Miracle-dz/UniFAR-FTUs huggingface dataset:
The Psychology of Risk and Taxes

M y accountant told me about a client who came to him following a very profitable real estate transaction. "Congratulations," said the accountant, "you made a million dollars. " "Thank you very much," said the client sarcastically, "I made a million and a half dollars before I stepped into your office. " This client, like many people, sees her advisor as a messenger from the US. Internal Revenue Service (IRS). It is as if financial advisors had the power to banish taxes and deliver high returns without risk. "Taxes are the price that we pay for a civilized society," said U.S. Chief Justice Oliver Wendell Holmes. Well, yes. We know that we have to pay taxes, but why do we have to pay so much? The search for tax shelters is long standing. The title of an article in The World's Work magazine of May 1914 is "Of Buying Stocks to Dodge Taxes. " It tells the sad story of a clergyman searching for tax-free returns who ended with no returns at all. The article states, "Doubtless the time will come when all the states will recognize the necessity of working out, along sensible and scientific lines, the problem of the taxation of investment securities. " Keep on wishing. We will never work out taxes along sensible and scientific lines. There is something impossible about taxes. It is not only that we have taxes but that we have many kinds of taxes: income taxes, sales taxes, property taxes, and more. Why so many kinds? Ask the tax authorities, and they will tell you the reason is that people cheat on their taxes. We have so many kinds of taxes, they say, for the same reason we have so many tollbooths on roads: Drivers are able to evade some tollbooths, but they cannot evade them all. Ask taxpayers the same question, and they will tell you that they are good, patriotic citizens. It is the
Deep Learning for Case-based Reasoning through Prototypes: A Neural Network that Explains its Predictions

Deep neural networks are widely used for classification. These deep models often suffer from a lack of interpretability---they are particularly difficult to understand because of their non-linear nature. As a result, neural networks are often treated as "black box" models, and in the past, have been trained purely to optimize the accuracy of predictions. In this work, we create a novel network architecture for deep learning that naturally explains its own reasoning for each prediction. This architecture contains an autoencoder and a special prototype layer, where each unit of that layer stores a weight vector that resembles an encoded training input. The encoder of the autoencoder allows us to do comparisons within the latent space, while the decoder allows us to visualize the learned prototypes. The training objective has four terms: an accuracy term, a term that encourages every prototype to be similar to at least one encoded input, a term that encourages every encoded input to be close to at least one prototype, and a term that encourages faithful reconstruction by the autoencoder. The distances computed in the prototype layer are used as part of the classification process. Since the prototypes are learned during training, the learned network naturally comes with explanations for each prediction, and the explanations are loyal to what the network actually computes.
Losing Ground: American Social Policy, 1950-1980

* Prologue A Generous Revolution * The Kennedy Transition * The System Is to Blame * Implementing the Elite Wisdom Being Poor, Being Black: 19501980 * Poverty * Employment * Wages and Occupations * Education * Crime * The Family * The View from 1966 Interpreting The Data * The Scoial Scientists and the Great Experiment * Incentives to Fail I: Maximizing Short-Term Gains * The Destruction of Status Rewards Rethinking Social Policy * What do we want to Accomplish? * The Constraints on Helping * Choosing a Future
Weight in the First Year of Life: Associations with Maternal Prepregnancy Body Mass Index and Gestational Weight Gain—Findings from a Longitudinal Pregnancy Cohort

Abstract Objective To investigate the obesogenic influence of maternal prepregnancy body mass index (BMI) and gestational weight gain (GWG) on infant weight at birth and 12 months postpartum in an Australian general population sample. Methods Data on 1,305 pregnant women were collected on prepregnancy BMI and GWG through maternal interview, on infant weight at birth through hospital records, and on infant weight 12 months postbirth through direct measurement. Relationships between prepregnancy, gestational weight exposures, and infant weight outcomes were assessed with and without adjustment for potential confounding. Results We observed a 14 to 24 g increase in infant birth weight for every 1 kg increase in maternal weight (infant birth weight: β(BMI) = 0.014, p < 0.000; β(GWG) = 0.012, p < 0.000; and 12 months: β(BMI) = 0.018, p < 0.000; β(GWG) = 0.024, p < 0.000). Effects remained after adjustment for potential confounders (infant birth weight: β(BMI) = 0.014, p < 0.000; β(GWG) = 0.012, p < 0.001; and 12 months: β(BMI)= 0.017, p ≤ 0.033; β(GWG) = 0.023, p = 0.001). However, the effects observed were small, and there was no evidence that GWG mediated relationships between preconception BMI and infant weight. Conclusion In a general population sample, there is a significant but not substantial observed relationship between maternal prepregnancy BMI and GWG and infant weight outcomes, suggesting a minor role for these factors at a population level.
Forecasting with generalized bayesian vector auto regressions

