity. 3D geometric models of humans and the environment are combined with the material and light models to form a system capable of predicting lighting conditions and visibility conditions in space.
System Simulation of Combustion in Direct-Injection Spark-Ignition Engines

Future constraints on pollutant emissions pushed car manufacturers towards gasoline direct injection (GDI) technologies to improve engine performances and reduce fuel consumption and emissions. New challenges are then introduced in terms of combustion optimization due to a more complex phenomenology while system models require additional calibration parameters.This PhD work presents the development and validation of a Zero-Dimensional (0D) model of GDI combustion for system simulation. The proposed model focuses on physics of atomization and drop evaporation, fuel/air mixing, flame propagation in heterogeneous charge and mutual interaction between these phenomena.The liquid phase is discretized in parcels grouping drops of the same size. An empirical atomization model based on injection velocity, fuel characteristics and thermodynamic conditions provides initial diameters. A Lagrangian model including drag-inertia dynamics, heat-up and forced convection describes drop parcel penetration and evaporation. Fuel / air mixing is described using a discrete Probability Density Function (PDF) approach, based on constant-mixture-fraction classes interacting with each other and with the drop parcels. Flame propagation takes into account mixture heterogeneity effects on flame speed and pollutant production is modelled.The model was implemented in the Simcenter Amesim platform for multi-physical modelling and integrated in a generic Spark Ignition (SI) combustion chamber submodel, CFM1D, from the IFP-Engine library.Fuel evaporation, spray dynamics and mixture formation modelling approaches, inspired by literature on Diesel engines, were adapted to GDI operating conditions. The model was first validated on a constant-volume vessel with quiescent gas in different thermodynamic conditions by means of experiments and 3D RANS CFD simulations performed with IFP-C3D. A tumble vortex in a constant volume vessel, in a first time, and rapid variations of the vessel volume, in a second time, were then added to the numerical experiment in order to test the model response to in-cylinder flow aerodynamics and variable thermodynamic conditions, respectively, in terms of fuel evaporation, spray development and fuel/air mixing and equivalence ratio distribution. Computations of fuel injections in a motored engine complete the model validation campaign in variable thermodynamic conditions and with realistic aerodynamics and the results were compared to both experiments and CFD computations.
Introduction: The past and the future

The question of marriage has been the subject of discussion and activism from the beginning of the LGBT rights movement in the United States. In 1953, ONE, the first American magazine aimed at advancing the equality rights of sexual minorities, published an essay on the question of marriage. At the time, of course, the idea that two individuals of the same sex could legally marry seemed fanciful, but the author nonetheless urged the nascent homophile movement to consider the implications of seeking marital rights.1 Sixteen years later, during the summer following the Stonewall riots, Metropolitan Community Church ministers began conducting marriage ceremonies for samesex couples. In the early 1970s, about a dozen samesex couples around the country applied for marriage licenses. After officials turned down the applications, three of the couples sued, arguing that the denials violated their constitutional rights. The courts rejected the constitutional claims out of hand, a fact that contributed to a twentyyearlong hiatus in the filing of new marriage equality lawsuits.2 But some activists continued to push for marriage, focusing their efforts primarily on local officials. In 1975, the county clerk in Boulder, Colorado, who was also the president of the local chapter of the National Organization for Women, became the first government official in the United States to issue marriage licenses to samesex couples (later deemed legally invalid by the state attorney general). Also in 1975, a Washington, DC, city council member introduced a marriage reform bill that called for the adoption of nofault divorce and referred to spouses in genderneutral terms in order to encourage a debate over marriage by samesex couples.3 In that same year, two members of the
Cortex-M4 Optimizations for \{R, M\}LWE Schemes

This paper proposes various optimizations for lattice-based key-encapsulation mechanisms (KEM) using the Number Theoretic Transform (NTT) on the popular ARM Cortex-M4 microcontroller. Improvements come in the form of a faster code using more efficient modular reductions, small polynomial multiplications and more aggressive layer merging in the NTT but also reduced stack usage. We test those optimizations in software implementations of Kyber and NewHope, both round 2 candidates in the NIST post-quantum project and also NewHope-Compact, a recently proposed derivative of NewHope with smaller parameters. Our software is the first implementation of NewHope-Compact on Cortex-M4 and shows speed improvements over previous high-speed implementations on the same platform for Kyber and NewHope. Moreover, it gives a common framework to compare those algorithms with the same level of optimization. Our results show that NewHope-Compact is the faster algorithm, followed by Kyber and finally NewHope that seems to suffer from its large modulus and error distribution for small dimensions.
Testing Unsatisfiability of Constraint Satisfaction Problems via Tensor Products