The effects of using different distributions to parameterize the prior beliefs in a Bayesian analysis of vector autoregressions are studied. The well-known Minnesota prior of Litterman as well as four less restrictive distributions are considered. Two of these prior distributions are new to vector autoregressive models. When the forecasting performance of the different parameterizations of the prior beliefs are compared it is found that the prior distributions that allow for dependencies between the equations of the VAR give rise to better forecasts.
Handwritten Line Detection via an EM Algorithm

In this paper we present a handwritten line segmentation method devised to work on documents composed of several paragraphs with multiple line orientations. The method is based on a variation of the EM algorithm for the estimation of a set of regression lines between the connected components that compose the image. We evaluated our method on the ICDAR2009 handwriting segmentation contest dataset with promising results that overcome most of the presented methods. In addition, we prove the usability of the presented method by performing line segmentation on the George Washington database obtaining encouraging results.
Simultaneous depletion of Atm and Mdl rebalances cytosolic Fe‐S cluster assembly but not heme import into the mitochondrion of Trypanosoma brucei

ABC transporter mitochondrial 1 (Atm1) and multidrug resistance‐like 1 (Mdl) are mitochondrial ABC transporters. Although Atm1 was recently suggested to transport different forms of glutathione from the mitochondrion, which are used for iron‐sulfur (Fe‐S) cluster maturation in the cytosol, the function of Mdl remains elusive. In Trypanosoma brucei, we identified one homolog of each of these genes, TbAtm and TbMdl, which were downregulated either separately or simultaneously using RNA interference. Individual depletion of TbAtm and TbMdl led to limited growth defects. In cells downregulated for TbAtm, the enzymatic activities of the Fe‐S cluster proteins aconitase and fumarase significantly decreased in the cytosol but not in the mitochondrion. Downregulation of TbMdl did not cause any change in activities of the Fe‐S proteins. Unexpectedly, the simultaneous downregulation of TbAtm and TbMdl did not result in any growth defect, nor were the Fe‐S cluster protein activities altered in either the cytosolic or mitochondrial compartments. Additionally, TbAtm and TbMdl were able to partially restore the growth of the Saccharomyces cerevisiae Δatm1 and Δmdl2 null mutants, respectively. Because T. brucei completely lost the heme b biosynthesis pathway, this cofactor has to be obtained from the host. Based on our results, TbMdl is a candidate for mitochondrial import of heme b, which was markedly decreased in both TbMdl and TbAtm + TbMdl knockdowns. Moreover, the levels of heme a were strongly decreased in the same knockdowns, suggesting that TbMdl plays a key role in heme a biosynthesis, thus affecting the overall heme homeostasis in T. brucei.
Brief Report: Chimeric Pigs Produced from Induced Pluripotent Stem Cells Demonstrate Germline Transmission and No Evidence of Tumor Formation in Young Pigs

The recent development of porcine induced pluripotent stem cells (piPSCs) capable of generating chimeric animals, a feat not previously accomplished with embryonic stem cells or iPSCs in a species outside of rodents, has opened the doors for in‐depth study of iPSC tumorigenicity, autologous transplantation, and other key aspects to safely move iPSC therapies to the clinic. The study of iPSC tumorigenicity is critical as previous research in the mouse showed that iPSC‐derived chimeras possessed large numbers of tumors, rising significant concerns about the safety of iPSC therapies. Additionally, piPSCs capable of generating germline chimeras could revolutionize the transgenic animal field by enabling complex genetic manipulations (e.g., knockout or knockin of genes) to produce biomedically important large animal models or improve livestock production. In this study, we demonstrate for the first time in a nonrodent species germline transmission of iPSCs with the live birth of a transgenic piglet that possessed genome integration of the human POU5F1 and NANOG genes. In addition, gross and histological examination of necropsied porcine chimeras at 2, 7, and 9 months showed that these animals lacked tumor formation and demonstrated normal development. Tissue samples positive for human POU5F1 DNA showed no C‐MYC gene expression, further implicating C‐MYC as a cause of tumorigenicity. The development of germline‐competent porcine iPSCs that do not produce tumors in young chimeric animals presents an attractive and powerful translational model to study the efficacy and safety of stem cell therapies and perhaps to efficiently produce complex transgenic animals. STEM CELLS 2011;29:1640–1643
Design methodology for an Energy Neutral Health Monitoring Wireless Sensor Node