We study the design of stochastic local search methods to prove unsatisfiability of a constraint satisfaction problem (CSP). For a binary CSP, such methods have been designed using the microstructure of the CSP. Here, we develop a method to decompose the microstructure into graph tensors. We show how to use the tensor decomposition to compute a proof of unsatisfiability efficiently and in parallel. We also offer substantial empirical evidence that our approach improves the praxis. For instance, one decomposition yields proofs of unsatisfiability in half the time without sacrificing the quality. Another decomposition is twenty times faster and effective three-tenths of the times compared to the prior method. Our method is applicable to arbitrary CSPs using the well known dual and hidden variable transformations from an arbitrary CSP to a binary CSP.
Succession Planning In Polygamous Family Businesses: A Case of a Family Business in Zimbabwe

Most businesses in Zimbabwe are started as family businesses and thrive under the guidance of the founding member. However in cases when these businesses are expanding, the owners marry other wives who are made responsible for some business units like shops in rural areas.After the death of the founding member, a successor has to be appointed butthis appointment leads to family disputes and a gradual decline in the size and overall performance of the business.Generally, succession planning in family businesses is problematic in Zimbabwe. Succession challenges are more acute in polygamous families where businessgrowth is linked to a large number of wives and children. This study showed that succession planning in polygamous families tended to be haphazard. The first step was to have each wife responsible for a business unit(s) but answerable to the husband with very limited decision making responsibility. On the death of the founder, an heir is appointed and disputes arise leading to some family members exiting business altogether and renting out the premises as sources of income for their upkeep. Successionplanning in polygamous family businesses is necessary to ensure continuity after the death of the founder member. The creation of private companies rather than sole traders, where family members can be issued with share which can be disposed of without affecting business structure should be considered. --------------------------------------------------------------------------------------------------------------------------------------Date of Submission: 13-08-2018 Date of acceptance: 30-08-2018 --------------------------------------------------------------------------------------------------------------------------------------
The Impact of using Social Network on Academic Performance by using Contextual and Localized Data Analysis of Facebook Groups

Social Networks due to their intrinsic nature of being addictive have become an integral part of our civilization and plays an important role in our daily interactions. Facebook being the largest global online network, is used as a primary platform for carrying out our study and hypothesis testing. We built a web crawler for data extraction and used that data for our analysis. Primary goal of this study is to identify patterns among members of a Facebook group using a contextual and localised approach. We also intend to validate some hypotheses using a data driven approach like comparison of student’s social participation and activeness with actual class participation and its impact on his/her grades. We have also used user interactions in Facebook groups for identifying close relationships. The polarity of content in a group’s comments and posts defines a lot about that group and is also conferred in this paper.
Heart Disease Prediction based on External Factors: A Machine Learning Approach

Technology has immensely changed the world over the last decade. As a consequence, the life of the people is undergoing multiple changes that directly have positive and negative effects on health. Less physical activity and a lot of virtual involvements are pushing people into various health-related issues and heart disease is one of them. Currently, it has gained a great deal of attention among various life-threatening diseases. Heart disease can be detected or diagnosed by different medical tests by considering various internal factors. However, this type of approach is not only time-consuming but also expensive. Concurrently, there are very few studies conducted on heart disease prediction based on external factors. To bridge this gap, we proposed a heart disease prediction model based on the machine learning approach which enables predicting heart disease with 95% accuracy. To acquire the best result, 6 distinct machine learning classifiers (Decision Tree, Random Forest, Naive Bayes, Support Vector Machine, Quadratic Discriminant, and Logistic Regression) were used. At the same time, sklearn.ensemble. ExtraTreesClassifier has been used to extract relevant features to improve predictive accuracy and control over-fitting. Findings reveal that Support Vector Machine (SVM) outperforms the others with greater accuracy (95%).