This paper investigates an Energy Neutral System using Micro Energy Harvesters for a Health Monitoring Wireless Sensor Node (HM-WSN). We have implemented HM-WSN consisting of a solar cell energy harvester, a Power Management Unit (PMU) and the controller, sensor and transmitter acting as load. A battery extender and a switching logic circuit for sensor node is designed to evaluate the efficiency of the proposed system. The HM-WSN senses body temperature with improvement in lifetime of coincell. The HM-WSN is built using commercially available off the shelf (COTS) components and consumes 47. 2μW for data being updated every 134s. It can be operated for approximately 1.32 years only on a coincell. Furthermore, the HM-WSN can be operated with solar energy at 200lux, achieving 93.72 years with a combination of a coincell with an additional capacitor of 2.2mF. It attains 4.07 years to 16.87 years lifetime with the load capacitor varying from 1mF to 1.5mF at 350lux lighting condition. With the same ambient condition and a 2mF load capacitor, HM-WSN attains complete energy autonomy.
Shmoo plotting: the black art of IC testing

Modern IC testing has developed many different techniques each with specific targets for improving quality and controlling the process to improve the yield over the life time of the product. Shmoo plotting of digital ICs is part of the early product characterisation procedures intended to establish that the design is stable within the process and can be manufactured with virtually zero yield loss, except for spot defects. In this sense Shmoo plotting verifies that the IC has been implemented to six-sigma design principles, or better. It also allows the wafer/batch monitoring parameters to be accurately established for the product. For the vast majority of ICs for which no performance binning is performed it avoids testing all of the parts in production for process variations. This is economically essential for high volume production because testing a device for process variations expressly implies functional testing, instead of simple defect oriented testing such a scan testing or IDDQ. Thus, knowledge gained from Shmoo plotting can be used to optimize the process, design and final test program.
Collective synchronization induced by epidemic dynamics on complex networks with communities.

Much recent empirical evidence shows that community structure is ubiquitous in the real-world networks. In this paper we propose a growth model to create scale-free networks with the tunable strength (noted by Q ) of community structure and investigate the influence of community strength upon the collective synchronization induced by Susceptive-Infected-Recovery-Susceptive (SIRS) epidemiological process. Global and local synchronizability of the system is studied by means of an order parameter and the relevant finite-size scaling analysis is provided. The numerical results show that a phase transition occurs at Qc approximately or equal to 0.835 from global synchronization to desynchronization and the local synchronization is weakened in a range of intermediately large Q. Moreover, we study the impact of mean degree upon synchronization on scale-free networks.
Corporate Social Responsibility Within the U.S. Lodging Industry: an Exploratory Study

This exploratory study examines corporate social responsibility (CSR) in the lodging industry by measuring the contributions to communities made by lodging properties throughout the United States. This work is significant for two reasons: (a) It attempts to develop a theoretical foundation for CSR within the U.S. lodging industry; (b) it signifies one of the first attempts to quantify one component of CSR, corporate giving, within the U.S. lodging industry. A random sample of lodging properties in the United States was surveyed, and they were asked to report their various contributions to community. Although a limited response prevented generalizability, based on 421 responding properties, regression analysis suggested that the industry made various contributions worth more than $815 million or 3.6% of total industry profits in 2005. The results also showed that a number of structural variables affected the quantified social component of CSR among lodging properties as well as the existence of CSR written policies, processes, and principles.
Multi-Factor Stochastic Model and Estimation Procedure for the Valuation and Hedging of Commodity Contingent Claims

In this paper we provide a new multi-factor stochastic model of commodity futures prices and propose a Kalman filter estimation procedure that may be applied to a panel data with missing observations. This model may be used to implement financial engineering applications which require the valuation or hedging of commodity-linked assets. We calibrate our model using daily oil futures prices and analyze its goodness-of-fit to observed prices and empirical volatilities.
Knowledge Transfer and Resource Sharing as Value Creation Mechanisms in Inbound Continental European Acquisitions

ABSTRACT Despite the growing popularity of acquisitions as a European market entry strategy, existing research reveals that many such acquisitions fail to generate positive shareholder returns. In an extension of the literature on operational synergy and value creation within acquisitions, this paper examines the types of knowledge transfer and resource sharing sought by inbound European acquirers together with their relative success rates. Data from 121 acquisitions into Continental Europe reveal that acquirers emphasize the attainment of operational synergy via knowledge transfer, particularly within the marketing and distribution functions. Disturbingly, however, the results also show that firms' ability to successfully transfer functional knowledge consistently falls short of their expectations, as does their ability to share resources within those activities that have a high local market knowledge content. These results are interpreted in the light of the recent knowledge-based literature and implica.
Apolipoprotein epsilon3 allele is associated with persistent hepatitis C virus infection.

BACKGROUND Host genetic factors may significantly influence the ability to clear hepatitis C virus (HCV) following infection. HCV is associated with very low density lipoproteins (VLDL) and low density lipoproteins (LDL) in the host's circulation. Apolipoprotein E (APOE) is found in VLDL and binds to potential receptors involved in HCV entry into cells, the LDL receptor, and the scavenger receptor protein SR-B1. The APOE gene is polymorphic with three alleles coding for three isoforms: Apo-epsilon2, Apo-epsilon3, and Apo-epsilon4. The aim of this study was to assess if these functional polymorphisms determine disease outcome in HCV infected individuals. METHODS The APOE genotype was determined in 420 Northern European patients with evidence of exposure to HCV. Genotype and allele distribution were compared with those of 288 healthy controls and progression of liver disease and viral clearance were analysed according to APOE allele status. RESULTS The APOE*E2 and APOE*E4 alleles were both associated with a reduced likelihood of chronic infection (odds ratio (OR) 0.39 (95% confidence interval (CI) 0.211-0.728), p = 0.003; and OR 0.6 (95% CI 0.38-0.96), p = 0.032) and there was a notable absence of the E2E2 genotype in the HCV antibody positive group compared with the control population (p = 0.0067). Overall the genotypes carrying the E2 allele (E2,E3 and E2,E4) were associated with the equivalent of a 3-5-fold reduction in the risk of chronic HCV infection (genotype relative risk 0.36 and 0.20, respectively). CONCLUSION This study indicates that functional APOE gene polymorphisms may be a determinant of outcome in HCV infection. We hypothesise that the E2 allele may protect against viral persistence via defective binding of HCV lipoviral particles to the cellular receptors involved in entry of these infectious particles.
ON THE ORIGIN OF THE HUMAN TREPONEMATOSES (PINTA, YAWS, ENDEMIC SYPHILIS AND VENEREAL SYPHILIS).

A close relationship between the four human treponematoses is suggested by their clinical and epidemiological characteristics and by such limited knowledge of the treponemes as there is at present. No treponeme of this group (except for that of the rabbit) is known other than in man, but the human treponemes probably arose long ago from an animal infection. The long period of infectiousness of pinta suggests that it may have been the earliest human treponematosis. It may have been spread throughout the world by about 15 000 B.C., being subsequently isolated in the Americas when the Bering Strait was flooded. About 10 000 B.C. in the Afro-Asian land mass environmental conditions might have favoured treponeme mutants leading to yaws; from these, about 7000 B.C., endemic syphilis perhaps developed, to give rise to venereal syphilis about 3000 B.C. in south-west Asia as big cities developed there. Towards the end of the fifteenth century A.D. a further mutation may have resulted in a more severe venereal syphilis in Europe which, with European exploration and geographical expansion, was subsequently carried throughout the then treponemally uncommitted world. These suggestions find some tentative support in climatic changes which might have influenced the selection of those treponemes which still survive in humid or arid climates. Venereal transmission would presumably remove the treponeme from the direct influence of climate. The author makes a plea for further investigation of many aspects of this subject while this is still possible.
Modeling structure and aerosol concentration with fused radar and LiDAR data in environments with changing visibility

LiDAR scanners are commonly used for mapping and localization with mobile robots. But, they cannot see through occlusions, as it occurs in harsh environments, containing smoke, fog or dust. Radar scanners can overcome this problem, but they have lower range and angular resolution, and cannot represent an environment in the same quality. In the following article, we present the integration of fused LiDAR and radar data into a SLAM cycle and continue our work from [1], where we presented first results regarding a feature based and a scan matching-based approach for SLAM in environments with changing visibility using LiDAR and radar sensors. New content in this article, the data fusion takes place on scan level as well as on map level and aims to result in an optimum map quality considering the visibility situation. Additionally, we collected more data during an indoor experiment involving real fog (see Fig. 1). Besides the structure of the environment, we can model aerosol concentration with fused LiDAR and Radar data in parallel to the mapping process with a finite difference model without involving a smoke or gas sensor. Overall, our method allows the modeling of the structure of an environment including dynamic distribution of aerosol concentration.
Multi-dimensional scaling and MODELLER-based evolutionary algorithms for protein model refinement

Protein structure prediction, i.e., computationally predicting the three-dimensional structure of a protein from its primary sequence, is one of the most important and challenging problems in bioinformatics. Model refinement is a key step in the prediction process, where improved structures are constructed based on a pool of initially generated models. Since the refinement category was added to the biennial Critical Assessment of Structure Prediction (CASP) in 2008, CASP results show that it is a challenge for existing model refinement methods to improve model quality consistently. This paper presents three evolutionary algorithms for protein model refinement, in which multidimensional scaling(MDS), the MODELLER software, and a hybrid of both are used as crossover operators, respectively. The MDS-based method takes a purely geometrical approach and generates a child model by combining the contact maps of multiple parents. The MODELLER-based method takes a statistical and energy minimization approach, and uses the remodeling module in MODELLER program to generate new models from multiple parents. The hybrid method first generates models using the MDS-based method and then run them through the MODELLER-based method, aiming at combining the strength of both. Promising results have been obtained in experiments using CASP datasets. The MDS-based method improved the best of a pool of predicted models in terms of the global distance test score (GDT-TS) in 9 out of 16test targets.